From dfb34c7be4334f0b46a6a08cd2949ea464dc875b Mon Sep 17 00:00:00 2001 From: Eric Hare Date: Wed, 20 May 2026 12:46:51 -0700 Subject: [PATCH] fix(agent): scope chat history retrieval by flow_id to prevent cross-flow leak (#13087) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * fix(agent): scope chat history retrieval by flow_id to prevent cross-flow leak (#13059) AgentComponent.get_memory_data filtered chat history by session_id only, so the playground's default session names (e.g. "New Session 0") leaked conversation history across unrelated flows whenever two flows happened to use the same default name. The fix replaces the ad-hoc MemoryComponent spawn (whose internal _vertex is None and therefore cannot see the running flow's flow_id) with a direct aget_messages call that passes flow_id from the agent's own graph context. MemoryComponent.retrieve_messages is also updated to scope by self.graph.flow_id when available, so the standalone Message History component does not have the same leak. Adds regression tests covering: UUID coercion of string flow_ids, isolation of two flows that share a session name, graceful fallback when flow_id is missing or non-UUID, untouched external-memory path, and filtering out the current input message. * fix(agent): preserve n_messages=0 disable-memory contract and apply scoping to Cuga Review follow-up on #13087: - AgentComponent.get_memory_data regressed n_messages=0: before this PR the call went through MemoryComponent.retrieve_messages which short-circuits to [] when n_messages==0. The direct aget_messages call lost that contract, because `if self.n_messages:` is falsy for 0 and `messages[-0:]` returns every message fetched under limit=10000. - CugaComponent.get_memory_data had the same leak pattern (issue #13059) because it also spawned an ad-hoc MemoryComponent whose _vertex is None. Extract aget_agent_chat_history(session_id, flow_id, context_id, n_messages) into memory.py: * returns [] when n_messages == 0 * coerces flow_id to UUID (gracefully falls back on invalid values) * applies n_messages slicing Both AgentComponent and CugaComponent now route through it. Tests: * aget_agent_chat_history: passes flow_id as UUID, short-circuits on n_messages=0, slices to most-recent N, returns all on n_messages=None, falls back to unscoped query on invalid flow_id. * AgentComponent integration: routes through helper with flow_id, filters out current input, n_messages=0 disables memory (asserts aget_messages is never awaited so a future regression resurfaces as a real DB call). * CugaComponent integration: scopes by flow_id, n_messages=0 disables memory. Module-import gated for envs without optional Cuga deps. * [autofix.ci] apply automated fixes * [autofix.ci] apply automated fixes (attempt 2/3) * [autofix.ci] apply automated fixes * Template update * [autofix.ci] apply automated fixes * Update .secrets.baseline * [autofix.ci] apply automated fixes * Update starter projects * [autofix.ci] apply automated fixes * Update .secrets.baseline * [autofix.ci] apply automated fixes * [autofix.ci] apply automated fixes * fix(memory): address PR #13087 review — symmetric flow_id, DESC fetch, loud fallback Addresses the review comments on PR #13087: I1 (symmetric flow_id handling): `store_message` now routes the internal-memory write through `_coerce_flow_id_to_uuid(_safe_graph_flow_id(self))` instead of accessing `self.graph.flow_id` directly, so an ad-hoc / custom-subclass MemoryComponent without `_vertex` no longer crashes on the write half before reaching the safe read half. Both calls share a single `flow_id_scope` variable. I2 (>10k row ordering bug): both `aget_agent_chat_history` and `MemoryComponent.retrieve_messages` (internal-memory branch) now query in `DESC` order with `limit=n_messages` (falling back to `MAX_CHAT_HISTORY_FETCH_LIMIT` when no explicit limit is set), then reverse to the caller's preferred order. The previous ASC + slice shape returned the chronological FIRST window once sessions exceeded 10k rows, silently serving stale history. I3 (loud fallback observability): when `_coerce_flow_id_to_uuid` is forced to return `None` for a malformed `flow_id`, the log call is now `logger.error` (was `warning`) and carries a structured `event` tag (`memory_flow_id_unscoped`). The unbounded-fetch ceiling-hit path emits a parallel `memory_chat_history_limit_reached` warning. Both events are explicit alert hooks for observability pipelines so a regression cannot silently re-enable the cross-flow leak that motivated the PR. R1 (magic number): extracted `MAX_CHAT_HISTORY_FETCH_LIMIT = 10_000` as a module-level constant; reused in both call sites and the test suite. R2 (signature): tightened `_coerce_flow_id_to_uuid` and `_safe_graph_flow_id` from `Any` to `str | UUID | None`; tightened `aget_agent_chat_history`'s `flow_id` argument accordingly. Tests: extended `test_memory_flow_id_scoping.py` with coverage for each of the above — symmetric store/read flow_id (I1), DESC+reverse ordering on retrieve_messages (I2), ceiling-warning emission (I2), structured error log on flow_id fallback (I3), and explicit-limit suppression of the warning. All 26 tests pass. * [autofix.ci] apply automated fixes * Template update --------- Co-authored-by: autofix-ci[bot] <114827586+autofix-ci[bot]@users.noreply.github.com> --- .secrets.baseline | 8 +- .../Custom Component Generator.json | 4 +- .../Instagram Copywriter.json | 4 +- .../starter_projects/Invoice Summarizer.json | 4 +- .../starter_projects/Market Research.json | 4 +- .../starter_projects/Meeting Summary.json | 4 +- .../starter_projects/Memory Chatbot.json | 4 +- .../starter_projects/News Aggregator.json | 4 +- .../starter_projects/Nvidia Remix.json | 4 +- .../starter_projects/Pokédex Agent.json | 4 +- .../starter_projects/Price Deal Finder.json | 4 +- .../starter_projects/Research Agent.json | 4 +- .../starter_projects/SaaS Pricing.json | 4 +- .../starter_projects/Search agent.json | 4 +- .../Sequential Tasks Agents.json | 12 +- .../starter_projects/Simple Agent.json | 4 +- .../starter_projects/Social Media Agent.json | 4 +- .../Travel Planning Agents.json | 12 +- .../starter_projects/Youtube Analysis.json | 4 +- .../test_memory_flow_id_scoping.py | 540 ++++++++++++++++++ src/lfx/src/lfx/_assets/component_index.json | 14 +- src/lfx/src/lfx/components/cuga/cuga_agent.py | 13 +- .../lfx/components/models_and_agents/agent.py | 21 +- .../components/models_and_agents/memory.py | 136 ++++- 24 files changed, 743 insertions(+), 77 deletions(-) create mode 100644 src/backend/tests/unit/components/models_and_agents/test_memory_flow_id_scoping.py diff --git a/.secrets.baseline b/.secrets.baseline index 712a23c72e..863bd41e76 100644 --- a/.secrets.baseline +++ b/.secrets.baseline @@ -2438,7 +2438,7 @@ "filename": "src/backend/base/langflow/initial_setup/starter_projects/Nvidia Remix.json", "hashed_secret": "b8205f6293ceac2bf593580250e865708c8a9aeb", "is_verified": false, - "line_number": 2136 + "line_number": 2157 } ], "src/backend/base/langflow/initial_setup/starter_projects/Pok\u00e9dex Agent.json": [ @@ -2833,14 +2833,14 @@ "filename": "src/backend/base/langflow/initial_setup/starter_projects/Youtube Analysis.json", "hashed_secret": "d6e6d7b4b115cd3b9d172623199f8c403055fecc", "is_verified": false, - "line_number": 1323 + "line_number": 1344 }, { "type": "Hex High Entropy String", "filename": "src/backend/base/langflow/initial_setup/starter_projects/Youtube Analysis.json", "hashed_secret": "54ed260e3bc31bc77ee06754dff850981d39a66c", "is_verified": false, - "line_number": 2095, + "line_number": 2116, "is_secret": false } ], @@ -9031,5 +9031,5 @@ } ] }, - "generated_at": "2026-05-19T17:11:47Z" + "generated_at": "2026-05-20T18:39:19Z" } diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Custom Component Generator.json b/src/backend/base/langflow/initial_setup/starter_projects/Custom Component Generator.json index 6efa69415e..6ed834088f 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Custom Component Generator.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Custom Component Generator.json @@ -244,7 +244,7 @@ "legacy": false, "lf_version": "1.6.0", "metadata": { - "code_hash": "7608453efb4e", + "code_hash": "5619cf9008d5", "dependencies": { "dependencies": [ { @@ -306,7 +306,7 @@ "show": true, "title_case": false, "type": "code", - "value": "from typing import Any, cast\n\nfrom lfx.custom.custom_component.component import Component\nfrom lfx.helpers.data import data_to_text\nfrom lfx.inputs.inputs import DropdownInput, HandleInput, IntInput, MessageTextInput, MultilineInput, TabInput\nfrom lfx.memory import aget_messages, astore_message\nfrom lfx.schema.data import Data\nfrom lfx.schema.dataframe import DataFrame\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.template.field.base import Output\nfrom lfx.utils.component_utils import set_current_fields, set_field_display\nfrom lfx.utils.constants import MESSAGE_SENDER_AI, MESSAGE_SENDER_NAME_AI, MESSAGE_SENDER_USER\n\n\nclass MemoryComponent(Component):\n display_name = \"Message History\"\n description = \"Stores or retrieves stored chat messages from Langflow tables or an external memory.\"\n documentation: str = \"https://docs.langflow.org/message-history\"\n icon = \"message-square-more\"\n name = \"Memory\"\n default_keys = [\"mode\", \"memory\", \"session_id\", \"context_id\"]\n mode_config = {\n \"Store\": [\"message\", \"memory\", \"sender\", \"sender_name\", \"session_id\", \"context_id\"],\n \"Retrieve\": [\"n_messages\", \"order\", \"template\", \"memory\", \"session_id\", \"context_id\"],\n }\n\n inputs = [\n TabInput(\n name=\"mode\",\n display_name=\"Mode\",\n options=[\"Retrieve\", \"Store\"],\n value=\"Retrieve\",\n info=\"Operation mode: Store messages or Retrieve messages.\",\n real_time_refresh=True,\n ),\n MessageTextInput(\n name=\"message\",\n display_name=\"Message\",\n info=\"The chat message to be stored.\",\n tool_mode=True,\n dynamic=True,\n show=False,\n ),\n HandleInput(\n name=\"memory\",\n display_name=\"External Memory\",\n input_types=[\"Memory\"],\n info=\"Retrieve messages from an external memory. If empty, it will use the Langflow tables.\",\n advanced=True,\n ),\n DropdownInput(\n name=\"sender_type\",\n display_name=\"Sender Type\",\n options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER, \"Machine and User\"],\n value=\"Machine and User\",\n info=\"Filter by sender type.\",\n advanced=True,\n ),\n MessageTextInput(\n name=\"sender\",\n display_name=\"Sender\",\n info=\"The sender of the message. Might be Machine or User. \"\n \"If empty, the current sender parameter will be used.\",\n advanced=True,\n ),\n MessageTextInput(\n name=\"sender_name\",\n display_name=\"Sender Name\",\n info=\"Filter by sender name.\",\n advanced=True,\n show=False,\n ),\n IntInput(\n name=\"n_messages\",\n display_name=\"Number of Messages\",\n value=100,\n info=\"Number of messages to retrieve.\",\n advanced=True,\n show=True,\n ),\n MessageTextInput(\n name=\"session_id\",\n display_name=\"Session ID\",\n info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n value=\"\",\n advanced=True,\n ),\n MessageTextInput(\n name=\"context_id\",\n display_name=\"Context ID\",\n info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n value=\"\",\n advanced=True,\n ),\n DropdownInput(\n name=\"order\",\n display_name=\"Order\",\n options=[\"Ascending\", \"Descending\"],\n value=\"Ascending\",\n info=\"Order of the messages.\",\n advanced=True,\n tool_mode=True,\n required=True,\n ),\n MultilineInput(\n name=\"template\",\n display_name=\"Template\",\n info=\"The template to use for formatting the data. \"\n \"It can contain the keys {text}, {sender} or any other key in the message data.\",\n value=\"{sender_name}: {text}\",\n advanced=True,\n show=False,\n ),\n ]\n\n outputs = [\n Output(\n display_name=\"Messages\",\n name=\"messages_text\",\n method=\"retrieve_messages_as_text\",\n types=[\"Message\"],\n selected=\"Message\",\n dynamic=True,\n ),\n Output(\n display_name=\"Table\",\n name=\"dataframe\",\n method=\"retrieve_messages_dataframe\",\n types=[\"Table\"],\n selected=\"Table\",\n dynamic=True,\n ),\n ]\n\n def update_outputs(self, frontend_node: dict, field_name: str, field_value: Any) -> dict:\n \"\"\"Dynamically show only the relevant output based on the selected output type.\"\"\"\n if field_name == \"mode\":\n # Start with empty outputs\n frontend_node[\"outputs\"] = []\n if field_value == \"Store\":\n frontend_node[\"outputs\"] = [\n Output(\n display_name=\"Stored Messages\",\n name=\"stored_messages\",\n method=\"store_message\",\n types=[\"Message\"],\n selected=\"Message\",\n hidden=True,\n dynamic=True,\n )\n ]\n if field_value == \"Retrieve\":\n frontend_node[\"outputs\"] = [\n Output(\n display_name=\"Messages\",\n name=\"messages_text\",\n method=\"retrieve_messages_as_text\",\n types=[\"Message\"],\n selected=\"Message\",\n dynamic=True,\n ),\n Output(\n display_name=\"Table\",\n name=\"dataframe\",\n method=\"retrieve_messages_dataframe\",\n types=[\"Table\"],\n selected=\"Table\",\n dynamic=True,\n ),\n ]\n return frontend_node\n\n async def store_message(self) -> Message:\n message = Message(text=self.message) if isinstance(self.message, str) else self.message\n\n message.context_id = self.context_id or message.context_id\n message.session_id = self.session_id or message.session_id\n message.sender = self.sender or message.sender or MESSAGE_SENDER_AI\n message.sender_name = self.sender_name or message.sender_name or MESSAGE_SENDER_NAME_AI\n\n stored_messages: list[Message] = []\n\n if self.memory:\n self.memory.context_id = message.context_id\n self.memory.session_id = message.session_id\n lc_message = message.to_lc_message()\n await self.memory.aadd_messages([lc_message])\n\n stored_messages = await self.memory.aget_messages() or []\n\n stored_messages = [Message.from_lc_message(m) for m in stored_messages] if stored_messages else []\n\n if message.sender:\n stored_messages = [m for m in stored_messages if m.sender == message.sender]\n else:\n await astore_message(message, flow_id=self.graph.flow_id)\n stored_messages = (\n await aget_messages(\n session_id=message.session_id,\n context_id=message.context_id,\n sender_name=message.sender_name,\n sender=message.sender,\n )\n or []\n )\n\n if not stored_messages:\n msg = \"No messages were stored. Please ensure that the session ID and sender are properly set.\"\n raise ValueError(msg)\n\n stored_message = stored_messages[0]\n self.status = stored_message\n return stored_message\n\n async def retrieve_messages(self) -> Data:\n sender_type = self.sender_type\n sender_name = self.sender_name\n session_id = self.session_id\n context_id = self.context_id\n n_messages = self.n_messages\n order = \"DESC\" if self.order == \"Descending\" else \"ASC\"\n\n if sender_type == \"Machine and User\":\n sender_type = None\n\n if self.memory and not hasattr(self.memory, \"aget_messages\"):\n memory_name = type(self.memory).__name__\n err_msg = f\"External Memory object ({memory_name}) must have 'aget_messages' method.\"\n raise AttributeError(err_msg)\n # Check if n_messages is None or 0\n if n_messages == 0:\n stored = []\n elif self.memory:\n # override session_id\n self.memory.session_id = session_id\n self.memory.context_id = context_id\n\n stored = await self.memory.aget_messages()\n # langchain memories are supposed to return messages in ascending order\n\n if n_messages:\n stored = stored[-n_messages:] # Get last N messages first\n\n if order == \"DESC\":\n stored = stored[::-1] # Then reverse if needed\n\n stored = [Message.from_lc_message(m) for m in stored]\n if sender_type:\n expected_type = MESSAGE_SENDER_AI if sender_type == MESSAGE_SENDER_AI else MESSAGE_SENDER_USER\n stored = [m for m in stored if m.type == expected_type]\n else:\n # For internal memory, we always fetch the last N messages by ordering by DESC\n stored = await aget_messages(\n sender=sender_type,\n sender_name=sender_name,\n session_id=session_id,\n context_id=context_id,\n limit=10000,\n order=order,\n )\n if n_messages:\n stored = stored[-n_messages:] # Get last N messages\n\n # self.status = stored\n return cast(\"Data\", stored)\n\n async def retrieve_messages_as_text(self) -> Message:\n stored_text = data_to_text(self.template, await self.retrieve_messages())\n # self.status = stored_text\n return Message(text=stored_text)\n\n async def retrieve_messages_dataframe(self) -> DataFrame:\n \"\"\"Convert the retrieved messages into a DataFrame.\n\n Returns:\n DataFrame: A DataFrame containing the message data.\n \"\"\"\n messages = await self.retrieve_messages()\n return DataFrame(messages)\n\n def update_build_config(\n self,\n build_config: dotdict,\n field_value: Any, # noqa: ARG002\n field_name: str | None = None, # noqa: ARG002\n ) -> dotdict:\n return set_current_fields(\n build_config=build_config,\n action_fields=self.mode_config,\n selected_action=build_config[\"mode\"][\"value\"],\n default_fields=self.default_keys,\n func=set_field_display,\n )\n" + "value": "from typing import Any, cast\nfrom uuid import UUID\n\nfrom lfx.custom.custom_component.component import Component\nfrom lfx.helpers.data import data_to_text\nfrom lfx.inputs.inputs import DropdownInput, HandleInput, IntInput, MessageTextInput, MultilineInput, TabInput\nfrom lfx.log.logger import logger\nfrom lfx.memory import aget_messages, astore_message\nfrom lfx.schema.data import Data\nfrom lfx.schema.dataframe import DataFrame\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.template.field.base import Output\nfrom lfx.utils.component_utils import set_current_fields, set_field_display\nfrom lfx.utils.constants import MESSAGE_SENDER_AI, MESSAGE_SENDER_NAME_AI, MESSAGE_SENDER_USER\n\n# Cap on rows we will pull from the DB when the caller has not supplied an\n# explicit ``n_messages`` limit. This is a guardrail against unbounded fetches\n# (DB pressure, memory) on sessions that accumulate huge histories. The\n# trade-off is that sessions with more rows than this cap will not surface\n# their oldest messages when no ``n_messages`` is set. See ``aget_messages``\n# usage sites below for how this interacts with ordering.\nMAX_CHAT_HISTORY_FETCH_LIMIT = 10_000\n\n\ndef _coerce_flow_id_to_uuid(flow_id: str | UUID | None) -> UUID | None:\n \"\"\"Coerce a graph flow_id (typically str) to UUID for DB filtering.\n\n Returns ``None`` when ``flow_id`` is missing or cannot be parsed. The\n caller then falls back to the previous **unscoped** retrieval, which\n re-introduces the cross-flow leak that motivated PR #13087. We emit a\n structured ``error`` log on that path (rather than ``warning``) so\n observability can alert on it — see issue #13059 / PR #13087.\n \"\"\"\n if flow_id is None or flow_id == \"\":\n return None\n if isinstance(flow_id, UUID):\n return flow_id\n try:\n return UUID(str(flow_id))\n except (ValueError, TypeError, AttributeError):\n # Loud, structured signal — this path means chat history is being\n # served without flow-scoping, which is the privacy bug PR #13087\n # was created to close. Anything matching this event is a candidate\n # for an observability alert.\n logger.error(\n \"memory_flow_id_unscoped: flow_id %r is not a valid UUID; \"\n \"chat history will NOT be scoped by flow_id. This re-enables \"\n \"cross-flow leakage (issue #13059) for this request.\",\n flow_id,\n extra={\"event\": \"memory_flow_id_unscoped\", \"flow_id_repr\": repr(flow_id)},\n )\n return None\n\n\ndef _safe_graph_flow_id(component: Component) -> str | UUID | None:\n \"\"\"Best-effort lookup of the component's graph flow_id.\n\n ``Component.graph`` is a property that reaches into ``self._vertex.graph``;\n when a MemoryComponent is constructed ad-hoc (e.g. by the Agent component\n via ``MemoryComponent(**self.get_base_args())``), ``_vertex`` is ``None`` and\n accessing the property raises ``AttributeError``. Swallow that here so\n retrieval falls back to the previous unscoped behavior rather than crashing.\n \"\"\"\n try:\n graph = component.graph\n except AttributeError:\n return None\n return getattr(graph, \"flow_id\", None)\n\n\nasync def aget_agent_chat_history(\n *,\n session_id: str | UUID | None,\n flow_id: str | UUID | None,\n context_id: str | None = None,\n n_messages: int | None = None,\n) -> list[Message]:\n \"\"\"Fetch chat history for an agent, scoped to a single flow.\n\n Centralizes the contract previously implemented by\n ``MemoryComponent.retrieve_messages`` for agent callers:\n\n * Returns ``[]`` when ``n_messages == 0`` (memory explicitly disabled).\n Without this short-circuit, the bounded query would still execute and\n the caller's ``messages[-0:]`` would return everything.\n * Scopes by ``flow_id`` (coerced to ``UUID``) so default playground\n session names cannot leak history across flows (issue #13059).\n * Returns up to ``n_messages`` most recent messages in ascending order.\n Queries in ``DESC`` order with ``limit=n_messages`` so sessions with\n more than ``MAX_CHAT_HISTORY_FETCH_LIMIT`` rows still see the genuine\n most-recent slice, not the chronological first window.\n \"\"\"\n if n_messages == 0:\n return []\n fetch_limit = n_messages if n_messages else MAX_CHAT_HISTORY_FETCH_LIMIT\n messages = await aget_messages(\n session_id=session_id,\n context_id=context_id,\n flow_id=_coerce_flow_id_to_uuid(flow_id),\n limit=fetch_limit,\n order=\"DESC\",\n )\n if not n_messages and len(messages) == MAX_CHAT_HISTORY_FETCH_LIMIT:\n # We hit the unbounded-fetch ceiling. The caller will likely see\n # stale \"most-recent\" history. Flag this so on-call has something\n # to grep when a user reports forgotten context.\n logger.warning(\n \"memory_chat_history_limit_reached: hit MAX_CHAT_HISTORY_FETCH_LIMIT=%d \"\n \"without an explicit n_messages; older messages were not returned.\",\n MAX_CHAT_HISTORY_FETCH_LIMIT,\n extra={\"event\": \"memory_chat_history_limit_reached\"},\n )\n # ``aget_messages`` returned DESC; reverse to ASC for the agent's prompt.\n return list(reversed(messages))\n\n\nclass MemoryComponent(Component):\n display_name = \"Message History\"\n description = \"Stores or retrieves stored chat messages from Langflow tables or an external memory.\"\n documentation: str = \"https://docs.langflow.org/message-history\"\n icon = \"message-square-more\"\n name = \"Memory\"\n default_keys = [\"mode\", \"memory\", \"session_id\", \"context_id\"]\n mode_config = {\n \"Store\": [\"message\", \"memory\", \"sender\", \"sender_name\", \"session_id\", \"context_id\"],\n \"Retrieve\": [\"n_messages\", \"order\", \"template\", \"memory\", \"session_id\", \"context_id\"],\n }\n\n inputs = [\n TabInput(\n name=\"mode\",\n display_name=\"Mode\",\n options=[\"Retrieve\", \"Store\"],\n value=\"Retrieve\",\n info=\"Operation mode: Store messages or Retrieve messages.\",\n real_time_refresh=True,\n ),\n MessageTextInput(\n name=\"message\",\n display_name=\"Message\",\n info=\"The chat message to be stored.\",\n tool_mode=True,\n dynamic=True,\n show=False,\n ),\n HandleInput(\n name=\"memory\",\n display_name=\"External Memory\",\n input_types=[\"Memory\"],\n info=\"Retrieve messages from an external memory. If empty, it will use the Langflow tables.\",\n advanced=True,\n ),\n DropdownInput(\n name=\"sender_type\",\n display_name=\"Sender Type\",\n options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER, \"Machine and User\"],\n value=\"Machine and User\",\n info=\"Filter by sender type.\",\n advanced=True,\n ),\n MessageTextInput(\n name=\"sender\",\n display_name=\"Sender\",\n info=\"The sender of the message. Might be Machine or User. \"\n \"If empty, the current sender parameter will be used.\",\n advanced=True,\n ),\n MessageTextInput(\n name=\"sender_name\",\n display_name=\"Sender Name\",\n info=\"Filter by sender name.\",\n advanced=True,\n show=False,\n ),\n IntInput(\n name=\"n_messages\",\n display_name=\"Number of Messages\",\n value=100,\n info=\"Number of messages to retrieve.\",\n advanced=True,\n show=True,\n ),\n MessageTextInput(\n name=\"session_id\",\n display_name=\"Session ID\",\n info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n value=\"\",\n advanced=True,\n ),\n MessageTextInput(\n name=\"context_id\",\n display_name=\"Context ID\",\n info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n value=\"\",\n advanced=True,\n ),\n DropdownInput(\n name=\"order\",\n display_name=\"Order\",\n options=[\"Ascending\", \"Descending\"],\n value=\"Ascending\",\n info=\"Order of the messages.\",\n advanced=True,\n tool_mode=True,\n required=True,\n ),\n MultilineInput(\n name=\"template\",\n display_name=\"Template\",\n info=\"The template to use for formatting the data. \"\n \"It can contain the keys {text}, {sender} or any other key in the message data.\",\n value=\"{sender_name}: {text}\",\n advanced=True,\n show=False,\n ),\n ]\n\n outputs = [\n Output(\n display_name=\"Messages\",\n name=\"messages_text\",\n method=\"retrieve_messages_as_text\",\n types=[\"Message\"],\n selected=\"Message\",\n dynamic=True,\n ),\n Output(\n display_name=\"Table\",\n name=\"dataframe\",\n method=\"retrieve_messages_dataframe\",\n types=[\"Table\"],\n selected=\"Table\",\n dynamic=True,\n ),\n ]\n\n def update_outputs(self, frontend_node: dict, field_name: str, field_value: Any) -> dict:\n \"\"\"Dynamically show only the relevant output based on the selected output type.\"\"\"\n if field_name == \"mode\":\n # Start with empty outputs\n frontend_node[\"outputs\"] = []\n if field_value == \"Store\":\n frontend_node[\"outputs\"] = [\n Output(\n display_name=\"Stored Messages\",\n name=\"stored_messages\",\n method=\"store_message\",\n types=[\"Message\"],\n selected=\"Message\",\n hidden=True,\n dynamic=True,\n )\n ]\n if field_value == \"Retrieve\":\n frontend_node[\"outputs\"] = [\n Output(\n display_name=\"Messages\",\n name=\"messages_text\",\n method=\"retrieve_messages_as_text\",\n types=[\"Message\"],\n selected=\"Message\",\n dynamic=True,\n ),\n Output(\n display_name=\"Table\",\n name=\"dataframe\",\n method=\"retrieve_messages_dataframe\",\n types=[\"Table\"],\n selected=\"Table\",\n dynamic=True,\n ),\n ]\n return frontend_node\n\n async def store_message(self) -> Message:\n message = Message(text=self.message) if isinstance(self.message, str) else self.message\n\n message.context_id = self.context_id or message.context_id\n message.session_id = self.session_id or message.session_id\n message.sender = self.sender or message.sender or MESSAGE_SENDER_AI\n message.sender_name = self.sender_name or message.sender_name or MESSAGE_SENDER_NAME_AI\n\n stored_messages: list[Message] = []\n\n if self.memory:\n self.memory.context_id = message.context_id\n self.memory.session_id = message.session_id\n lc_message = message.to_lc_message()\n await self.memory.aadd_messages([lc_message])\n\n stored_messages = await self.memory.aget_messages() or []\n\n stored_messages = [Message.from_lc_message(m) for m in stored_messages] if stored_messages else []\n\n if message.sender:\n stored_messages = [m for m in stored_messages if m.sender == message.sender]\n else:\n # Single coerced scope used for both the write and the read-back,\n # so a missing/ad-hoc ``_vertex`` cannot crash the write half while\n # the read half degrades gracefully. See PR #13087 review I1.\n flow_id_scope = _coerce_flow_id_to_uuid(_safe_graph_flow_id(self))\n await astore_message(message, flow_id=flow_id_scope)\n stored_messages = (\n await aget_messages(\n session_id=message.session_id,\n context_id=message.context_id,\n sender_name=message.sender_name,\n sender=message.sender,\n flow_id=flow_id_scope,\n )\n or []\n )\n\n if not stored_messages:\n msg = \"No messages were stored. Please ensure that the session ID and sender are properly set.\"\n raise ValueError(msg)\n\n stored_message = stored_messages[0]\n self.status = stored_message\n return stored_message\n\n async def retrieve_messages(self) -> Data:\n sender_type = self.sender_type\n sender_name = self.sender_name\n session_id = self.session_id\n context_id = self.context_id\n n_messages = self.n_messages\n order = \"DESC\" if self.order == \"Descending\" else \"ASC\"\n\n if sender_type == \"Machine and User\":\n sender_type = None\n\n if self.memory and not hasattr(self.memory, \"aget_messages\"):\n memory_name = type(self.memory).__name__\n err_msg = f\"External Memory object ({memory_name}) must have 'aget_messages' method.\"\n raise AttributeError(err_msg)\n # Check if n_messages is None or 0\n if n_messages == 0:\n stored = []\n elif self.memory:\n # override session_id\n self.memory.session_id = session_id\n self.memory.context_id = context_id\n\n stored = await self.memory.aget_messages()\n # langchain memories are supposed to return messages in ascending order\n\n if n_messages:\n stored = stored[-n_messages:] # Get last N messages first\n\n if order == \"DESC\":\n stored = stored[::-1] # Then reverse if needed\n\n stored = [Message.from_lc_message(m) for m in stored]\n if sender_type:\n expected_type = MESSAGE_SENDER_AI if sender_type == MESSAGE_SENDER_AI else MESSAGE_SENDER_USER\n stored = [m for m in stored if m.type == expected_type]\n else:\n # For internal memory, fetch the last N messages by ordering DESC at the\n # DB layer so sessions with more than ``MAX_CHAT_HISTORY_FETCH_LIMIT``\n # rows still return the genuine most-recent slice rather than the\n # chronological first window. See PR #13087 review I2.\n #\n # Scope by flow_id so default session names (e.g. \"New Session 0\") do not\n # leak chat history across unrelated flows. See issue #13059.\n flow_id_scope = _coerce_flow_id_to_uuid(_safe_graph_flow_id(self))\n fetch_limit = n_messages if n_messages else MAX_CHAT_HISTORY_FETCH_LIMIT\n stored = await aget_messages(\n sender=sender_type,\n sender_name=sender_name,\n session_id=session_id,\n context_id=context_id,\n flow_id=flow_id_scope,\n limit=fetch_limit,\n order=\"DESC\",\n )\n if not n_messages and len(stored) == MAX_CHAT_HISTORY_FETCH_LIMIT:\n logger.warning(\n \"memory_chat_history_limit_reached: hit MAX_CHAT_HISTORY_FETCH_LIMIT=%d \"\n \"without an explicit n_messages; older messages were not returned.\",\n MAX_CHAT_HISTORY_FETCH_LIMIT,\n extra={\"event\": \"memory_chat_history_limit_reached\"},\n )\n # Honor the user-selected order: we fetched DESC, reverse if ASC requested.\n if order == \"ASC\":\n stored = list(reversed(stored))\n\n # self.status = stored\n return cast(\"Data\", stored)\n\n async def retrieve_messages_as_text(self) -> Message:\n stored_text = data_to_text(self.template, await self.retrieve_messages())\n # self.status = stored_text\n return Message(text=stored_text)\n\n async def retrieve_messages_dataframe(self) -> DataFrame:\n \"\"\"Convert the retrieved messages into a DataFrame.\n\n Returns:\n DataFrame: A DataFrame containing the message data.\n \"\"\"\n messages = await self.retrieve_messages()\n return DataFrame(messages)\n\n def update_build_config(\n self,\n build_config: dotdict,\n field_value: Any, # noqa: ARG002\n field_name: str | None = None, # noqa: ARG002\n ) -> dotdict:\n return set_current_fields(\n build_config=build_config,\n action_fields=self.mode_config,\n selected_action=build_config[\"mode\"][\"value\"],\n default_fields=self.default_keys,\n func=set_field_display,\n )\n" }, "context_id": { "_input_type": "MessageTextInput", diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Instagram Copywriter.json b/src/backend/base/langflow/initial_setup/starter_projects/Instagram Copywriter.json index 58f1118272..0d9852b84d 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Instagram Copywriter.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Instagram Copywriter.json @@ -2074,7 +2074,7 @@ "last_updated": "2025-12-11T21:41:48.407Z", "legacy": false, "metadata": { - "code_hash": "ebac74bfcae6", + "code_hash": "443159257161", "dependencies": { "dependencies": [ { @@ -2263,7 +2263,7 @@ "show": true, "title_case": false, "type": "code", - "value": "from __future__ import annotations\n\nfrom contextlib import contextmanager\nfrom datetime import datetime, timezone\nfrom typing import TYPE_CHECKING, Any\n\nfrom lfx.components.models_and_agents.memory import MemoryComponent\n\nif TYPE_CHECKING:\n from langchain_core.tools import Tool\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.agents.default_system_prompt import DEFAULT_SYSTEM_PROMPT_TEMPLATE\nfrom lfx.base.agents.events import ExceptionWithMessageError\nfrom lfx.base.constants import STREAM_INFO_TEXT\nfrom lfx.base.models.unified_models import (\n get_language_model_options,\n get_llm,\n handle_model_input_update,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.components.agentics.helpers.model_config import validate_model_selection\nfrom lfx.components.helpers import CalculatorComponent, CurrentDateComponent\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.custom.custom_component.component import get_component_toolkit\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, ModelInput, StrInput\nfrom lfx.io import IntInput, MessageTextInput, MultilineInput, Output, SecretStrInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.schema.data import Data\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.schema.table import EditMode\n\n\ndef set_advanced_true(component_input):\n component_input.advanced = True\n return component_input\n\n\ndef _extract_text_content(value) -> str:\n \"\"\"Pull a string payload from a Message-like, AIMessage-like, or string value.\"\"\"\n if isinstance(value, str):\n return value\n text = getattr(value, \"text\", None)\n if isinstance(text, str):\n return text\n content = getattr(value, \"content\", None)\n if isinstance(content, str):\n return content\n return str(value) if value is not None else \"\"\n\n\n@contextmanager\ndef _suppress_send_message(component: Any):\n \"\"\"Temporarily replace component.send_message with a no-op for the duration of the block.\n\n Used during the structured-output prompt fallback: run_agent streams the agent's\n final answer through self.send_message (correct for message_response), but in\n json_response the orchestrator parses that text into structured Data which the\n downstream Chat Output emits — leaving the original emission in place produces a\n duplicate message in the playground. The original method is always restored on exit,\n even when the wrapped call raises.\n \"\"\"\n original = component.send_message\n\n async def _noop(message, *_args, **_kwargs):\n return message\n\n component.send_message = _noop\n try:\n yield\n finally:\n component.send_message = original\n\n\nclass AgentComponent(ToolCallingAgentComponent):\n display_name: str = \"Agent\"\n description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n documentation: str = \"https://docs.langflow.org/agents\"\n icon = \"bot\"\n beta = False\n name = \"Agent\"\n\n memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n inputs = [\n ModelInput(\n name=\"model\",\n display_name=\"Language Model\",\n info=\"Select your model provider\",\n real_time_refresh=True,\n required=True,\n # Agents require tool calling — the filter is honored by\n # ``handle_model_input_update`` so models that can't run with\n # tools never reach the picker (and any saved selection that\n # no longer satisfies the constraint is auto-replaced).\n filters={\"tool_calling\": True},\n ),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"API Key\",\n info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n real_time_refresh=True,\n advanced=True,\n ),\n DropdownInput(\n name=\"base_url_ibm_watsonx\",\n display_name=\"watsonx API Endpoint\",\n info=\"The base URL of the API (IBM watsonx.ai only)\",\n options=IBM_WATSONX_URLS,\n value=IBM_WATSONX_URLS[0],\n combobox=True,\n show=False,\n real_time_refresh=True,\n ),\n StrInput(\n name=\"project_id\",\n display_name=\"watsonx Project ID\",\n info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n show=False,\n required=False,\n ),\n MultilineInput(\n name=\"system_prompt\",\n display_name=\"Agent Instructions\",\n info=(\n \"System Prompt: Initial instructions and context provided to guide the agent's behavior. \"\n \"Supports dynamic placeholders: {current_date}, {model_name}, {optional_user_context}.\"\n ),\n value=DEFAULT_SYSTEM_PROMPT_TEMPLATE,\n advanced=False,\n ),\n MessageTextInput(\n name=\"context_id\",\n display_name=\"Context ID\",\n info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n value=\"\",\n advanced=True,\n ),\n IntInput(\n name=\"n_messages\",\n display_name=\"Number of Chat History Messages\",\n value=100,\n info=\"Number of chat history messages to retrieve.\",\n advanced=True,\n show=True,\n ),\n IntInput(\n name=\"max_tokens\",\n display_name=\"Max Tokens\",\n info=\"Maximum number of tokens to generate. Field name varies by provider.\",\n advanced=True,\n range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n ),\n MultilineInput(\n name=\"format_instructions\",\n display_name=\"Output Format Instructions\",\n info=\"Generic Template for structured output formatting. Valid only with Structured response.\",\n value=(\n \"You are an AI that extracts structured JSON objects from unstructured text. \"\n \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n \"Fill missing or ambiguous values with defaults: null for missing values. \"\n \"Remove exact duplicates but keep variations that have different field values. \"\n \"Always return valid JSON in the expected format, never throw errors. \"\n \"If multiple objects can be extracted, return them all in the structured format.\"\n ),\n advanced=True,\n ),\n TableInput(\n name=\"output_schema\",\n display_name=\"Output Schema\",\n info=(\n \"Schema Validation: Define the structure and data types for structured output. \"\n \"No validation if no output schema.\"\n ),\n advanced=True,\n required=False,\n value=[],\n table_schema=[\n {\n \"name\": \"name\",\n \"display_name\": \"Name\",\n \"type\": \"str\",\n \"description\": \"Specify the name of the output field.\",\n \"default\": \"field\",\n \"edit_mode\": EditMode.INLINE,\n },\n {\n \"name\": \"description\",\n \"display_name\": \"Description\",\n \"type\": \"str\",\n \"description\": \"Describe the purpose of the output field.\",\n \"default\": \"description of field\",\n \"edit_mode\": EditMode.POPOVER,\n },\n {\n \"name\": \"type\",\n \"display_name\": \"Type\",\n \"type\": \"str\",\n \"edit_mode\": EditMode.INLINE,\n \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n \"default\": \"str\",\n },\n {\n \"name\": \"multiple\",\n \"display_name\": \"As List\",\n \"type\": \"boolean\",\n \"description\": \"Set to True if this output field should be a list of the specified type.\",\n \"default\": \"False\",\n \"edit_mode\": EditMode.INLINE,\n },\n ],\n ),\n *LCToolsAgentComponent.get_base_inputs(),\n # removed memory inputs from agent component\n # *memory_inputs,\n BoolInput(\n name=\"stream\",\n display_name=\"Stream\",\n info=STREAM_INFO_TEXT,\n value=False,\n advanced=True,\n ),\n BoolInput(\n name=\"add_current_date_tool\",\n display_name=\"Current Date\",\n advanced=True,\n info=\"If true, will add a tool to the agent that returns the current date.\",\n value=True,\n ),\n BoolInput(\n name=\"add_calculator_tool\",\n display_name=\"Calculator\",\n advanced=True,\n info=(\n \"If true, adds a zero-config arithmetic calculator tool to the agent \"\n \"(safe: only +, -, *, /, ** operators via AST).\"\n ),\n value=True,\n ),\n ]\n outputs = [\n Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n Output(\n name=\"structured_response\",\n display_name=\"Structured Response\",\n method=\"json_response\",\n types=[\"Data\"],\n ),\n ]\n\n def _resolve_selected_model(self):\n \"\"\"Resolve the selected model, including legacy agent_llm/model_name inputs.\"\"\"\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n if isinstance(self.model, BaseLanguageModel):\n return self.model\n except ImportError:\n pass\n\n if isinstance(self.model, list) and self.model:\n return self.model\n\n legacy_provider = getattr(self, \"agent_llm\", None)\n legacy_model_name = getattr(self, \"model_name\", None)\n if not legacy_provider or not legacy_model_name:\n return self.model\n\n options = get_language_model_options(user_id=self.user_id)\n for option in options:\n if option.get(\"provider\") == legacy_provider and option.get(\"name\") == legacy_model_name:\n return [option]\n\n return [\n {\n \"name\": legacy_model_name,\n \"provider\": legacy_provider,\n \"metadata\": {},\n }\n ]\n\n def _get_max_tokens_value(self):\n \"\"\"Return the user-supplied max_tokens or None when unset/zero.\"\"\"\n val = getattr(self, \"max_tokens\", None)\n if val in {\"\", 0}:\n return None\n return val\n\n def _get_llm(self):\n \"\"\"Override parent to include max_tokens from the Agent's input field.\"\"\"\n return get_llm(\n model=self.model,\n user_id=self.user_id,\n api_key=getattr(self, \"api_key\", None),\n stream=bool(getattr(self, \"stream\", False)),\n max_tokens=self._get_max_tokens_value(),\n watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n watsonx_project_id=getattr(self, \"project_id\", None),\n )\n\n async def get_agent_requirements(self):\n \"\"\"Get the agent requirements for the agent.\"\"\"\n from langchain_core.tools import StructuredTool\n\n selected_model = self._resolve_selected_model()\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n is_connected_model = isinstance(selected_model, BaseLanguageModel)\n except ImportError:\n is_connected_model = False\n\n if not is_connected_model:\n validate_model_selection(selected_model)\n\n # Ensure _get_llm() uses the resolved model (e.g. from legacy agent_llm/model_name)\n self.model = selected_model\n llm_model = self._get_llm()\n if llm_model is None:\n msg = \"No language model selected. Please choose a model to proceed.\"\n raise ValueError(msg)\n\n # Get memory data\n self.chat_history = await self.get_memory_data()\n await logger.adebug(f\"Retrieved {len(self.chat_history)} chat history messages\")\n if isinstance(self.chat_history, Message):\n self.chat_history = [self.chat_history]\n\n # Add current date tool if enabled\n if self.add_current_date_tool:\n if not isinstance(self.tools, list): # type: ignore[has-type]\n self.tools = []\n current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n if not isinstance(current_date_tool, StructuredTool):\n msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n raise TypeError(msg)\n # Skip if an externally-connected tool already provides the same name.\n # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n if not any(getattr(t, \"name\", None) == current_date_tool.name for t in self.tools):\n self.tools.append(current_date_tool)\n\n # Add calculator tool if enabled (zero-config arithmetic)\n if getattr(self, \"add_calculator_tool\", False):\n if not isinstance(self.tools, list): # type: ignore[has-type]\n self.tools = []\n calculator_tool = (await CalculatorComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n if not isinstance(calculator_tool, StructuredTool):\n msg = \"CalculatorComponent must be converted to a StructuredTool\"\n raise TypeError(msg)\n # Skip if an externally-connected tool already provides the same name.\n # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n if not any(getattr(t, \"name\", None) == calculator_tool.name for t in self.tools):\n self.tools.append(calculator_tool)\n\n # Set shared callbacks for tracing the tools used by the agent\n self.set_tools_callbacks(self.tools, self._get_shared_callbacks())\n\n return llm_model, self.chat_history, self.tools\n\n def _get_resolved_model_name(self) -> str:\n \"\"\"Best-effort human-readable model name for {model_name} injection.\"\"\"\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n if isinstance(self.model, BaseLanguageModel):\n return type(self.model).__name__\n except ImportError:\n pass\n\n if isinstance(self.model, list) and self.model:\n first = self.model[0]\n if isinstance(first, dict):\n name = first.get(\"name\")\n if isinstance(name, str) and name:\n return name\n\n legacy_model_name = getattr(self, \"model_name\", None)\n if isinstance(legacy_model_name, str) and legacy_model_name:\n return legacy_model_name\n return \"\"\n\n def _inject_dynamic_prompt_values(self, prompt: str | None) -> str | None:\n \"\"\"Replace known env placeholders in the system prompt.\n\n Handles {current_date}, {model_name}, and {optional_user_context} (the\n last one ships with the structured DEFAULT_SYSTEM_PROMPT_TEMPLATE and\n is currently unused at the AgentComponent layer, so it resolves to \"\").\n Uses str.replace (not str.format) so user prompts containing literal\n braces such as JSON examples ({\"key\": 1}) never break the agent.\n \"\"\"\n if not prompt:\n return prompt\n replacements = {\n \"{current_date}\": datetime.now(tz=timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S UTC\"),\n \"{model_name}\": self._get_resolved_model_name(),\n \"{optional_user_context}\": \"\",\n }\n for placeholder, value in replacements.items():\n prompt = prompt.replace(placeholder, value)\n return prompt\n\n async def message_response(self) -> Message:\n try:\n llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n # Set up and run agent\n self.set(\n llm=llm_model,\n tools=self.tools or [],\n chat_history=self.chat_history,\n input_value=self.input_value,\n system_prompt=self._inject_dynamic_prompt_values(self.system_prompt),\n )\n agent = self.create_agent_runnable()\n result = await self.run_agent(agent)\n\n # Store result for potential JSON output\n self._agent_result = result\n\n except (ValueError, TypeError, KeyError) as e:\n await logger.aerror(f\"{type(e).__name__}: {e!s}\")\n raise\n except ExceptionWithMessageError as e:\n await logger.aerror(f\"ExceptionWithMessageError occurred: {e}\")\n raise\n # Avoid catching blind Exception; let truly unexpected exceptions propagate\n except Exception as e:\n await logger.aerror(f\"Unexpected error: {e!s}\")\n raise\n else:\n return result\n\n async def json_response(self) -> Data:\n \"\"\"Produce structured Data via native LLM structured output, with prompt-based fallback.\n\n Native path (no tools, llm has with_structured_output) bypasses the agent loop and\n returns provider-validated JSON. When tools are attached, falls back to running the\n agent with a schema-augmented system prompt and parsing the final message content.\n \"\"\"\n from lfx.components.models_and_agents.structured_output.structured_output_orchestrator import (\n orchestrate_structured_output,\n )\n\n try:\n llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n except (ValueError, TypeError) as exc:\n await logger.aerror(f\"json_response.requirements_failed: {exc}\")\n return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n injected_system_prompt = self._inject_dynamic_prompt_values(getattr(self, \"system_prompt\", \"\") or \"\") or \"\"\n format_instructions = getattr(self, \"format_instructions\", \"\") or \"\"\n output_schema = getattr(self, \"output_schema\", None) or []\n has_tools = bool(self.tools)\n\n async def _run_agent_for_fallback(augmented_prompt: str) -> str:\n self.set(\n llm=llm_model,\n tools=self.tools or [],\n chat_history=self.chat_history,\n input_value=self.input_value,\n system_prompt=augmented_prompt,\n )\n agent_runnable = self.create_agent_runnable()\n with _suppress_send_message(self):\n result = await self.run_agent(agent_runnable)\n return _extract_text_content(result)\n\n try:\n return await orchestrate_structured_output(\n llm=llm_model,\n output_schema=output_schema,\n system_prompt=injected_system_prompt,\n format_instructions=format_instructions,\n input_value=_extract_text_content(self.input_value),\n run_prompt_fallback=_run_agent_for_fallback,\n prefer_native=not has_tools,\n )\n except (\n ExceptionWithMessageError,\n ValueError,\n TypeError,\n NotImplementedError,\n AttributeError,\n ) as exc:\n await logger.aerror(f\"json_response.orchestration_failed: {exc}\")\n return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n async def get_memory_data(self):\n # TODO: This is a temporary fix to avoid message duplication. We should develop a function for this.\n messages = (\n await MemoryComponent(**self.get_base_args())\n .set(\n session_id=self.graph.session_id,\n context_id=self.context_id,\n order=\"Ascending\",\n n_messages=self.n_messages,\n )\n .retrieve_messages()\n )\n return [\n message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n ]\n\n def update_input_types(self, build_config: dotdict) -> dotdict:\n \"\"\"Update input types for all fields in build_config.\"\"\"\n for key, value in build_config.items():\n if isinstance(value, dict):\n if value.get(\"input_types\") is None:\n build_config[key][\"input_types\"] = []\n elif hasattr(value, \"input_types\") and value.input_types is None:\n value.input_types = []\n return build_config\n\n async def update_build_config(\n self,\n build_config: dotdict,\n field_value: list[dict],\n field_name: str | None = None,\n ) -> dotdict:\n # Update model options with caching (for all field changes).\n # The tool-calling constraint lives on the ModelInput's ``filters``\n # field (declared above); ``handle_model_input_update`` reads it\n # and applies the filter to both the dropdown options and the\n # sticky-default re-injection path.\n build_config = handle_model_input_update(\n component=self,\n build_config=dict(build_config),\n field_value=field_value,\n field_name=field_name,\n )\n build_config = dotdict(build_config)\n\n if field_name == \"model\":\n build_config = self.update_input_types(build_config)\n\n # Validate required keys\n default_keys = [\n \"code\",\n \"_type\",\n \"model\",\n \"tools\",\n \"input_value\",\n \"add_current_date_tool\",\n \"add_calculator_tool\",\n \"system_prompt\",\n \"agent_description\",\n \"max_iterations\",\n \"handle_parsing_errors\",\n \"verbose\",\n ]\n missing_keys = [key for key in default_keys if key not in build_config]\n if missing_keys:\n msg = f\"Missing required keys in build_config: {missing_keys}\"\n raise ValueError(msg)\n return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n async def _get_tools(self) -> list[Tool]:\n component_toolkit = get_component_toolkit()\n tools_names = self._build_tools_names()\n agent_description = self.get_tool_description()\n # TODO: Agent Description Depreciated Feature to be removed\n description = f\"{agent_description}{tools_names}\"\n\n tools = component_toolkit(component=self).get_tools(\n tool_name=\"Call_Agent\",\n tool_description=description,\n # here we do not use the shared callbacks as we are exposing the agent as a tool\n callbacks=self.get_langchain_callbacks(),\n )\n if hasattr(self, \"tools_metadata\"):\n tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n\n return tools\n" + "value": "from __future__ import annotations\n\nfrom contextlib import contextmanager\nfrom datetime import datetime, timezone\nfrom typing import TYPE_CHECKING, Any\n\nfrom lfx.components.models_and_agents.memory import MemoryComponent, aget_agent_chat_history\n\nif TYPE_CHECKING:\n from langchain_core.tools import Tool\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.agents.default_system_prompt import DEFAULT_SYSTEM_PROMPT_TEMPLATE\nfrom lfx.base.agents.events import ExceptionWithMessageError\nfrom lfx.base.constants import STREAM_INFO_TEXT\nfrom lfx.base.models.unified_models import (\n get_language_model_options,\n get_llm,\n handle_model_input_update,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.components.agentics.helpers.model_config import validate_model_selection\nfrom lfx.components.helpers import CalculatorComponent, CurrentDateComponent\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.custom.custom_component.component import get_component_toolkit\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, ModelInput, StrInput\nfrom lfx.io import IntInput, MessageTextInput, MultilineInput, Output, SecretStrInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.schema.data import Data\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.schema.table import EditMode\n\n\ndef set_advanced_true(component_input):\n component_input.advanced = True\n return component_input\n\n\ndef _extract_text_content(value) -> str:\n \"\"\"Pull a string payload from a Message-like, AIMessage-like, or string value.\"\"\"\n if isinstance(value, str):\n return value\n text = getattr(value, \"text\", None)\n if isinstance(text, str):\n return text\n content = getattr(value, \"content\", None)\n if isinstance(content, str):\n return content\n return str(value) if value is not None else \"\"\n\n\n@contextmanager\ndef _suppress_send_message(component: Any):\n \"\"\"Temporarily replace component.send_message with a no-op for the duration of the block.\n\n Used during the structured-output prompt fallback: run_agent streams the agent's\n final answer through self.send_message (correct for message_response), but in\n json_response the orchestrator parses that text into structured Data which the\n downstream Chat Output emits — leaving the original emission in place produces a\n duplicate message in the playground. The original method is always restored on exit,\n even when the wrapped call raises.\n \"\"\"\n original = component.send_message\n\n async def _noop(message, *_args, **_kwargs):\n return message\n\n component.send_message = _noop\n try:\n yield\n finally:\n component.send_message = original\n\n\nclass AgentComponent(ToolCallingAgentComponent):\n display_name: str = \"Agent\"\n description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n documentation: str = \"https://docs.langflow.org/agents\"\n icon = \"bot\"\n beta = False\n name = \"Agent\"\n\n memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n inputs = [\n ModelInput(\n name=\"model\",\n display_name=\"Language Model\",\n info=\"Select your model provider\",\n real_time_refresh=True,\n required=True,\n # Agents require tool calling — the filter is honored by\n # ``handle_model_input_update`` so models that can't run with\n # tools never reach the picker (and any saved selection that\n # no longer satisfies the constraint is auto-replaced).\n filters={\"tool_calling\": True},\n ),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"API Key\",\n info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n real_time_refresh=True,\n advanced=True,\n ),\n DropdownInput(\n name=\"base_url_ibm_watsonx\",\n display_name=\"watsonx API Endpoint\",\n info=\"The base URL of the API (IBM watsonx.ai only)\",\n options=IBM_WATSONX_URLS,\n value=IBM_WATSONX_URLS[0],\n combobox=True,\n show=False,\n real_time_refresh=True,\n ),\n StrInput(\n name=\"project_id\",\n display_name=\"watsonx Project ID\",\n info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n show=False,\n required=False,\n ),\n MultilineInput(\n name=\"system_prompt\",\n display_name=\"Agent Instructions\",\n info=(\n \"System Prompt: Initial instructions and context provided to guide the agent's behavior. \"\n \"Supports dynamic placeholders: {current_date}, {model_name}, {optional_user_context}.\"\n ),\n value=DEFAULT_SYSTEM_PROMPT_TEMPLATE,\n advanced=False,\n ),\n MessageTextInput(\n name=\"context_id\",\n display_name=\"Context ID\",\n info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n value=\"\",\n advanced=True,\n ),\n IntInput(\n name=\"n_messages\",\n display_name=\"Number of Chat History Messages\",\n value=100,\n info=\"Number of chat history messages to retrieve.\",\n advanced=True,\n show=True,\n ),\n IntInput(\n name=\"max_tokens\",\n display_name=\"Max Tokens\",\n info=\"Maximum number of tokens to generate. Field name varies by provider.\",\n advanced=True,\n range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n ),\n MultilineInput(\n name=\"format_instructions\",\n display_name=\"Output Format Instructions\",\n info=\"Generic Template for structured output formatting. Valid only with Structured response.\",\n value=(\n \"You are an AI that extracts structured JSON objects from unstructured text. \"\n \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n \"Fill missing or ambiguous values with defaults: null for missing values. \"\n \"Remove exact duplicates but keep variations that have different field values. \"\n \"Always return valid JSON in the expected format, never throw errors. \"\n \"If multiple objects can be extracted, return them all in the structured format.\"\n ),\n advanced=True,\n ),\n TableInput(\n name=\"output_schema\",\n display_name=\"Output Schema\",\n info=(\n \"Schema Validation: Define the structure and data types for structured output. \"\n \"No validation if no output schema.\"\n ),\n advanced=True,\n required=False,\n value=[],\n table_schema=[\n {\n \"name\": \"name\",\n \"display_name\": \"Name\",\n \"type\": \"str\",\n \"description\": \"Specify the name of the output field.\",\n \"default\": \"field\",\n \"edit_mode\": EditMode.INLINE,\n },\n {\n \"name\": \"description\",\n \"display_name\": \"Description\",\n \"type\": \"str\",\n \"description\": \"Describe the purpose of the output field.\",\n \"default\": \"description of field\",\n \"edit_mode\": EditMode.POPOVER,\n },\n {\n \"name\": \"type\",\n \"display_name\": \"Type\",\n \"type\": \"str\",\n \"edit_mode\": EditMode.INLINE,\n \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n \"default\": \"str\",\n },\n {\n \"name\": \"multiple\",\n \"display_name\": \"As List\",\n \"type\": \"boolean\",\n \"description\": \"Set to True if this output field should be a list of the specified type.\",\n \"default\": \"False\",\n \"edit_mode\": EditMode.INLINE,\n },\n ],\n ),\n *LCToolsAgentComponent.get_base_inputs(),\n # removed memory inputs from agent component\n # *memory_inputs,\n BoolInput(\n name=\"stream\",\n display_name=\"Stream\",\n info=STREAM_INFO_TEXT,\n value=False,\n advanced=True,\n ),\n BoolInput(\n name=\"add_current_date_tool\",\n display_name=\"Current Date\",\n advanced=True,\n info=\"If true, will add a tool to the agent that returns the current date.\",\n value=True,\n ),\n BoolInput(\n name=\"add_calculator_tool\",\n display_name=\"Calculator\",\n advanced=True,\n info=(\n \"If true, adds a zero-config arithmetic calculator tool to the agent \"\n \"(safe: only +, -, *, /, ** operators via AST).\"\n ),\n value=True,\n ),\n ]\n outputs = [\n Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n Output(\n name=\"structured_response\",\n display_name=\"Structured Response\",\n method=\"json_response\",\n types=[\"Data\"],\n ),\n ]\n\n def _resolve_selected_model(self):\n \"\"\"Resolve the selected model, including legacy agent_llm/model_name inputs.\"\"\"\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n if isinstance(self.model, BaseLanguageModel):\n return self.model\n except ImportError:\n pass\n\n if isinstance(self.model, list) and self.model:\n return self.model\n\n legacy_provider = getattr(self, \"agent_llm\", None)\n legacy_model_name = getattr(self, \"model_name\", None)\n if not legacy_provider or not legacy_model_name:\n return self.model\n\n options = get_language_model_options(user_id=self.user_id)\n for option in options:\n if option.get(\"provider\") == legacy_provider and option.get(\"name\") == legacy_model_name:\n return [option]\n\n return [\n {\n \"name\": legacy_model_name,\n \"provider\": legacy_provider,\n \"metadata\": {},\n }\n ]\n\n def _get_max_tokens_value(self):\n \"\"\"Return the user-supplied max_tokens or None when unset/zero.\"\"\"\n val = getattr(self, \"max_tokens\", None)\n if val in {\"\", 0}:\n return None\n return val\n\n def _get_llm(self):\n \"\"\"Override parent to include max_tokens from the Agent's input field.\"\"\"\n return get_llm(\n model=self.model,\n user_id=self.user_id,\n api_key=getattr(self, \"api_key\", None),\n stream=bool(getattr(self, \"stream\", False)),\n max_tokens=self._get_max_tokens_value(),\n watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n watsonx_project_id=getattr(self, \"project_id\", None),\n )\n\n async def get_agent_requirements(self):\n \"\"\"Get the agent requirements for the agent.\"\"\"\n from langchain_core.tools import StructuredTool\n\n selected_model = self._resolve_selected_model()\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n is_connected_model = isinstance(selected_model, BaseLanguageModel)\n except ImportError:\n is_connected_model = False\n\n if not is_connected_model:\n validate_model_selection(selected_model)\n\n # Ensure _get_llm() uses the resolved model (e.g. from legacy agent_llm/model_name)\n self.model = selected_model\n llm_model = self._get_llm()\n if llm_model is None:\n msg = \"No language model selected. Please choose a model to proceed.\"\n raise ValueError(msg)\n\n # Get memory data\n self.chat_history = await self.get_memory_data()\n await logger.adebug(f\"Retrieved {len(self.chat_history)} chat history messages\")\n if isinstance(self.chat_history, Message):\n self.chat_history = [self.chat_history]\n\n # Add current date tool if enabled\n if self.add_current_date_tool:\n if not isinstance(self.tools, list): # type: ignore[has-type]\n self.tools = []\n current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n if not isinstance(current_date_tool, StructuredTool):\n msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n raise TypeError(msg)\n # Skip if an externally-connected tool already provides the same name.\n # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n if not any(getattr(t, \"name\", None) == current_date_tool.name for t in self.tools):\n self.tools.append(current_date_tool)\n\n # Add calculator tool if enabled (zero-config arithmetic)\n if getattr(self, \"add_calculator_tool\", False):\n if not isinstance(self.tools, list): # type: ignore[has-type]\n self.tools = []\n calculator_tool = (await CalculatorComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n if not isinstance(calculator_tool, StructuredTool):\n msg = \"CalculatorComponent must be converted to a StructuredTool\"\n raise TypeError(msg)\n # Skip if an externally-connected tool already provides the same name.\n # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n if not any(getattr(t, \"name\", None) == calculator_tool.name for t in self.tools):\n self.tools.append(calculator_tool)\n\n # Set shared callbacks for tracing the tools used by the agent\n self.set_tools_callbacks(self.tools, self._get_shared_callbacks())\n\n return llm_model, self.chat_history, self.tools\n\n def _get_resolved_model_name(self) -> str:\n \"\"\"Best-effort human-readable model name for {model_name} injection.\"\"\"\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n if isinstance(self.model, BaseLanguageModel):\n return type(self.model).__name__\n except ImportError:\n pass\n\n if isinstance(self.model, list) and self.model:\n first = self.model[0]\n if isinstance(first, dict):\n name = first.get(\"name\")\n if isinstance(name, str) and name:\n return name\n\n legacy_model_name = getattr(self, \"model_name\", None)\n if isinstance(legacy_model_name, str) and legacy_model_name:\n return legacy_model_name\n return \"\"\n\n def _inject_dynamic_prompt_values(self, prompt: str | None) -> str | None:\n \"\"\"Replace known env placeholders in the system prompt.\n\n Handles {current_date}, {model_name}, and {optional_user_context} (the\n last one ships with the structured DEFAULT_SYSTEM_PROMPT_TEMPLATE and\n is currently unused at the AgentComponent layer, so it resolves to \"\").\n Uses str.replace (not str.format) so user prompts containing literal\n braces such as JSON examples ({\"key\": 1}) never break the agent.\n \"\"\"\n if not prompt:\n return prompt\n replacements = {\n \"{current_date}\": datetime.now(tz=timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S UTC\"),\n \"{model_name}\": self._get_resolved_model_name(),\n \"{optional_user_context}\": \"\",\n }\n for placeholder, value in replacements.items():\n prompt = prompt.replace(placeholder, value)\n return prompt\n\n async def message_response(self) -> Message:\n try:\n llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n # Set up and run agent\n self.set(\n llm=llm_model,\n tools=self.tools or [],\n chat_history=self.chat_history,\n input_value=self.input_value,\n system_prompt=self._inject_dynamic_prompt_values(self.system_prompt),\n )\n agent = self.create_agent_runnable()\n result = await self.run_agent(agent)\n\n # Store result for potential JSON output\n self._agent_result = result\n\n except (ValueError, TypeError, KeyError) as e:\n await logger.aerror(f\"{type(e).__name__}: {e!s}\")\n raise\n except ExceptionWithMessageError as e:\n await logger.aerror(f\"ExceptionWithMessageError occurred: {e}\")\n raise\n # Avoid catching blind Exception; let truly unexpected exceptions propagate\n except Exception as e:\n await logger.aerror(f\"Unexpected error: {e!s}\")\n raise\n else:\n return result\n\n async def json_response(self) -> Data:\n \"\"\"Produce structured Data via native LLM structured output, with prompt-based fallback.\n\n Native path (no tools, llm has with_structured_output) bypasses the agent loop and\n returns provider-validated JSON. When tools are attached, falls back to running the\n agent with a schema-augmented system prompt and parsing the final message content.\n \"\"\"\n from lfx.components.models_and_agents.structured_output.structured_output_orchestrator import (\n orchestrate_structured_output,\n )\n\n try:\n llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n except (ValueError, TypeError) as exc:\n await logger.aerror(f\"json_response.requirements_failed: {exc}\")\n return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n injected_system_prompt = self._inject_dynamic_prompt_values(getattr(self, \"system_prompt\", \"\") or \"\") or \"\"\n format_instructions = getattr(self, \"format_instructions\", \"\") or \"\"\n output_schema = getattr(self, \"output_schema\", None) or []\n has_tools = bool(self.tools)\n\n async def _run_agent_for_fallback(augmented_prompt: str) -> str:\n self.set(\n llm=llm_model,\n tools=self.tools or [],\n chat_history=self.chat_history,\n input_value=self.input_value,\n system_prompt=augmented_prompt,\n )\n agent_runnable = self.create_agent_runnable()\n with _suppress_send_message(self):\n result = await self.run_agent(agent_runnable)\n return _extract_text_content(result)\n\n try:\n return await orchestrate_structured_output(\n llm=llm_model,\n output_schema=output_schema,\n system_prompt=injected_system_prompt,\n format_instructions=format_instructions,\n input_value=_extract_text_content(self.input_value),\n run_prompt_fallback=_run_agent_for_fallback,\n prefer_native=not has_tools,\n )\n except (\n ExceptionWithMessageError,\n ValueError,\n TypeError,\n NotImplementedError,\n AttributeError,\n ) as exc:\n await logger.aerror(f\"json_response.orchestration_failed: {exc}\")\n return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n async def get_memory_data(self):\n # Scope by flow_id so default playground session names (e.g. \"New Session 0\")\n # cannot leak chat history across unrelated flows. See issue #13059.\n # The helper also returns [] when n_messages == 0, preserving the\n # explicit \"memory disabled\" contract from MemoryComponent.retrieve_messages.\n messages = await aget_agent_chat_history(\n session_id=self.graph.session_id,\n flow_id=getattr(self.graph, \"flow_id\", None),\n context_id=self.context_id,\n n_messages=self.n_messages,\n )\n return [\n message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n ]\n\n def update_input_types(self, build_config: dotdict) -> dotdict:\n \"\"\"Update input types for all fields in build_config.\"\"\"\n for key, value in build_config.items():\n if isinstance(value, dict):\n if value.get(\"input_types\") is None:\n build_config[key][\"input_types\"] = []\n elif hasattr(value, \"input_types\") and value.input_types is None:\n value.input_types = []\n return build_config\n\n async def update_build_config(\n self,\n build_config: dotdict,\n field_value: list[dict],\n field_name: str | None = None,\n ) -> dotdict:\n # Update model options with caching (for all field changes).\n # The tool-calling constraint lives on the ModelInput's ``filters``\n # field (declared above); ``handle_model_input_update`` reads it\n # and applies the filter to both the dropdown options and the\n # sticky-default re-injection path.\n build_config = handle_model_input_update(\n component=self,\n build_config=dict(build_config),\n field_value=field_value,\n field_name=field_name,\n )\n build_config = dotdict(build_config)\n\n if field_name == \"model\":\n build_config = self.update_input_types(build_config)\n\n # Validate required keys\n default_keys = [\n \"code\",\n \"_type\",\n \"model\",\n \"tools\",\n \"input_value\",\n \"add_current_date_tool\",\n \"add_calculator_tool\",\n \"system_prompt\",\n \"agent_description\",\n \"max_iterations\",\n \"handle_parsing_errors\",\n \"verbose\",\n ]\n missing_keys = [key for key in default_keys if key not in build_config]\n if missing_keys:\n msg = f\"Missing required keys in build_config: {missing_keys}\"\n raise ValueError(msg)\n return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n async def _get_tools(self) -> list[Tool]:\n component_toolkit = get_component_toolkit()\n tools_names = self._build_tools_names()\n agent_description = self.get_tool_description()\n # TODO: Agent Description Depreciated Feature to be removed\n description = f\"{agent_description}{tools_names}\"\n\n tools = component_toolkit(component=self).get_tools(\n tool_name=\"Call_Agent\",\n tool_description=description,\n # here we do not use the shared callbacks as we are exposing the agent as a tool\n callbacks=self.get_langchain_callbacks(),\n )\n if hasattr(self, \"tools_metadata\"):\n tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n\n return tools\n" }, "context_id": { "_input_type": "MessageTextInput", diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Invoice Summarizer.json b/src/backend/base/langflow/initial_setup/starter_projects/Invoice Summarizer.json index c34ce7faea..ee047c8d94 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Invoice Summarizer.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Invoice Summarizer.json @@ -1184,7 +1184,7 @@ "last_updated": "2025-12-11T21:41:48.407Z", "legacy": false, "metadata": { - "code_hash": "ebac74bfcae6", + "code_hash": "443159257161", "dependencies": { "dependencies": [ { @@ -1373,7 +1373,7 @@ "show": true, "title_case": false, "type": "code", - "value": "from __future__ import annotations\n\nfrom contextlib import contextmanager\nfrom datetime import datetime, timezone\nfrom typing import TYPE_CHECKING, Any\n\nfrom lfx.components.models_and_agents.memory import MemoryComponent\n\nif TYPE_CHECKING:\n from langchain_core.tools import Tool\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.agents.default_system_prompt import DEFAULT_SYSTEM_PROMPT_TEMPLATE\nfrom lfx.base.agents.events import ExceptionWithMessageError\nfrom lfx.base.constants import STREAM_INFO_TEXT\nfrom lfx.base.models.unified_models import (\n get_language_model_options,\n get_llm,\n handle_model_input_update,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.components.agentics.helpers.model_config import validate_model_selection\nfrom lfx.components.helpers import CalculatorComponent, CurrentDateComponent\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.custom.custom_component.component import get_component_toolkit\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, ModelInput, StrInput\nfrom lfx.io import IntInput, MessageTextInput, MultilineInput, Output, SecretStrInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.schema.data import Data\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.schema.table import EditMode\n\n\ndef set_advanced_true(component_input):\n component_input.advanced = True\n return component_input\n\n\ndef _extract_text_content(value) -> str:\n \"\"\"Pull a string payload from a Message-like, AIMessage-like, or string value.\"\"\"\n if isinstance(value, str):\n return value\n text = getattr(value, \"text\", None)\n if isinstance(text, str):\n return text\n content = getattr(value, \"content\", None)\n if isinstance(content, str):\n return content\n return str(value) if value is not None else \"\"\n\n\n@contextmanager\ndef _suppress_send_message(component: Any):\n \"\"\"Temporarily replace component.send_message with a no-op for the duration of the block.\n\n Used during the structured-output prompt fallback: run_agent streams the agent's\n final answer through self.send_message (correct for message_response), but in\n json_response the orchestrator parses that text into structured Data which the\n downstream Chat Output emits — leaving the original emission in place produces a\n duplicate message in the playground. The original method is always restored on exit,\n even when the wrapped call raises.\n \"\"\"\n original = component.send_message\n\n async def _noop(message, *_args, **_kwargs):\n return message\n\n component.send_message = _noop\n try:\n yield\n finally:\n component.send_message = original\n\n\nclass AgentComponent(ToolCallingAgentComponent):\n display_name: str = \"Agent\"\n description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n documentation: str = \"https://docs.langflow.org/agents\"\n icon = \"bot\"\n beta = False\n name = \"Agent\"\n\n memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n inputs = [\n ModelInput(\n name=\"model\",\n display_name=\"Language Model\",\n info=\"Select your model provider\",\n real_time_refresh=True,\n required=True,\n # Agents require tool calling — the filter is honored by\n # ``handle_model_input_update`` so models that can't run with\n # tools never reach the picker (and any saved selection that\n # no longer satisfies the constraint is auto-replaced).\n filters={\"tool_calling\": True},\n ),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"API Key\",\n info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n real_time_refresh=True,\n advanced=True,\n ),\n DropdownInput(\n name=\"base_url_ibm_watsonx\",\n display_name=\"watsonx API Endpoint\",\n info=\"The base URL of the API (IBM watsonx.ai only)\",\n options=IBM_WATSONX_URLS,\n value=IBM_WATSONX_URLS[0],\n combobox=True,\n show=False,\n real_time_refresh=True,\n ),\n StrInput(\n name=\"project_id\",\n display_name=\"watsonx Project ID\",\n info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n show=False,\n required=False,\n ),\n MultilineInput(\n name=\"system_prompt\",\n display_name=\"Agent Instructions\",\n info=(\n \"System Prompt: Initial instructions and context provided to guide the agent's behavior. \"\n \"Supports dynamic placeholders: {current_date}, {model_name}, {optional_user_context}.\"\n ),\n value=DEFAULT_SYSTEM_PROMPT_TEMPLATE,\n advanced=False,\n ),\n MessageTextInput(\n name=\"context_id\",\n display_name=\"Context ID\",\n info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n value=\"\",\n advanced=True,\n ),\n IntInput(\n name=\"n_messages\",\n display_name=\"Number of Chat History Messages\",\n value=100,\n info=\"Number of chat history messages to retrieve.\",\n advanced=True,\n show=True,\n ),\n IntInput(\n name=\"max_tokens\",\n display_name=\"Max Tokens\",\n info=\"Maximum number of tokens to generate. Field name varies by provider.\",\n advanced=True,\n range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n ),\n MultilineInput(\n name=\"format_instructions\",\n display_name=\"Output Format Instructions\",\n info=\"Generic Template for structured output formatting. Valid only with Structured response.\",\n value=(\n \"You are an AI that extracts structured JSON objects from unstructured text. \"\n \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n \"Fill missing or ambiguous values with defaults: null for missing values. \"\n \"Remove exact duplicates but keep variations that have different field values. \"\n \"Always return valid JSON in the expected format, never throw errors. \"\n \"If multiple objects can be extracted, return them all in the structured format.\"\n ),\n advanced=True,\n ),\n TableInput(\n name=\"output_schema\",\n display_name=\"Output Schema\",\n info=(\n \"Schema Validation: Define the structure and data types for structured output. \"\n \"No validation if no output schema.\"\n ),\n advanced=True,\n required=False,\n value=[],\n table_schema=[\n {\n \"name\": \"name\",\n \"display_name\": \"Name\",\n \"type\": \"str\",\n \"description\": \"Specify the name of the output field.\",\n \"default\": \"field\",\n \"edit_mode\": EditMode.INLINE,\n },\n {\n \"name\": \"description\",\n \"display_name\": \"Description\",\n \"type\": \"str\",\n \"description\": \"Describe the purpose of the output field.\",\n \"default\": \"description of field\",\n \"edit_mode\": EditMode.POPOVER,\n },\n {\n \"name\": \"type\",\n \"display_name\": \"Type\",\n \"type\": \"str\",\n \"edit_mode\": EditMode.INLINE,\n \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n \"default\": \"str\",\n },\n {\n \"name\": \"multiple\",\n \"display_name\": \"As List\",\n \"type\": \"boolean\",\n \"description\": \"Set to True if this output field should be a list of the specified type.\",\n \"default\": \"False\",\n \"edit_mode\": EditMode.INLINE,\n },\n ],\n ),\n *LCToolsAgentComponent.get_base_inputs(),\n # removed memory inputs from agent component\n # *memory_inputs,\n BoolInput(\n name=\"stream\",\n display_name=\"Stream\",\n info=STREAM_INFO_TEXT,\n value=False,\n advanced=True,\n ),\n BoolInput(\n name=\"add_current_date_tool\",\n display_name=\"Current Date\",\n advanced=True,\n info=\"If true, will add a tool to the agent that returns the current date.\",\n value=True,\n ),\n BoolInput(\n name=\"add_calculator_tool\",\n display_name=\"Calculator\",\n advanced=True,\n info=(\n \"If true, adds a zero-config arithmetic calculator tool to the agent \"\n \"(safe: only +, -, *, /, ** operators via AST).\"\n ),\n value=True,\n ),\n ]\n outputs = [\n Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n Output(\n name=\"structured_response\",\n display_name=\"Structured Response\",\n method=\"json_response\",\n types=[\"Data\"],\n ),\n ]\n\n def _resolve_selected_model(self):\n \"\"\"Resolve the selected model, including legacy agent_llm/model_name inputs.\"\"\"\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n if isinstance(self.model, BaseLanguageModel):\n return self.model\n except ImportError:\n pass\n\n if isinstance(self.model, list) and self.model:\n return self.model\n\n legacy_provider = getattr(self, \"agent_llm\", None)\n legacy_model_name = getattr(self, \"model_name\", None)\n if not legacy_provider or not legacy_model_name:\n return self.model\n\n options = get_language_model_options(user_id=self.user_id)\n for option in options:\n if option.get(\"provider\") == legacy_provider and option.get(\"name\") == legacy_model_name:\n return [option]\n\n return [\n {\n \"name\": legacy_model_name,\n \"provider\": legacy_provider,\n \"metadata\": {},\n }\n ]\n\n def _get_max_tokens_value(self):\n \"\"\"Return the user-supplied max_tokens or None when unset/zero.\"\"\"\n val = getattr(self, \"max_tokens\", None)\n if val in {\"\", 0}:\n return None\n return val\n\n def _get_llm(self):\n \"\"\"Override parent to include max_tokens from the Agent's input field.\"\"\"\n return get_llm(\n model=self.model,\n user_id=self.user_id,\n api_key=getattr(self, \"api_key\", None),\n stream=bool(getattr(self, \"stream\", False)),\n max_tokens=self._get_max_tokens_value(),\n watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n watsonx_project_id=getattr(self, \"project_id\", None),\n )\n\n async def get_agent_requirements(self):\n \"\"\"Get the agent requirements for the agent.\"\"\"\n from langchain_core.tools import StructuredTool\n\n selected_model = self._resolve_selected_model()\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n is_connected_model = isinstance(selected_model, BaseLanguageModel)\n except ImportError:\n is_connected_model = False\n\n if not is_connected_model:\n validate_model_selection(selected_model)\n\n # Ensure _get_llm() uses the resolved model (e.g. from legacy agent_llm/model_name)\n self.model = selected_model\n llm_model = self._get_llm()\n if llm_model is None:\n msg = \"No language model selected. Please choose a model to proceed.\"\n raise ValueError(msg)\n\n # Get memory data\n self.chat_history = await self.get_memory_data()\n await logger.adebug(f\"Retrieved {len(self.chat_history)} chat history messages\")\n if isinstance(self.chat_history, Message):\n self.chat_history = [self.chat_history]\n\n # Add current date tool if enabled\n if self.add_current_date_tool:\n if not isinstance(self.tools, list): # type: ignore[has-type]\n self.tools = []\n current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n if not isinstance(current_date_tool, StructuredTool):\n msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n raise TypeError(msg)\n # Skip if an externally-connected tool already provides the same name.\n # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n if not any(getattr(t, \"name\", None) == current_date_tool.name for t in self.tools):\n self.tools.append(current_date_tool)\n\n # Add calculator tool if enabled (zero-config arithmetic)\n if getattr(self, \"add_calculator_tool\", False):\n if not isinstance(self.tools, list): # type: ignore[has-type]\n self.tools = []\n calculator_tool = (await CalculatorComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n if not isinstance(calculator_tool, StructuredTool):\n msg = \"CalculatorComponent must be converted to a StructuredTool\"\n raise TypeError(msg)\n # Skip if an externally-connected tool already provides the same name.\n # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n if not any(getattr(t, \"name\", None) == calculator_tool.name for t in self.tools):\n self.tools.append(calculator_tool)\n\n # Set shared callbacks for tracing the tools used by the agent\n self.set_tools_callbacks(self.tools, self._get_shared_callbacks())\n\n return llm_model, self.chat_history, self.tools\n\n def _get_resolved_model_name(self) -> str:\n \"\"\"Best-effort human-readable model name for {model_name} injection.\"\"\"\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n if isinstance(self.model, BaseLanguageModel):\n return type(self.model).__name__\n except ImportError:\n pass\n\n if isinstance(self.model, list) and self.model:\n first = self.model[0]\n if isinstance(first, dict):\n name = first.get(\"name\")\n if isinstance(name, str) and name:\n return name\n\n legacy_model_name = getattr(self, \"model_name\", None)\n if isinstance(legacy_model_name, str) and legacy_model_name:\n return legacy_model_name\n return \"\"\n\n def _inject_dynamic_prompt_values(self, prompt: str | None) -> str | None:\n \"\"\"Replace known env placeholders in the system prompt.\n\n Handles {current_date}, {model_name}, and {optional_user_context} (the\n last one ships with the structured DEFAULT_SYSTEM_PROMPT_TEMPLATE and\n is currently unused at the AgentComponent layer, so it resolves to \"\").\n Uses str.replace (not str.format) so user prompts containing literal\n braces such as JSON examples ({\"key\": 1}) never break the agent.\n \"\"\"\n if not prompt:\n return prompt\n replacements = {\n \"{current_date}\": datetime.now(tz=timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S UTC\"),\n \"{model_name}\": self._get_resolved_model_name(),\n \"{optional_user_context}\": \"\",\n }\n for placeholder, value in replacements.items():\n prompt = prompt.replace(placeholder, value)\n return prompt\n\n async def message_response(self) -> Message:\n try:\n llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n # Set up and run agent\n self.set(\n llm=llm_model,\n tools=self.tools or [],\n chat_history=self.chat_history,\n input_value=self.input_value,\n system_prompt=self._inject_dynamic_prompt_values(self.system_prompt),\n )\n agent = self.create_agent_runnable()\n result = await self.run_agent(agent)\n\n # Store result for potential JSON output\n self._agent_result = result\n\n except (ValueError, TypeError, KeyError) as e:\n await logger.aerror(f\"{type(e).__name__}: {e!s}\")\n raise\n except ExceptionWithMessageError as e:\n await logger.aerror(f\"ExceptionWithMessageError occurred: {e}\")\n raise\n # Avoid catching blind Exception; let truly unexpected exceptions propagate\n except Exception as e:\n await logger.aerror(f\"Unexpected error: {e!s}\")\n raise\n else:\n return result\n\n async def json_response(self) -> Data:\n \"\"\"Produce structured Data via native LLM structured output, with prompt-based fallback.\n\n Native path (no tools, llm has with_structured_output) bypasses the agent loop and\n returns provider-validated JSON. When tools are attached, falls back to running the\n agent with a schema-augmented system prompt and parsing the final message content.\n \"\"\"\n from lfx.components.models_and_agents.structured_output.structured_output_orchestrator import (\n orchestrate_structured_output,\n )\n\n try:\n llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n except (ValueError, TypeError) as exc:\n await logger.aerror(f\"json_response.requirements_failed: {exc}\")\n return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n injected_system_prompt = self._inject_dynamic_prompt_values(getattr(self, \"system_prompt\", \"\") or \"\") or \"\"\n format_instructions = getattr(self, \"format_instructions\", \"\") or \"\"\n output_schema = getattr(self, \"output_schema\", None) or []\n has_tools = bool(self.tools)\n\n async def _run_agent_for_fallback(augmented_prompt: str) -> str:\n self.set(\n llm=llm_model,\n tools=self.tools or [],\n chat_history=self.chat_history,\n input_value=self.input_value,\n system_prompt=augmented_prompt,\n )\n agent_runnable = self.create_agent_runnable()\n with _suppress_send_message(self):\n result = await self.run_agent(agent_runnable)\n return _extract_text_content(result)\n\n try:\n return await orchestrate_structured_output(\n llm=llm_model,\n output_schema=output_schema,\n system_prompt=injected_system_prompt,\n format_instructions=format_instructions,\n input_value=_extract_text_content(self.input_value),\n run_prompt_fallback=_run_agent_for_fallback,\n prefer_native=not has_tools,\n )\n except (\n ExceptionWithMessageError,\n ValueError,\n TypeError,\n NotImplementedError,\n AttributeError,\n ) as exc:\n await logger.aerror(f\"json_response.orchestration_failed: {exc}\")\n return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n async def get_memory_data(self):\n # TODO: This is a temporary fix to avoid message duplication. We should develop a function for this.\n messages = (\n await MemoryComponent(**self.get_base_args())\n .set(\n session_id=self.graph.session_id,\n context_id=self.context_id,\n order=\"Ascending\",\n n_messages=self.n_messages,\n )\n .retrieve_messages()\n )\n return [\n message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n ]\n\n def update_input_types(self, build_config: dotdict) -> dotdict:\n \"\"\"Update input types for all fields in build_config.\"\"\"\n for key, value in build_config.items():\n if isinstance(value, dict):\n if value.get(\"input_types\") is None:\n build_config[key][\"input_types\"] = []\n elif hasattr(value, \"input_types\") and value.input_types is None:\n value.input_types = []\n return build_config\n\n async def update_build_config(\n self,\n build_config: dotdict,\n field_value: list[dict],\n field_name: str | None = None,\n ) -> dotdict:\n # Update model options with caching (for all field changes).\n # The tool-calling constraint lives on the ModelInput's ``filters``\n # field (declared above); ``handle_model_input_update`` reads it\n # and applies the filter to both the dropdown options and the\n # sticky-default re-injection path.\n build_config = handle_model_input_update(\n component=self,\n build_config=dict(build_config),\n field_value=field_value,\n field_name=field_name,\n )\n build_config = dotdict(build_config)\n\n if field_name == \"model\":\n build_config = self.update_input_types(build_config)\n\n # Validate required keys\n default_keys = [\n \"code\",\n \"_type\",\n \"model\",\n \"tools\",\n \"input_value\",\n \"add_current_date_tool\",\n \"add_calculator_tool\",\n \"system_prompt\",\n \"agent_description\",\n \"max_iterations\",\n \"handle_parsing_errors\",\n \"verbose\",\n ]\n missing_keys = [key for key in default_keys if key not in build_config]\n if missing_keys:\n msg = f\"Missing required keys in build_config: {missing_keys}\"\n raise ValueError(msg)\n return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n async def _get_tools(self) -> list[Tool]:\n component_toolkit = get_component_toolkit()\n tools_names = self._build_tools_names()\n agent_description = self.get_tool_description()\n # TODO: Agent Description Depreciated Feature to be removed\n description = f\"{agent_description}{tools_names}\"\n\n tools = component_toolkit(component=self).get_tools(\n tool_name=\"Call_Agent\",\n tool_description=description,\n # here we do not use the shared callbacks as we are exposing the agent as a tool\n callbacks=self.get_langchain_callbacks(),\n )\n if hasattr(self, \"tools_metadata\"):\n tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n\n return tools\n" + "value": "from __future__ import annotations\n\nfrom contextlib import contextmanager\nfrom datetime import datetime, timezone\nfrom typing import TYPE_CHECKING, Any\n\nfrom lfx.components.models_and_agents.memory import MemoryComponent, aget_agent_chat_history\n\nif TYPE_CHECKING:\n from langchain_core.tools import Tool\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.agents.default_system_prompt import DEFAULT_SYSTEM_PROMPT_TEMPLATE\nfrom lfx.base.agents.events import ExceptionWithMessageError\nfrom lfx.base.constants import STREAM_INFO_TEXT\nfrom lfx.base.models.unified_models import (\n get_language_model_options,\n get_llm,\n handle_model_input_update,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.components.agentics.helpers.model_config import validate_model_selection\nfrom lfx.components.helpers import CalculatorComponent, CurrentDateComponent\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.custom.custom_component.component import get_component_toolkit\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, ModelInput, StrInput\nfrom lfx.io import IntInput, MessageTextInput, MultilineInput, Output, SecretStrInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.schema.data import Data\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.schema.table import EditMode\n\n\ndef set_advanced_true(component_input):\n component_input.advanced = True\n return component_input\n\n\ndef _extract_text_content(value) -> str:\n \"\"\"Pull a string payload from a Message-like, AIMessage-like, or string value.\"\"\"\n if isinstance(value, str):\n return value\n text = getattr(value, \"text\", None)\n if isinstance(text, str):\n return text\n content = getattr(value, \"content\", None)\n if isinstance(content, str):\n return content\n return str(value) if value is not None else \"\"\n\n\n@contextmanager\ndef _suppress_send_message(component: Any):\n \"\"\"Temporarily replace component.send_message with a no-op for the duration of the block.\n\n Used during the structured-output prompt fallback: run_agent streams the agent's\n final answer through self.send_message (correct for message_response), but in\n json_response the orchestrator parses that text into structured Data which the\n downstream Chat Output emits — leaving the original emission in place produces a\n duplicate message in the playground. The original method is always restored on exit,\n even when the wrapped call raises.\n \"\"\"\n original = component.send_message\n\n async def _noop(message, *_args, **_kwargs):\n return message\n\n component.send_message = _noop\n try:\n yield\n finally:\n component.send_message = original\n\n\nclass AgentComponent(ToolCallingAgentComponent):\n display_name: str = \"Agent\"\n description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n documentation: str = \"https://docs.langflow.org/agents\"\n icon = \"bot\"\n beta = False\n name = \"Agent\"\n\n memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n inputs = [\n ModelInput(\n name=\"model\",\n display_name=\"Language Model\",\n info=\"Select your model provider\",\n real_time_refresh=True,\n required=True,\n # Agents require tool calling — the filter is honored by\n # ``handle_model_input_update`` so models that can't run with\n # tools never reach the picker (and any saved selection that\n # no longer satisfies the constraint is auto-replaced).\n filters={\"tool_calling\": True},\n ),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"API Key\",\n info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n real_time_refresh=True,\n advanced=True,\n ),\n DropdownInput(\n name=\"base_url_ibm_watsonx\",\n display_name=\"watsonx API Endpoint\",\n info=\"The base URL of the API (IBM watsonx.ai only)\",\n options=IBM_WATSONX_URLS,\n value=IBM_WATSONX_URLS[0],\n combobox=True,\n show=False,\n real_time_refresh=True,\n ),\n StrInput(\n name=\"project_id\",\n display_name=\"watsonx Project ID\",\n info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n show=False,\n required=False,\n ),\n MultilineInput(\n name=\"system_prompt\",\n display_name=\"Agent Instructions\",\n info=(\n \"System Prompt: Initial instructions and context provided to guide the agent's behavior. \"\n \"Supports dynamic placeholders: {current_date}, {model_name}, {optional_user_context}.\"\n ),\n value=DEFAULT_SYSTEM_PROMPT_TEMPLATE,\n advanced=False,\n ),\n MessageTextInput(\n name=\"context_id\",\n display_name=\"Context ID\",\n info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n value=\"\",\n advanced=True,\n ),\n IntInput(\n name=\"n_messages\",\n display_name=\"Number of Chat History Messages\",\n value=100,\n info=\"Number of chat history messages to retrieve.\",\n advanced=True,\n show=True,\n ),\n IntInput(\n name=\"max_tokens\",\n display_name=\"Max Tokens\",\n info=\"Maximum number of tokens to generate. Field name varies by provider.\",\n advanced=True,\n range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n ),\n MultilineInput(\n name=\"format_instructions\",\n display_name=\"Output Format Instructions\",\n info=\"Generic Template for structured output formatting. Valid only with Structured response.\",\n value=(\n \"You are an AI that extracts structured JSON objects from unstructured text. \"\n \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n \"Fill missing or ambiguous values with defaults: null for missing values. \"\n \"Remove exact duplicates but keep variations that have different field values. \"\n \"Always return valid JSON in the expected format, never throw errors. \"\n \"If multiple objects can be extracted, return them all in the structured format.\"\n ),\n advanced=True,\n ),\n TableInput(\n name=\"output_schema\",\n display_name=\"Output Schema\",\n info=(\n \"Schema Validation: Define the structure and data types for structured output. \"\n \"No validation if no output schema.\"\n ),\n advanced=True,\n required=False,\n value=[],\n table_schema=[\n {\n \"name\": \"name\",\n \"display_name\": \"Name\",\n \"type\": \"str\",\n \"description\": \"Specify the name of the output field.\",\n \"default\": \"field\",\n \"edit_mode\": EditMode.INLINE,\n },\n {\n \"name\": \"description\",\n \"display_name\": \"Description\",\n \"type\": \"str\",\n \"description\": \"Describe the purpose of the output field.\",\n \"default\": \"description of field\",\n \"edit_mode\": EditMode.POPOVER,\n },\n {\n \"name\": \"type\",\n \"display_name\": \"Type\",\n \"type\": \"str\",\n \"edit_mode\": EditMode.INLINE,\n \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n \"default\": \"str\",\n },\n {\n \"name\": \"multiple\",\n \"display_name\": \"As List\",\n \"type\": \"boolean\",\n \"description\": \"Set to True if this output field should be a list of the specified type.\",\n \"default\": \"False\",\n \"edit_mode\": EditMode.INLINE,\n },\n ],\n ),\n *LCToolsAgentComponent.get_base_inputs(),\n # removed memory inputs from agent component\n # *memory_inputs,\n BoolInput(\n name=\"stream\",\n display_name=\"Stream\",\n info=STREAM_INFO_TEXT,\n value=False,\n advanced=True,\n ),\n BoolInput(\n name=\"add_current_date_tool\",\n display_name=\"Current Date\",\n advanced=True,\n info=\"If true, will add a tool to the agent that returns the current date.\",\n value=True,\n ),\n BoolInput(\n name=\"add_calculator_tool\",\n display_name=\"Calculator\",\n advanced=True,\n info=(\n \"If true, adds a zero-config arithmetic calculator tool to the agent \"\n \"(safe: only +, -, *, /, ** operators via AST).\"\n ),\n value=True,\n ),\n ]\n outputs = [\n Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n Output(\n name=\"structured_response\",\n display_name=\"Structured Response\",\n method=\"json_response\",\n types=[\"Data\"],\n ),\n ]\n\n def _resolve_selected_model(self):\n \"\"\"Resolve the selected model, including legacy agent_llm/model_name inputs.\"\"\"\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n if isinstance(self.model, BaseLanguageModel):\n return self.model\n except ImportError:\n pass\n\n if isinstance(self.model, list) and self.model:\n return self.model\n\n legacy_provider = getattr(self, \"agent_llm\", None)\n legacy_model_name = getattr(self, \"model_name\", None)\n if not legacy_provider or not legacy_model_name:\n return self.model\n\n options = get_language_model_options(user_id=self.user_id)\n for option in options:\n if option.get(\"provider\") == legacy_provider and option.get(\"name\") == legacy_model_name:\n return [option]\n\n return [\n {\n \"name\": legacy_model_name,\n \"provider\": legacy_provider,\n \"metadata\": {},\n }\n ]\n\n def _get_max_tokens_value(self):\n \"\"\"Return the user-supplied max_tokens or None when unset/zero.\"\"\"\n val = getattr(self, \"max_tokens\", None)\n if val in {\"\", 0}:\n return None\n return val\n\n def _get_llm(self):\n \"\"\"Override parent to include max_tokens from the Agent's input field.\"\"\"\n return get_llm(\n model=self.model,\n user_id=self.user_id,\n api_key=getattr(self, \"api_key\", None),\n stream=bool(getattr(self, \"stream\", False)),\n max_tokens=self._get_max_tokens_value(),\n watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n watsonx_project_id=getattr(self, \"project_id\", None),\n )\n\n async def get_agent_requirements(self):\n \"\"\"Get the agent requirements for the agent.\"\"\"\n from langchain_core.tools import StructuredTool\n\n selected_model = self._resolve_selected_model()\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n is_connected_model = isinstance(selected_model, BaseLanguageModel)\n except ImportError:\n is_connected_model = False\n\n if not is_connected_model:\n validate_model_selection(selected_model)\n\n # Ensure _get_llm() uses the resolved model (e.g. from legacy agent_llm/model_name)\n self.model = selected_model\n llm_model = self._get_llm()\n if llm_model is None:\n msg = \"No language model selected. Please choose a model to proceed.\"\n raise ValueError(msg)\n\n # Get memory data\n self.chat_history = await self.get_memory_data()\n await logger.adebug(f\"Retrieved {len(self.chat_history)} chat history messages\")\n if isinstance(self.chat_history, Message):\n self.chat_history = [self.chat_history]\n\n # Add current date tool if enabled\n if self.add_current_date_tool:\n if not isinstance(self.tools, list): # type: ignore[has-type]\n self.tools = []\n current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n if not isinstance(current_date_tool, StructuredTool):\n msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n raise TypeError(msg)\n # Skip if an externally-connected tool already provides the same name.\n # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n if not any(getattr(t, \"name\", None) == current_date_tool.name for t in self.tools):\n self.tools.append(current_date_tool)\n\n # Add calculator tool if enabled (zero-config arithmetic)\n if getattr(self, \"add_calculator_tool\", False):\n if not isinstance(self.tools, list): # type: ignore[has-type]\n self.tools = []\n calculator_tool = (await CalculatorComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n if not isinstance(calculator_tool, StructuredTool):\n msg = \"CalculatorComponent must be converted to a StructuredTool\"\n raise TypeError(msg)\n # Skip if an externally-connected tool already provides the same name.\n # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n if not any(getattr(t, \"name\", None) == calculator_tool.name for t in self.tools):\n self.tools.append(calculator_tool)\n\n # Set shared callbacks for tracing the tools used by the agent\n self.set_tools_callbacks(self.tools, self._get_shared_callbacks())\n\n return llm_model, self.chat_history, self.tools\n\n def _get_resolved_model_name(self) -> str:\n \"\"\"Best-effort human-readable model name for {model_name} injection.\"\"\"\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n if isinstance(self.model, BaseLanguageModel):\n return type(self.model).__name__\n except ImportError:\n pass\n\n if isinstance(self.model, list) and self.model:\n first = self.model[0]\n if isinstance(first, dict):\n name = first.get(\"name\")\n if isinstance(name, str) and name:\n return name\n\n legacy_model_name = getattr(self, \"model_name\", None)\n if isinstance(legacy_model_name, str) and legacy_model_name:\n return legacy_model_name\n return \"\"\n\n def _inject_dynamic_prompt_values(self, prompt: str | None) -> str | None:\n \"\"\"Replace known env placeholders in the system prompt.\n\n Handles {current_date}, {model_name}, and {optional_user_context} (the\n last one ships with the structured DEFAULT_SYSTEM_PROMPT_TEMPLATE and\n is currently unused at the AgentComponent layer, so it resolves to \"\").\n Uses str.replace (not str.format) so user prompts containing literal\n braces such as JSON examples ({\"key\": 1}) never break the agent.\n \"\"\"\n if not prompt:\n return prompt\n replacements = {\n \"{current_date}\": datetime.now(tz=timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S UTC\"),\n \"{model_name}\": self._get_resolved_model_name(),\n \"{optional_user_context}\": \"\",\n }\n for placeholder, value in replacements.items():\n prompt = prompt.replace(placeholder, value)\n return prompt\n\n async def message_response(self) -> Message:\n try:\n llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n # Set up and run agent\n self.set(\n llm=llm_model,\n tools=self.tools or [],\n chat_history=self.chat_history,\n input_value=self.input_value,\n system_prompt=self._inject_dynamic_prompt_values(self.system_prompt),\n )\n agent = self.create_agent_runnable()\n result = await self.run_agent(agent)\n\n # Store result for potential JSON output\n self._agent_result = result\n\n except (ValueError, TypeError, KeyError) as e:\n await logger.aerror(f\"{type(e).__name__}: {e!s}\")\n raise\n except ExceptionWithMessageError as e:\n await logger.aerror(f\"ExceptionWithMessageError occurred: {e}\")\n raise\n # Avoid catching blind Exception; let truly unexpected exceptions propagate\n except Exception as e:\n await logger.aerror(f\"Unexpected error: {e!s}\")\n raise\n else:\n return result\n\n async def json_response(self) -> Data:\n \"\"\"Produce structured Data via native LLM structured output, with prompt-based fallback.\n\n Native path (no tools, llm has with_structured_output) bypasses the agent loop and\n returns provider-validated JSON. When tools are attached, falls back to running the\n agent with a schema-augmented system prompt and parsing the final message content.\n \"\"\"\n from lfx.components.models_and_agents.structured_output.structured_output_orchestrator import (\n orchestrate_structured_output,\n )\n\n try:\n llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n except (ValueError, TypeError) as exc:\n await logger.aerror(f\"json_response.requirements_failed: {exc}\")\n return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n injected_system_prompt = self._inject_dynamic_prompt_values(getattr(self, \"system_prompt\", \"\") or \"\") or \"\"\n format_instructions = getattr(self, \"format_instructions\", \"\") or \"\"\n output_schema = getattr(self, \"output_schema\", None) or []\n has_tools = bool(self.tools)\n\n async def _run_agent_for_fallback(augmented_prompt: str) -> str:\n self.set(\n llm=llm_model,\n tools=self.tools or [],\n chat_history=self.chat_history,\n input_value=self.input_value,\n system_prompt=augmented_prompt,\n )\n agent_runnable = self.create_agent_runnable()\n with _suppress_send_message(self):\n result = await self.run_agent(agent_runnable)\n return _extract_text_content(result)\n\n try:\n return await orchestrate_structured_output(\n llm=llm_model,\n output_schema=output_schema,\n system_prompt=injected_system_prompt,\n format_instructions=format_instructions,\n input_value=_extract_text_content(self.input_value),\n run_prompt_fallback=_run_agent_for_fallback,\n prefer_native=not has_tools,\n )\n except (\n ExceptionWithMessageError,\n ValueError,\n TypeError,\n NotImplementedError,\n AttributeError,\n ) as exc:\n await logger.aerror(f\"json_response.orchestration_failed: {exc}\")\n return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n async def get_memory_data(self):\n # Scope by flow_id so default playground session names (e.g. \"New Session 0\")\n # cannot leak chat history across unrelated flows. See issue #13059.\n # The helper also returns [] when n_messages == 0, preserving the\n # explicit \"memory disabled\" contract from MemoryComponent.retrieve_messages.\n messages = await aget_agent_chat_history(\n session_id=self.graph.session_id,\n flow_id=getattr(self.graph, \"flow_id\", None),\n context_id=self.context_id,\n n_messages=self.n_messages,\n )\n return [\n message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n ]\n\n def update_input_types(self, build_config: dotdict) -> dotdict:\n \"\"\"Update input types for all fields in build_config.\"\"\"\n for key, value in build_config.items():\n if isinstance(value, dict):\n if value.get(\"input_types\") is None:\n build_config[key][\"input_types\"] = []\n elif hasattr(value, \"input_types\") and value.input_types is None:\n value.input_types = []\n return build_config\n\n async def update_build_config(\n self,\n build_config: dotdict,\n field_value: list[dict],\n field_name: str | None = None,\n ) -> dotdict:\n # Update model options with caching (for all field changes).\n # The tool-calling constraint lives on the ModelInput's ``filters``\n # field (declared above); ``handle_model_input_update`` reads it\n # and applies the filter to both the dropdown options and the\n # sticky-default re-injection path.\n build_config = handle_model_input_update(\n component=self,\n build_config=dict(build_config),\n field_value=field_value,\n field_name=field_name,\n )\n build_config = dotdict(build_config)\n\n if field_name == \"model\":\n build_config = self.update_input_types(build_config)\n\n # Validate required keys\n default_keys = [\n \"code\",\n \"_type\",\n \"model\",\n \"tools\",\n \"input_value\",\n \"add_current_date_tool\",\n \"add_calculator_tool\",\n \"system_prompt\",\n \"agent_description\",\n \"max_iterations\",\n \"handle_parsing_errors\",\n \"verbose\",\n ]\n missing_keys = [key for key in default_keys if key not in build_config]\n if missing_keys:\n msg = f\"Missing required keys in build_config: {missing_keys}\"\n raise ValueError(msg)\n return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n async def _get_tools(self) -> list[Tool]:\n component_toolkit = get_component_toolkit()\n tools_names = self._build_tools_names()\n agent_description = self.get_tool_description()\n # TODO: Agent Description Depreciated Feature to be removed\n description = f\"{agent_description}{tools_names}\"\n\n tools = component_toolkit(component=self).get_tools(\n tool_name=\"Call_Agent\",\n tool_description=description,\n # here we do not use the shared callbacks as we are exposing the agent as a tool\n callbacks=self.get_langchain_callbacks(),\n )\n if hasattr(self, \"tools_metadata\"):\n tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n\n return tools\n" }, "context_id": { "_input_type": "MessageTextInput", diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Market Research.json b/src/backend/base/langflow/initial_setup/starter_projects/Market Research.json index 99fbae1cad..0f4360f593 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Market Research.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Market Research.json @@ -1194,7 +1194,7 @@ "last_updated": "2025-12-11T21:41:48.407Z", "legacy": false, "metadata": { - "code_hash": "ebac74bfcae6", + "code_hash": "443159257161", "dependencies": { "dependencies": [ { @@ -1383,7 +1383,7 @@ "show": true, "title_case": false, "type": "code", - "value": "from __future__ import annotations\n\nfrom contextlib import contextmanager\nfrom datetime import datetime, timezone\nfrom typing import TYPE_CHECKING, Any\n\nfrom lfx.components.models_and_agents.memory import MemoryComponent\n\nif TYPE_CHECKING:\n from langchain_core.tools import Tool\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.agents.default_system_prompt import DEFAULT_SYSTEM_PROMPT_TEMPLATE\nfrom lfx.base.agents.events import ExceptionWithMessageError\nfrom lfx.base.constants import STREAM_INFO_TEXT\nfrom lfx.base.models.unified_models import (\n get_language_model_options,\n get_llm,\n handle_model_input_update,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.components.agentics.helpers.model_config import validate_model_selection\nfrom lfx.components.helpers import CalculatorComponent, CurrentDateComponent\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.custom.custom_component.component import get_component_toolkit\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, ModelInput, StrInput\nfrom lfx.io import IntInput, MessageTextInput, MultilineInput, Output, SecretStrInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.schema.data import Data\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.schema.table import EditMode\n\n\ndef set_advanced_true(component_input):\n component_input.advanced = True\n return component_input\n\n\ndef _extract_text_content(value) -> str:\n \"\"\"Pull a string payload from a Message-like, AIMessage-like, or string value.\"\"\"\n if isinstance(value, str):\n return value\n text = getattr(value, \"text\", None)\n if isinstance(text, str):\n return text\n content = getattr(value, \"content\", None)\n if isinstance(content, str):\n return content\n return str(value) if value is not None else \"\"\n\n\n@contextmanager\ndef _suppress_send_message(component: Any):\n \"\"\"Temporarily replace component.send_message with a no-op for the duration of the block.\n\n Used during the structured-output prompt fallback: run_agent streams the agent's\n final answer through self.send_message (correct for message_response), but in\n json_response the orchestrator parses that text into structured Data which the\n downstream Chat Output emits — leaving the original emission in place produces a\n duplicate message in the playground. The original method is always restored on exit,\n even when the wrapped call raises.\n \"\"\"\n original = component.send_message\n\n async def _noop(message, *_args, **_kwargs):\n return message\n\n component.send_message = _noop\n try:\n yield\n finally:\n component.send_message = original\n\n\nclass AgentComponent(ToolCallingAgentComponent):\n display_name: str = \"Agent\"\n description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n documentation: str = \"https://docs.langflow.org/agents\"\n icon = \"bot\"\n beta = False\n name = \"Agent\"\n\n memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n inputs = [\n ModelInput(\n name=\"model\",\n display_name=\"Language Model\",\n info=\"Select your model provider\",\n real_time_refresh=True,\n required=True,\n # Agents require tool calling — the filter is honored by\n # ``handle_model_input_update`` so models that can't run with\n # tools never reach the picker (and any saved selection that\n # no longer satisfies the constraint is auto-replaced).\n filters={\"tool_calling\": True},\n ),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"API Key\",\n info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n real_time_refresh=True,\n advanced=True,\n ),\n DropdownInput(\n name=\"base_url_ibm_watsonx\",\n display_name=\"watsonx API Endpoint\",\n info=\"The base URL of the API (IBM watsonx.ai only)\",\n options=IBM_WATSONX_URLS,\n value=IBM_WATSONX_URLS[0],\n combobox=True,\n show=False,\n real_time_refresh=True,\n ),\n StrInput(\n name=\"project_id\",\n display_name=\"watsonx Project ID\",\n info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n show=False,\n required=False,\n ),\n MultilineInput(\n name=\"system_prompt\",\n display_name=\"Agent Instructions\",\n info=(\n \"System Prompt: Initial instructions and context provided to guide the agent's behavior. \"\n \"Supports dynamic placeholders: {current_date}, {model_name}, {optional_user_context}.\"\n ),\n value=DEFAULT_SYSTEM_PROMPT_TEMPLATE,\n advanced=False,\n ),\n MessageTextInput(\n name=\"context_id\",\n display_name=\"Context ID\",\n info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n value=\"\",\n advanced=True,\n ),\n IntInput(\n name=\"n_messages\",\n display_name=\"Number of Chat History Messages\",\n value=100,\n info=\"Number of chat history messages to retrieve.\",\n advanced=True,\n show=True,\n ),\n IntInput(\n name=\"max_tokens\",\n display_name=\"Max Tokens\",\n info=\"Maximum number of tokens to generate. Field name varies by provider.\",\n advanced=True,\n range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n ),\n MultilineInput(\n name=\"format_instructions\",\n display_name=\"Output Format Instructions\",\n info=\"Generic Template for structured output formatting. Valid only with Structured response.\",\n value=(\n \"You are an AI that extracts structured JSON objects from unstructured text. \"\n \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n \"Fill missing or ambiguous values with defaults: null for missing values. \"\n \"Remove exact duplicates but keep variations that have different field values. \"\n \"Always return valid JSON in the expected format, never throw errors. \"\n \"If multiple objects can be extracted, return them all in the structured format.\"\n ),\n advanced=True,\n ),\n TableInput(\n name=\"output_schema\",\n display_name=\"Output Schema\",\n info=(\n \"Schema Validation: Define the structure and data types for structured output. \"\n \"No validation if no output schema.\"\n ),\n advanced=True,\n required=False,\n value=[],\n table_schema=[\n {\n \"name\": \"name\",\n \"display_name\": \"Name\",\n \"type\": \"str\",\n \"description\": \"Specify the name of the output field.\",\n \"default\": \"field\",\n \"edit_mode\": EditMode.INLINE,\n },\n {\n \"name\": \"description\",\n \"display_name\": \"Description\",\n \"type\": \"str\",\n \"description\": \"Describe the purpose of the output field.\",\n \"default\": \"description of field\",\n \"edit_mode\": EditMode.POPOVER,\n },\n {\n \"name\": \"type\",\n \"display_name\": \"Type\",\n \"type\": \"str\",\n \"edit_mode\": EditMode.INLINE,\n \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n \"default\": \"str\",\n },\n {\n \"name\": \"multiple\",\n \"display_name\": \"As List\",\n \"type\": \"boolean\",\n \"description\": \"Set to True if this output field should be a list of the specified type.\",\n \"default\": \"False\",\n \"edit_mode\": EditMode.INLINE,\n },\n ],\n ),\n *LCToolsAgentComponent.get_base_inputs(),\n # removed memory inputs from agent component\n # *memory_inputs,\n BoolInput(\n name=\"stream\",\n display_name=\"Stream\",\n info=STREAM_INFO_TEXT,\n value=False,\n advanced=True,\n ),\n BoolInput(\n name=\"add_current_date_tool\",\n display_name=\"Current Date\",\n advanced=True,\n info=\"If true, will add a tool to the agent that returns the current date.\",\n value=True,\n ),\n BoolInput(\n name=\"add_calculator_tool\",\n display_name=\"Calculator\",\n advanced=True,\n info=(\n \"If true, adds a zero-config arithmetic calculator tool to the agent \"\n \"(safe: only +, -, *, /, ** operators via AST).\"\n ),\n value=True,\n ),\n ]\n outputs = [\n Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n Output(\n name=\"structured_response\",\n display_name=\"Structured Response\",\n method=\"json_response\",\n types=[\"Data\"],\n ),\n ]\n\n def _resolve_selected_model(self):\n \"\"\"Resolve the selected model, including legacy agent_llm/model_name inputs.\"\"\"\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n if isinstance(self.model, BaseLanguageModel):\n return self.model\n except ImportError:\n pass\n\n if isinstance(self.model, list) and self.model:\n return self.model\n\n legacy_provider = getattr(self, \"agent_llm\", None)\n legacy_model_name = getattr(self, \"model_name\", None)\n if not legacy_provider or not legacy_model_name:\n return self.model\n\n options = get_language_model_options(user_id=self.user_id)\n for option in options:\n if option.get(\"provider\") == legacy_provider and option.get(\"name\") == legacy_model_name:\n return [option]\n\n return [\n {\n \"name\": legacy_model_name,\n \"provider\": legacy_provider,\n \"metadata\": {},\n }\n ]\n\n def _get_max_tokens_value(self):\n \"\"\"Return the user-supplied max_tokens or None when unset/zero.\"\"\"\n val = getattr(self, \"max_tokens\", None)\n if val in {\"\", 0}:\n return None\n return val\n\n def _get_llm(self):\n \"\"\"Override parent to include max_tokens from the Agent's input field.\"\"\"\n return get_llm(\n model=self.model,\n user_id=self.user_id,\n api_key=getattr(self, \"api_key\", None),\n stream=bool(getattr(self, \"stream\", False)),\n max_tokens=self._get_max_tokens_value(),\n watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n watsonx_project_id=getattr(self, \"project_id\", None),\n )\n\n async def get_agent_requirements(self):\n \"\"\"Get the agent requirements for the agent.\"\"\"\n from langchain_core.tools import StructuredTool\n\n selected_model = self._resolve_selected_model()\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n is_connected_model = isinstance(selected_model, BaseLanguageModel)\n except ImportError:\n is_connected_model = False\n\n if not is_connected_model:\n validate_model_selection(selected_model)\n\n # Ensure _get_llm() uses the resolved model (e.g. from legacy agent_llm/model_name)\n self.model = selected_model\n llm_model = self._get_llm()\n if llm_model is None:\n msg = \"No language model selected. Please choose a model to proceed.\"\n raise ValueError(msg)\n\n # Get memory data\n self.chat_history = await self.get_memory_data()\n await logger.adebug(f\"Retrieved {len(self.chat_history)} chat history messages\")\n if isinstance(self.chat_history, Message):\n self.chat_history = [self.chat_history]\n\n # Add current date tool if enabled\n if self.add_current_date_tool:\n if not isinstance(self.tools, list): # type: ignore[has-type]\n self.tools = []\n current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n if not isinstance(current_date_tool, StructuredTool):\n msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n raise TypeError(msg)\n # Skip if an externally-connected tool already provides the same name.\n # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n if not any(getattr(t, \"name\", None) == current_date_tool.name for t in self.tools):\n self.tools.append(current_date_tool)\n\n # Add calculator tool if enabled (zero-config arithmetic)\n if getattr(self, \"add_calculator_tool\", False):\n if not isinstance(self.tools, list): # type: ignore[has-type]\n self.tools = []\n calculator_tool = (await CalculatorComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n if not isinstance(calculator_tool, StructuredTool):\n msg = \"CalculatorComponent must be converted to a StructuredTool\"\n raise TypeError(msg)\n # Skip if an externally-connected tool already provides the same name.\n # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n if not any(getattr(t, \"name\", None) == calculator_tool.name for t in self.tools):\n self.tools.append(calculator_tool)\n\n # Set shared callbacks for tracing the tools used by the agent\n self.set_tools_callbacks(self.tools, self._get_shared_callbacks())\n\n return llm_model, self.chat_history, self.tools\n\n def _get_resolved_model_name(self) -> str:\n \"\"\"Best-effort human-readable model name for {model_name} injection.\"\"\"\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n if isinstance(self.model, BaseLanguageModel):\n return type(self.model).__name__\n except ImportError:\n pass\n\n if isinstance(self.model, list) and self.model:\n first = self.model[0]\n if isinstance(first, dict):\n name = first.get(\"name\")\n if isinstance(name, str) and name:\n return name\n\n legacy_model_name = getattr(self, \"model_name\", None)\n if isinstance(legacy_model_name, str) and legacy_model_name:\n return legacy_model_name\n return \"\"\n\n def _inject_dynamic_prompt_values(self, prompt: str | None) -> str | None:\n \"\"\"Replace known env placeholders in the system prompt.\n\n Handles {current_date}, {model_name}, and {optional_user_context} (the\n last one ships with the structured DEFAULT_SYSTEM_PROMPT_TEMPLATE and\n is currently unused at the AgentComponent layer, so it resolves to \"\").\n Uses str.replace (not str.format) so user prompts containing literal\n braces such as JSON examples ({\"key\": 1}) never break the agent.\n \"\"\"\n if not prompt:\n return prompt\n replacements = {\n \"{current_date}\": datetime.now(tz=timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S UTC\"),\n \"{model_name}\": self._get_resolved_model_name(),\n \"{optional_user_context}\": \"\",\n }\n for placeholder, value in replacements.items():\n prompt = prompt.replace(placeholder, value)\n return prompt\n\n async def message_response(self) -> Message:\n try:\n llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n # Set up and run agent\n self.set(\n llm=llm_model,\n tools=self.tools or [],\n chat_history=self.chat_history,\n input_value=self.input_value,\n system_prompt=self._inject_dynamic_prompt_values(self.system_prompt),\n )\n agent = self.create_agent_runnable()\n result = await self.run_agent(agent)\n\n # Store result for potential JSON output\n self._agent_result = result\n\n except (ValueError, TypeError, KeyError) as e:\n await logger.aerror(f\"{type(e).__name__}: {e!s}\")\n raise\n except ExceptionWithMessageError as e:\n await logger.aerror(f\"ExceptionWithMessageError occurred: {e}\")\n raise\n # Avoid catching blind Exception; let truly unexpected exceptions propagate\n except Exception as e:\n await logger.aerror(f\"Unexpected error: {e!s}\")\n raise\n else:\n return result\n\n async def json_response(self) -> Data:\n \"\"\"Produce structured Data via native LLM structured output, with prompt-based fallback.\n\n Native path (no tools, llm has with_structured_output) bypasses the agent loop and\n returns provider-validated JSON. When tools are attached, falls back to running the\n agent with a schema-augmented system prompt and parsing the final message content.\n \"\"\"\n from lfx.components.models_and_agents.structured_output.structured_output_orchestrator import (\n orchestrate_structured_output,\n )\n\n try:\n llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n except (ValueError, TypeError) as exc:\n await logger.aerror(f\"json_response.requirements_failed: {exc}\")\n return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n injected_system_prompt = self._inject_dynamic_prompt_values(getattr(self, \"system_prompt\", \"\") or \"\") or \"\"\n format_instructions = getattr(self, \"format_instructions\", \"\") or \"\"\n output_schema = getattr(self, \"output_schema\", None) or []\n has_tools = bool(self.tools)\n\n async def _run_agent_for_fallback(augmented_prompt: str) -> str:\n self.set(\n llm=llm_model,\n tools=self.tools or [],\n chat_history=self.chat_history,\n input_value=self.input_value,\n system_prompt=augmented_prompt,\n )\n agent_runnable = self.create_agent_runnable()\n with _suppress_send_message(self):\n result = await self.run_agent(agent_runnable)\n return _extract_text_content(result)\n\n try:\n return await orchestrate_structured_output(\n llm=llm_model,\n output_schema=output_schema,\n system_prompt=injected_system_prompt,\n format_instructions=format_instructions,\n input_value=_extract_text_content(self.input_value),\n run_prompt_fallback=_run_agent_for_fallback,\n prefer_native=not has_tools,\n )\n except (\n ExceptionWithMessageError,\n ValueError,\n TypeError,\n NotImplementedError,\n AttributeError,\n ) as exc:\n await logger.aerror(f\"json_response.orchestration_failed: {exc}\")\n return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n async def get_memory_data(self):\n # TODO: This is a temporary fix to avoid message duplication. We should develop a function for this.\n messages = (\n await MemoryComponent(**self.get_base_args())\n .set(\n session_id=self.graph.session_id,\n context_id=self.context_id,\n order=\"Ascending\",\n n_messages=self.n_messages,\n )\n .retrieve_messages()\n )\n return [\n message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n ]\n\n def update_input_types(self, build_config: dotdict) -> dotdict:\n \"\"\"Update input types for all fields in build_config.\"\"\"\n for key, value in build_config.items():\n if isinstance(value, dict):\n if value.get(\"input_types\") is None:\n build_config[key][\"input_types\"] = []\n elif hasattr(value, \"input_types\") and value.input_types is None:\n value.input_types = []\n return build_config\n\n async def update_build_config(\n self,\n build_config: dotdict,\n field_value: list[dict],\n field_name: str | None = None,\n ) -> dotdict:\n # Update model options with caching (for all field changes).\n # The tool-calling constraint lives on the ModelInput's ``filters``\n # field (declared above); ``handle_model_input_update`` reads it\n # and applies the filter to both the dropdown options and the\n # sticky-default re-injection path.\n build_config = handle_model_input_update(\n component=self,\n build_config=dict(build_config),\n field_value=field_value,\n field_name=field_name,\n )\n build_config = dotdict(build_config)\n\n if field_name == \"model\":\n build_config = self.update_input_types(build_config)\n\n # Validate required keys\n default_keys = [\n \"code\",\n \"_type\",\n \"model\",\n \"tools\",\n \"input_value\",\n \"add_current_date_tool\",\n \"add_calculator_tool\",\n \"system_prompt\",\n \"agent_description\",\n \"max_iterations\",\n \"handle_parsing_errors\",\n \"verbose\",\n ]\n missing_keys = [key for key in default_keys if key not in build_config]\n if missing_keys:\n msg = f\"Missing required keys in build_config: {missing_keys}\"\n raise ValueError(msg)\n return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n async def _get_tools(self) -> list[Tool]:\n component_toolkit = get_component_toolkit()\n tools_names = self._build_tools_names()\n agent_description = self.get_tool_description()\n # TODO: Agent Description Depreciated Feature to be removed\n description = f\"{agent_description}{tools_names}\"\n\n tools = component_toolkit(component=self).get_tools(\n tool_name=\"Call_Agent\",\n tool_description=description,\n # here we do not use the shared callbacks as we are exposing the agent as a tool\n callbacks=self.get_langchain_callbacks(),\n )\n if hasattr(self, \"tools_metadata\"):\n tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n\n return tools\n" + "value": "from __future__ import annotations\n\nfrom contextlib import contextmanager\nfrom datetime import datetime, timezone\nfrom typing import TYPE_CHECKING, Any\n\nfrom lfx.components.models_and_agents.memory import MemoryComponent, aget_agent_chat_history\n\nif TYPE_CHECKING:\n from langchain_core.tools import Tool\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.agents.default_system_prompt import DEFAULT_SYSTEM_PROMPT_TEMPLATE\nfrom lfx.base.agents.events import ExceptionWithMessageError\nfrom lfx.base.constants import STREAM_INFO_TEXT\nfrom lfx.base.models.unified_models import (\n get_language_model_options,\n get_llm,\n handle_model_input_update,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.components.agentics.helpers.model_config import validate_model_selection\nfrom lfx.components.helpers import CalculatorComponent, CurrentDateComponent\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.custom.custom_component.component import get_component_toolkit\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, ModelInput, StrInput\nfrom lfx.io import IntInput, MessageTextInput, MultilineInput, Output, SecretStrInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.schema.data import Data\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.schema.table import EditMode\n\n\ndef set_advanced_true(component_input):\n component_input.advanced = True\n return component_input\n\n\ndef _extract_text_content(value) -> str:\n \"\"\"Pull a string payload from a Message-like, AIMessage-like, or string value.\"\"\"\n if isinstance(value, str):\n return value\n text = getattr(value, \"text\", None)\n if isinstance(text, str):\n return text\n content = getattr(value, \"content\", None)\n if isinstance(content, str):\n return content\n return str(value) if value is not None else \"\"\n\n\n@contextmanager\ndef _suppress_send_message(component: Any):\n \"\"\"Temporarily replace component.send_message with a no-op for the duration of the block.\n\n Used during the structured-output prompt fallback: run_agent streams the agent's\n final answer through self.send_message (correct for message_response), but in\n json_response the orchestrator parses that text into structured Data which the\n downstream Chat Output emits — leaving the original emission in place produces a\n duplicate message in the playground. The original method is always restored on exit,\n even when the wrapped call raises.\n \"\"\"\n original = component.send_message\n\n async def _noop(message, *_args, **_kwargs):\n return message\n\n component.send_message = _noop\n try:\n yield\n finally:\n component.send_message = original\n\n\nclass AgentComponent(ToolCallingAgentComponent):\n display_name: str = \"Agent\"\n description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n documentation: str = \"https://docs.langflow.org/agents\"\n icon = \"bot\"\n beta = False\n name = \"Agent\"\n\n memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n inputs = [\n ModelInput(\n name=\"model\",\n display_name=\"Language Model\",\n info=\"Select your model provider\",\n real_time_refresh=True,\n required=True,\n # Agents require tool calling — the filter is honored by\n # ``handle_model_input_update`` so models that can't run with\n # tools never reach the picker (and any saved selection that\n # no longer satisfies the constraint is auto-replaced).\n filters={\"tool_calling\": True},\n ),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"API Key\",\n info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n real_time_refresh=True,\n advanced=True,\n ),\n DropdownInput(\n name=\"base_url_ibm_watsonx\",\n display_name=\"watsonx API Endpoint\",\n info=\"The base URL of the API (IBM watsonx.ai only)\",\n options=IBM_WATSONX_URLS,\n value=IBM_WATSONX_URLS[0],\n combobox=True,\n show=False,\n real_time_refresh=True,\n ),\n StrInput(\n name=\"project_id\",\n display_name=\"watsonx Project ID\",\n info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n show=False,\n required=False,\n ),\n MultilineInput(\n name=\"system_prompt\",\n display_name=\"Agent Instructions\",\n info=(\n \"System Prompt: Initial instructions and context provided to guide the agent's behavior. \"\n \"Supports dynamic placeholders: {current_date}, {model_name}, {optional_user_context}.\"\n ),\n value=DEFAULT_SYSTEM_PROMPT_TEMPLATE,\n advanced=False,\n ),\n MessageTextInput(\n name=\"context_id\",\n display_name=\"Context ID\",\n info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n value=\"\",\n advanced=True,\n ),\n IntInput(\n name=\"n_messages\",\n display_name=\"Number of Chat History Messages\",\n value=100,\n info=\"Number of chat history messages to retrieve.\",\n advanced=True,\n show=True,\n ),\n IntInput(\n name=\"max_tokens\",\n display_name=\"Max Tokens\",\n info=\"Maximum number of tokens to generate. Field name varies by provider.\",\n advanced=True,\n range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n ),\n MultilineInput(\n name=\"format_instructions\",\n display_name=\"Output Format Instructions\",\n info=\"Generic Template for structured output formatting. Valid only with Structured response.\",\n value=(\n \"You are an AI that extracts structured JSON objects from unstructured text. \"\n \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n \"Fill missing or ambiguous values with defaults: null for missing values. \"\n \"Remove exact duplicates but keep variations that have different field values. \"\n \"Always return valid JSON in the expected format, never throw errors. \"\n \"If multiple objects can be extracted, return them all in the structured format.\"\n ),\n advanced=True,\n ),\n TableInput(\n name=\"output_schema\",\n display_name=\"Output Schema\",\n info=(\n \"Schema Validation: Define the structure and data types for structured output. \"\n \"No validation if no output schema.\"\n ),\n advanced=True,\n required=False,\n value=[],\n table_schema=[\n {\n \"name\": \"name\",\n \"display_name\": \"Name\",\n \"type\": \"str\",\n \"description\": \"Specify the name of the output field.\",\n \"default\": \"field\",\n \"edit_mode\": EditMode.INLINE,\n },\n {\n \"name\": \"description\",\n \"display_name\": \"Description\",\n \"type\": \"str\",\n \"description\": \"Describe the purpose of the output field.\",\n \"default\": \"description of field\",\n \"edit_mode\": EditMode.POPOVER,\n },\n {\n \"name\": \"type\",\n \"display_name\": \"Type\",\n \"type\": \"str\",\n \"edit_mode\": EditMode.INLINE,\n \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n \"default\": \"str\",\n },\n {\n \"name\": \"multiple\",\n \"display_name\": \"As List\",\n \"type\": \"boolean\",\n \"description\": \"Set to True if this output field should be a list of the specified type.\",\n \"default\": \"False\",\n \"edit_mode\": EditMode.INLINE,\n },\n ],\n ),\n *LCToolsAgentComponent.get_base_inputs(),\n # removed memory inputs from agent component\n # *memory_inputs,\n BoolInput(\n name=\"stream\",\n display_name=\"Stream\",\n info=STREAM_INFO_TEXT,\n value=False,\n advanced=True,\n ),\n BoolInput(\n name=\"add_current_date_tool\",\n display_name=\"Current Date\",\n advanced=True,\n info=\"If true, will add a tool to the agent that returns the current date.\",\n value=True,\n ),\n BoolInput(\n name=\"add_calculator_tool\",\n display_name=\"Calculator\",\n advanced=True,\n info=(\n \"If true, adds a zero-config arithmetic calculator tool to the agent \"\n \"(safe: only +, -, *, /, ** operators via AST).\"\n ),\n value=True,\n ),\n ]\n outputs = [\n Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n Output(\n name=\"structured_response\",\n display_name=\"Structured Response\",\n method=\"json_response\",\n types=[\"Data\"],\n ),\n ]\n\n def _resolve_selected_model(self):\n \"\"\"Resolve the selected model, including legacy agent_llm/model_name inputs.\"\"\"\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n if isinstance(self.model, BaseLanguageModel):\n return self.model\n except ImportError:\n pass\n\n if isinstance(self.model, list) and self.model:\n return self.model\n\n legacy_provider = getattr(self, \"agent_llm\", None)\n legacy_model_name = getattr(self, \"model_name\", None)\n if not legacy_provider or not legacy_model_name:\n return self.model\n\n options = get_language_model_options(user_id=self.user_id)\n for option in options:\n if option.get(\"provider\") == legacy_provider and option.get(\"name\") == legacy_model_name:\n return [option]\n\n return [\n {\n \"name\": legacy_model_name,\n \"provider\": legacy_provider,\n \"metadata\": {},\n }\n ]\n\n def _get_max_tokens_value(self):\n \"\"\"Return the user-supplied max_tokens or None when unset/zero.\"\"\"\n val = getattr(self, \"max_tokens\", None)\n if val in {\"\", 0}:\n return None\n return val\n\n def _get_llm(self):\n \"\"\"Override parent to include max_tokens from the Agent's input field.\"\"\"\n return get_llm(\n model=self.model,\n user_id=self.user_id,\n api_key=getattr(self, \"api_key\", None),\n stream=bool(getattr(self, \"stream\", False)),\n max_tokens=self._get_max_tokens_value(),\n watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n watsonx_project_id=getattr(self, \"project_id\", None),\n )\n\n async def get_agent_requirements(self):\n \"\"\"Get the agent requirements for the agent.\"\"\"\n from langchain_core.tools import StructuredTool\n\n selected_model = self._resolve_selected_model()\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n is_connected_model = isinstance(selected_model, BaseLanguageModel)\n except ImportError:\n is_connected_model = False\n\n if not is_connected_model:\n validate_model_selection(selected_model)\n\n # Ensure _get_llm() uses the resolved model (e.g. from legacy agent_llm/model_name)\n self.model = selected_model\n llm_model = self._get_llm()\n if llm_model is None:\n msg = \"No language model selected. Please choose a model to proceed.\"\n raise ValueError(msg)\n\n # Get memory data\n self.chat_history = await self.get_memory_data()\n await logger.adebug(f\"Retrieved {len(self.chat_history)} chat history messages\")\n if isinstance(self.chat_history, Message):\n self.chat_history = [self.chat_history]\n\n # Add current date tool if enabled\n if self.add_current_date_tool:\n if not isinstance(self.tools, list): # type: ignore[has-type]\n self.tools = []\n current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n if not isinstance(current_date_tool, StructuredTool):\n msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n raise TypeError(msg)\n # Skip if an externally-connected tool already provides the same name.\n # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n if not any(getattr(t, \"name\", None) == current_date_tool.name for t in self.tools):\n self.tools.append(current_date_tool)\n\n # Add calculator tool if enabled (zero-config arithmetic)\n if getattr(self, \"add_calculator_tool\", False):\n if not isinstance(self.tools, list): # type: ignore[has-type]\n self.tools = []\n calculator_tool = (await CalculatorComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n if not isinstance(calculator_tool, StructuredTool):\n msg = \"CalculatorComponent must be converted to a StructuredTool\"\n raise TypeError(msg)\n # Skip if an externally-connected tool already provides the same name.\n # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n if not any(getattr(t, \"name\", None) == calculator_tool.name for t in self.tools):\n self.tools.append(calculator_tool)\n\n # Set shared callbacks for tracing the tools used by the agent\n self.set_tools_callbacks(self.tools, self._get_shared_callbacks())\n\n return llm_model, self.chat_history, self.tools\n\n def _get_resolved_model_name(self) -> str:\n \"\"\"Best-effort human-readable model name for {model_name} injection.\"\"\"\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n if isinstance(self.model, BaseLanguageModel):\n return type(self.model).__name__\n except ImportError:\n pass\n\n if isinstance(self.model, list) and self.model:\n first = self.model[0]\n if isinstance(first, dict):\n name = first.get(\"name\")\n if isinstance(name, str) and name:\n return name\n\n legacy_model_name = getattr(self, \"model_name\", None)\n if isinstance(legacy_model_name, str) and legacy_model_name:\n return legacy_model_name\n return \"\"\n\n def _inject_dynamic_prompt_values(self, prompt: str | None) -> str | None:\n \"\"\"Replace known env placeholders in the system prompt.\n\n Handles {current_date}, {model_name}, and {optional_user_context} (the\n last one ships with the structured DEFAULT_SYSTEM_PROMPT_TEMPLATE and\n is currently unused at the AgentComponent layer, so it resolves to \"\").\n Uses str.replace (not str.format) so user prompts containing literal\n braces such as JSON examples ({\"key\": 1}) never break the agent.\n \"\"\"\n if not prompt:\n return prompt\n replacements = {\n \"{current_date}\": datetime.now(tz=timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S UTC\"),\n \"{model_name}\": self._get_resolved_model_name(),\n \"{optional_user_context}\": \"\",\n }\n for placeholder, value in replacements.items():\n prompt = prompt.replace(placeholder, value)\n return prompt\n\n async def message_response(self) -> Message:\n try:\n llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n # Set up and run agent\n self.set(\n llm=llm_model,\n tools=self.tools or [],\n chat_history=self.chat_history,\n input_value=self.input_value,\n system_prompt=self._inject_dynamic_prompt_values(self.system_prompt),\n )\n agent = self.create_agent_runnable()\n result = await self.run_agent(agent)\n\n # Store result for potential JSON output\n self._agent_result = result\n\n except (ValueError, TypeError, KeyError) as e:\n await logger.aerror(f\"{type(e).__name__}: {e!s}\")\n raise\n except ExceptionWithMessageError as e:\n await logger.aerror(f\"ExceptionWithMessageError occurred: {e}\")\n raise\n # Avoid catching blind Exception; let truly unexpected exceptions propagate\n except Exception as e:\n await logger.aerror(f\"Unexpected error: {e!s}\")\n raise\n else:\n return result\n\n async def json_response(self) -> Data:\n \"\"\"Produce structured Data via native LLM structured output, with prompt-based fallback.\n\n Native path (no tools, llm has with_structured_output) bypasses the agent loop and\n returns provider-validated JSON. When tools are attached, falls back to running the\n agent with a schema-augmented system prompt and parsing the final message content.\n \"\"\"\n from lfx.components.models_and_agents.structured_output.structured_output_orchestrator import (\n orchestrate_structured_output,\n )\n\n try:\n llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n except (ValueError, TypeError) as exc:\n await logger.aerror(f\"json_response.requirements_failed: {exc}\")\n return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n injected_system_prompt = self._inject_dynamic_prompt_values(getattr(self, \"system_prompt\", \"\") or \"\") or \"\"\n format_instructions = getattr(self, \"format_instructions\", \"\") or \"\"\n output_schema = getattr(self, \"output_schema\", None) or []\n has_tools = bool(self.tools)\n\n async def _run_agent_for_fallback(augmented_prompt: str) -> str:\n self.set(\n llm=llm_model,\n tools=self.tools or [],\n chat_history=self.chat_history,\n input_value=self.input_value,\n system_prompt=augmented_prompt,\n )\n agent_runnable = self.create_agent_runnable()\n with _suppress_send_message(self):\n result = await self.run_agent(agent_runnable)\n return _extract_text_content(result)\n\n try:\n return await orchestrate_structured_output(\n llm=llm_model,\n output_schema=output_schema,\n system_prompt=injected_system_prompt,\n format_instructions=format_instructions,\n input_value=_extract_text_content(self.input_value),\n run_prompt_fallback=_run_agent_for_fallback,\n prefer_native=not has_tools,\n )\n except (\n ExceptionWithMessageError,\n ValueError,\n TypeError,\n NotImplementedError,\n AttributeError,\n ) as exc:\n await logger.aerror(f\"json_response.orchestration_failed: {exc}\")\n return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n async def get_memory_data(self):\n # Scope by flow_id so default playground session names (e.g. \"New Session 0\")\n # cannot leak chat history across unrelated flows. See issue #13059.\n # The helper also returns [] when n_messages == 0, preserving the\n # explicit \"memory disabled\" contract from MemoryComponent.retrieve_messages.\n messages = await aget_agent_chat_history(\n session_id=self.graph.session_id,\n flow_id=getattr(self.graph, \"flow_id\", None),\n context_id=self.context_id,\n n_messages=self.n_messages,\n )\n return [\n message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n ]\n\n def update_input_types(self, build_config: dotdict) -> dotdict:\n \"\"\"Update input types for all fields in build_config.\"\"\"\n for key, value in build_config.items():\n if isinstance(value, dict):\n if value.get(\"input_types\") is None:\n build_config[key][\"input_types\"] = []\n elif hasattr(value, \"input_types\") and value.input_types is None:\n value.input_types = []\n return build_config\n\n async def update_build_config(\n self,\n build_config: dotdict,\n field_value: list[dict],\n field_name: str | None = None,\n ) -> dotdict:\n # Update model options with caching (for all field changes).\n # The tool-calling constraint lives on the ModelInput's ``filters``\n # field (declared above); ``handle_model_input_update`` reads it\n # and applies the filter to both the dropdown options and the\n # sticky-default re-injection path.\n build_config = handle_model_input_update(\n component=self,\n build_config=dict(build_config),\n field_value=field_value,\n field_name=field_name,\n )\n build_config = dotdict(build_config)\n\n if field_name == \"model\":\n build_config = self.update_input_types(build_config)\n\n # Validate required keys\n default_keys = [\n \"code\",\n \"_type\",\n \"model\",\n \"tools\",\n \"input_value\",\n \"add_current_date_tool\",\n \"add_calculator_tool\",\n \"system_prompt\",\n \"agent_description\",\n \"max_iterations\",\n \"handle_parsing_errors\",\n \"verbose\",\n ]\n missing_keys = [key for key in default_keys if key not in build_config]\n if missing_keys:\n msg = f\"Missing required keys in build_config: {missing_keys}\"\n raise ValueError(msg)\n return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n async def _get_tools(self) -> list[Tool]:\n component_toolkit = get_component_toolkit()\n tools_names = self._build_tools_names()\n agent_description = self.get_tool_description()\n # TODO: Agent Description Depreciated Feature to be removed\n description = f\"{agent_description}{tools_names}\"\n\n tools = component_toolkit(component=self).get_tools(\n tool_name=\"Call_Agent\",\n tool_description=description,\n # here we do not use the shared callbacks as we are exposing the agent as a tool\n callbacks=self.get_langchain_callbacks(),\n )\n if hasattr(self, \"tools_metadata\"):\n tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n\n return tools\n" }, "context_id": { "_input_type": "MessageTextInput", diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Meeting Summary.json b/src/backend/base/langflow/initial_setup/starter_projects/Meeting Summary.json index f94f998b04..73a8fa69a4 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Meeting Summary.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Meeting Summary.json @@ -1734,7 +1734,7 @@ "legacy": false, "lf_version": "1.1.5", "metadata": { - "code_hash": "7608453efb4e", + "code_hash": "5619cf9008d5", "dependencies": { "dependencies": [ { @@ -1797,7 +1797,7 @@ "show": true, "title_case": false, "type": "code", - "value": "from typing import Any, cast\n\nfrom lfx.custom.custom_component.component import Component\nfrom lfx.helpers.data import data_to_text\nfrom lfx.inputs.inputs import DropdownInput, HandleInput, IntInput, MessageTextInput, MultilineInput, TabInput\nfrom lfx.memory import aget_messages, astore_message\nfrom lfx.schema.data import Data\nfrom lfx.schema.dataframe import DataFrame\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.template.field.base import Output\nfrom lfx.utils.component_utils import set_current_fields, set_field_display\nfrom lfx.utils.constants import MESSAGE_SENDER_AI, MESSAGE_SENDER_NAME_AI, MESSAGE_SENDER_USER\n\n\nclass MemoryComponent(Component):\n display_name = \"Message History\"\n description = \"Stores or retrieves stored chat messages from Langflow tables or an external memory.\"\n documentation: str = \"https://docs.langflow.org/message-history\"\n icon = \"message-square-more\"\n name = \"Memory\"\n default_keys = [\"mode\", \"memory\", \"session_id\", \"context_id\"]\n mode_config = {\n \"Store\": [\"message\", \"memory\", \"sender\", \"sender_name\", \"session_id\", \"context_id\"],\n \"Retrieve\": [\"n_messages\", \"order\", \"template\", \"memory\", \"session_id\", \"context_id\"],\n }\n\n inputs = [\n TabInput(\n name=\"mode\",\n display_name=\"Mode\",\n options=[\"Retrieve\", \"Store\"],\n value=\"Retrieve\",\n info=\"Operation mode: Store messages or Retrieve messages.\",\n real_time_refresh=True,\n ),\n MessageTextInput(\n name=\"message\",\n display_name=\"Message\",\n info=\"The chat message to be stored.\",\n tool_mode=True,\n dynamic=True,\n show=False,\n ),\n HandleInput(\n name=\"memory\",\n display_name=\"External Memory\",\n input_types=[\"Memory\"],\n info=\"Retrieve messages from an external memory. If empty, it will use the Langflow tables.\",\n advanced=True,\n ),\n DropdownInput(\n name=\"sender_type\",\n display_name=\"Sender Type\",\n options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER, \"Machine and User\"],\n value=\"Machine and User\",\n info=\"Filter by sender type.\",\n advanced=True,\n ),\n MessageTextInput(\n name=\"sender\",\n display_name=\"Sender\",\n info=\"The sender of the message. Might be Machine or User. \"\n \"If empty, the current sender parameter will be used.\",\n advanced=True,\n ),\n MessageTextInput(\n name=\"sender_name\",\n display_name=\"Sender Name\",\n info=\"Filter by sender name.\",\n advanced=True,\n show=False,\n ),\n IntInput(\n name=\"n_messages\",\n display_name=\"Number of Messages\",\n value=100,\n info=\"Number of messages to retrieve.\",\n advanced=True,\n show=True,\n ),\n MessageTextInput(\n name=\"session_id\",\n display_name=\"Session ID\",\n info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n value=\"\",\n advanced=True,\n ),\n MessageTextInput(\n name=\"context_id\",\n display_name=\"Context ID\",\n info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n value=\"\",\n advanced=True,\n ),\n DropdownInput(\n name=\"order\",\n display_name=\"Order\",\n options=[\"Ascending\", \"Descending\"],\n value=\"Ascending\",\n info=\"Order of the messages.\",\n advanced=True,\n tool_mode=True,\n required=True,\n ),\n MultilineInput(\n name=\"template\",\n display_name=\"Template\",\n info=\"The template to use for formatting the data. \"\n \"It can contain the keys {text}, {sender} or any other key in the message data.\",\n value=\"{sender_name}: {text}\",\n advanced=True,\n show=False,\n ),\n ]\n\n outputs = [\n Output(\n display_name=\"Messages\",\n name=\"messages_text\",\n method=\"retrieve_messages_as_text\",\n types=[\"Message\"],\n selected=\"Message\",\n dynamic=True,\n ),\n Output(\n display_name=\"Table\",\n name=\"dataframe\",\n method=\"retrieve_messages_dataframe\",\n types=[\"Table\"],\n selected=\"Table\",\n dynamic=True,\n ),\n ]\n\n def update_outputs(self, frontend_node: dict, field_name: str, field_value: Any) -> dict:\n \"\"\"Dynamically show only the relevant output based on the selected output type.\"\"\"\n if field_name == \"mode\":\n # Start with empty outputs\n frontend_node[\"outputs\"] = []\n if field_value == \"Store\":\n frontend_node[\"outputs\"] = [\n Output(\n display_name=\"Stored Messages\",\n name=\"stored_messages\",\n method=\"store_message\",\n types=[\"Message\"],\n selected=\"Message\",\n hidden=True,\n dynamic=True,\n )\n ]\n if field_value == \"Retrieve\":\n frontend_node[\"outputs\"] = [\n Output(\n display_name=\"Messages\",\n name=\"messages_text\",\n method=\"retrieve_messages_as_text\",\n types=[\"Message\"],\n selected=\"Message\",\n dynamic=True,\n ),\n Output(\n display_name=\"Table\",\n name=\"dataframe\",\n method=\"retrieve_messages_dataframe\",\n types=[\"Table\"],\n selected=\"Table\",\n dynamic=True,\n ),\n ]\n return frontend_node\n\n async def store_message(self) -> Message:\n message = Message(text=self.message) if isinstance(self.message, str) else self.message\n\n message.context_id = self.context_id or message.context_id\n message.session_id = self.session_id or message.session_id\n message.sender = self.sender or message.sender or MESSAGE_SENDER_AI\n message.sender_name = self.sender_name or message.sender_name or MESSAGE_SENDER_NAME_AI\n\n stored_messages: list[Message] = []\n\n if self.memory:\n self.memory.context_id = message.context_id\n self.memory.session_id = message.session_id\n lc_message = message.to_lc_message()\n await self.memory.aadd_messages([lc_message])\n\n stored_messages = await self.memory.aget_messages() or []\n\n stored_messages = [Message.from_lc_message(m) for m in stored_messages] if stored_messages else []\n\n if message.sender:\n stored_messages = [m for m in stored_messages if m.sender == message.sender]\n else:\n await astore_message(message, flow_id=self.graph.flow_id)\n stored_messages = (\n await aget_messages(\n session_id=message.session_id,\n context_id=message.context_id,\n sender_name=message.sender_name,\n sender=message.sender,\n )\n or []\n )\n\n if not stored_messages:\n msg = \"No messages were stored. Please ensure that the session ID and sender are properly set.\"\n raise ValueError(msg)\n\n stored_message = stored_messages[0]\n self.status = stored_message\n return stored_message\n\n async def retrieve_messages(self) -> Data:\n sender_type = self.sender_type\n sender_name = self.sender_name\n session_id = self.session_id\n context_id = self.context_id\n n_messages = self.n_messages\n order = \"DESC\" if self.order == \"Descending\" else \"ASC\"\n\n if sender_type == \"Machine and User\":\n sender_type = None\n\n if self.memory and not hasattr(self.memory, \"aget_messages\"):\n memory_name = type(self.memory).__name__\n err_msg = f\"External Memory object ({memory_name}) must have 'aget_messages' method.\"\n raise AttributeError(err_msg)\n # Check if n_messages is None or 0\n if n_messages == 0:\n stored = []\n elif self.memory:\n # override session_id\n self.memory.session_id = session_id\n self.memory.context_id = context_id\n\n stored = await self.memory.aget_messages()\n # langchain memories are supposed to return messages in ascending order\n\n if n_messages:\n stored = stored[-n_messages:] # Get last N messages first\n\n if order == \"DESC\":\n stored = stored[::-1] # Then reverse if needed\n\n stored = [Message.from_lc_message(m) for m in stored]\n if sender_type:\n expected_type = MESSAGE_SENDER_AI if sender_type == MESSAGE_SENDER_AI else MESSAGE_SENDER_USER\n stored = [m for m in stored if m.type == expected_type]\n else:\n # For internal memory, we always fetch the last N messages by ordering by DESC\n stored = await aget_messages(\n sender=sender_type,\n sender_name=sender_name,\n session_id=session_id,\n context_id=context_id,\n limit=10000,\n order=order,\n )\n if n_messages:\n stored = stored[-n_messages:] # Get last N messages\n\n # self.status = stored\n return cast(\"Data\", stored)\n\n async def retrieve_messages_as_text(self) -> Message:\n stored_text = data_to_text(self.template, await self.retrieve_messages())\n # self.status = stored_text\n return Message(text=stored_text)\n\n async def retrieve_messages_dataframe(self) -> DataFrame:\n \"\"\"Convert the retrieved messages into a DataFrame.\n\n Returns:\n DataFrame: A DataFrame containing the message data.\n \"\"\"\n messages = await self.retrieve_messages()\n return DataFrame(messages)\n\n def update_build_config(\n self,\n build_config: dotdict,\n field_value: Any, # noqa: ARG002\n field_name: str | None = None, # noqa: ARG002\n ) -> dotdict:\n return set_current_fields(\n build_config=build_config,\n action_fields=self.mode_config,\n selected_action=build_config[\"mode\"][\"value\"],\n default_fields=self.default_keys,\n func=set_field_display,\n )\n" + "value": "from typing import Any, cast\nfrom uuid import UUID\n\nfrom lfx.custom.custom_component.component import Component\nfrom lfx.helpers.data import data_to_text\nfrom lfx.inputs.inputs import DropdownInput, HandleInput, IntInput, MessageTextInput, MultilineInput, TabInput\nfrom lfx.log.logger import logger\nfrom lfx.memory import aget_messages, astore_message\nfrom lfx.schema.data import Data\nfrom lfx.schema.dataframe import DataFrame\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.template.field.base import Output\nfrom lfx.utils.component_utils import set_current_fields, set_field_display\nfrom lfx.utils.constants import MESSAGE_SENDER_AI, MESSAGE_SENDER_NAME_AI, MESSAGE_SENDER_USER\n\n# Cap on rows we will pull from the DB when the caller has not supplied an\n# explicit ``n_messages`` limit. This is a guardrail against unbounded fetches\n# (DB pressure, memory) on sessions that accumulate huge histories. The\n# trade-off is that sessions with more rows than this cap will not surface\n# their oldest messages when no ``n_messages`` is set. See ``aget_messages``\n# usage sites below for how this interacts with ordering.\nMAX_CHAT_HISTORY_FETCH_LIMIT = 10_000\n\n\ndef _coerce_flow_id_to_uuid(flow_id: str | UUID | None) -> UUID | None:\n \"\"\"Coerce a graph flow_id (typically str) to UUID for DB filtering.\n\n Returns ``None`` when ``flow_id`` is missing or cannot be parsed. The\n caller then falls back to the previous **unscoped** retrieval, which\n re-introduces the cross-flow leak that motivated PR #13087. We emit a\n structured ``error`` log on that path (rather than ``warning``) so\n observability can alert on it — see issue #13059 / PR #13087.\n \"\"\"\n if flow_id is None or flow_id == \"\":\n return None\n if isinstance(flow_id, UUID):\n return flow_id\n try:\n return UUID(str(flow_id))\n except (ValueError, TypeError, AttributeError):\n # Loud, structured signal — this path means chat history is being\n # served without flow-scoping, which is the privacy bug PR #13087\n # was created to close. Anything matching this event is a candidate\n # for an observability alert.\n logger.error(\n \"memory_flow_id_unscoped: flow_id %r is not a valid UUID; \"\n \"chat history will NOT be scoped by flow_id. This re-enables \"\n \"cross-flow leakage (issue #13059) for this request.\",\n flow_id,\n extra={\"event\": \"memory_flow_id_unscoped\", \"flow_id_repr\": repr(flow_id)},\n )\n return None\n\n\ndef _safe_graph_flow_id(component: Component) -> str | UUID | None:\n \"\"\"Best-effort lookup of the component's graph flow_id.\n\n ``Component.graph`` is a property that reaches into ``self._vertex.graph``;\n when a MemoryComponent is constructed ad-hoc (e.g. by the Agent component\n via ``MemoryComponent(**self.get_base_args())``), ``_vertex`` is ``None`` and\n accessing the property raises ``AttributeError``. Swallow that here so\n retrieval falls back to the previous unscoped behavior rather than crashing.\n \"\"\"\n try:\n graph = component.graph\n except AttributeError:\n return None\n return getattr(graph, \"flow_id\", None)\n\n\nasync def aget_agent_chat_history(\n *,\n session_id: str | UUID | None,\n flow_id: str | UUID | None,\n context_id: str | None = None,\n n_messages: int | None = None,\n) -> list[Message]:\n \"\"\"Fetch chat history for an agent, scoped to a single flow.\n\n Centralizes the contract previously implemented by\n ``MemoryComponent.retrieve_messages`` for agent callers:\n\n * Returns ``[]`` when ``n_messages == 0`` (memory explicitly disabled).\n Without this short-circuit, the bounded query would still execute and\n the caller's ``messages[-0:]`` would return everything.\n * Scopes by ``flow_id`` (coerced to ``UUID``) so default playground\n session names cannot leak history across flows (issue #13059).\n * Returns up to ``n_messages`` most recent messages in ascending order.\n Queries in ``DESC`` order with ``limit=n_messages`` so sessions with\n more than ``MAX_CHAT_HISTORY_FETCH_LIMIT`` rows still see the genuine\n most-recent slice, not the chronological first window.\n \"\"\"\n if n_messages == 0:\n return []\n fetch_limit = n_messages if n_messages else MAX_CHAT_HISTORY_FETCH_LIMIT\n messages = await aget_messages(\n session_id=session_id,\n context_id=context_id,\n flow_id=_coerce_flow_id_to_uuid(flow_id),\n limit=fetch_limit,\n order=\"DESC\",\n )\n if not n_messages and len(messages) == MAX_CHAT_HISTORY_FETCH_LIMIT:\n # We hit the unbounded-fetch ceiling. The caller will likely see\n # stale \"most-recent\" history. Flag this so on-call has something\n # to grep when a user reports forgotten context.\n logger.warning(\n \"memory_chat_history_limit_reached: hit MAX_CHAT_HISTORY_FETCH_LIMIT=%d \"\n \"without an explicit n_messages; older messages were not returned.\",\n MAX_CHAT_HISTORY_FETCH_LIMIT,\n extra={\"event\": \"memory_chat_history_limit_reached\"},\n )\n # ``aget_messages`` returned DESC; reverse to ASC for the agent's prompt.\n return list(reversed(messages))\n\n\nclass MemoryComponent(Component):\n display_name = \"Message History\"\n description = \"Stores or retrieves stored chat messages from Langflow tables or an external memory.\"\n documentation: str = \"https://docs.langflow.org/message-history\"\n icon = \"message-square-more\"\n name = \"Memory\"\n default_keys = [\"mode\", \"memory\", \"session_id\", \"context_id\"]\n mode_config = {\n \"Store\": [\"message\", \"memory\", \"sender\", \"sender_name\", \"session_id\", \"context_id\"],\n \"Retrieve\": [\"n_messages\", \"order\", \"template\", \"memory\", \"session_id\", \"context_id\"],\n }\n\n inputs = [\n TabInput(\n name=\"mode\",\n display_name=\"Mode\",\n options=[\"Retrieve\", \"Store\"],\n value=\"Retrieve\",\n info=\"Operation mode: Store messages or Retrieve messages.\",\n real_time_refresh=True,\n ),\n MessageTextInput(\n name=\"message\",\n display_name=\"Message\",\n info=\"The chat message to be stored.\",\n tool_mode=True,\n dynamic=True,\n show=False,\n ),\n HandleInput(\n name=\"memory\",\n display_name=\"External Memory\",\n input_types=[\"Memory\"],\n info=\"Retrieve messages from an external memory. If empty, it will use the Langflow tables.\",\n advanced=True,\n ),\n DropdownInput(\n name=\"sender_type\",\n display_name=\"Sender Type\",\n options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER, \"Machine and User\"],\n value=\"Machine and User\",\n info=\"Filter by sender type.\",\n advanced=True,\n ),\n MessageTextInput(\n name=\"sender\",\n display_name=\"Sender\",\n info=\"The sender of the message. Might be Machine or User. \"\n \"If empty, the current sender parameter will be used.\",\n advanced=True,\n ),\n MessageTextInput(\n name=\"sender_name\",\n display_name=\"Sender Name\",\n info=\"Filter by sender name.\",\n advanced=True,\n show=False,\n ),\n IntInput(\n name=\"n_messages\",\n display_name=\"Number of Messages\",\n value=100,\n info=\"Number of messages to retrieve.\",\n advanced=True,\n show=True,\n ),\n MessageTextInput(\n name=\"session_id\",\n display_name=\"Session ID\",\n info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n value=\"\",\n advanced=True,\n ),\n MessageTextInput(\n name=\"context_id\",\n display_name=\"Context ID\",\n info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n value=\"\",\n advanced=True,\n ),\n DropdownInput(\n name=\"order\",\n display_name=\"Order\",\n options=[\"Ascending\", \"Descending\"],\n value=\"Ascending\",\n info=\"Order of the messages.\",\n advanced=True,\n tool_mode=True,\n required=True,\n ),\n MultilineInput(\n name=\"template\",\n display_name=\"Template\",\n info=\"The template to use for formatting the data. \"\n \"It can contain the keys {text}, {sender} or any other key in the message data.\",\n value=\"{sender_name}: {text}\",\n advanced=True,\n show=False,\n ),\n ]\n\n outputs = [\n Output(\n display_name=\"Messages\",\n name=\"messages_text\",\n method=\"retrieve_messages_as_text\",\n types=[\"Message\"],\n selected=\"Message\",\n dynamic=True,\n ),\n Output(\n display_name=\"Table\",\n name=\"dataframe\",\n method=\"retrieve_messages_dataframe\",\n types=[\"Table\"],\n selected=\"Table\",\n dynamic=True,\n ),\n ]\n\n def update_outputs(self, frontend_node: dict, field_name: str, field_value: Any) -> dict:\n \"\"\"Dynamically show only the relevant output based on the selected output type.\"\"\"\n if field_name == \"mode\":\n # Start with empty outputs\n frontend_node[\"outputs\"] = []\n if field_value == \"Store\":\n frontend_node[\"outputs\"] = [\n Output(\n display_name=\"Stored Messages\",\n name=\"stored_messages\",\n method=\"store_message\",\n types=[\"Message\"],\n selected=\"Message\",\n hidden=True,\n dynamic=True,\n )\n ]\n if field_value == \"Retrieve\":\n frontend_node[\"outputs\"] = [\n Output(\n display_name=\"Messages\",\n name=\"messages_text\",\n method=\"retrieve_messages_as_text\",\n types=[\"Message\"],\n selected=\"Message\",\n dynamic=True,\n ),\n Output(\n display_name=\"Table\",\n name=\"dataframe\",\n method=\"retrieve_messages_dataframe\",\n types=[\"Table\"],\n selected=\"Table\",\n dynamic=True,\n ),\n ]\n return frontend_node\n\n async def store_message(self) -> Message:\n message = Message(text=self.message) if isinstance(self.message, str) else self.message\n\n message.context_id = self.context_id or message.context_id\n message.session_id = self.session_id or message.session_id\n message.sender = self.sender or message.sender or MESSAGE_SENDER_AI\n message.sender_name = self.sender_name or message.sender_name or MESSAGE_SENDER_NAME_AI\n\n stored_messages: list[Message] = []\n\n if self.memory:\n self.memory.context_id = message.context_id\n self.memory.session_id = message.session_id\n lc_message = message.to_lc_message()\n await self.memory.aadd_messages([lc_message])\n\n stored_messages = await self.memory.aget_messages() or []\n\n stored_messages = [Message.from_lc_message(m) for m in stored_messages] if stored_messages else []\n\n if message.sender:\n stored_messages = [m for m in stored_messages if m.sender == message.sender]\n else:\n # Single coerced scope used for both the write and the read-back,\n # so a missing/ad-hoc ``_vertex`` cannot crash the write half while\n # the read half degrades gracefully. See PR #13087 review I1.\n flow_id_scope = _coerce_flow_id_to_uuid(_safe_graph_flow_id(self))\n await astore_message(message, flow_id=flow_id_scope)\n stored_messages = (\n await aget_messages(\n session_id=message.session_id,\n context_id=message.context_id,\n sender_name=message.sender_name,\n sender=message.sender,\n flow_id=flow_id_scope,\n )\n or []\n )\n\n if not stored_messages:\n msg = \"No messages were stored. Please ensure that the session ID and sender are properly set.\"\n raise ValueError(msg)\n\n stored_message = stored_messages[0]\n self.status = stored_message\n return stored_message\n\n async def retrieve_messages(self) -> Data:\n sender_type = self.sender_type\n sender_name = self.sender_name\n session_id = self.session_id\n context_id = self.context_id\n n_messages = self.n_messages\n order = \"DESC\" if self.order == \"Descending\" else \"ASC\"\n\n if sender_type == \"Machine and User\":\n sender_type = None\n\n if self.memory and not hasattr(self.memory, \"aget_messages\"):\n memory_name = type(self.memory).__name__\n err_msg = f\"External Memory object ({memory_name}) must have 'aget_messages' method.\"\n raise AttributeError(err_msg)\n # Check if n_messages is None or 0\n if n_messages == 0:\n stored = []\n elif self.memory:\n # override session_id\n self.memory.session_id = session_id\n self.memory.context_id = context_id\n\n stored = await self.memory.aget_messages()\n # langchain memories are supposed to return messages in ascending order\n\n if n_messages:\n stored = stored[-n_messages:] # Get last N messages first\n\n if order == \"DESC\":\n stored = stored[::-1] # Then reverse if needed\n\n stored = [Message.from_lc_message(m) for m in stored]\n if sender_type:\n expected_type = MESSAGE_SENDER_AI if sender_type == MESSAGE_SENDER_AI else MESSAGE_SENDER_USER\n stored = [m for m in stored if m.type == expected_type]\n else:\n # For internal memory, fetch the last N messages by ordering DESC at the\n # DB layer so sessions with more than ``MAX_CHAT_HISTORY_FETCH_LIMIT``\n # rows still return the genuine most-recent slice rather than the\n # chronological first window. See PR #13087 review I2.\n #\n # Scope by flow_id so default session names (e.g. \"New Session 0\") do not\n # leak chat history across unrelated flows. See issue #13059.\n flow_id_scope = _coerce_flow_id_to_uuid(_safe_graph_flow_id(self))\n fetch_limit = n_messages if n_messages else MAX_CHAT_HISTORY_FETCH_LIMIT\n stored = await aget_messages(\n sender=sender_type,\n sender_name=sender_name,\n session_id=session_id,\n context_id=context_id,\n flow_id=flow_id_scope,\n limit=fetch_limit,\n order=\"DESC\",\n )\n if not n_messages and len(stored) == MAX_CHAT_HISTORY_FETCH_LIMIT:\n logger.warning(\n \"memory_chat_history_limit_reached: hit MAX_CHAT_HISTORY_FETCH_LIMIT=%d \"\n \"without an explicit n_messages; older messages were not returned.\",\n MAX_CHAT_HISTORY_FETCH_LIMIT,\n extra={\"event\": \"memory_chat_history_limit_reached\"},\n )\n # Honor the user-selected order: we fetched DESC, reverse if ASC requested.\n if order == \"ASC\":\n stored = list(reversed(stored))\n\n # self.status = stored\n return cast(\"Data\", stored)\n\n async def retrieve_messages_as_text(self) -> Message:\n stored_text = data_to_text(self.template, await self.retrieve_messages())\n # self.status = stored_text\n return Message(text=stored_text)\n\n async def retrieve_messages_dataframe(self) -> DataFrame:\n \"\"\"Convert the retrieved messages into a DataFrame.\n\n Returns:\n DataFrame: A DataFrame containing the message data.\n \"\"\"\n messages = await self.retrieve_messages()\n return DataFrame(messages)\n\n def update_build_config(\n self,\n build_config: dotdict,\n field_value: Any, # noqa: ARG002\n field_name: str | None = None, # noqa: ARG002\n ) -> dotdict:\n return set_current_fields(\n build_config=build_config,\n action_fields=self.mode_config,\n selected_action=build_config[\"mode\"][\"value\"],\n default_fields=self.default_keys,\n func=set_field_display,\n )\n" }, "context_id": { "_input_type": "MessageTextInput", diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Memory Chatbot.json b/src/backend/base/langflow/initial_setup/starter_projects/Memory Chatbot.json index e5b74d186d..af24539527 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Memory Chatbot.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Memory Chatbot.json @@ -930,7 +930,7 @@ "legacy": false, "lf_version": "1.4.3", "metadata": { - "code_hash": "7608453efb4e", + "code_hash": "5619cf9008d5", "dependencies": { "dependencies": [ { @@ -994,7 +994,7 @@ "show": true, "title_case": false, "type": "code", - "value": "from typing import Any, cast\n\nfrom lfx.custom.custom_component.component import Component\nfrom lfx.helpers.data import data_to_text\nfrom lfx.inputs.inputs import DropdownInput, HandleInput, IntInput, MessageTextInput, MultilineInput, TabInput\nfrom lfx.memory import aget_messages, astore_message\nfrom lfx.schema.data import Data\nfrom lfx.schema.dataframe import DataFrame\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.template.field.base import Output\nfrom lfx.utils.component_utils import set_current_fields, set_field_display\nfrom lfx.utils.constants import MESSAGE_SENDER_AI, MESSAGE_SENDER_NAME_AI, MESSAGE_SENDER_USER\n\n\nclass MemoryComponent(Component):\n display_name = \"Message History\"\n description = \"Stores or retrieves stored chat messages from Langflow tables or an external memory.\"\n documentation: str = \"https://docs.langflow.org/message-history\"\n icon = \"message-square-more\"\n name = \"Memory\"\n default_keys = [\"mode\", \"memory\", \"session_id\", \"context_id\"]\n mode_config = {\n \"Store\": [\"message\", \"memory\", \"sender\", \"sender_name\", \"session_id\", \"context_id\"],\n \"Retrieve\": [\"n_messages\", \"order\", \"template\", \"memory\", \"session_id\", \"context_id\"],\n }\n\n inputs = [\n TabInput(\n name=\"mode\",\n display_name=\"Mode\",\n options=[\"Retrieve\", \"Store\"],\n value=\"Retrieve\",\n info=\"Operation mode: Store messages or Retrieve messages.\",\n real_time_refresh=True,\n ),\n MessageTextInput(\n name=\"message\",\n display_name=\"Message\",\n info=\"The chat message to be stored.\",\n tool_mode=True,\n dynamic=True,\n show=False,\n ),\n HandleInput(\n name=\"memory\",\n display_name=\"External Memory\",\n input_types=[\"Memory\"],\n info=\"Retrieve messages from an external memory. If empty, it will use the Langflow tables.\",\n advanced=True,\n ),\n DropdownInput(\n name=\"sender_type\",\n display_name=\"Sender Type\",\n options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER, \"Machine and User\"],\n value=\"Machine and User\",\n info=\"Filter by sender type.\",\n advanced=True,\n ),\n MessageTextInput(\n name=\"sender\",\n display_name=\"Sender\",\n info=\"The sender of the message. Might be Machine or User. \"\n \"If empty, the current sender parameter will be used.\",\n advanced=True,\n ),\n MessageTextInput(\n name=\"sender_name\",\n display_name=\"Sender Name\",\n info=\"Filter by sender name.\",\n advanced=True,\n show=False,\n ),\n IntInput(\n name=\"n_messages\",\n display_name=\"Number of Messages\",\n value=100,\n info=\"Number of messages to retrieve.\",\n advanced=True,\n show=True,\n ),\n MessageTextInput(\n name=\"session_id\",\n display_name=\"Session ID\",\n info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n value=\"\",\n advanced=True,\n ),\n MessageTextInput(\n name=\"context_id\",\n display_name=\"Context ID\",\n info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n value=\"\",\n advanced=True,\n ),\n DropdownInput(\n name=\"order\",\n display_name=\"Order\",\n options=[\"Ascending\", \"Descending\"],\n value=\"Ascending\",\n info=\"Order of the messages.\",\n advanced=True,\n tool_mode=True,\n required=True,\n ),\n MultilineInput(\n name=\"template\",\n display_name=\"Template\",\n info=\"The template to use for formatting the data. \"\n \"It can contain the keys {text}, {sender} or any other key in the message data.\",\n value=\"{sender_name}: {text}\",\n advanced=True,\n show=False,\n ),\n ]\n\n outputs = [\n Output(\n display_name=\"Messages\",\n name=\"messages_text\",\n method=\"retrieve_messages_as_text\",\n types=[\"Message\"],\n selected=\"Message\",\n dynamic=True,\n ),\n Output(\n display_name=\"Table\",\n name=\"dataframe\",\n method=\"retrieve_messages_dataframe\",\n types=[\"Table\"],\n selected=\"Table\",\n dynamic=True,\n ),\n ]\n\n def update_outputs(self, frontend_node: dict, field_name: str, field_value: Any) -> dict:\n \"\"\"Dynamically show only the relevant output based on the selected output type.\"\"\"\n if field_name == \"mode\":\n # Start with empty outputs\n frontend_node[\"outputs\"] = []\n if field_value == \"Store\":\n frontend_node[\"outputs\"] = [\n Output(\n display_name=\"Stored Messages\",\n name=\"stored_messages\",\n method=\"store_message\",\n types=[\"Message\"],\n selected=\"Message\",\n hidden=True,\n dynamic=True,\n )\n ]\n if field_value == \"Retrieve\":\n frontend_node[\"outputs\"] = [\n Output(\n display_name=\"Messages\",\n name=\"messages_text\",\n method=\"retrieve_messages_as_text\",\n types=[\"Message\"],\n selected=\"Message\",\n dynamic=True,\n ),\n Output(\n display_name=\"Table\",\n name=\"dataframe\",\n method=\"retrieve_messages_dataframe\",\n types=[\"Table\"],\n selected=\"Table\",\n dynamic=True,\n ),\n ]\n return frontend_node\n\n async def store_message(self) -> Message:\n message = Message(text=self.message) if isinstance(self.message, str) else self.message\n\n message.context_id = self.context_id or message.context_id\n message.session_id = self.session_id or message.session_id\n message.sender = self.sender or message.sender or MESSAGE_SENDER_AI\n message.sender_name = self.sender_name or message.sender_name or MESSAGE_SENDER_NAME_AI\n\n stored_messages: list[Message] = []\n\n if self.memory:\n self.memory.context_id = message.context_id\n self.memory.session_id = message.session_id\n lc_message = message.to_lc_message()\n await self.memory.aadd_messages([lc_message])\n\n stored_messages = await self.memory.aget_messages() or []\n\n stored_messages = [Message.from_lc_message(m) for m in stored_messages] if stored_messages else []\n\n if message.sender:\n stored_messages = [m for m in stored_messages if m.sender == message.sender]\n else:\n await astore_message(message, flow_id=self.graph.flow_id)\n stored_messages = (\n await aget_messages(\n session_id=message.session_id,\n context_id=message.context_id,\n sender_name=message.sender_name,\n sender=message.sender,\n )\n or []\n )\n\n if not stored_messages:\n msg = \"No messages were stored. Please ensure that the session ID and sender are properly set.\"\n raise ValueError(msg)\n\n stored_message = stored_messages[0]\n self.status = stored_message\n return stored_message\n\n async def retrieve_messages(self) -> Data:\n sender_type = self.sender_type\n sender_name = self.sender_name\n session_id = self.session_id\n context_id = self.context_id\n n_messages = self.n_messages\n order = \"DESC\" if self.order == \"Descending\" else \"ASC\"\n\n if sender_type == \"Machine and User\":\n sender_type = None\n\n if self.memory and not hasattr(self.memory, \"aget_messages\"):\n memory_name = type(self.memory).__name__\n err_msg = f\"External Memory object ({memory_name}) must have 'aget_messages' method.\"\n raise AttributeError(err_msg)\n # Check if n_messages is None or 0\n if n_messages == 0:\n stored = []\n elif self.memory:\n # override session_id\n self.memory.session_id = session_id\n self.memory.context_id = context_id\n\n stored = await self.memory.aget_messages()\n # langchain memories are supposed to return messages in ascending order\n\n if n_messages:\n stored = stored[-n_messages:] # Get last N messages first\n\n if order == \"DESC\":\n stored = stored[::-1] # Then reverse if needed\n\n stored = [Message.from_lc_message(m) for m in stored]\n if sender_type:\n expected_type = MESSAGE_SENDER_AI if sender_type == MESSAGE_SENDER_AI else MESSAGE_SENDER_USER\n stored = [m for m in stored if m.type == expected_type]\n else:\n # For internal memory, we always fetch the last N messages by ordering by DESC\n stored = await aget_messages(\n sender=sender_type,\n sender_name=sender_name,\n session_id=session_id,\n context_id=context_id,\n limit=10000,\n order=order,\n )\n if n_messages:\n stored = stored[-n_messages:] # Get last N messages\n\n # self.status = stored\n return cast(\"Data\", stored)\n\n async def retrieve_messages_as_text(self) -> Message:\n stored_text = data_to_text(self.template, await self.retrieve_messages())\n # self.status = stored_text\n return Message(text=stored_text)\n\n async def retrieve_messages_dataframe(self) -> DataFrame:\n \"\"\"Convert the retrieved messages into a DataFrame.\n\n Returns:\n DataFrame: A DataFrame containing the message data.\n \"\"\"\n messages = await self.retrieve_messages()\n return DataFrame(messages)\n\n def update_build_config(\n self,\n build_config: dotdict,\n field_value: Any, # noqa: ARG002\n field_name: str | None = None, # noqa: ARG002\n ) -> dotdict:\n return set_current_fields(\n build_config=build_config,\n action_fields=self.mode_config,\n selected_action=build_config[\"mode\"][\"value\"],\n default_fields=self.default_keys,\n func=set_field_display,\n )\n" + "value": "from typing import Any, cast\nfrom uuid import UUID\n\nfrom lfx.custom.custom_component.component import Component\nfrom lfx.helpers.data import data_to_text\nfrom lfx.inputs.inputs import DropdownInput, HandleInput, IntInput, MessageTextInput, MultilineInput, TabInput\nfrom lfx.log.logger import logger\nfrom lfx.memory import aget_messages, astore_message\nfrom lfx.schema.data import Data\nfrom lfx.schema.dataframe import DataFrame\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.template.field.base import Output\nfrom lfx.utils.component_utils import set_current_fields, set_field_display\nfrom lfx.utils.constants import MESSAGE_SENDER_AI, MESSAGE_SENDER_NAME_AI, MESSAGE_SENDER_USER\n\n# Cap on rows we will pull from the DB when the caller has not supplied an\n# explicit ``n_messages`` limit. This is a guardrail against unbounded fetches\n# (DB pressure, memory) on sessions that accumulate huge histories. The\n# trade-off is that sessions with more rows than this cap will not surface\n# their oldest messages when no ``n_messages`` is set. See ``aget_messages``\n# usage sites below for how this interacts with ordering.\nMAX_CHAT_HISTORY_FETCH_LIMIT = 10_000\n\n\ndef _coerce_flow_id_to_uuid(flow_id: str | UUID | None) -> UUID | None:\n \"\"\"Coerce a graph flow_id (typically str) to UUID for DB filtering.\n\n Returns ``None`` when ``flow_id`` is missing or cannot be parsed. The\n caller then falls back to the previous **unscoped** retrieval, which\n re-introduces the cross-flow leak that motivated PR #13087. We emit a\n structured ``error`` log on that path (rather than ``warning``) so\n observability can alert on it — see issue #13059 / PR #13087.\n \"\"\"\n if flow_id is None or flow_id == \"\":\n return None\n if isinstance(flow_id, UUID):\n return flow_id\n try:\n return UUID(str(flow_id))\n except (ValueError, TypeError, AttributeError):\n # Loud, structured signal — this path means chat history is being\n # served without flow-scoping, which is the privacy bug PR #13087\n # was created to close. Anything matching this event is a candidate\n # for an observability alert.\n logger.error(\n \"memory_flow_id_unscoped: flow_id %r is not a valid UUID; \"\n \"chat history will NOT be scoped by flow_id. This re-enables \"\n \"cross-flow leakage (issue #13059) for this request.\",\n flow_id,\n extra={\"event\": \"memory_flow_id_unscoped\", \"flow_id_repr\": repr(flow_id)},\n )\n return None\n\n\ndef _safe_graph_flow_id(component: Component) -> str | UUID | None:\n \"\"\"Best-effort lookup of the component's graph flow_id.\n\n ``Component.graph`` is a property that reaches into ``self._vertex.graph``;\n when a MemoryComponent is constructed ad-hoc (e.g. by the Agent component\n via ``MemoryComponent(**self.get_base_args())``), ``_vertex`` is ``None`` and\n accessing the property raises ``AttributeError``. Swallow that here so\n retrieval falls back to the previous unscoped behavior rather than crashing.\n \"\"\"\n try:\n graph = component.graph\n except AttributeError:\n return None\n return getattr(graph, \"flow_id\", None)\n\n\nasync def aget_agent_chat_history(\n *,\n session_id: str | UUID | None,\n flow_id: str | UUID | None,\n context_id: str | None = None,\n n_messages: int | None = None,\n) -> list[Message]:\n \"\"\"Fetch chat history for an agent, scoped to a single flow.\n\n Centralizes the contract previously implemented by\n ``MemoryComponent.retrieve_messages`` for agent callers:\n\n * Returns ``[]`` when ``n_messages == 0`` (memory explicitly disabled).\n Without this short-circuit, the bounded query would still execute and\n the caller's ``messages[-0:]`` would return everything.\n * Scopes by ``flow_id`` (coerced to ``UUID``) so default playground\n session names cannot leak history across flows (issue #13059).\n * Returns up to ``n_messages`` most recent messages in ascending order.\n Queries in ``DESC`` order with ``limit=n_messages`` so sessions with\n more than ``MAX_CHAT_HISTORY_FETCH_LIMIT`` rows still see the genuine\n most-recent slice, not the chronological first window.\n \"\"\"\n if n_messages == 0:\n return []\n fetch_limit = n_messages if n_messages else MAX_CHAT_HISTORY_FETCH_LIMIT\n messages = await aget_messages(\n session_id=session_id,\n context_id=context_id,\n flow_id=_coerce_flow_id_to_uuid(flow_id),\n limit=fetch_limit,\n order=\"DESC\",\n )\n if not n_messages and len(messages) == MAX_CHAT_HISTORY_FETCH_LIMIT:\n # We hit the unbounded-fetch ceiling. The caller will likely see\n # stale \"most-recent\" history. Flag this so on-call has something\n # to grep when a user reports forgotten context.\n logger.warning(\n \"memory_chat_history_limit_reached: hit MAX_CHAT_HISTORY_FETCH_LIMIT=%d \"\n \"without an explicit n_messages; older messages were not returned.\",\n MAX_CHAT_HISTORY_FETCH_LIMIT,\n extra={\"event\": \"memory_chat_history_limit_reached\"},\n )\n # ``aget_messages`` returned DESC; reverse to ASC for the agent's prompt.\n return list(reversed(messages))\n\n\nclass MemoryComponent(Component):\n display_name = \"Message History\"\n description = \"Stores or retrieves stored chat messages from Langflow tables or an external memory.\"\n documentation: str = \"https://docs.langflow.org/message-history\"\n icon = \"message-square-more\"\n name = \"Memory\"\n default_keys = [\"mode\", \"memory\", \"session_id\", \"context_id\"]\n mode_config = {\n \"Store\": [\"message\", \"memory\", \"sender\", \"sender_name\", \"session_id\", \"context_id\"],\n \"Retrieve\": [\"n_messages\", \"order\", \"template\", \"memory\", \"session_id\", \"context_id\"],\n }\n\n inputs = [\n TabInput(\n name=\"mode\",\n display_name=\"Mode\",\n options=[\"Retrieve\", \"Store\"],\n value=\"Retrieve\",\n info=\"Operation mode: Store messages or Retrieve messages.\",\n real_time_refresh=True,\n ),\n MessageTextInput(\n name=\"message\",\n display_name=\"Message\",\n info=\"The chat message to be stored.\",\n tool_mode=True,\n dynamic=True,\n show=False,\n ),\n HandleInput(\n name=\"memory\",\n display_name=\"External Memory\",\n input_types=[\"Memory\"],\n info=\"Retrieve messages from an external memory. If empty, it will use the Langflow tables.\",\n advanced=True,\n ),\n DropdownInput(\n name=\"sender_type\",\n display_name=\"Sender Type\",\n options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER, \"Machine and User\"],\n value=\"Machine and User\",\n info=\"Filter by sender type.\",\n advanced=True,\n ),\n MessageTextInput(\n name=\"sender\",\n display_name=\"Sender\",\n info=\"The sender of the message. Might be Machine or User. \"\n \"If empty, the current sender parameter will be used.\",\n advanced=True,\n ),\n MessageTextInput(\n name=\"sender_name\",\n display_name=\"Sender Name\",\n info=\"Filter by sender name.\",\n advanced=True,\n show=False,\n ),\n IntInput(\n name=\"n_messages\",\n display_name=\"Number of Messages\",\n value=100,\n info=\"Number of messages to retrieve.\",\n advanced=True,\n show=True,\n ),\n MessageTextInput(\n name=\"session_id\",\n display_name=\"Session ID\",\n info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n value=\"\",\n advanced=True,\n ),\n MessageTextInput(\n name=\"context_id\",\n display_name=\"Context ID\",\n info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n value=\"\",\n advanced=True,\n ),\n DropdownInput(\n name=\"order\",\n display_name=\"Order\",\n options=[\"Ascending\", \"Descending\"],\n value=\"Ascending\",\n info=\"Order of the messages.\",\n advanced=True,\n tool_mode=True,\n required=True,\n ),\n MultilineInput(\n name=\"template\",\n display_name=\"Template\",\n info=\"The template to use for formatting the data. \"\n \"It can contain the keys {text}, {sender} or any other key in the message data.\",\n value=\"{sender_name}: {text}\",\n advanced=True,\n show=False,\n ),\n ]\n\n outputs = [\n Output(\n display_name=\"Messages\",\n name=\"messages_text\",\n method=\"retrieve_messages_as_text\",\n types=[\"Message\"],\n selected=\"Message\",\n dynamic=True,\n ),\n Output(\n display_name=\"Table\",\n name=\"dataframe\",\n method=\"retrieve_messages_dataframe\",\n types=[\"Table\"],\n selected=\"Table\",\n dynamic=True,\n ),\n ]\n\n def update_outputs(self, frontend_node: dict, field_name: str, field_value: Any) -> dict:\n \"\"\"Dynamically show only the relevant output based on the selected output type.\"\"\"\n if field_name == \"mode\":\n # Start with empty outputs\n frontend_node[\"outputs\"] = []\n if field_value == \"Store\":\n frontend_node[\"outputs\"] = [\n Output(\n display_name=\"Stored Messages\",\n name=\"stored_messages\",\n method=\"store_message\",\n types=[\"Message\"],\n selected=\"Message\",\n hidden=True,\n dynamic=True,\n )\n ]\n if field_value == \"Retrieve\":\n frontend_node[\"outputs\"] = [\n Output(\n display_name=\"Messages\",\n name=\"messages_text\",\n method=\"retrieve_messages_as_text\",\n types=[\"Message\"],\n selected=\"Message\",\n dynamic=True,\n ),\n Output(\n display_name=\"Table\",\n name=\"dataframe\",\n method=\"retrieve_messages_dataframe\",\n types=[\"Table\"],\n selected=\"Table\",\n dynamic=True,\n ),\n ]\n return frontend_node\n\n async def store_message(self) -> Message:\n message = Message(text=self.message) if isinstance(self.message, str) else self.message\n\n message.context_id = self.context_id or message.context_id\n message.session_id = self.session_id or message.session_id\n message.sender = self.sender or message.sender or MESSAGE_SENDER_AI\n message.sender_name = self.sender_name or message.sender_name or MESSAGE_SENDER_NAME_AI\n\n stored_messages: list[Message] = []\n\n if self.memory:\n self.memory.context_id = message.context_id\n self.memory.session_id = message.session_id\n lc_message = message.to_lc_message()\n await self.memory.aadd_messages([lc_message])\n\n stored_messages = await self.memory.aget_messages() or []\n\n stored_messages = [Message.from_lc_message(m) for m in stored_messages] if stored_messages else []\n\n if message.sender:\n stored_messages = [m for m in stored_messages if m.sender == message.sender]\n else:\n # Single coerced scope used for both the write and the read-back,\n # so a missing/ad-hoc ``_vertex`` cannot crash the write half while\n # the read half degrades gracefully. See PR #13087 review I1.\n flow_id_scope = _coerce_flow_id_to_uuid(_safe_graph_flow_id(self))\n await astore_message(message, flow_id=flow_id_scope)\n stored_messages = (\n await aget_messages(\n session_id=message.session_id,\n context_id=message.context_id,\n sender_name=message.sender_name,\n sender=message.sender,\n flow_id=flow_id_scope,\n )\n or []\n )\n\n if not stored_messages:\n msg = \"No messages were stored. Please ensure that the session ID and sender are properly set.\"\n raise ValueError(msg)\n\n stored_message = stored_messages[0]\n self.status = stored_message\n return stored_message\n\n async def retrieve_messages(self) -> Data:\n sender_type = self.sender_type\n sender_name = self.sender_name\n session_id = self.session_id\n context_id = self.context_id\n n_messages = self.n_messages\n order = \"DESC\" if self.order == \"Descending\" else \"ASC\"\n\n if sender_type == \"Machine and User\":\n sender_type = None\n\n if self.memory and not hasattr(self.memory, \"aget_messages\"):\n memory_name = type(self.memory).__name__\n err_msg = f\"External Memory object ({memory_name}) must have 'aget_messages' method.\"\n raise AttributeError(err_msg)\n # Check if n_messages is None or 0\n if n_messages == 0:\n stored = []\n elif self.memory:\n # override session_id\n self.memory.session_id = session_id\n self.memory.context_id = context_id\n\n stored = await self.memory.aget_messages()\n # langchain memories are supposed to return messages in ascending order\n\n if n_messages:\n stored = stored[-n_messages:] # Get last N messages first\n\n if order == \"DESC\":\n stored = stored[::-1] # Then reverse if needed\n\n stored = [Message.from_lc_message(m) for m in stored]\n if sender_type:\n expected_type = MESSAGE_SENDER_AI if sender_type == MESSAGE_SENDER_AI else MESSAGE_SENDER_USER\n stored = [m for m in stored if m.type == expected_type]\n else:\n # For internal memory, fetch the last N messages by ordering DESC at the\n # DB layer so sessions with more than ``MAX_CHAT_HISTORY_FETCH_LIMIT``\n # rows still return the genuine most-recent slice rather than the\n # chronological first window. See PR #13087 review I2.\n #\n # Scope by flow_id so default session names (e.g. \"New Session 0\") do not\n # leak chat history across unrelated flows. See issue #13059.\n flow_id_scope = _coerce_flow_id_to_uuid(_safe_graph_flow_id(self))\n fetch_limit = n_messages if n_messages else MAX_CHAT_HISTORY_FETCH_LIMIT\n stored = await aget_messages(\n sender=sender_type,\n sender_name=sender_name,\n session_id=session_id,\n context_id=context_id,\n flow_id=flow_id_scope,\n limit=fetch_limit,\n order=\"DESC\",\n )\n if not n_messages and len(stored) == MAX_CHAT_HISTORY_FETCH_LIMIT:\n logger.warning(\n \"memory_chat_history_limit_reached: hit MAX_CHAT_HISTORY_FETCH_LIMIT=%d \"\n \"without an explicit n_messages; older messages were not returned.\",\n MAX_CHAT_HISTORY_FETCH_LIMIT,\n extra={\"event\": \"memory_chat_history_limit_reached\"},\n )\n # Honor the user-selected order: we fetched DESC, reverse if ASC requested.\n if order == \"ASC\":\n stored = list(reversed(stored))\n\n # self.status = stored\n return cast(\"Data\", stored)\n\n async def retrieve_messages_as_text(self) -> Message:\n stored_text = data_to_text(self.template, await self.retrieve_messages())\n # self.status = stored_text\n return Message(text=stored_text)\n\n async def retrieve_messages_dataframe(self) -> DataFrame:\n \"\"\"Convert the retrieved messages into a DataFrame.\n\n Returns:\n DataFrame: A DataFrame containing the message data.\n \"\"\"\n messages = await self.retrieve_messages()\n return DataFrame(messages)\n\n def update_build_config(\n self,\n build_config: dotdict,\n field_value: Any, # noqa: ARG002\n field_name: str | None = None, # noqa: ARG002\n ) -> dotdict:\n return set_current_fields(\n build_config=build_config,\n action_fields=self.mode_config,\n selected_action=build_config[\"mode\"][\"value\"],\n default_fields=self.default_keys,\n func=set_field_display,\n )\n" }, "context_id": { "_input_type": "MessageTextInput", diff --git a/src/backend/base/langflow/initial_setup/starter_projects/News Aggregator.json b/src/backend/base/langflow/initial_setup/starter_projects/News Aggregator.json index 26e248fd94..b9858a589d 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/News Aggregator.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/News Aggregator.json @@ -1178,7 +1178,7 @@ "last_updated": "2025-12-11T21:41:48.407Z", "legacy": false, "metadata": { - "code_hash": "ebac74bfcae6", + "code_hash": "443159257161", "dependencies": { "dependencies": [ { @@ -1367,7 +1367,7 @@ "show": true, "title_case": false, "type": "code", - "value": "from __future__ import annotations\n\nfrom contextlib import contextmanager\nfrom datetime import datetime, timezone\nfrom typing import TYPE_CHECKING, Any\n\nfrom lfx.components.models_and_agents.memory import MemoryComponent\n\nif TYPE_CHECKING:\n from langchain_core.tools import Tool\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.agents.default_system_prompt import DEFAULT_SYSTEM_PROMPT_TEMPLATE\nfrom lfx.base.agents.events import ExceptionWithMessageError\nfrom lfx.base.constants import STREAM_INFO_TEXT\nfrom lfx.base.models.unified_models import (\n get_language_model_options,\n get_llm,\n handle_model_input_update,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.components.agentics.helpers.model_config import validate_model_selection\nfrom lfx.components.helpers import CalculatorComponent, CurrentDateComponent\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.custom.custom_component.component import get_component_toolkit\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, ModelInput, StrInput\nfrom lfx.io import IntInput, MessageTextInput, MultilineInput, Output, SecretStrInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.schema.data import Data\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.schema.table import EditMode\n\n\ndef set_advanced_true(component_input):\n component_input.advanced = True\n return component_input\n\n\ndef _extract_text_content(value) -> str:\n \"\"\"Pull a string payload from a Message-like, AIMessage-like, or string value.\"\"\"\n if isinstance(value, str):\n return value\n text = getattr(value, \"text\", None)\n if isinstance(text, str):\n return text\n content = getattr(value, \"content\", None)\n if isinstance(content, str):\n return content\n return str(value) if value is not None else \"\"\n\n\n@contextmanager\ndef _suppress_send_message(component: Any):\n \"\"\"Temporarily replace component.send_message with a no-op for the duration of the block.\n\n Used during the structured-output prompt fallback: run_agent streams the agent's\n final answer through self.send_message (correct for message_response), but in\n json_response the orchestrator parses that text into structured Data which the\n downstream Chat Output emits — leaving the original emission in place produces a\n duplicate message in the playground. The original method is always restored on exit,\n even when the wrapped call raises.\n \"\"\"\n original = component.send_message\n\n async def _noop(message, *_args, **_kwargs):\n return message\n\n component.send_message = _noop\n try:\n yield\n finally:\n component.send_message = original\n\n\nclass AgentComponent(ToolCallingAgentComponent):\n display_name: str = \"Agent\"\n description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n documentation: str = \"https://docs.langflow.org/agents\"\n icon = \"bot\"\n beta = False\n name = \"Agent\"\n\n memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n inputs = [\n ModelInput(\n name=\"model\",\n display_name=\"Language Model\",\n info=\"Select your model provider\",\n real_time_refresh=True,\n required=True,\n # Agents require tool calling — the filter is honored by\n # ``handle_model_input_update`` so models that can't run with\n # tools never reach the picker (and any saved selection that\n # no longer satisfies the constraint is auto-replaced).\n filters={\"tool_calling\": True},\n ),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"API Key\",\n info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n real_time_refresh=True,\n advanced=True,\n ),\n DropdownInput(\n name=\"base_url_ibm_watsonx\",\n display_name=\"watsonx API Endpoint\",\n info=\"The base URL of the API (IBM watsonx.ai only)\",\n options=IBM_WATSONX_URLS,\n value=IBM_WATSONX_URLS[0],\n combobox=True,\n show=False,\n real_time_refresh=True,\n ),\n StrInput(\n name=\"project_id\",\n display_name=\"watsonx Project ID\",\n info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n show=False,\n required=False,\n ),\n MultilineInput(\n name=\"system_prompt\",\n display_name=\"Agent Instructions\",\n info=(\n \"System Prompt: Initial instructions and context provided to guide the agent's behavior. \"\n \"Supports dynamic placeholders: {current_date}, {model_name}, {optional_user_context}.\"\n ),\n value=DEFAULT_SYSTEM_PROMPT_TEMPLATE,\n advanced=False,\n ),\n MessageTextInput(\n name=\"context_id\",\n display_name=\"Context ID\",\n info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n value=\"\",\n advanced=True,\n ),\n IntInput(\n name=\"n_messages\",\n display_name=\"Number of Chat History Messages\",\n value=100,\n info=\"Number of chat history messages to retrieve.\",\n advanced=True,\n show=True,\n ),\n IntInput(\n name=\"max_tokens\",\n display_name=\"Max Tokens\",\n info=\"Maximum number of tokens to generate. Field name varies by provider.\",\n advanced=True,\n range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n ),\n MultilineInput(\n name=\"format_instructions\",\n display_name=\"Output Format Instructions\",\n info=\"Generic Template for structured output formatting. Valid only with Structured response.\",\n value=(\n \"You are an AI that extracts structured JSON objects from unstructured text. \"\n \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n \"Fill missing or ambiguous values with defaults: null for missing values. \"\n \"Remove exact duplicates but keep variations that have different field values. \"\n \"Always return valid JSON in the expected format, never throw errors. \"\n \"If multiple objects can be extracted, return them all in the structured format.\"\n ),\n advanced=True,\n ),\n TableInput(\n name=\"output_schema\",\n display_name=\"Output Schema\",\n info=(\n \"Schema Validation: Define the structure and data types for structured output. \"\n \"No validation if no output schema.\"\n ),\n advanced=True,\n required=False,\n value=[],\n table_schema=[\n {\n \"name\": \"name\",\n \"display_name\": \"Name\",\n \"type\": \"str\",\n \"description\": \"Specify the name of the output field.\",\n \"default\": \"field\",\n \"edit_mode\": EditMode.INLINE,\n },\n {\n \"name\": \"description\",\n \"display_name\": \"Description\",\n \"type\": \"str\",\n \"description\": \"Describe the purpose of the output field.\",\n \"default\": \"description of field\",\n \"edit_mode\": EditMode.POPOVER,\n },\n {\n \"name\": \"type\",\n \"display_name\": \"Type\",\n \"type\": \"str\",\n \"edit_mode\": EditMode.INLINE,\n \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n \"default\": \"str\",\n },\n {\n \"name\": \"multiple\",\n \"display_name\": \"As List\",\n \"type\": \"boolean\",\n \"description\": \"Set to True if this output field should be a list of the specified type.\",\n \"default\": \"False\",\n \"edit_mode\": EditMode.INLINE,\n },\n ],\n ),\n *LCToolsAgentComponent.get_base_inputs(),\n # removed memory inputs from agent component\n # *memory_inputs,\n BoolInput(\n name=\"stream\",\n display_name=\"Stream\",\n info=STREAM_INFO_TEXT,\n value=False,\n advanced=True,\n ),\n BoolInput(\n name=\"add_current_date_tool\",\n display_name=\"Current Date\",\n advanced=True,\n info=\"If true, will add a tool to the agent that returns the current date.\",\n value=True,\n ),\n BoolInput(\n name=\"add_calculator_tool\",\n display_name=\"Calculator\",\n advanced=True,\n info=(\n \"If true, adds a zero-config arithmetic calculator tool to the agent \"\n \"(safe: only +, -, *, /, ** operators via AST).\"\n ),\n value=True,\n ),\n ]\n outputs = [\n Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n Output(\n name=\"structured_response\",\n display_name=\"Structured Response\",\n method=\"json_response\",\n types=[\"Data\"],\n ),\n ]\n\n def _resolve_selected_model(self):\n \"\"\"Resolve the selected model, including legacy agent_llm/model_name inputs.\"\"\"\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n if isinstance(self.model, BaseLanguageModel):\n return self.model\n except ImportError:\n pass\n\n if isinstance(self.model, list) and self.model:\n return self.model\n\n legacy_provider = getattr(self, \"agent_llm\", None)\n legacy_model_name = getattr(self, \"model_name\", None)\n if not legacy_provider or not legacy_model_name:\n return self.model\n\n options = get_language_model_options(user_id=self.user_id)\n for option in options:\n if option.get(\"provider\") == legacy_provider and option.get(\"name\") == legacy_model_name:\n return [option]\n\n return [\n {\n \"name\": legacy_model_name,\n \"provider\": legacy_provider,\n \"metadata\": {},\n }\n ]\n\n def _get_max_tokens_value(self):\n \"\"\"Return the user-supplied max_tokens or None when unset/zero.\"\"\"\n val = getattr(self, \"max_tokens\", None)\n if val in {\"\", 0}:\n return None\n return val\n\n def _get_llm(self):\n \"\"\"Override parent to include max_tokens from the Agent's input field.\"\"\"\n return get_llm(\n model=self.model,\n user_id=self.user_id,\n api_key=getattr(self, \"api_key\", None),\n stream=bool(getattr(self, \"stream\", False)),\n max_tokens=self._get_max_tokens_value(),\n watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n watsonx_project_id=getattr(self, \"project_id\", None),\n )\n\n async def get_agent_requirements(self):\n \"\"\"Get the agent requirements for the agent.\"\"\"\n from langchain_core.tools import StructuredTool\n\n selected_model = self._resolve_selected_model()\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n is_connected_model = isinstance(selected_model, BaseLanguageModel)\n except ImportError:\n is_connected_model = False\n\n if not is_connected_model:\n validate_model_selection(selected_model)\n\n # Ensure _get_llm() uses the resolved model (e.g. from legacy agent_llm/model_name)\n self.model = selected_model\n llm_model = self._get_llm()\n if llm_model is None:\n msg = \"No language model selected. Please choose a model to proceed.\"\n raise ValueError(msg)\n\n # Get memory data\n self.chat_history = await self.get_memory_data()\n await logger.adebug(f\"Retrieved {len(self.chat_history)} chat history messages\")\n if isinstance(self.chat_history, Message):\n self.chat_history = [self.chat_history]\n\n # Add current date tool if enabled\n if self.add_current_date_tool:\n if not isinstance(self.tools, list): # type: ignore[has-type]\n self.tools = []\n current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n if not isinstance(current_date_tool, StructuredTool):\n msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n raise TypeError(msg)\n # Skip if an externally-connected tool already provides the same name.\n # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n if not any(getattr(t, \"name\", None) == current_date_tool.name for t in self.tools):\n self.tools.append(current_date_tool)\n\n # Add calculator tool if enabled (zero-config arithmetic)\n if getattr(self, \"add_calculator_tool\", False):\n if not isinstance(self.tools, list): # type: ignore[has-type]\n self.tools = []\n calculator_tool = (await CalculatorComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n if not isinstance(calculator_tool, StructuredTool):\n msg = \"CalculatorComponent must be converted to a StructuredTool\"\n raise TypeError(msg)\n # Skip if an externally-connected tool already provides the same name.\n # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n if not any(getattr(t, \"name\", None) == calculator_tool.name for t in self.tools):\n self.tools.append(calculator_tool)\n\n # Set shared callbacks for tracing the tools used by the agent\n self.set_tools_callbacks(self.tools, self._get_shared_callbacks())\n\n return llm_model, self.chat_history, self.tools\n\n def _get_resolved_model_name(self) -> str:\n \"\"\"Best-effort human-readable model name for {model_name} injection.\"\"\"\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n if isinstance(self.model, BaseLanguageModel):\n return type(self.model).__name__\n except ImportError:\n pass\n\n if isinstance(self.model, list) and self.model:\n first = self.model[0]\n if isinstance(first, dict):\n name = first.get(\"name\")\n if isinstance(name, str) and name:\n return name\n\n legacy_model_name = getattr(self, \"model_name\", None)\n if isinstance(legacy_model_name, str) and legacy_model_name:\n return legacy_model_name\n return \"\"\n\n def _inject_dynamic_prompt_values(self, prompt: str | None) -> str | None:\n \"\"\"Replace known env placeholders in the system prompt.\n\n Handles {current_date}, {model_name}, and {optional_user_context} (the\n last one ships with the structured DEFAULT_SYSTEM_PROMPT_TEMPLATE and\n is currently unused at the AgentComponent layer, so it resolves to \"\").\n Uses str.replace (not str.format) so user prompts containing literal\n braces such as JSON examples ({\"key\": 1}) never break the agent.\n \"\"\"\n if not prompt:\n return prompt\n replacements = {\n \"{current_date}\": datetime.now(tz=timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S UTC\"),\n \"{model_name}\": self._get_resolved_model_name(),\n \"{optional_user_context}\": \"\",\n }\n for placeholder, value in replacements.items():\n prompt = prompt.replace(placeholder, value)\n return prompt\n\n async def message_response(self) -> Message:\n try:\n llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n # Set up and run agent\n self.set(\n llm=llm_model,\n tools=self.tools or [],\n chat_history=self.chat_history,\n input_value=self.input_value,\n system_prompt=self._inject_dynamic_prompt_values(self.system_prompt),\n )\n agent = self.create_agent_runnable()\n result = await self.run_agent(agent)\n\n # Store result for potential JSON output\n self._agent_result = result\n\n except (ValueError, TypeError, KeyError) as e:\n await logger.aerror(f\"{type(e).__name__}: {e!s}\")\n raise\n except ExceptionWithMessageError as e:\n await logger.aerror(f\"ExceptionWithMessageError occurred: {e}\")\n raise\n # Avoid catching blind Exception; let truly unexpected exceptions propagate\n except Exception as e:\n await logger.aerror(f\"Unexpected error: {e!s}\")\n raise\n else:\n return result\n\n async def json_response(self) -> Data:\n \"\"\"Produce structured Data via native LLM structured output, with prompt-based fallback.\n\n Native path (no tools, llm has with_structured_output) bypasses the agent loop and\n returns provider-validated JSON. When tools are attached, falls back to running the\n agent with a schema-augmented system prompt and parsing the final message content.\n \"\"\"\n from lfx.components.models_and_agents.structured_output.structured_output_orchestrator import (\n orchestrate_structured_output,\n )\n\n try:\n llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n except (ValueError, TypeError) as exc:\n await logger.aerror(f\"json_response.requirements_failed: {exc}\")\n return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n injected_system_prompt = self._inject_dynamic_prompt_values(getattr(self, \"system_prompt\", \"\") or \"\") or \"\"\n format_instructions = getattr(self, \"format_instructions\", \"\") or \"\"\n output_schema = getattr(self, \"output_schema\", None) or []\n has_tools = bool(self.tools)\n\n async def _run_agent_for_fallback(augmented_prompt: str) -> str:\n self.set(\n llm=llm_model,\n tools=self.tools or [],\n chat_history=self.chat_history,\n input_value=self.input_value,\n system_prompt=augmented_prompt,\n )\n agent_runnable = self.create_agent_runnable()\n with _suppress_send_message(self):\n result = await self.run_agent(agent_runnable)\n return _extract_text_content(result)\n\n try:\n return await orchestrate_structured_output(\n llm=llm_model,\n output_schema=output_schema,\n system_prompt=injected_system_prompt,\n format_instructions=format_instructions,\n input_value=_extract_text_content(self.input_value),\n run_prompt_fallback=_run_agent_for_fallback,\n prefer_native=not has_tools,\n )\n except (\n ExceptionWithMessageError,\n ValueError,\n TypeError,\n NotImplementedError,\n AttributeError,\n ) as exc:\n await logger.aerror(f\"json_response.orchestration_failed: {exc}\")\n return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n async def get_memory_data(self):\n # TODO: This is a temporary fix to avoid message duplication. We should develop a function for this.\n messages = (\n await MemoryComponent(**self.get_base_args())\n .set(\n session_id=self.graph.session_id,\n context_id=self.context_id,\n order=\"Ascending\",\n n_messages=self.n_messages,\n )\n .retrieve_messages()\n )\n return [\n message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n ]\n\n def update_input_types(self, build_config: dotdict) -> dotdict:\n \"\"\"Update input types for all fields in build_config.\"\"\"\n for key, value in build_config.items():\n if isinstance(value, dict):\n if value.get(\"input_types\") is None:\n build_config[key][\"input_types\"] = []\n elif hasattr(value, \"input_types\") and value.input_types is None:\n value.input_types = []\n return build_config\n\n async def update_build_config(\n self,\n build_config: dotdict,\n field_value: list[dict],\n field_name: str | None = None,\n ) -> dotdict:\n # Update model options with caching (for all field changes).\n # The tool-calling constraint lives on the ModelInput's ``filters``\n # field (declared above); ``handle_model_input_update`` reads it\n # and applies the filter to both the dropdown options and the\n # sticky-default re-injection path.\n build_config = handle_model_input_update(\n component=self,\n build_config=dict(build_config),\n field_value=field_value,\n field_name=field_name,\n )\n build_config = dotdict(build_config)\n\n if field_name == \"model\":\n build_config = self.update_input_types(build_config)\n\n # Validate required keys\n default_keys = [\n \"code\",\n \"_type\",\n \"model\",\n \"tools\",\n \"input_value\",\n \"add_current_date_tool\",\n \"add_calculator_tool\",\n \"system_prompt\",\n \"agent_description\",\n \"max_iterations\",\n \"handle_parsing_errors\",\n \"verbose\",\n ]\n missing_keys = [key for key in default_keys if key not in build_config]\n if missing_keys:\n msg = f\"Missing required keys in build_config: {missing_keys}\"\n raise ValueError(msg)\n return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n async def _get_tools(self) -> list[Tool]:\n component_toolkit = get_component_toolkit()\n tools_names = self._build_tools_names()\n agent_description = self.get_tool_description()\n # TODO: Agent Description Depreciated Feature to be removed\n description = f\"{agent_description}{tools_names}\"\n\n tools = component_toolkit(component=self).get_tools(\n tool_name=\"Call_Agent\",\n tool_description=description,\n # here we do not use the shared callbacks as we are exposing the agent as a tool\n callbacks=self.get_langchain_callbacks(),\n )\n if hasattr(self, \"tools_metadata\"):\n tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n\n return tools\n" + "value": "from __future__ import annotations\n\nfrom contextlib import contextmanager\nfrom datetime import datetime, timezone\nfrom typing import TYPE_CHECKING, Any\n\nfrom lfx.components.models_and_agents.memory import MemoryComponent, aget_agent_chat_history\n\nif TYPE_CHECKING:\n from langchain_core.tools import Tool\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.agents.default_system_prompt import DEFAULT_SYSTEM_PROMPT_TEMPLATE\nfrom lfx.base.agents.events import ExceptionWithMessageError\nfrom lfx.base.constants import STREAM_INFO_TEXT\nfrom lfx.base.models.unified_models import (\n get_language_model_options,\n get_llm,\n handle_model_input_update,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.components.agentics.helpers.model_config import validate_model_selection\nfrom lfx.components.helpers import CalculatorComponent, CurrentDateComponent\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.custom.custom_component.component import get_component_toolkit\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, ModelInput, StrInput\nfrom lfx.io import IntInput, MessageTextInput, MultilineInput, Output, SecretStrInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.schema.data import Data\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.schema.table import EditMode\n\n\ndef set_advanced_true(component_input):\n component_input.advanced = True\n return component_input\n\n\ndef _extract_text_content(value) -> str:\n \"\"\"Pull a string payload from a Message-like, AIMessage-like, or string value.\"\"\"\n if isinstance(value, str):\n return value\n text = getattr(value, \"text\", None)\n if isinstance(text, str):\n return text\n content = getattr(value, \"content\", None)\n if isinstance(content, str):\n return content\n return str(value) if value is not None else \"\"\n\n\n@contextmanager\ndef _suppress_send_message(component: Any):\n \"\"\"Temporarily replace component.send_message with a no-op for the duration of the block.\n\n Used during the structured-output prompt fallback: run_agent streams the agent's\n final answer through self.send_message (correct for message_response), but in\n json_response the orchestrator parses that text into structured Data which the\n downstream Chat Output emits — leaving the original emission in place produces a\n duplicate message in the playground. The original method is always restored on exit,\n even when the wrapped call raises.\n \"\"\"\n original = component.send_message\n\n async def _noop(message, *_args, **_kwargs):\n return message\n\n component.send_message = _noop\n try:\n yield\n finally:\n component.send_message = original\n\n\nclass AgentComponent(ToolCallingAgentComponent):\n display_name: str = \"Agent\"\n description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n documentation: str = \"https://docs.langflow.org/agents\"\n icon = \"bot\"\n beta = False\n name = \"Agent\"\n\n memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n inputs = [\n ModelInput(\n name=\"model\",\n display_name=\"Language Model\",\n info=\"Select your model provider\",\n real_time_refresh=True,\n required=True,\n # Agents require tool calling — the filter is honored by\n # ``handle_model_input_update`` so models that can't run with\n # tools never reach the picker (and any saved selection that\n # no longer satisfies the constraint is auto-replaced).\n filters={\"tool_calling\": True},\n ),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"API Key\",\n info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n real_time_refresh=True,\n advanced=True,\n ),\n DropdownInput(\n name=\"base_url_ibm_watsonx\",\n display_name=\"watsonx API Endpoint\",\n info=\"The base URL of the API (IBM watsonx.ai only)\",\n options=IBM_WATSONX_URLS,\n value=IBM_WATSONX_URLS[0],\n combobox=True,\n show=False,\n real_time_refresh=True,\n ),\n StrInput(\n name=\"project_id\",\n display_name=\"watsonx Project ID\",\n info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n show=False,\n required=False,\n ),\n MultilineInput(\n name=\"system_prompt\",\n display_name=\"Agent Instructions\",\n info=(\n \"System Prompt: Initial instructions and context provided to guide the agent's behavior. \"\n \"Supports dynamic placeholders: {current_date}, {model_name}, {optional_user_context}.\"\n ),\n value=DEFAULT_SYSTEM_PROMPT_TEMPLATE,\n advanced=False,\n ),\n MessageTextInput(\n name=\"context_id\",\n display_name=\"Context ID\",\n info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n value=\"\",\n advanced=True,\n ),\n IntInput(\n name=\"n_messages\",\n display_name=\"Number of Chat History Messages\",\n value=100,\n info=\"Number of chat history messages to retrieve.\",\n advanced=True,\n show=True,\n ),\n IntInput(\n name=\"max_tokens\",\n display_name=\"Max Tokens\",\n info=\"Maximum number of tokens to generate. Field name varies by provider.\",\n advanced=True,\n range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n ),\n MultilineInput(\n name=\"format_instructions\",\n display_name=\"Output Format Instructions\",\n info=\"Generic Template for structured output formatting. Valid only with Structured response.\",\n value=(\n \"You are an AI that extracts structured JSON objects from unstructured text. \"\n \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n \"Fill missing or ambiguous values with defaults: null for missing values. \"\n \"Remove exact duplicates but keep variations that have different field values. \"\n \"Always return valid JSON in the expected format, never throw errors. \"\n \"If multiple objects can be extracted, return them all in the structured format.\"\n ),\n advanced=True,\n ),\n TableInput(\n name=\"output_schema\",\n display_name=\"Output Schema\",\n info=(\n \"Schema Validation: Define the structure and data types for structured output. \"\n \"No validation if no output schema.\"\n ),\n advanced=True,\n required=False,\n value=[],\n table_schema=[\n {\n \"name\": \"name\",\n \"display_name\": \"Name\",\n \"type\": \"str\",\n \"description\": \"Specify the name of the output field.\",\n \"default\": \"field\",\n \"edit_mode\": EditMode.INLINE,\n },\n {\n \"name\": \"description\",\n \"display_name\": \"Description\",\n \"type\": \"str\",\n \"description\": \"Describe the purpose of the output field.\",\n \"default\": \"description of field\",\n \"edit_mode\": EditMode.POPOVER,\n },\n {\n \"name\": \"type\",\n \"display_name\": \"Type\",\n \"type\": \"str\",\n \"edit_mode\": EditMode.INLINE,\n \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n \"default\": \"str\",\n },\n {\n \"name\": \"multiple\",\n \"display_name\": \"As List\",\n \"type\": \"boolean\",\n \"description\": \"Set to True if this output field should be a list of the specified type.\",\n \"default\": \"False\",\n \"edit_mode\": EditMode.INLINE,\n },\n ],\n ),\n *LCToolsAgentComponent.get_base_inputs(),\n # removed memory inputs from agent component\n # *memory_inputs,\n BoolInput(\n name=\"stream\",\n display_name=\"Stream\",\n info=STREAM_INFO_TEXT,\n value=False,\n advanced=True,\n ),\n BoolInput(\n name=\"add_current_date_tool\",\n display_name=\"Current Date\",\n advanced=True,\n info=\"If true, will add a tool to the agent that returns the current date.\",\n value=True,\n ),\n BoolInput(\n name=\"add_calculator_tool\",\n display_name=\"Calculator\",\n advanced=True,\n info=(\n \"If true, adds a zero-config arithmetic calculator tool to the agent \"\n \"(safe: only +, -, *, /, ** operators via AST).\"\n ),\n value=True,\n ),\n ]\n outputs = [\n Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n Output(\n name=\"structured_response\",\n display_name=\"Structured Response\",\n method=\"json_response\",\n types=[\"Data\"],\n ),\n ]\n\n def _resolve_selected_model(self):\n \"\"\"Resolve the selected model, including legacy agent_llm/model_name inputs.\"\"\"\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n if isinstance(self.model, BaseLanguageModel):\n return self.model\n except ImportError:\n pass\n\n if isinstance(self.model, list) and self.model:\n return self.model\n\n legacy_provider = getattr(self, \"agent_llm\", None)\n legacy_model_name = getattr(self, \"model_name\", None)\n if not legacy_provider or not legacy_model_name:\n return self.model\n\n options = get_language_model_options(user_id=self.user_id)\n for option in options:\n if option.get(\"provider\") == legacy_provider and option.get(\"name\") == legacy_model_name:\n return [option]\n\n return [\n {\n \"name\": legacy_model_name,\n \"provider\": legacy_provider,\n \"metadata\": {},\n }\n ]\n\n def _get_max_tokens_value(self):\n \"\"\"Return the user-supplied max_tokens or None when unset/zero.\"\"\"\n val = getattr(self, \"max_tokens\", None)\n if val in {\"\", 0}:\n return None\n return val\n\n def _get_llm(self):\n \"\"\"Override parent to include max_tokens from the Agent's input field.\"\"\"\n return get_llm(\n model=self.model,\n user_id=self.user_id,\n api_key=getattr(self, \"api_key\", None),\n stream=bool(getattr(self, \"stream\", False)),\n max_tokens=self._get_max_tokens_value(),\n watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n watsonx_project_id=getattr(self, \"project_id\", None),\n )\n\n async def get_agent_requirements(self):\n \"\"\"Get the agent requirements for the agent.\"\"\"\n from langchain_core.tools import StructuredTool\n\n selected_model = self._resolve_selected_model()\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n is_connected_model = isinstance(selected_model, BaseLanguageModel)\n except ImportError:\n is_connected_model = False\n\n if not is_connected_model:\n validate_model_selection(selected_model)\n\n # Ensure _get_llm() uses the resolved model (e.g. from legacy agent_llm/model_name)\n self.model = selected_model\n llm_model = self._get_llm()\n if llm_model is None:\n msg = \"No language model selected. Please choose a model to proceed.\"\n raise ValueError(msg)\n\n # Get memory data\n self.chat_history = await self.get_memory_data()\n await logger.adebug(f\"Retrieved {len(self.chat_history)} chat history messages\")\n if isinstance(self.chat_history, Message):\n self.chat_history = [self.chat_history]\n\n # Add current date tool if enabled\n if self.add_current_date_tool:\n if not isinstance(self.tools, list): # type: ignore[has-type]\n self.tools = []\n current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n if not isinstance(current_date_tool, StructuredTool):\n msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n raise TypeError(msg)\n # Skip if an externally-connected tool already provides the same name.\n # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n if not any(getattr(t, \"name\", None) == current_date_tool.name for t in self.tools):\n self.tools.append(current_date_tool)\n\n # Add calculator tool if enabled (zero-config arithmetic)\n if getattr(self, \"add_calculator_tool\", False):\n if not isinstance(self.tools, list): # type: ignore[has-type]\n self.tools = []\n calculator_tool = (await CalculatorComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n if not isinstance(calculator_tool, StructuredTool):\n msg = \"CalculatorComponent must be converted to a StructuredTool\"\n raise TypeError(msg)\n # Skip if an externally-connected tool already provides the same name.\n # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n if not any(getattr(t, \"name\", None) == calculator_tool.name for t in self.tools):\n self.tools.append(calculator_tool)\n\n # Set shared callbacks for tracing the tools used by the agent\n self.set_tools_callbacks(self.tools, self._get_shared_callbacks())\n\n return llm_model, self.chat_history, self.tools\n\n def _get_resolved_model_name(self) -> str:\n \"\"\"Best-effort human-readable model name for {model_name} injection.\"\"\"\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n if isinstance(self.model, BaseLanguageModel):\n return type(self.model).__name__\n except ImportError:\n pass\n\n if isinstance(self.model, list) and self.model:\n first = self.model[0]\n if isinstance(first, dict):\n name = first.get(\"name\")\n if isinstance(name, str) and name:\n return name\n\n legacy_model_name = getattr(self, \"model_name\", None)\n if isinstance(legacy_model_name, str) and legacy_model_name:\n return legacy_model_name\n return \"\"\n\n def _inject_dynamic_prompt_values(self, prompt: str | None) -> str | None:\n \"\"\"Replace known env placeholders in the system prompt.\n\n Handles {current_date}, {model_name}, and {optional_user_context} (the\n last one ships with the structured DEFAULT_SYSTEM_PROMPT_TEMPLATE and\n is currently unused at the AgentComponent layer, so it resolves to \"\").\n Uses str.replace (not str.format) so user prompts containing literal\n braces such as JSON examples ({\"key\": 1}) never break the agent.\n \"\"\"\n if not prompt:\n return prompt\n replacements = {\n \"{current_date}\": datetime.now(tz=timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S UTC\"),\n \"{model_name}\": self._get_resolved_model_name(),\n \"{optional_user_context}\": \"\",\n }\n for placeholder, value in replacements.items():\n prompt = prompt.replace(placeholder, value)\n return prompt\n\n async def message_response(self) -> Message:\n try:\n llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n # Set up and run agent\n self.set(\n llm=llm_model,\n tools=self.tools or [],\n chat_history=self.chat_history,\n input_value=self.input_value,\n system_prompt=self._inject_dynamic_prompt_values(self.system_prompt),\n )\n agent = self.create_agent_runnable()\n result = await self.run_agent(agent)\n\n # Store result for potential JSON output\n self._agent_result = result\n\n except (ValueError, TypeError, KeyError) as e:\n await logger.aerror(f\"{type(e).__name__}: {e!s}\")\n raise\n except ExceptionWithMessageError as e:\n await logger.aerror(f\"ExceptionWithMessageError occurred: {e}\")\n raise\n # Avoid catching blind Exception; let truly unexpected exceptions propagate\n except Exception as e:\n await logger.aerror(f\"Unexpected error: {e!s}\")\n raise\n else:\n return result\n\n async def json_response(self) -> Data:\n \"\"\"Produce structured Data via native LLM structured output, with prompt-based fallback.\n\n Native path (no tools, llm has with_structured_output) bypasses the agent loop and\n returns provider-validated JSON. When tools are attached, falls back to running the\n agent with a schema-augmented system prompt and parsing the final message content.\n \"\"\"\n from lfx.components.models_and_agents.structured_output.structured_output_orchestrator import (\n orchestrate_structured_output,\n )\n\n try:\n llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n except (ValueError, TypeError) as exc:\n await logger.aerror(f\"json_response.requirements_failed: {exc}\")\n return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n injected_system_prompt = self._inject_dynamic_prompt_values(getattr(self, \"system_prompt\", \"\") or \"\") or \"\"\n format_instructions = getattr(self, \"format_instructions\", \"\") or \"\"\n output_schema = getattr(self, \"output_schema\", None) or []\n has_tools = bool(self.tools)\n\n async def _run_agent_for_fallback(augmented_prompt: str) -> str:\n self.set(\n llm=llm_model,\n tools=self.tools or [],\n chat_history=self.chat_history,\n input_value=self.input_value,\n system_prompt=augmented_prompt,\n )\n agent_runnable = self.create_agent_runnable()\n with _suppress_send_message(self):\n result = await self.run_agent(agent_runnable)\n return _extract_text_content(result)\n\n try:\n return await orchestrate_structured_output(\n llm=llm_model,\n output_schema=output_schema,\n system_prompt=injected_system_prompt,\n format_instructions=format_instructions,\n input_value=_extract_text_content(self.input_value),\n run_prompt_fallback=_run_agent_for_fallback,\n prefer_native=not has_tools,\n )\n except (\n ExceptionWithMessageError,\n ValueError,\n TypeError,\n NotImplementedError,\n AttributeError,\n ) as exc:\n await logger.aerror(f\"json_response.orchestration_failed: {exc}\")\n return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n async def get_memory_data(self):\n # Scope by flow_id so default playground session names (e.g. \"New Session 0\")\n # cannot leak chat history across unrelated flows. See issue #13059.\n # The helper also returns [] when n_messages == 0, preserving the\n # explicit \"memory disabled\" contract from MemoryComponent.retrieve_messages.\n messages = await aget_agent_chat_history(\n session_id=self.graph.session_id,\n flow_id=getattr(self.graph, \"flow_id\", None),\n context_id=self.context_id,\n n_messages=self.n_messages,\n )\n return [\n message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n ]\n\n def update_input_types(self, build_config: dotdict) -> dotdict:\n \"\"\"Update input types for all fields in build_config.\"\"\"\n for key, value in build_config.items():\n if isinstance(value, dict):\n if value.get(\"input_types\") is None:\n build_config[key][\"input_types\"] = []\n elif hasattr(value, \"input_types\") and value.input_types is None:\n value.input_types = []\n return build_config\n\n async def update_build_config(\n self,\n build_config: dotdict,\n field_value: list[dict],\n field_name: str | None = None,\n ) -> dotdict:\n # Update model options with caching (for all field changes).\n # The tool-calling constraint lives on the ModelInput's ``filters``\n # field (declared above); ``handle_model_input_update`` reads it\n # and applies the filter to both the dropdown options and the\n # sticky-default re-injection path.\n build_config = handle_model_input_update(\n component=self,\n build_config=dict(build_config),\n field_value=field_value,\n field_name=field_name,\n )\n build_config = dotdict(build_config)\n\n if field_name == \"model\":\n build_config = self.update_input_types(build_config)\n\n # Validate required keys\n default_keys = [\n \"code\",\n \"_type\",\n \"model\",\n \"tools\",\n \"input_value\",\n \"add_current_date_tool\",\n \"add_calculator_tool\",\n \"system_prompt\",\n \"agent_description\",\n \"max_iterations\",\n \"handle_parsing_errors\",\n \"verbose\",\n ]\n missing_keys = [key for key in default_keys if key not in build_config]\n if missing_keys:\n msg = f\"Missing required keys in build_config: {missing_keys}\"\n raise ValueError(msg)\n return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n async def _get_tools(self) -> list[Tool]:\n component_toolkit = get_component_toolkit()\n tools_names = self._build_tools_names()\n agent_description = self.get_tool_description()\n # TODO: Agent Description Depreciated Feature to be removed\n description = f\"{agent_description}{tools_names}\"\n\n tools = component_toolkit(component=self).get_tools(\n tool_name=\"Call_Agent\",\n tool_description=description,\n # here we do not use the shared callbacks as we are exposing the agent as a tool\n callbacks=self.get_langchain_callbacks(),\n )\n if hasattr(self, \"tools_metadata\"):\n tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n\n return tools\n" }, "context_id": { "_input_type": "MessageTextInput", diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Nvidia Remix.json b/src/backend/base/langflow/initial_setup/starter_projects/Nvidia Remix.json index d6f5966fff..c9d11dacc5 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Nvidia Remix.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Nvidia Remix.json @@ -802,7 +802,7 @@ "last_updated": "2026-03-20T22:35:04.094Z", "legacy": false, "metadata": { - "code_hash": "ebac74bfcae6", + "code_hash": "443159257161", "dependencies": { "dependencies": [ { @@ -993,7 +993,7 @@ "show": true, "title_case": false, "type": "code", - "value": "from __future__ import annotations\n\nfrom contextlib import contextmanager\nfrom datetime import datetime, timezone\nfrom typing import TYPE_CHECKING, Any\n\nfrom lfx.components.models_and_agents.memory import MemoryComponent\n\nif TYPE_CHECKING:\n from langchain_core.tools import Tool\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.agents.default_system_prompt import DEFAULT_SYSTEM_PROMPT_TEMPLATE\nfrom lfx.base.agents.events import ExceptionWithMessageError\nfrom lfx.base.constants import STREAM_INFO_TEXT\nfrom lfx.base.models.unified_models import (\n get_language_model_options,\n get_llm,\n handle_model_input_update,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.components.agentics.helpers.model_config import validate_model_selection\nfrom lfx.components.helpers import CalculatorComponent, CurrentDateComponent\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.custom.custom_component.component import get_component_toolkit\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, ModelInput, StrInput\nfrom lfx.io import IntInput, MessageTextInput, MultilineInput, Output, SecretStrInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.schema.data import Data\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.schema.table import EditMode\n\n\ndef set_advanced_true(component_input):\n component_input.advanced = True\n return component_input\n\n\ndef _extract_text_content(value) -> str:\n \"\"\"Pull a string payload from a Message-like, AIMessage-like, or string value.\"\"\"\n if isinstance(value, str):\n return value\n text = getattr(value, \"text\", None)\n if isinstance(text, str):\n return text\n content = getattr(value, \"content\", None)\n if isinstance(content, str):\n return content\n return str(value) if value is not None else \"\"\n\n\n@contextmanager\ndef _suppress_send_message(component: Any):\n \"\"\"Temporarily replace component.send_message with a no-op for the duration of the block.\n\n Used during the structured-output prompt fallback: run_agent streams the agent's\n final answer through self.send_message (correct for message_response), but in\n json_response the orchestrator parses that text into structured Data which the\n downstream Chat Output emits — leaving the original emission in place produces a\n duplicate message in the playground. The original method is always restored on exit,\n even when the wrapped call raises.\n \"\"\"\n original = component.send_message\n\n async def _noop(message, *_args, **_kwargs):\n return message\n\n component.send_message = _noop\n try:\n yield\n finally:\n component.send_message = original\n\n\nclass AgentComponent(ToolCallingAgentComponent):\n display_name: str = \"Agent\"\n description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n documentation: str = \"https://docs.langflow.org/agents\"\n icon = \"bot\"\n beta = False\n name = \"Agent\"\n\n memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n inputs = [\n ModelInput(\n name=\"model\",\n display_name=\"Language Model\",\n info=\"Select your model provider\",\n real_time_refresh=True,\n required=True,\n # Agents require tool calling — the filter is honored by\n # ``handle_model_input_update`` so models that can't run with\n # tools never reach the picker (and any saved selection that\n # no longer satisfies the constraint is auto-replaced).\n filters={\"tool_calling\": True},\n ),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"API Key\",\n info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n real_time_refresh=True,\n advanced=True,\n ),\n DropdownInput(\n name=\"base_url_ibm_watsonx\",\n display_name=\"watsonx API Endpoint\",\n info=\"The base URL of the API (IBM watsonx.ai only)\",\n options=IBM_WATSONX_URLS,\n value=IBM_WATSONX_URLS[0],\n combobox=True,\n show=False,\n real_time_refresh=True,\n ),\n StrInput(\n name=\"project_id\",\n display_name=\"watsonx Project ID\",\n info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n show=False,\n required=False,\n ),\n MultilineInput(\n name=\"system_prompt\",\n display_name=\"Agent Instructions\",\n info=(\n \"System Prompt: Initial instructions and context provided to guide the agent's behavior. \"\n \"Supports dynamic placeholders: {current_date}, {model_name}, {optional_user_context}.\"\n ),\n value=DEFAULT_SYSTEM_PROMPT_TEMPLATE,\n advanced=False,\n ),\n MessageTextInput(\n name=\"context_id\",\n display_name=\"Context ID\",\n info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n value=\"\",\n advanced=True,\n ),\n IntInput(\n name=\"n_messages\",\n display_name=\"Number of Chat History Messages\",\n value=100,\n info=\"Number of chat history messages to retrieve.\",\n advanced=True,\n show=True,\n ),\n IntInput(\n name=\"max_tokens\",\n display_name=\"Max Tokens\",\n info=\"Maximum number of tokens to generate. Field name varies by provider.\",\n advanced=True,\n range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n ),\n MultilineInput(\n name=\"format_instructions\",\n display_name=\"Output Format Instructions\",\n info=\"Generic Template for structured output formatting. Valid only with Structured response.\",\n value=(\n \"You are an AI that extracts structured JSON objects from unstructured text. \"\n \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n \"Fill missing or ambiguous values with defaults: null for missing values. \"\n \"Remove exact duplicates but keep variations that have different field values. \"\n \"Always return valid JSON in the expected format, never throw errors. \"\n \"If multiple objects can be extracted, return them all in the structured format.\"\n ),\n advanced=True,\n ),\n TableInput(\n name=\"output_schema\",\n display_name=\"Output Schema\",\n info=(\n \"Schema Validation: Define the structure and data types for structured output. \"\n \"No validation if no output schema.\"\n ),\n advanced=True,\n required=False,\n value=[],\n table_schema=[\n {\n \"name\": \"name\",\n \"display_name\": \"Name\",\n \"type\": \"str\",\n \"description\": \"Specify the name of the output field.\",\n \"default\": \"field\",\n \"edit_mode\": EditMode.INLINE,\n },\n {\n \"name\": \"description\",\n \"display_name\": \"Description\",\n \"type\": \"str\",\n \"description\": \"Describe the purpose of the output field.\",\n \"default\": \"description of field\",\n \"edit_mode\": EditMode.POPOVER,\n },\n {\n \"name\": \"type\",\n \"display_name\": \"Type\",\n \"type\": \"str\",\n \"edit_mode\": EditMode.INLINE,\n \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n \"default\": \"str\",\n },\n {\n \"name\": \"multiple\",\n \"display_name\": \"As List\",\n \"type\": \"boolean\",\n \"description\": \"Set to True if this output field should be a list of the specified type.\",\n \"default\": \"False\",\n \"edit_mode\": EditMode.INLINE,\n },\n ],\n ),\n *LCToolsAgentComponent.get_base_inputs(),\n # removed memory inputs from agent component\n # *memory_inputs,\n BoolInput(\n name=\"stream\",\n display_name=\"Stream\",\n info=STREAM_INFO_TEXT,\n value=False,\n advanced=True,\n ),\n BoolInput(\n name=\"add_current_date_tool\",\n display_name=\"Current Date\",\n advanced=True,\n info=\"If true, will add a tool to the agent that returns the current date.\",\n value=True,\n ),\n BoolInput(\n name=\"add_calculator_tool\",\n display_name=\"Calculator\",\n advanced=True,\n info=(\n \"If true, adds a zero-config arithmetic calculator tool to the agent \"\n \"(safe: only +, -, *, /, ** operators via AST).\"\n ),\n value=True,\n ),\n ]\n outputs = [\n Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n Output(\n name=\"structured_response\",\n display_name=\"Structured Response\",\n method=\"json_response\",\n types=[\"Data\"],\n ),\n ]\n\n def _resolve_selected_model(self):\n \"\"\"Resolve the selected model, including legacy agent_llm/model_name inputs.\"\"\"\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n if isinstance(self.model, BaseLanguageModel):\n return self.model\n except ImportError:\n pass\n\n if isinstance(self.model, list) and self.model:\n return self.model\n\n legacy_provider = getattr(self, \"agent_llm\", None)\n legacy_model_name = getattr(self, \"model_name\", None)\n if not legacy_provider or not legacy_model_name:\n return self.model\n\n options = get_language_model_options(user_id=self.user_id)\n for option in options:\n if option.get(\"provider\") == legacy_provider and option.get(\"name\") == legacy_model_name:\n return [option]\n\n return [\n {\n \"name\": legacy_model_name,\n \"provider\": legacy_provider,\n \"metadata\": {},\n }\n ]\n\n def _get_max_tokens_value(self):\n \"\"\"Return the user-supplied max_tokens or None when unset/zero.\"\"\"\n val = getattr(self, \"max_tokens\", None)\n if val in {\"\", 0}:\n return None\n return val\n\n def _get_llm(self):\n \"\"\"Override parent to include max_tokens from the Agent's input field.\"\"\"\n return get_llm(\n model=self.model,\n user_id=self.user_id,\n api_key=getattr(self, \"api_key\", None),\n stream=bool(getattr(self, \"stream\", False)),\n max_tokens=self._get_max_tokens_value(),\n watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n watsonx_project_id=getattr(self, \"project_id\", None),\n )\n\n async def get_agent_requirements(self):\n \"\"\"Get the agent requirements for the agent.\"\"\"\n from langchain_core.tools import StructuredTool\n\n selected_model = self._resolve_selected_model()\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n is_connected_model = isinstance(selected_model, BaseLanguageModel)\n except ImportError:\n is_connected_model = False\n\n if not is_connected_model:\n validate_model_selection(selected_model)\n\n # Ensure _get_llm() uses the resolved model (e.g. from legacy agent_llm/model_name)\n self.model = selected_model\n llm_model = self._get_llm()\n if llm_model is None:\n msg = \"No language model selected. Please choose a model to proceed.\"\n raise ValueError(msg)\n\n # Get memory data\n self.chat_history = await self.get_memory_data()\n await logger.adebug(f\"Retrieved {len(self.chat_history)} chat history messages\")\n if isinstance(self.chat_history, Message):\n self.chat_history = [self.chat_history]\n\n # Add current date tool if enabled\n if self.add_current_date_tool:\n if not isinstance(self.tools, list): # type: ignore[has-type]\n self.tools = []\n current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n if not isinstance(current_date_tool, StructuredTool):\n msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n raise TypeError(msg)\n # Skip if an externally-connected tool already provides the same name.\n # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n if not any(getattr(t, \"name\", None) == current_date_tool.name for t in self.tools):\n self.tools.append(current_date_tool)\n\n # Add calculator tool if enabled (zero-config arithmetic)\n if getattr(self, \"add_calculator_tool\", False):\n if not isinstance(self.tools, list): # type: ignore[has-type]\n self.tools = []\n calculator_tool = (await CalculatorComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n if not isinstance(calculator_tool, StructuredTool):\n msg = \"CalculatorComponent must be converted to a StructuredTool\"\n raise TypeError(msg)\n # Skip if an externally-connected tool already provides the same name.\n # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n if not any(getattr(t, \"name\", None) == calculator_tool.name for t in self.tools):\n self.tools.append(calculator_tool)\n\n # Set shared callbacks for tracing the tools used by the agent\n self.set_tools_callbacks(self.tools, self._get_shared_callbacks())\n\n return llm_model, self.chat_history, self.tools\n\n def _get_resolved_model_name(self) -> str:\n \"\"\"Best-effort human-readable model name for {model_name} injection.\"\"\"\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n if isinstance(self.model, BaseLanguageModel):\n return type(self.model).__name__\n except ImportError:\n pass\n\n if isinstance(self.model, list) and self.model:\n first = self.model[0]\n if isinstance(first, dict):\n name = first.get(\"name\")\n if isinstance(name, str) and name:\n return name\n\n legacy_model_name = getattr(self, \"model_name\", None)\n if isinstance(legacy_model_name, str) and legacy_model_name:\n return legacy_model_name\n return \"\"\n\n def _inject_dynamic_prompt_values(self, prompt: str | None) -> str | None:\n \"\"\"Replace known env placeholders in the system prompt.\n\n Handles {current_date}, {model_name}, and {optional_user_context} (the\n last one ships with the structured DEFAULT_SYSTEM_PROMPT_TEMPLATE and\n is currently unused at the AgentComponent layer, so it resolves to \"\").\n Uses str.replace (not str.format) so user prompts containing literal\n braces such as JSON examples ({\"key\": 1}) never break the agent.\n \"\"\"\n if not prompt:\n return prompt\n replacements = {\n \"{current_date}\": datetime.now(tz=timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S UTC\"),\n \"{model_name}\": self._get_resolved_model_name(),\n \"{optional_user_context}\": \"\",\n }\n for placeholder, value in replacements.items():\n prompt = prompt.replace(placeholder, value)\n return prompt\n\n async def message_response(self) -> Message:\n try:\n llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n # Set up and run agent\n self.set(\n llm=llm_model,\n tools=self.tools or [],\n chat_history=self.chat_history,\n input_value=self.input_value,\n system_prompt=self._inject_dynamic_prompt_values(self.system_prompt),\n )\n agent = self.create_agent_runnable()\n result = await self.run_agent(agent)\n\n # Store result for potential JSON output\n self._agent_result = result\n\n except (ValueError, TypeError, KeyError) as e:\n await logger.aerror(f\"{type(e).__name__}: {e!s}\")\n raise\n except ExceptionWithMessageError as e:\n await logger.aerror(f\"ExceptionWithMessageError occurred: {e}\")\n raise\n # Avoid catching blind Exception; let truly unexpected exceptions propagate\n except Exception as e:\n await logger.aerror(f\"Unexpected error: {e!s}\")\n raise\n else:\n return result\n\n async def json_response(self) -> Data:\n \"\"\"Produce structured Data via native LLM structured output, with prompt-based fallback.\n\n Native path (no tools, llm has with_structured_output) bypasses the agent loop and\n returns provider-validated JSON. When tools are attached, falls back to running the\n agent with a schema-augmented system prompt and parsing the final message content.\n \"\"\"\n from lfx.components.models_and_agents.structured_output.structured_output_orchestrator import (\n orchestrate_structured_output,\n )\n\n try:\n llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n except (ValueError, TypeError) as exc:\n await logger.aerror(f\"json_response.requirements_failed: {exc}\")\n return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n injected_system_prompt = self._inject_dynamic_prompt_values(getattr(self, \"system_prompt\", \"\") or \"\") or \"\"\n format_instructions = getattr(self, \"format_instructions\", \"\") or \"\"\n output_schema = getattr(self, \"output_schema\", None) or []\n has_tools = bool(self.tools)\n\n async def _run_agent_for_fallback(augmented_prompt: str) -> str:\n self.set(\n llm=llm_model,\n tools=self.tools or [],\n chat_history=self.chat_history,\n input_value=self.input_value,\n system_prompt=augmented_prompt,\n )\n agent_runnable = self.create_agent_runnable()\n with _suppress_send_message(self):\n result = await self.run_agent(agent_runnable)\n return _extract_text_content(result)\n\n try:\n return await orchestrate_structured_output(\n llm=llm_model,\n output_schema=output_schema,\n system_prompt=injected_system_prompt,\n format_instructions=format_instructions,\n input_value=_extract_text_content(self.input_value),\n run_prompt_fallback=_run_agent_for_fallback,\n prefer_native=not has_tools,\n )\n except (\n ExceptionWithMessageError,\n ValueError,\n TypeError,\n NotImplementedError,\n AttributeError,\n ) as exc:\n await logger.aerror(f\"json_response.orchestration_failed: {exc}\")\n return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n async def get_memory_data(self):\n # TODO: This is a temporary fix to avoid message duplication. We should develop a function for this.\n messages = (\n await MemoryComponent(**self.get_base_args())\n .set(\n session_id=self.graph.session_id,\n context_id=self.context_id,\n order=\"Ascending\",\n n_messages=self.n_messages,\n )\n .retrieve_messages()\n )\n return [\n message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n ]\n\n def update_input_types(self, build_config: dotdict) -> dotdict:\n \"\"\"Update input types for all fields in build_config.\"\"\"\n for key, value in build_config.items():\n if isinstance(value, dict):\n if value.get(\"input_types\") is None:\n build_config[key][\"input_types\"] = []\n elif hasattr(value, \"input_types\") and value.input_types is None:\n value.input_types = []\n return build_config\n\n async def update_build_config(\n self,\n build_config: dotdict,\n field_value: list[dict],\n field_name: str | None = None,\n ) -> dotdict:\n # Update model options with caching (for all field changes).\n # The tool-calling constraint lives on the ModelInput's ``filters``\n # field (declared above); ``handle_model_input_update`` reads it\n # and applies the filter to both the dropdown options and the\n # sticky-default re-injection path.\n build_config = handle_model_input_update(\n component=self,\n build_config=dict(build_config),\n field_value=field_value,\n field_name=field_name,\n )\n build_config = dotdict(build_config)\n\n if field_name == \"model\":\n build_config = self.update_input_types(build_config)\n\n # Validate required keys\n default_keys = [\n \"code\",\n \"_type\",\n \"model\",\n \"tools\",\n \"input_value\",\n \"add_current_date_tool\",\n \"add_calculator_tool\",\n \"system_prompt\",\n \"agent_description\",\n \"max_iterations\",\n \"handle_parsing_errors\",\n \"verbose\",\n ]\n missing_keys = [key for key in default_keys if key not in build_config]\n if missing_keys:\n msg = f\"Missing required keys in build_config: {missing_keys}\"\n raise ValueError(msg)\n return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n async def _get_tools(self) -> list[Tool]:\n component_toolkit = get_component_toolkit()\n tools_names = self._build_tools_names()\n agent_description = self.get_tool_description()\n # TODO: Agent Description Depreciated Feature to be removed\n description = f\"{agent_description}{tools_names}\"\n\n tools = component_toolkit(component=self).get_tools(\n tool_name=\"Call_Agent\",\n tool_description=description,\n # here we do not use the shared callbacks as we are exposing the agent as a tool\n callbacks=self.get_langchain_callbacks(),\n )\n if hasattr(self, \"tools_metadata\"):\n tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n\n return tools\n" + "value": "from __future__ import annotations\n\nfrom contextlib import contextmanager\nfrom datetime import datetime, timezone\nfrom typing import TYPE_CHECKING, Any\n\nfrom lfx.components.models_and_agents.memory import MemoryComponent, aget_agent_chat_history\n\nif TYPE_CHECKING:\n from langchain_core.tools import Tool\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.agents.default_system_prompt import DEFAULT_SYSTEM_PROMPT_TEMPLATE\nfrom lfx.base.agents.events import ExceptionWithMessageError\nfrom lfx.base.constants import STREAM_INFO_TEXT\nfrom lfx.base.models.unified_models import (\n get_language_model_options,\n get_llm,\n handle_model_input_update,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.components.agentics.helpers.model_config import validate_model_selection\nfrom lfx.components.helpers import CalculatorComponent, CurrentDateComponent\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.custom.custom_component.component import get_component_toolkit\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, ModelInput, StrInput\nfrom lfx.io import IntInput, MessageTextInput, MultilineInput, Output, SecretStrInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.schema.data import Data\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.schema.table import EditMode\n\n\ndef set_advanced_true(component_input):\n component_input.advanced = True\n return component_input\n\n\ndef _extract_text_content(value) -> str:\n \"\"\"Pull a string payload from a Message-like, AIMessage-like, or string value.\"\"\"\n if isinstance(value, str):\n return value\n text = getattr(value, \"text\", None)\n if isinstance(text, str):\n return text\n content = getattr(value, \"content\", None)\n if isinstance(content, str):\n return content\n return str(value) if value is not None else \"\"\n\n\n@contextmanager\ndef _suppress_send_message(component: Any):\n \"\"\"Temporarily replace component.send_message with a no-op for the duration of the block.\n\n Used during the structured-output prompt fallback: run_agent streams the agent's\n final answer through self.send_message (correct for message_response), but in\n json_response the orchestrator parses that text into structured Data which the\n downstream Chat Output emits — leaving the original emission in place produces a\n duplicate message in the playground. The original method is always restored on exit,\n even when the wrapped call raises.\n \"\"\"\n original = component.send_message\n\n async def _noop(message, *_args, **_kwargs):\n return message\n\n component.send_message = _noop\n try:\n yield\n finally:\n component.send_message = original\n\n\nclass AgentComponent(ToolCallingAgentComponent):\n display_name: str = \"Agent\"\n description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n documentation: str = \"https://docs.langflow.org/agents\"\n icon = \"bot\"\n beta = False\n name = \"Agent\"\n\n memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n inputs = [\n ModelInput(\n name=\"model\",\n display_name=\"Language Model\",\n info=\"Select your model provider\",\n real_time_refresh=True,\n required=True,\n # Agents require tool calling — the filter is honored by\n # ``handle_model_input_update`` so models that can't run with\n # tools never reach the picker (and any saved selection that\n # no longer satisfies the constraint is auto-replaced).\n filters={\"tool_calling\": True},\n ),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"API Key\",\n info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n real_time_refresh=True,\n advanced=True,\n ),\n DropdownInput(\n name=\"base_url_ibm_watsonx\",\n display_name=\"watsonx API Endpoint\",\n info=\"The base URL of the API (IBM watsonx.ai only)\",\n options=IBM_WATSONX_URLS,\n value=IBM_WATSONX_URLS[0],\n combobox=True,\n show=False,\n real_time_refresh=True,\n ),\n StrInput(\n name=\"project_id\",\n display_name=\"watsonx Project ID\",\n info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n show=False,\n required=False,\n ),\n MultilineInput(\n name=\"system_prompt\",\n display_name=\"Agent Instructions\",\n info=(\n \"System Prompt: Initial instructions and context provided to guide the agent's behavior. \"\n \"Supports dynamic placeholders: {current_date}, {model_name}, {optional_user_context}.\"\n ),\n value=DEFAULT_SYSTEM_PROMPT_TEMPLATE,\n advanced=False,\n ),\n MessageTextInput(\n name=\"context_id\",\n display_name=\"Context ID\",\n info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n value=\"\",\n advanced=True,\n ),\n IntInput(\n name=\"n_messages\",\n display_name=\"Number of Chat History Messages\",\n value=100,\n info=\"Number of chat history messages to retrieve.\",\n advanced=True,\n show=True,\n ),\n IntInput(\n name=\"max_tokens\",\n display_name=\"Max Tokens\",\n info=\"Maximum number of tokens to generate. Field name varies by provider.\",\n advanced=True,\n range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n ),\n MultilineInput(\n name=\"format_instructions\",\n display_name=\"Output Format Instructions\",\n info=\"Generic Template for structured output formatting. Valid only with Structured response.\",\n value=(\n \"You are an AI that extracts structured JSON objects from unstructured text. \"\n \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n \"Fill missing or ambiguous values with defaults: null for missing values. \"\n \"Remove exact duplicates but keep variations that have different field values. \"\n \"Always return valid JSON in the expected format, never throw errors. \"\n \"If multiple objects can be extracted, return them all in the structured format.\"\n ),\n advanced=True,\n ),\n TableInput(\n name=\"output_schema\",\n display_name=\"Output Schema\",\n info=(\n \"Schema Validation: Define the structure and data types for structured output. \"\n \"No validation if no output schema.\"\n ),\n advanced=True,\n required=False,\n value=[],\n table_schema=[\n {\n \"name\": \"name\",\n \"display_name\": \"Name\",\n \"type\": \"str\",\n \"description\": \"Specify the name of the output field.\",\n \"default\": \"field\",\n \"edit_mode\": EditMode.INLINE,\n },\n {\n \"name\": \"description\",\n \"display_name\": \"Description\",\n \"type\": \"str\",\n \"description\": \"Describe the purpose of the output field.\",\n \"default\": \"description of field\",\n \"edit_mode\": EditMode.POPOVER,\n },\n {\n \"name\": \"type\",\n \"display_name\": \"Type\",\n \"type\": \"str\",\n \"edit_mode\": EditMode.INLINE,\n \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n \"default\": \"str\",\n },\n {\n \"name\": \"multiple\",\n \"display_name\": \"As List\",\n \"type\": \"boolean\",\n \"description\": \"Set to True if this output field should be a list of the specified type.\",\n \"default\": \"False\",\n \"edit_mode\": EditMode.INLINE,\n },\n ],\n ),\n *LCToolsAgentComponent.get_base_inputs(),\n # removed memory inputs from agent component\n # *memory_inputs,\n BoolInput(\n name=\"stream\",\n display_name=\"Stream\",\n info=STREAM_INFO_TEXT,\n value=False,\n advanced=True,\n ),\n BoolInput(\n name=\"add_current_date_tool\",\n display_name=\"Current Date\",\n advanced=True,\n info=\"If true, will add a tool to the agent that returns the current date.\",\n value=True,\n ),\n BoolInput(\n name=\"add_calculator_tool\",\n display_name=\"Calculator\",\n advanced=True,\n info=(\n \"If true, adds a zero-config arithmetic calculator tool to the agent \"\n \"(safe: only +, -, *, /, ** operators via AST).\"\n ),\n value=True,\n ),\n ]\n outputs = [\n Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n Output(\n name=\"structured_response\",\n display_name=\"Structured Response\",\n method=\"json_response\",\n types=[\"Data\"],\n ),\n ]\n\n def _resolve_selected_model(self):\n \"\"\"Resolve the selected model, including legacy agent_llm/model_name inputs.\"\"\"\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n if isinstance(self.model, BaseLanguageModel):\n return self.model\n except ImportError:\n pass\n\n if isinstance(self.model, list) and self.model:\n return self.model\n\n legacy_provider = getattr(self, \"agent_llm\", None)\n legacy_model_name = getattr(self, \"model_name\", None)\n if not legacy_provider or not legacy_model_name:\n return self.model\n\n options = get_language_model_options(user_id=self.user_id)\n for option in options:\n if option.get(\"provider\") == legacy_provider and option.get(\"name\") == legacy_model_name:\n return [option]\n\n return [\n {\n \"name\": legacy_model_name,\n \"provider\": legacy_provider,\n \"metadata\": {},\n }\n ]\n\n def _get_max_tokens_value(self):\n \"\"\"Return the user-supplied max_tokens or None when unset/zero.\"\"\"\n val = getattr(self, \"max_tokens\", None)\n if val in {\"\", 0}:\n return None\n return val\n\n def _get_llm(self):\n \"\"\"Override parent to include max_tokens from the Agent's input field.\"\"\"\n return get_llm(\n model=self.model,\n user_id=self.user_id,\n api_key=getattr(self, \"api_key\", None),\n stream=bool(getattr(self, \"stream\", False)),\n max_tokens=self._get_max_tokens_value(),\n watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n watsonx_project_id=getattr(self, \"project_id\", None),\n )\n\n async def get_agent_requirements(self):\n \"\"\"Get the agent requirements for the agent.\"\"\"\n from langchain_core.tools import StructuredTool\n\n selected_model = self._resolve_selected_model()\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n is_connected_model = isinstance(selected_model, BaseLanguageModel)\n except ImportError:\n is_connected_model = False\n\n if not is_connected_model:\n validate_model_selection(selected_model)\n\n # Ensure _get_llm() uses the resolved model (e.g. from legacy agent_llm/model_name)\n self.model = selected_model\n llm_model = self._get_llm()\n if llm_model is None:\n msg = \"No language model selected. Please choose a model to proceed.\"\n raise ValueError(msg)\n\n # Get memory data\n self.chat_history = await self.get_memory_data()\n await logger.adebug(f\"Retrieved {len(self.chat_history)} chat history messages\")\n if isinstance(self.chat_history, Message):\n self.chat_history = [self.chat_history]\n\n # Add current date tool if enabled\n if self.add_current_date_tool:\n if not isinstance(self.tools, list): # type: ignore[has-type]\n self.tools = []\n current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n if not isinstance(current_date_tool, StructuredTool):\n msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n raise TypeError(msg)\n # Skip if an externally-connected tool already provides the same name.\n # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n if not any(getattr(t, \"name\", None) == current_date_tool.name for t in self.tools):\n self.tools.append(current_date_tool)\n\n # Add calculator tool if enabled (zero-config arithmetic)\n if getattr(self, \"add_calculator_tool\", False):\n if not isinstance(self.tools, list): # type: ignore[has-type]\n self.tools = []\n calculator_tool = (await CalculatorComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n if not isinstance(calculator_tool, StructuredTool):\n msg = \"CalculatorComponent must be converted to a StructuredTool\"\n raise TypeError(msg)\n # Skip if an externally-connected tool already provides the same name.\n # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n if not any(getattr(t, \"name\", None) == calculator_tool.name for t in self.tools):\n self.tools.append(calculator_tool)\n\n # Set shared callbacks for tracing the tools used by the agent\n self.set_tools_callbacks(self.tools, self._get_shared_callbacks())\n\n return llm_model, self.chat_history, self.tools\n\n def _get_resolved_model_name(self) -> str:\n \"\"\"Best-effort human-readable model name for {model_name} injection.\"\"\"\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n if isinstance(self.model, BaseLanguageModel):\n return type(self.model).__name__\n except ImportError:\n pass\n\n if isinstance(self.model, list) and self.model:\n first = self.model[0]\n if isinstance(first, dict):\n name = first.get(\"name\")\n if isinstance(name, str) and name:\n return name\n\n legacy_model_name = getattr(self, \"model_name\", None)\n if isinstance(legacy_model_name, str) and legacy_model_name:\n return legacy_model_name\n return \"\"\n\n def _inject_dynamic_prompt_values(self, prompt: str | None) -> str | None:\n \"\"\"Replace known env placeholders in the system prompt.\n\n Handles {current_date}, {model_name}, and {optional_user_context} (the\n last one ships with the structured DEFAULT_SYSTEM_PROMPT_TEMPLATE and\n is currently unused at the AgentComponent layer, so it resolves to \"\").\n Uses str.replace (not str.format) so user prompts containing literal\n braces such as JSON examples ({\"key\": 1}) never break the agent.\n \"\"\"\n if not prompt:\n return prompt\n replacements = {\n \"{current_date}\": datetime.now(tz=timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S UTC\"),\n \"{model_name}\": self._get_resolved_model_name(),\n \"{optional_user_context}\": \"\",\n }\n for placeholder, value in replacements.items():\n prompt = prompt.replace(placeholder, value)\n return prompt\n\n async def message_response(self) -> Message:\n try:\n llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n # Set up and run agent\n self.set(\n llm=llm_model,\n tools=self.tools or [],\n chat_history=self.chat_history,\n input_value=self.input_value,\n system_prompt=self._inject_dynamic_prompt_values(self.system_prompt),\n )\n agent = self.create_agent_runnable()\n result = await self.run_agent(agent)\n\n # Store result for potential JSON output\n self._agent_result = result\n\n except (ValueError, TypeError, KeyError) as e:\n await logger.aerror(f\"{type(e).__name__}: {e!s}\")\n raise\n except ExceptionWithMessageError as e:\n await logger.aerror(f\"ExceptionWithMessageError occurred: {e}\")\n raise\n # Avoid catching blind Exception; let truly unexpected exceptions propagate\n except Exception as e:\n await logger.aerror(f\"Unexpected error: {e!s}\")\n raise\n else:\n return result\n\n async def json_response(self) -> Data:\n \"\"\"Produce structured Data via native LLM structured output, with prompt-based fallback.\n\n Native path (no tools, llm has with_structured_output) bypasses the agent loop and\n returns provider-validated JSON. When tools are attached, falls back to running the\n agent with a schema-augmented system prompt and parsing the final message content.\n \"\"\"\n from lfx.components.models_and_agents.structured_output.structured_output_orchestrator import (\n orchestrate_structured_output,\n )\n\n try:\n llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n except (ValueError, TypeError) as exc:\n await logger.aerror(f\"json_response.requirements_failed: {exc}\")\n return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n injected_system_prompt = self._inject_dynamic_prompt_values(getattr(self, \"system_prompt\", \"\") or \"\") or \"\"\n format_instructions = getattr(self, \"format_instructions\", \"\") or \"\"\n output_schema = getattr(self, \"output_schema\", None) or []\n has_tools = bool(self.tools)\n\n async def _run_agent_for_fallback(augmented_prompt: str) -> str:\n self.set(\n llm=llm_model,\n tools=self.tools or [],\n chat_history=self.chat_history,\n input_value=self.input_value,\n system_prompt=augmented_prompt,\n )\n agent_runnable = self.create_agent_runnable()\n with _suppress_send_message(self):\n result = await self.run_agent(agent_runnable)\n return _extract_text_content(result)\n\n try:\n return await orchestrate_structured_output(\n llm=llm_model,\n output_schema=output_schema,\n system_prompt=injected_system_prompt,\n format_instructions=format_instructions,\n input_value=_extract_text_content(self.input_value),\n run_prompt_fallback=_run_agent_for_fallback,\n prefer_native=not has_tools,\n )\n except (\n ExceptionWithMessageError,\n ValueError,\n TypeError,\n NotImplementedError,\n AttributeError,\n ) as exc:\n await logger.aerror(f\"json_response.orchestration_failed: {exc}\")\n return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n async def get_memory_data(self):\n # Scope by flow_id so default playground session names (e.g. \"New Session 0\")\n # cannot leak chat history across unrelated flows. See issue #13059.\n # The helper also returns [] when n_messages == 0, preserving the\n # explicit \"memory disabled\" contract from MemoryComponent.retrieve_messages.\n messages = await aget_agent_chat_history(\n session_id=self.graph.session_id,\n flow_id=getattr(self.graph, \"flow_id\", None),\n context_id=self.context_id,\n n_messages=self.n_messages,\n )\n return [\n message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n ]\n\n def update_input_types(self, build_config: dotdict) -> dotdict:\n \"\"\"Update input types for all fields in build_config.\"\"\"\n for key, value in build_config.items():\n if isinstance(value, dict):\n if value.get(\"input_types\") is None:\n build_config[key][\"input_types\"] = []\n elif hasattr(value, \"input_types\") and value.input_types is None:\n value.input_types = []\n return build_config\n\n async def update_build_config(\n self,\n build_config: dotdict,\n field_value: list[dict],\n field_name: str | None = None,\n ) -> dotdict:\n # Update model options with caching (for all field changes).\n # The tool-calling constraint lives on the ModelInput's ``filters``\n # field (declared above); ``handle_model_input_update`` reads it\n # and applies the filter to both the dropdown options and the\n # sticky-default re-injection path.\n build_config = handle_model_input_update(\n component=self,\n build_config=dict(build_config),\n field_value=field_value,\n field_name=field_name,\n )\n build_config = dotdict(build_config)\n\n if field_name == \"model\":\n build_config = self.update_input_types(build_config)\n\n # Validate required keys\n default_keys = [\n \"code\",\n \"_type\",\n \"model\",\n \"tools\",\n \"input_value\",\n \"add_current_date_tool\",\n \"add_calculator_tool\",\n \"system_prompt\",\n \"agent_description\",\n \"max_iterations\",\n \"handle_parsing_errors\",\n \"verbose\",\n ]\n missing_keys = [key for key in default_keys if key not in build_config]\n if missing_keys:\n msg = f\"Missing required keys in build_config: {missing_keys}\"\n raise ValueError(msg)\n return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n async def _get_tools(self) -> list[Tool]:\n component_toolkit = get_component_toolkit()\n tools_names = self._build_tools_names()\n agent_description = self.get_tool_description()\n # TODO: Agent Description Depreciated Feature to be removed\n description = f\"{agent_description}{tools_names}\"\n\n tools = component_toolkit(component=self).get_tools(\n tool_name=\"Call_Agent\",\n tool_description=description,\n # here we do not use the shared callbacks as we are exposing the agent as a tool\n callbacks=self.get_langchain_callbacks(),\n )\n if hasattr(self, \"tools_metadata\"):\n tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n\n return tools\n" }, "context_id": { "_input_type": "MessageTextInput", diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Pokédex Agent.json b/src/backend/base/langflow/initial_setup/starter_projects/Pokédex Agent.json index 239aa26f6a..a8fc9a5655 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Pokédex Agent.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Pokédex Agent.json @@ -1243,7 +1243,7 @@ "last_updated": "2025-12-11T21:41:48.407Z", "legacy": false, "metadata": { - "code_hash": "ebac74bfcae6", + "code_hash": "443159257161", "dependencies": { "dependencies": [ { @@ -1432,7 +1432,7 @@ "show": true, "title_case": false, "type": "code", - "value": "from __future__ import annotations\n\nfrom contextlib import contextmanager\nfrom datetime import datetime, timezone\nfrom typing import TYPE_CHECKING, Any\n\nfrom lfx.components.models_and_agents.memory import MemoryComponent\n\nif TYPE_CHECKING:\n from langchain_core.tools import Tool\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.agents.default_system_prompt import DEFAULT_SYSTEM_PROMPT_TEMPLATE\nfrom lfx.base.agents.events import ExceptionWithMessageError\nfrom lfx.base.constants import STREAM_INFO_TEXT\nfrom lfx.base.models.unified_models import (\n get_language_model_options,\n get_llm,\n handle_model_input_update,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.components.agentics.helpers.model_config import validate_model_selection\nfrom lfx.components.helpers import CalculatorComponent, CurrentDateComponent\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.custom.custom_component.component import get_component_toolkit\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, ModelInput, StrInput\nfrom lfx.io import IntInput, MessageTextInput, MultilineInput, Output, SecretStrInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.schema.data import Data\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.schema.table import EditMode\n\n\ndef set_advanced_true(component_input):\n component_input.advanced = True\n return component_input\n\n\ndef _extract_text_content(value) -> str:\n \"\"\"Pull a string payload from a Message-like, AIMessage-like, or string value.\"\"\"\n if isinstance(value, str):\n return value\n text = getattr(value, \"text\", None)\n if isinstance(text, str):\n return text\n content = getattr(value, \"content\", None)\n if isinstance(content, str):\n return content\n return str(value) if value is not None else \"\"\n\n\n@contextmanager\ndef _suppress_send_message(component: Any):\n \"\"\"Temporarily replace component.send_message with a no-op for the duration of the block.\n\n Used during the structured-output prompt fallback: run_agent streams the agent's\n final answer through self.send_message (correct for message_response), but in\n json_response the orchestrator parses that text into structured Data which the\n downstream Chat Output emits — leaving the original emission in place produces a\n duplicate message in the playground. The original method is always restored on exit,\n even when the wrapped call raises.\n \"\"\"\n original = component.send_message\n\n async def _noop(message, *_args, **_kwargs):\n return message\n\n component.send_message = _noop\n try:\n yield\n finally:\n component.send_message = original\n\n\nclass AgentComponent(ToolCallingAgentComponent):\n display_name: str = \"Agent\"\n description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n documentation: str = \"https://docs.langflow.org/agents\"\n icon = \"bot\"\n beta = False\n name = \"Agent\"\n\n memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n inputs = [\n ModelInput(\n name=\"model\",\n display_name=\"Language Model\",\n info=\"Select your model provider\",\n real_time_refresh=True,\n required=True,\n # Agents require tool calling — the filter is honored by\n # ``handle_model_input_update`` so models that can't run with\n # tools never reach the picker (and any saved selection that\n # no longer satisfies the constraint is auto-replaced).\n filters={\"tool_calling\": True},\n ),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"API Key\",\n info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n real_time_refresh=True,\n advanced=True,\n ),\n DropdownInput(\n name=\"base_url_ibm_watsonx\",\n display_name=\"watsonx API Endpoint\",\n info=\"The base URL of the API (IBM watsonx.ai only)\",\n options=IBM_WATSONX_URLS,\n value=IBM_WATSONX_URLS[0],\n combobox=True,\n show=False,\n real_time_refresh=True,\n ),\n StrInput(\n name=\"project_id\",\n display_name=\"watsonx Project ID\",\n info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n show=False,\n required=False,\n ),\n MultilineInput(\n name=\"system_prompt\",\n display_name=\"Agent Instructions\",\n info=(\n \"System Prompt: Initial instructions and context provided to guide the agent's behavior. \"\n \"Supports dynamic placeholders: {current_date}, {model_name}, {optional_user_context}.\"\n ),\n value=DEFAULT_SYSTEM_PROMPT_TEMPLATE,\n advanced=False,\n ),\n MessageTextInput(\n name=\"context_id\",\n display_name=\"Context ID\",\n info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n value=\"\",\n advanced=True,\n ),\n IntInput(\n name=\"n_messages\",\n display_name=\"Number of Chat History Messages\",\n value=100,\n info=\"Number of chat history messages to retrieve.\",\n advanced=True,\n show=True,\n ),\n IntInput(\n name=\"max_tokens\",\n display_name=\"Max Tokens\",\n info=\"Maximum number of tokens to generate. Field name varies by provider.\",\n advanced=True,\n range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n ),\n MultilineInput(\n name=\"format_instructions\",\n display_name=\"Output Format Instructions\",\n info=\"Generic Template for structured output formatting. Valid only with Structured response.\",\n value=(\n \"You are an AI that extracts structured JSON objects from unstructured text. \"\n \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n \"Fill missing or ambiguous values with defaults: null for missing values. \"\n \"Remove exact duplicates but keep variations that have different field values. \"\n \"Always return valid JSON in the expected format, never throw errors. \"\n \"If multiple objects can be extracted, return them all in the structured format.\"\n ),\n advanced=True,\n ),\n TableInput(\n name=\"output_schema\",\n display_name=\"Output Schema\",\n info=(\n \"Schema Validation: Define the structure and data types for structured output. \"\n \"No validation if no output schema.\"\n ),\n advanced=True,\n required=False,\n value=[],\n table_schema=[\n {\n \"name\": \"name\",\n \"display_name\": \"Name\",\n \"type\": \"str\",\n \"description\": \"Specify the name of the output field.\",\n \"default\": \"field\",\n \"edit_mode\": EditMode.INLINE,\n },\n {\n \"name\": \"description\",\n \"display_name\": \"Description\",\n \"type\": \"str\",\n \"description\": \"Describe the purpose of the output field.\",\n \"default\": \"description of field\",\n \"edit_mode\": EditMode.POPOVER,\n },\n {\n \"name\": \"type\",\n \"display_name\": \"Type\",\n \"type\": \"str\",\n \"edit_mode\": EditMode.INLINE,\n \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n \"default\": \"str\",\n },\n {\n \"name\": \"multiple\",\n \"display_name\": \"As List\",\n \"type\": \"boolean\",\n \"description\": \"Set to True if this output field should be a list of the specified type.\",\n \"default\": \"False\",\n \"edit_mode\": EditMode.INLINE,\n },\n ],\n ),\n *LCToolsAgentComponent.get_base_inputs(),\n # removed memory inputs from agent component\n # *memory_inputs,\n BoolInput(\n name=\"stream\",\n display_name=\"Stream\",\n info=STREAM_INFO_TEXT,\n value=False,\n advanced=True,\n ),\n BoolInput(\n name=\"add_current_date_tool\",\n display_name=\"Current Date\",\n advanced=True,\n info=\"If true, will add a tool to the agent that returns the current date.\",\n value=True,\n ),\n BoolInput(\n name=\"add_calculator_tool\",\n display_name=\"Calculator\",\n advanced=True,\n info=(\n \"If true, adds a zero-config arithmetic calculator tool to the agent \"\n \"(safe: only +, -, *, /, ** operators via AST).\"\n ),\n value=True,\n ),\n ]\n outputs = [\n Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n Output(\n name=\"structured_response\",\n display_name=\"Structured Response\",\n method=\"json_response\",\n types=[\"Data\"],\n ),\n ]\n\n def _resolve_selected_model(self):\n \"\"\"Resolve the selected model, including legacy agent_llm/model_name inputs.\"\"\"\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n if isinstance(self.model, BaseLanguageModel):\n return self.model\n except ImportError:\n pass\n\n if isinstance(self.model, list) and self.model:\n return self.model\n\n legacy_provider = getattr(self, \"agent_llm\", None)\n legacy_model_name = getattr(self, \"model_name\", None)\n if not legacy_provider or not legacy_model_name:\n return self.model\n\n options = get_language_model_options(user_id=self.user_id)\n for option in options:\n if option.get(\"provider\") == legacy_provider and option.get(\"name\") == legacy_model_name:\n return [option]\n\n return [\n {\n \"name\": legacy_model_name,\n \"provider\": legacy_provider,\n \"metadata\": {},\n }\n ]\n\n def _get_max_tokens_value(self):\n \"\"\"Return the user-supplied max_tokens or None when unset/zero.\"\"\"\n val = getattr(self, \"max_tokens\", None)\n if val in {\"\", 0}:\n return None\n return val\n\n def _get_llm(self):\n \"\"\"Override parent to include max_tokens from the Agent's input field.\"\"\"\n return get_llm(\n model=self.model,\n user_id=self.user_id,\n api_key=getattr(self, \"api_key\", None),\n stream=bool(getattr(self, \"stream\", False)),\n max_tokens=self._get_max_tokens_value(),\n watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n watsonx_project_id=getattr(self, \"project_id\", None),\n )\n\n async def get_agent_requirements(self):\n \"\"\"Get the agent requirements for the agent.\"\"\"\n from langchain_core.tools import StructuredTool\n\n selected_model = self._resolve_selected_model()\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n is_connected_model = isinstance(selected_model, BaseLanguageModel)\n except ImportError:\n is_connected_model = False\n\n if not is_connected_model:\n validate_model_selection(selected_model)\n\n # Ensure _get_llm() uses the resolved model (e.g. from legacy agent_llm/model_name)\n self.model = selected_model\n llm_model = self._get_llm()\n if llm_model is None:\n msg = \"No language model selected. Please choose a model to proceed.\"\n raise ValueError(msg)\n\n # Get memory data\n self.chat_history = await self.get_memory_data()\n await logger.adebug(f\"Retrieved {len(self.chat_history)} chat history messages\")\n if isinstance(self.chat_history, Message):\n self.chat_history = [self.chat_history]\n\n # Add current date tool if enabled\n if self.add_current_date_tool:\n if not isinstance(self.tools, list): # type: ignore[has-type]\n self.tools = []\n current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n if not isinstance(current_date_tool, StructuredTool):\n msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n raise TypeError(msg)\n # Skip if an externally-connected tool already provides the same name.\n # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n if not any(getattr(t, \"name\", None) == current_date_tool.name for t in self.tools):\n self.tools.append(current_date_tool)\n\n # Add calculator tool if enabled (zero-config arithmetic)\n if getattr(self, \"add_calculator_tool\", False):\n if not isinstance(self.tools, list): # type: ignore[has-type]\n self.tools = []\n calculator_tool = (await CalculatorComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n if not isinstance(calculator_tool, StructuredTool):\n msg = \"CalculatorComponent must be converted to a StructuredTool\"\n raise TypeError(msg)\n # Skip if an externally-connected tool already provides the same name.\n # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n if not any(getattr(t, \"name\", None) == calculator_tool.name for t in self.tools):\n self.tools.append(calculator_tool)\n\n # Set shared callbacks for tracing the tools used by the agent\n self.set_tools_callbacks(self.tools, self._get_shared_callbacks())\n\n return llm_model, self.chat_history, self.tools\n\n def _get_resolved_model_name(self) -> str:\n \"\"\"Best-effort human-readable model name for {model_name} injection.\"\"\"\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n if isinstance(self.model, BaseLanguageModel):\n return type(self.model).__name__\n except ImportError:\n pass\n\n if isinstance(self.model, list) and self.model:\n first = self.model[0]\n if isinstance(first, dict):\n name = first.get(\"name\")\n if isinstance(name, str) and name:\n return name\n\n legacy_model_name = getattr(self, \"model_name\", None)\n if isinstance(legacy_model_name, str) and legacy_model_name:\n return legacy_model_name\n return \"\"\n\n def _inject_dynamic_prompt_values(self, prompt: str | None) -> str | None:\n \"\"\"Replace known env placeholders in the system prompt.\n\n Handles {current_date}, {model_name}, and {optional_user_context} (the\n last one ships with the structured DEFAULT_SYSTEM_PROMPT_TEMPLATE and\n is currently unused at the AgentComponent layer, so it resolves to \"\").\n Uses str.replace (not str.format) so user prompts containing literal\n braces such as JSON examples ({\"key\": 1}) never break the agent.\n \"\"\"\n if not prompt:\n return prompt\n replacements = {\n \"{current_date}\": datetime.now(tz=timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S UTC\"),\n \"{model_name}\": self._get_resolved_model_name(),\n \"{optional_user_context}\": \"\",\n }\n for placeholder, value in replacements.items():\n prompt = prompt.replace(placeholder, value)\n return prompt\n\n async def message_response(self) -> Message:\n try:\n llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n # Set up and run agent\n self.set(\n llm=llm_model,\n tools=self.tools or [],\n chat_history=self.chat_history,\n input_value=self.input_value,\n system_prompt=self._inject_dynamic_prompt_values(self.system_prompt),\n )\n agent = self.create_agent_runnable()\n result = await self.run_agent(agent)\n\n # Store result for potential JSON output\n self._agent_result = result\n\n except (ValueError, TypeError, KeyError) as e:\n await logger.aerror(f\"{type(e).__name__}: {e!s}\")\n raise\n except ExceptionWithMessageError as e:\n await logger.aerror(f\"ExceptionWithMessageError occurred: {e}\")\n raise\n # Avoid catching blind Exception; let truly unexpected exceptions propagate\n except Exception as e:\n await logger.aerror(f\"Unexpected error: {e!s}\")\n raise\n else:\n return result\n\n async def json_response(self) -> Data:\n \"\"\"Produce structured Data via native LLM structured output, with prompt-based fallback.\n\n Native path (no tools, llm has with_structured_output) bypasses the agent loop and\n returns provider-validated JSON. When tools are attached, falls back to running the\n agent with a schema-augmented system prompt and parsing the final message content.\n \"\"\"\n from lfx.components.models_and_agents.structured_output.structured_output_orchestrator import (\n orchestrate_structured_output,\n )\n\n try:\n llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n except (ValueError, TypeError) as exc:\n await logger.aerror(f\"json_response.requirements_failed: {exc}\")\n return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n injected_system_prompt = self._inject_dynamic_prompt_values(getattr(self, \"system_prompt\", \"\") or \"\") or \"\"\n format_instructions = getattr(self, \"format_instructions\", \"\") or \"\"\n output_schema = getattr(self, \"output_schema\", None) or []\n has_tools = bool(self.tools)\n\n async def _run_agent_for_fallback(augmented_prompt: str) -> str:\n self.set(\n llm=llm_model,\n tools=self.tools or [],\n chat_history=self.chat_history,\n input_value=self.input_value,\n system_prompt=augmented_prompt,\n )\n agent_runnable = self.create_agent_runnable()\n with _suppress_send_message(self):\n result = await self.run_agent(agent_runnable)\n return _extract_text_content(result)\n\n try:\n return await orchestrate_structured_output(\n llm=llm_model,\n output_schema=output_schema,\n system_prompt=injected_system_prompt,\n format_instructions=format_instructions,\n input_value=_extract_text_content(self.input_value),\n run_prompt_fallback=_run_agent_for_fallback,\n prefer_native=not has_tools,\n )\n except (\n ExceptionWithMessageError,\n ValueError,\n TypeError,\n NotImplementedError,\n AttributeError,\n ) as exc:\n await logger.aerror(f\"json_response.orchestration_failed: {exc}\")\n return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n async def get_memory_data(self):\n # TODO: This is a temporary fix to avoid message duplication. We should develop a function for this.\n messages = (\n await MemoryComponent(**self.get_base_args())\n .set(\n session_id=self.graph.session_id,\n context_id=self.context_id,\n order=\"Ascending\",\n n_messages=self.n_messages,\n )\n .retrieve_messages()\n )\n return [\n message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n ]\n\n def update_input_types(self, build_config: dotdict) -> dotdict:\n \"\"\"Update input types for all fields in build_config.\"\"\"\n for key, value in build_config.items():\n if isinstance(value, dict):\n if value.get(\"input_types\") is None:\n build_config[key][\"input_types\"] = []\n elif hasattr(value, \"input_types\") and value.input_types is None:\n value.input_types = []\n return build_config\n\n async def update_build_config(\n self,\n build_config: dotdict,\n field_value: list[dict],\n field_name: str | None = None,\n ) -> dotdict:\n # Update model options with caching (for all field changes).\n # The tool-calling constraint lives on the ModelInput's ``filters``\n # field (declared above); ``handle_model_input_update`` reads it\n # and applies the filter to both the dropdown options and the\n # sticky-default re-injection path.\n build_config = handle_model_input_update(\n component=self,\n build_config=dict(build_config),\n field_value=field_value,\n field_name=field_name,\n )\n build_config = dotdict(build_config)\n\n if field_name == \"model\":\n build_config = self.update_input_types(build_config)\n\n # Validate required keys\n default_keys = [\n \"code\",\n \"_type\",\n \"model\",\n \"tools\",\n \"input_value\",\n \"add_current_date_tool\",\n \"add_calculator_tool\",\n \"system_prompt\",\n \"agent_description\",\n \"max_iterations\",\n \"handle_parsing_errors\",\n \"verbose\",\n ]\n missing_keys = [key for key in default_keys if key not in build_config]\n if missing_keys:\n msg = f\"Missing required keys in build_config: {missing_keys}\"\n raise ValueError(msg)\n return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n async def _get_tools(self) -> list[Tool]:\n component_toolkit = get_component_toolkit()\n tools_names = self._build_tools_names()\n agent_description = self.get_tool_description()\n # TODO: Agent Description Depreciated Feature to be removed\n description = f\"{agent_description}{tools_names}\"\n\n tools = component_toolkit(component=self).get_tools(\n tool_name=\"Call_Agent\",\n tool_description=description,\n # here we do not use the shared callbacks as we are exposing the agent as a tool\n callbacks=self.get_langchain_callbacks(),\n )\n if hasattr(self, \"tools_metadata\"):\n tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n\n return tools\n" + "value": "from __future__ import annotations\n\nfrom contextlib import contextmanager\nfrom datetime import datetime, timezone\nfrom typing import TYPE_CHECKING, Any\n\nfrom lfx.components.models_and_agents.memory import MemoryComponent, aget_agent_chat_history\n\nif TYPE_CHECKING:\n from langchain_core.tools import Tool\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.agents.default_system_prompt import DEFAULT_SYSTEM_PROMPT_TEMPLATE\nfrom lfx.base.agents.events import ExceptionWithMessageError\nfrom lfx.base.constants import STREAM_INFO_TEXT\nfrom lfx.base.models.unified_models import (\n get_language_model_options,\n get_llm,\n handle_model_input_update,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.components.agentics.helpers.model_config import validate_model_selection\nfrom lfx.components.helpers import CalculatorComponent, CurrentDateComponent\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.custom.custom_component.component import get_component_toolkit\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, ModelInput, StrInput\nfrom lfx.io import IntInput, MessageTextInput, MultilineInput, Output, SecretStrInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.schema.data import Data\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.schema.table import EditMode\n\n\ndef set_advanced_true(component_input):\n component_input.advanced = True\n return component_input\n\n\ndef _extract_text_content(value) -> str:\n \"\"\"Pull a string payload from a Message-like, AIMessage-like, or string value.\"\"\"\n if isinstance(value, str):\n return value\n text = getattr(value, \"text\", None)\n if isinstance(text, str):\n return text\n content = getattr(value, \"content\", None)\n if isinstance(content, str):\n return content\n return str(value) if value is not None else \"\"\n\n\n@contextmanager\ndef _suppress_send_message(component: Any):\n \"\"\"Temporarily replace component.send_message with a no-op for the duration of the block.\n\n Used during the structured-output prompt fallback: run_agent streams the agent's\n final answer through self.send_message (correct for message_response), but in\n json_response the orchestrator parses that text into structured Data which the\n downstream Chat Output emits — leaving the original emission in place produces a\n duplicate message in the playground. The original method is always restored on exit,\n even when the wrapped call raises.\n \"\"\"\n original = component.send_message\n\n async def _noop(message, *_args, **_kwargs):\n return message\n\n component.send_message = _noop\n try:\n yield\n finally:\n component.send_message = original\n\n\nclass AgentComponent(ToolCallingAgentComponent):\n display_name: str = \"Agent\"\n description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n documentation: str = \"https://docs.langflow.org/agents\"\n icon = \"bot\"\n beta = False\n name = \"Agent\"\n\n memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n inputs = [\n ModelInput(\n name=\"model\",\n display_name=\"Language Model\",\n info=\"Select your model provider\",\n real_time_refresh=True,\n required=True,\n # Agents require tool calling — the filter is honored by\n # ``handle_model_input_update`` so models that can't run with\n # tools never reach the picker (and any saved selection that\n # no longer satisfies the constraint is auto-replaced).\n filters={\"tool_calling\": True},\n ),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"API Key\",\n info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n real_time_refresh=True,\n advanced=True,\n ),\n DropdownInput(\n name=\"base_url_ibm_watsonx\",\n display_name=\"watsonx API Endpoint\",\n info=\"The base URL of the API (IBM watsonx.ai only)\",\n options=IBM_WATSONX_URLS,\n value=IBM_WATSONX_URLS[0],\n combobox=True,\n show=False,\n real_time_refresh=True,\n ),\n StrInput(\n name=\"project_id\",\n display_name=\"watsonx Project ID\",\n info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n show=False,\n required=False,\n ),\n MultilineInput(\n name=\"system_prompt\",\n display_name=\"Agent Instructions\",\n info=(\n \"System Prompt: Initial instructions and context provided to guide the agent's behavior. \"\n \"Supports dynamic placeholders: {current_date}, {model_name}, {optional_user_context}.\"\n ),\n value=DEFAULT_SYSTEM_PROMPT_TEMPLATE,\n advanced=False,\n ),\n MessageTextInput(\n name=\"context_id\",\n display_name=\"Context ID\",\n info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n value=\"\",\n advanced=True,\n ),\n IntInput(\n name=\"n_messages\",\n display_name=\"Number of Chat History Messages\",\n value=100,\n info=\"Number of chat history messages to retrieve.\",\n advanced=True,\n show=True,\n ),\n IntInput(\n name=\"max_tokens\",\n display_name=\"Max Tokens\",\n info=\"Maximum number of tokens to generate. Field name varies by provider.\",\n advanced=True,\n range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n ),\n MultilineInput(\n name=\"format_instructions\",\n display_name=\"Output Format Instructions\",\n info=\"Generic Template for structured output formatting. Valid only with Structured response.\",\n value=(\n \"You are an AI that extracts structured JSON objects from unstructured text. \"\n \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n \"Fill missing or ambiguous values with defaults: null for missing values. \"\n \"Remove exact duplicates but keep variations that have different field values. \"\n \"Always return valid JSON in the expected format, never throw errors. \"\n \"If multiple objects can be extracted, return them all in the structured format.\"\n ),\n advanced=True,\n ),\n TableInput(\n name=\"output_schema\",\n display_name=\"Output Schema\",\n info=(\n \"Schema Validation: Define the structure and data types for structured output. \"\n \"No validation if no output schema.\"\n ),\n advanced=True,\n required=False,\n value=[],\n table_schema=[\n {\n \"name\": \"name\",\n \"display_name\": \"Name\",\n \"type\": \"str\",\n \"description\": \"Specify the name of the output field.\",\n \"default\": \"field\",\n \"edit_mode\": EditMode.INLINE,\n },\n {\n \"name\": \"description\",\n \"display_name\": \"Description\",\n \"type\": \"str\",\n \"description\": \"Describe the purpose of the output field.\",\n \"default\": \"description of field\",\n \"edit_mode\": EditMode.POPOVER,\n },\n {\n \"name\": \"type\",\n \"display_name\": \"Type\",\n \"type\": \"str\",\n \"edit_mode\": EditMode.INLINE,\n \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n \"default\": \"str\",\n },\n {\n \"name\": \"multiple\",\n \"display_name\": \"As List\",\n \"type\": \"boolean\",\n \"description\": \"Set to True if this output field should be a list of the specified type.\",\n \"default\": \"False\",\n \"edit_mode\": EditMode.INLINE,\n },\n ],\n ),\n *LCToolsAgentComponent.get_base_inputs(),\n # removed memory inputs from agent component\n # *memory_inputs,\n BoolInput(\n name=\"stream\",\n display_name=\"Stream\",\n info=STREAM_INFO_TEXT,\n value=False,\n advanced=True,\n ),\n BoolInput(\n name=\"add_current_date_tool\",\n display_name=\"Current Date\",\n advanced=True,\n info=\"If true, will add a tool to the agent that returns the current date.\",\n value=True,\n ),\n BoolInput(\n name=\"add_calculator_tool\",\n display_name=\"Calculator\",\n advanced=True,\n info=(\n \"If true, adds a zero-config arithmetic calculator tool to the agent \"\n \"(safe: only +, -, *, /, ** operators via AST).\"\n ),\n value=True,\n ),\n ]\n outputs = [\n Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n Output(\n name=\"structured_response\",\n display_name=\"Structured Response\",\n method=\"json_response\",\n types=[\"Data\"],\n ),\n ]\n\n def _resolve_selected_model(self):\n \"\"\"Resolve the selected model, including legacy agent_llm/model_name inputs.\"\"\"\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n if isinstance(self.model, BaseLanguageModel):\n return self.model\n except ImportError:\n pass\n\n if isinstance(self.model, list) and self.model:\n return self.model\n\n legacy_provider = getattr(self, \"agent_llm\", None)\n legacy_model_name = getattr(self, \"model_name\", None)\n if not legacy_provider or not legacy_model_name:\n return self.model\n\n options = get_language_model_options(user_id=self.user_id)\n for option in options:\n if option.get(\"provider\") == legacy_provider and option.get(\"name\") == legacy_model_name:\n return [option]\n\n return [\n {\n \"name\": legacy_model_name,\n \"provider\": legacy_provider,\n \"metadata\": {},\n }\n ]\n\n def _get_max_tokens_value(self):\n \"\"\"Return the user-supplied max_tokens or None when unset/zero.\"\"\"\n val = getattr(self, \"max_tokens\", None)\n if val in {\"\", 0}:\n return None\n return val\n\n def _get_llm(self):\n \"\"\"Override parent to include max_tokens from the Agent's input field.\"\"\"\n return get_llm(\n model=self.model,\n user_id=self.user_id,\n api_key=getattr(self, \"api_key\", None),\n stream=bool(getattr(self, \"stream\", False)),\n max_tokens=self._get_max_tokens_value(),\n watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n watsonx_project_id=getattr(self, \"project_id\", None),\n )\n\n async def get_agent_requirements(self):\n \"\"\"Get the agent requirements for the agent.\"\"\"\n from langchain_core.tools import StructuredTool\n\n selected_model = self._resolve_selected_model()\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n is_connected_model = isinstance(selected_model, BaseLanguageModel)\n except ImportError:\n is_connected_model = False\n\n if not is_connected_model:\n validate_model_selection(selected_model)\n\n # Ensure _get_llm() uses the resolved model (e.g. from legacy agent_llm/model_name)\n self.model = selected_model\n llm_model = self._get_llm()\n if llm_model is None:\n msg = \"No language model selected. Please choose a model to proceed.\"\n raise ValueError(msg)\n\n # Get memory data\n self.chat_history = await self.get_memory_data()\n await logger.adebug(f\"Retrieved {len(self.chat_history)} chat history messages\")\n if isinstance(self.chat_history, Message):\n self.chat_history = [self.chat_history]\n\n # Add current date tool if enabled\n if self.add_current_date_tool:\n if not isinstance(self.tools, list): # type: ignore[has-type]\n self.tools = []\n current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n if not isinstance(current_date_tool, StructuredTool):\n msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n raise TypeError(msg)\n # Skip if an externally-connected tool already provides the same name.\n # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n if not any(getattr(t, \"name\", None) == current_date_tool.name for t in self.tools):\n self.tools.append(current_date_tool)\n\n # Add calculator tool if enabled (zero-config arithmetic)\n if getattr(self, \"add_calculator_tool\", False):\n if not isinstance(self.tools, list): # type: ignore[has-type]\n self.tools = []\n calculator_tool = (await CalculatorComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n if not isinstance(calculator_tool, StructuredTool):\n msg = \"CalculatorComponent must be converted to a StructuredTool\"\n raise TypeError(msg)\n # Skip if an externally-connected tool already provides the same name.\n # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n if not any(getattr(t, \"name\", None) == calculator_tool.name for t in self.tools):\n self.tools.append(calculator_tool)\n\n # Set shared callbacks for tracing the tools used by the agent\n self.set_tools_callbacks(self.tools, self._get_shared_callbacks())\n\n return llm_model, self.chat_history, self.tools\n\n def _get_resolved_model_name(self) -> str:\n \"\"\"Best-effort human-readable model name for {model_name} injection.\"\"\"\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n if isinstance(self.model, BaseLanguageModel):\n return type(self.model).__name__\n except ImportError:\n pass\n\n if isinstance(self.model, list) and self.model:\n first = self.model[0]\n if isinstance(first, dict):\n name = first.get(\"name\")\n if isinstance(name, str) and name:\n return name\n\n legacy_model_name = getattr(self, \"model_name\", None)\n if isinstance(legacy_model_name, str) and legacy_model_name:\n return legacy_model_name\n return \"\"\n\n def _inject_dynamic_prompt_values(self, prompt: str | None) -> str | None:\n \"\"\"Replace known env placeholders in the system prompt.\n\n Handles {current_date}, {model_name}, and {optional_user_context} (the\n last one ships with the structured DEFAULT_SYSTEM_PROMPT_TEMPLATE and\n is currently unused at the AgentComponent layer, so it resolves to \"\").\n Uses str.replace (not str.format) so user prompts containing literal\n braces such as JSON examples ({\"key\": 1}) never break the agent.\n \"\"\"\n if not prompt:\n return prompt\n replacements = {\n \"{current_date}\": datetime.now(tz=timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S UTC\"),\n \"{model_name}\": self._get_resolved_model_name(),\n \"{optional_user_context}\": \"\",\n }\n for placeholder, value in replacements.items():\n prompt = prompt.replace(placeholder, value)\n return prompt\n\n async def message_response(self) -> Message:\n try:\n llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n # Set up and run agent\n self.set(\n llm=llm_model,\n tools=self.tools or [],\n chat_history=self.chat_history,\n input_value=self.input_value,\n system_prompt=self._inject_dynamic_prompt_values(self.system_prompt),\n )\n agent = self.create_agent_runnable()\n result = await self.run_agent(agent)\n\n # Store result for potential JSON output\n self._agent_result = result\n\n except (ValueError, TypeError, KeyError) as e:\n await logger.aerror(f\"{type(e).__name__}: {e!s}\")\n raise\n except ExceptionWithMessageError as e:\n await logger.aerror(f\"ExceptionWithMessageError occurred: {e}\")\n raise\n # Avoid catching blind Exception; let truly unexpected exceptions propagate\n except Exception as e:\n await logger.aerror(f\"Unexpected error: {e!s}\")\n raise\n else:\n return result\n\n async def json_response(self) -> Data:\n \"\"\"Produce structured Data via native LLM structured output, with prompt-based fallback.\n\n Native path (no tools, llm has with_structured_output) bypasses the agent loop and\n returns provider-validated JSON. When tools are attached, falls back to running the\n agent with a schema-augmented system prompt and parsing the final message content.\n \"\"\"\n from lfx.components.models_and_agents.structured_output.structured_output_orchestrator import (\n orchestrate_structured_output,\n )\n\n try:\n llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n except (ValueError, TypeError) as exc:\n await logger.aerror(f\"json_response.requirements_failed: {exc}\")\n return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n injected_system_prompt = self._inject_dynamic_prompt_values(getattr(self, \"system_prompt\", \"\") or \"\") or \"\"\n format_instructions = getattr(self, \"format_instructions\", \"\") or \"\"\n output_schema = getattr(self, \"output_schema\", None) or []\n has_tools = bool(self.tools)\n\n async def _run_agent_for_fallback(augmented_prompt: str) -> str:\n self.set(\n llm=llm_model,\n tools=self.tools or [],\n chat_history=self.chat_history,\n input_value=self.input_value,\n system_prompt=augmented_prompt,\n )\n agent_runnable = self.create_agent_runnable()\n with _suppress_send_message(self):\n result = await self.run_agent(agent_runnable)\n return _extract_text_content(result)\n\n try:\n return await orchestrate_structured_output(\n llm=llm_model,\n output_schema=output_schema,\n system_prompt=injected_system_prompt,\n format_instructions=format_instructions,\n input_value=_extract_text_content(self.input_value),\n run_prompt_fallback=_run_agent_for_fallback,\n prefer_native=not has_tools,\n )\n except (\n ExceptionWithMessageError,\n ValueError,\n TypeError,\n NotImplementedError,\n AttributeError,\n ) as exc:\n await logger.aerror(f\"json_response.orchestration_failed: {exc}\")\n return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n async def get_memory_data(self):\n # Scope by flow_id so default playground session names (e.g. \"New Session 0\")\n # cannot leak chat history across unrelated flows. See issue #13059.\n # The helper also returns [] when n_messages == 0, preserving the\n # explicit \"memory disabled\" contract from MemoryComponent.retrieve_messages.\n messages = await aget_agent_chat_history(\n session_id=self.graph.session_id,\n flow_id=getattr(self.graph, \"flow_id\", None),\n context_id=self.context_id,\n n_messages=self.n_messages,\n )\n return [\n message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n ]\n\n def update_input_types(self, build_config: dotdict) -> dotdict:\n \"\"\"Update input types for all fields in build_config.\"\"\"\n for key, value in build_config.items():\n if isinstance(value, dict):\n if value.get(\"input_types\") is None:\n build_config[key][\"input_types\"] = []\n elif hasattr(value, \"input_types\") and value.input_types is None:\n value.input_types = []\n return build_config\n\n async def update_build_config(\n self,\n build_config: dotdict,\n field_value: list[dict],\n field_name: str | None = None,\n ) -> dotdict:\n # Update model options with caching (for all field changes).\n # The tool-calling constraint lives on the ModelInput's ``filters``\n # field (declared above); ``handle_model_input_update`` reads it\n # and applies the filter to both the dropdown options and the\n # sticky-default re-injection path.\n build_config = handle_model_input_update(\n component=self,\n build_config=dict(build_config),\n field_value=field_value,\n field_name=field_name,\n )\n build_config = dotdict(build_config)\n\n if field_name == \"model\":\n build_config = self.update_input_types(build_config)\n\n # Validate required keys\n default_keys = [\n \"code\",\n \"_type\",\n \"model\",\n \"tools\",\n \"input_value\",\n \"add_current_date_tool\",\n \"add_calculator_tool\",\n \"system_prompt\",\n \"agent_description\",\n \"max_iterations\",\n \"handle_parsing_errors\",\n \"verbose\",\n ]\n missing_keys = [key for key in default_keys if key not in build_config]\n if missing_keys:\n msg = f\"Missing required keys in build_config: {missing_keys}\"\n raise ValueError(msg)\n return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n async def _get_tools(self) -> list[Tool]:\n component_toolkit = get_component_toolkit()\n tools_names = self._build_tools_names()\n agent_description = self.get_tool_description()\n # TODO: Agent Description Depreciated Feature to be removed\n description = f\"{agent_description}{tools_names}\"\n\n tools = component_toolkit(component=self).get_tools(\n tool_name=\"Call_Agent\",\n tool_description=description,\n # here we do not use the shared callbacks as we are exposing the agent as a tool\n callbacks=self.get_langchain_callbacks(),\n )\n if hasattr(self, \"tools_metadata\"):\n tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n\n return tools\n" }, "context_id": { "_input_type": "MessageTextInput", diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Price Deal Finder.json b/src/backend/base/langflow/initial_setup/starter_projects/Price Deal Finder.json index 5b13fe0530..10187591a5 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Price Deal Finder.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Price Deal Finder.json @@ -1613,7 +1613,7 @@ "last_updated": "2025-12-11T21:41:48.407Z", "legacy": false, "metadata": { - "code_hash": "ebac74bfcae6", + "code_hash": "443159257161", "dependencies": { "dependencies": [ { @@ -1802,7 +1802,7 @@ "show": true, "title_case": false, "type": "code", - "value": "from __future__ import annotations\n\nfrom contextlib import contextmanager\nfrom datetime import datetime, timezone\nfrom typing import TYPE_CHECKING, Any\n\nfrom lfx.components.models_and_agents.memory import MemoryComponent\n\nif TYPE_CHECKING:\n from langchain_core.tools import Tool\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.agents.default_system_prompt import DEFAULT_SYSTEM_PROMPT_TEMPLATE\nfrom lfx.base.agents.events import ExceptionWithMessageError\nfrom lfx.base.constants import STREAM_INFO_TEXT\nfrom lfx.base.models.unified_models import (\n get_language_model_options,\n get_llm,\n handle_model_input_update,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.components.agentics.helpers.model_config import validate_model_selection\nfrom lfx.components.helpers import CalculatorComponent, CurrentDateComponent\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.custom.custom_component.component import get_component_toolkit\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, ModelInput, StrInput\nfrom lfx.io import IntInput, MessageTextInput, MultilineInput, Output, SecretStrInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.schema.data import Data\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.schema.table import EditMode\n\n\ndef set_advanced_true(component_input):\n component_input.advanced = True\n return component_input\n\n\ndef _extract_text_content(value) -> str:\n \"\"\"Pull a string payload from a Message-like, AIMessage-like, or string value.\"\"\"\n if isinstance(value, str):\n return value\n text = getattr(value, \"text\", None)\n if isinstance(text, str):\n return text\n content = getattr(value, \"content\", None)\n if isinstance(content, str):\n return content\n return str(value) if value is not None else \"\"\n\n\n@contextmanager\ndef _suppress_send_message(component: Any):\n \"\"\"Temporarily replace component.send_message with a no-op for the duration of the block.\n\n Used during the structured-output prompt fallback: run_agent streams the agent's\n final answer through self.send_message (correct for message_response), but in\n json_response the orchestrator parses that text into structured Data which the\n downstream Chat Output emits — leaving the original emission in place produces a\n duplicate message in the playground. The original method is always restored on exit,\n even when the wrapped call raises.\n \"\"\"\n original = component.send_message\n\n async def _noop(message, *_args, **_kwargs):\n return message\n\n component.send_message = _noop\n try:\n yield\n finally:\n component.send_message = original\n\n\nclass AgentComponent(ToolCallingAgentComponent):\n display_name: str = \"Agent\"\n description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n documentation: str = \"https://docs.langflow.org/agents\"\n icon = \"bot\"\n beta = False\n name = \"Agent\"\n\n memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n inputs = [\n ModelInput(\n name=\"model\",\n display_name=\"Language Model\",\n info=\"Select your model provider\",\n real_time_refresh=True,\n required=True,\n # Agents require tool calling — the filter is honored by\n # ``handle_model_input_update`` so models that can't run with\n # tools never reach the picker (and any saved selection that\n # no longer satisfies the constraint is auto-replaced).\n filters={\"tool_calling\": True},\n ),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"API Key\",\n info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n real_time_refresh=True,\n advanced=True,\n ),\n DropdownInput(\n name=\"base_url_ibm_watsonx\",\n display_name=\"watsonx API Endpoint\",\n info=\"The base URL of the API (IBM watsonx.ai only)\",\n options=IBM_WATSONX_URLS,\n value=IBM_WATSONX_URLS[0],\n combobox=True,\n show=False,\n real_time_refresh=True,\n ),\n StrInput(\n name=\"project_id\",\n display_name=\"watsonx Project ID\",\n info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n show=False,\n required=False,\n ),\n MultilineInput(\n name=\"system_prompt\",\n display_name=\"Agent Instructions\",\n info=(\n \"System Prompt: Initial instructions and context provided to guide the agent's behavior. \"\n \"Supports dynamic placeholders: {current_date}, {model_name}, {optional_user_context}.\"\n ),\n value=DEFAULT_SYSTEM_PROMPT_TEMPLATE,\n advanced=False,\n ),\n MessageTextInput(\n name=\"context_id\",\n display_name=\"Context ID\",\n info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n value=\"\",\n advanced=True,\n ),\n IntInput(\n name=\"n_messages\",\n display_name=\"Number of Chat History Messages\",\n value=100,\n info=\"Number of chat history messages to retrieve.\",\n advanced=True,\n show=True,\n ),\n IntInput(\n name=\"max_tokens\",\n display_name=\"Max Tokens\",\n info=\"Maximum number of tokens to generate. Field name varies by provider.\",\n advanced=True,\n range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n ),\n MultilineInput(\n name=\"format_instructions\",\n display_name=\"Output Format Instructions\",\n info=\"Generic Template for structured output formatting. Valid only with Structured response.\",\n value=(\n \"You are an AI that extracts structured JSON objects from unstructured text. \"\n \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n \"Fill missing or ambiguous values with defaults: null for missing values. \"\n \"Remove exact duplicates but keep variations that have different field values. \"\n \"Always return valid JSON in the expected format, never throw errors. \"\n \"If multiple objects can be extracted, return them all in the structured format.\"\n ),\n advanced=True,\n ),\n TableInput(\n name=\"output_schema\",\n display_name=\"Output Schema\",\n info=(\n \"Schema Validation: Define the structure and data types for structured output. \"\n \"No validation if no output schema.\"\n ),\n advanced=True,\n required=False,\n value=[],\n table_schema=[\n {\n \"name\": \"name\",\n \"display_name\": \"Name\",\n \"type\": \"str\",\n \"description\": \"Specify the name of the output field.\",\n \"default\": \"field\",\n \"edit_mode\": EditMode.INLINE,\n },\n {\n \"name\": \"description\",\n \"display_name\": \"Description\",\n \"type\": \"str\",\n \"description\": \"Describe the purpose of the output field.\",\n \"default\": \"description of field\",\n \"edit_mode\": EditMode.POPOVER,\n },\n {\n \"name\": \"type\",\n \"display_name\": \"Type\",\n \"type\": \"str\",\n \"edit_mode\": EditMode.INLINE,\n \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n \"default\": \"str\",\n },\n {\n \"name\": \"multiple\",\n \"display_name\": \"As List\",\n \"type\": \"boolean\",\n \"description\": \"Set to True if this output field should be a list of the specified type.\",\n \"default\": \"False\",\n \"edit_mode\": EditMode.INLINE,\n },\n ],\n ),\n *LCToolsAgentComponent.get_base_inputs(),\n # removed memory inputs from agent component\n # *memory_inputs,\n BoolInput(\n name=\"stream\",\n display_name=\"Stream\",\n info=STREAM_INFO_TEXT,\n value=False,\n advanced=True,\n ),\n BoolInput(\n name=\"add_current_date_tool\",\n display_name=\"Current Date\",\n advanced=True,\n info=\"If true, will add a tool to the agent that returns the current date.\",\n value=True,\n ),\n BoolInput(\n name=\"add_calculator_tool\",\n display_name=\"Calculator\",\n advanced=True,\n info=(\n \"If true, adds a zero-config arithmetic calculator tool to the agent \"\n \"(safe: only +, -, *, /, ** operators via AST).\"\n ),\n value=True,\n ),\n ]\n outputs = [\n Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n Output(\n name=\"structured_response\",\n display_name=\"Structured Response\",\n method=\"json_response\",\n types=[\"Data\"],\n ),\n ]\n\n def _resolve_selected_model(self):\n \"\"\"Resolve the selected model, including legacy agent_llm/model_name inputs.\"\"\"\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n if isinstance(self.model, BaseLanguageModel):\n return self.model\n except ImportError:\n pass\n\n if isinstance(self.model, list) and self.model:\n return self.model\n\n legacy_provider = getattr(self, \"agent_llm\", None)\n legacy_model_name = getattr(self, \"model_name\", None)\n if not legacy_provider or not legacy_model_name:\n return self.model\n\n options = get_language_model_options(user_id=self.user_id)\n for option in options:\n if option.get(\"provider\") == legacy_provider and option.get(\"name\") == legacy_model_name:\n return [option]\n\n return [\n {\n \"name\": legacy_model_name,\n \"provider\": legacy_provider,\n \"metadata\": {},\n }\n ]\n\n def _get_max_tokens_value(self):\n \"\"\"Return the user-supplied max_tokens or None when unset/zero.\"\"\"\n val = getattr(self, \"max_tokens\", None)\n if val in {\"\", 0}:\n return None\n return val\n\n def _get_llm(self):\n \"\"\"Override parent to include max_tokens from the Agent's input field.\"\"\"\n return get_llm(\n model=self.model,\n user_id=self.user_id,\n api_key=getattr(self, \"api_key\", None),\n stream=bool(getattr(self, \"stream\", False)),\n max_tokens=self._get_max_tokens_value(),\n watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n watsonx_project_id=getattr(self, \"project_id\", None),\n )\n\n async def get_agent_requirements(self):\n \"\"\"Get the agent requirements for the agent.\"\"\"\n from langchain_core.tools import StructuredTool\n\n selected_model = self._resolve_selected_model()\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n is_connected_model = isinstance(selected_model, BaseLanguageModel)\n except ImportError:\n is_connected_model = False\n\n if not is_connected_model:\n validate_model_selection(selected_model)\n\n # Ensure _get_llm() uses the resolved model (e.g. from legacy agent_llm/model_name)\n self.model = selected_model\n llm_model = self._get_llm()\n if llm_model is None:\n msg = \"No language model selected. Please choose a model to proceed.\"\n raise ValueError(msg)\n\n # Get memory data\n self.chat_history = await self.get_memory_data()\n await logger.adebug(f\"Retrieved {len(self.chat_history)} chat history messages\")\n if isinstance(self.chat_history, Message):\n self.chat_history = [self.chat_history]\n\n # Add current date tool if enabled\n if self.add_current_date_tool:\n if not isinstance(self.tools, list): # type: ignore[has-type]\n self.tools = []\n current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n if not isinstance(current_date_tool, StructuredTool):\n msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n raise TypeError(msg)\n # Skip if an externally-connected tool already provides the same name.\n # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n if not any(getattr(t, \"name\", None) == current_date_tool.name for t in self.tools):\n self.tools.append(current_date_tool)\n\n # Add calculator tool if enabled (zero-config arithmetic)\n if getattr(self, \"add_calculator_tool\", False):\n if not isinstance(self.tools, list): # type: ignore[has-type]\n self.tools = []\n calculator_tool = (await CalculatorComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n if not isinstance(calculator_tool, StructuredTool):\n msg = \"CalculatorComponent must be converted to a StructuredTool\"\n raise TypeError(msg)\n # Skip if an externally-connected tool already provides the same name.\n # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n if not any(getattr(t, \"name\", None) == calculator_tool.name for t in self.tools):\n self.tools.append(calculator_tool)\n\n # Set shared callbacks for tracing the tools used by the agent\n self.set_tools_callbacks(self.tools, self._get_shared_callbacks())\n\n return llm_model, self.chat_history, self.tools\n\n def _get_resolved_model_name(self) -> str:\n \"\"\"Best-effort human-readable model name for {model_name} injection.\"\"\"\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n if isinstance(self.model, BaseLanguageModel):\n return type(self.model).__name__\n except ImportError:\n pass\n\n if isinstance(self.model, list) and self.model:\n first = self.model[0]\n if isinstance(first, dict):\n name = first.get(\"name\")\n if isinstance(name, str) and name:\n return name\n\n legacy_model_name = getattr(self, \"model_name\", None)\n if isinstance(legacy_model_name, str) and legacy_model_name:\n return legacy_model_name\n return \"\"\n\n def _inject_dynamic_prompt_values(self, prompt: str | None) -> str | None:\n \"\"\"Replace known env placeholders in the system prompt.\n\n Handles {current_date}, {model_name}, and {optional_user_context} (the\n last one ships with the structured DEFAULT_SYSTEM_PROMPT_TEMPLATE and\n is currently unused at the AgentComponent layer, so it resolves to \"\").\n Uses str.replace (not str.format) so user prompts containing literal\n braces such as JSON examples ({\"key\": 1}) never break the agent.\n \"\"\"\n if not prompt:\n return prompt\n replacements = {\n \"{current_date}\": datetime.now(tz=timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S UTC\"),\n \"{model_name}\": self._get_resolved_model_name(),\n \"{optional_user_context}\": \"\",\n }\n for placeholder, value in replacements.items():\n prompt = prompt.replace(placeholder, value)\n return prompt\n\n async def message_response(self) -> Message:\n try:\n llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n # Set up and run agent\n self.set(\n llm=llm_model,\n tools=self.tools or [],\n chat_history=self.chat_history,\n input_value=self.input_value,\n system_prompt=self._inject_dynamic_prompt_values(self.system_prompt),\n )\n agent = self.create_agent_runnable()\n result = await self.run_agent(agent)\n\n # Store result for potential JSON output\n self._agent_result = result\n\n except (ValueError, TypeError, KeyError) as e:\n await logger.aerror(f\"{type(e).__name__}: {e!s}\")\n raise\n except ExceptionWithMessageError as e:\n await logger.aerror(f\"ExceptionWithMessageError occurred: {e}\")\n raise\n # Avoid catching blind Exception; let truly unexpected exceptions propagate\n except Exception as e:\n await logger.aerror(f\"Unexpected error: {e!s}\")\n raise\n else:\n return result\n\n async def json_response(self) -> Data:\n \"\"\"Produce structured Data via native LLM structured output, with prompt-based fallback.\n\n Native path (no tools, llm has with_structured_output) bypasses the agent loop and\n returns provider-validated JSON. When tools are attached, falls back to running the\n agent with a schema-augmented system prompt and parsing the final message content.\n \"\"\"\n from lfx.components.models_and_agents.structured_output.structured_output_orchestrator import (\n orchestrate_structured_output,\n )\n\n try:\n llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n except (ValueError, TypeError) as exc:\n await logger.aerror(f\"json_response.requirements_failed: {exc}\")\n return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n injected_system_prompt = self._inject_dynamic_prompt_values(getattr(self, \"system_prompt\", \"\") or \"\") or \"\"\n format_instructions = getattr(self, \"format_instructions\", \"\") or \"\"\n output_schema = getattr(self, \"output_schema\", None) or []\n has_tools = bool(self.tools)\n\n async def _run_agent_for_fallback(augmented_prompt: str) -> str:\n self.set(\n llm=llm_model,\n tools=self.tools or [],\n chat_history=self.chat_history,\n input_value=self.input_value,\n system_prompt=augmented_prompt,\n )\n agent_runnable = self.create_agent_runnable()\n with _suppress_send_message(self):\n result = await self.run_agent(agent_runnable)\n return _extract_text_content(result)\n\n try:\n return await orchestrate_structured_output(\n llm=llm_model,\n output_schema=output_schema,\n system_prompt=injected_system_prompt,\n format_instructions=format_instructions,\n input_value=_extract_text_content(self.input_value),\n run_prompt_fallback=_run_agent_for_fallback,\n prefer_native=not has_tools,\n )\n except (\n ExceptionWithMessageError,\n ValueError,\n TypeError,\n NotImplementedError,\n AttributeError,\n ) as exc:\n await logger.aerror(f\"json_response.orchestration_failed: {exc}\")\n return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n async def get_memory_data(self):\n # TODO: This is a temporary fix to avoid message duplication. We should develop a function for this.\n messages = (\n await MemoryComponent(**self.get_base_args())\n .set(\n session_id=self.graph.session_id,\n context_id=self.context_id,\n order=\"Ascending\",\n n_messages=self.n_messages,\n )\n .retrieve_messages()\n )\n return [\n message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n ]\n\n def update_input_types(self, build_config: dotdict) -> dotdict:\n \"\"\"Update input types for all fields in build_config.\"\"\"\n for key, value in build_config.items():\n if isinstance(value, dict):\n if value.get(\"input_types\") is None:\n build_config[key][\"input_types\"] = []\n elif hasattr(value, \"input_types\") and value.input_types is None:\n value.input_types = []\n return build_config\n\n async def update_build_config(\n self,\n build_config: dotdict,\n field_value: list[dict],\n field_name: str | None = None,\n ) -> dotdict:\n # Update model options with caching (for all field changes).\n # The tool-calling constraint lives on the ModelInput's ``filters``\n # field (declared above); ``handle_model_input_update`` reads it\n # and applies the filter to both the dropdown options and the\n # sticky-default re-injection path.\n build_config = handle_model_input_update(\n component=self,\n build_config=dict(build_config),\n field_value=field_value,\n field_name=field_name,\n )\n build_config = dotdict(build_config)\n\n if field_name == \"model\":\n build_config = self.update_input_types(build_config)\n\n # Validate required keys\n default_keys = [\n \"code\",\n \"_type\",\n \"model\",\n \"tools\",\n \"input_value\",\n \"add_current_date_tool\",\n \"add_calculator_tool\",\n \"system_prompt\",\n \"agent_description\",\n \"max_iterations\",\n \"handle_parsing_errors\",\n \"verbose\",\n ]\n missing_keys = [key for key in default_keys if key not in build_config]\n if missing_keys:\n msg = f\"Missing required keys in build_config: {missing_keys}\"\n raise ValueError(msg)\n return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n async def _get_tools(self) -> list[Tool]:\n component_toolkit = get_component_toolkit()\n tools_names = self._build_tools_names()\n agent_description = self.get_tool_description()\n # TODO: Agent Description Depreciated Feature to be removed\n description = f\"{agent_description}{tools_names}\"\n\n tools = component_toolkit(component=self).get_tools(\n tool_name=\"Call_Agent\",\n tool_description=description,\n # here we do not use the shared callbacks as we are exposing the agent as a tool\n callbacks=self.get_langchain_callbacks(),\n )\n if hasattr(self, \"tools_metadata\"):\n tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n\n return tools\n" + "value": "from __future__ import annotations\n\nfrom contextlib import contextmanager\nfrom datetime import datetime, timezone\nfrom typing import TYPE_CHECKING, Any\n\nfrom lfx.components.models_and_agents.memory import MemoryComponent, aget_agent_chat_history\n\nif TYPE_CHECKING:\n from langchain_core.tools import Tool\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.agents.default_system_prompt import DEFAULT_SYSTEM_PROMPT_TEMPLATE\nfrom lfx.base.agents.events import ExceptionWithMessageError\nfrom lfx.base.constants import STREAM_INFO_TEXT\nfrom lfx.base.models.unified_models import (\n get_language_model_options,\n get_llm,\n handle_model_input_update,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.components.agentics.helpers.model_config import validate_model_selection\nfrom lfx.components.helpers import CalculatorComponent, CurrentDateComponent\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.custom.custom_component.component import get_component_toolkit\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, ModelInput, StrInput\nfrom lfx.io import IntInput, MessageTextInput, MultilineInput, Output, SecretStrInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.schema.data import Data\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.schema.table import EditMode\n\n\ndef set_advanced_true(component_input):\n component_input.advanced = True\n return component_input\n\n\ndef _extract_text_content(value) -> str:\n \"\"\"Pull a string payload from a Message-like, AIMessage-like, or string value.\"\"\"\n if isinstance(value, str):\n return value\n text = getattr(value, \"text\", None)\n if isinstance(text, str):\n return text\n content = getattr(value, \"content\", None)\n if isinstance(content, str):\n return content\n return str(value) if value is not None else \"\"\n\n\n@contextmanager\ndef _suppress_send_message(component: Any):\n \"\"\"Temporarily replace component.send_message with a no-op for the duration of the block.\n\n Used during the structured-output prompt fallback: run_agent streams the agent's\n final answer through self.send_message (correct for message_response), but in\n json_response the orchestrator parses that text into structured Data which the\n downstream Chat Output emits — leaving the original emission in place produces a\n duplicate message in the playground. The original method is always restored on exit,\n even when the wrapped call raises.\n \"\"\"\n original = component.send_message\n\n async def _noop(message, *_args, **_kwargs):\n return message\n\n component.send_message = _noop\n try:\n yield\n finally:\n component.send_message = original\n\n\nclass AgentComponent(ToolCallingAgentComponent):\n display_name: str = \"Agent\"\n description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n documentation: str = \"https://docs.langflow.org/agents\"\n icon = \"bot\"\n beta = False\n name = \"Agent\"\n\n memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n inputs = [\n ModelInput(\n name=\"model\",\n display_name=\"Language Model\",\n info=\"Select your model provider\",\n real_time_refresh=True,\n required=True,\n # Agents require tool calling — the filter is honored by\n # ``handle_model_input_update`` so models that can't run with\n # tools never reach the picker (and any saved selection that\n # no longer satisfies the constraint is auto-replaced).\n filters={\"tool_calling\": True},\n ),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"API Key\",\n info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n real_time_refresh=True,\n advanced=True,\n ),\n DropdownInput(\n name=\"base_url_ibm_watsonx\",\n display_name=\"watsonx API Endpoint\",\n info=\"The base URL of the API (IBM watsonx.ai only)\",\n options=IBM_WATSONX_URLS,\n value=IBM_WATSONX_URLS[0],\n combobox=True,\n show=False,\n real_time_refresh=True,\n ),\n StrInput(\n name=\"project_id\",\n display_name=\"watsonx Project ID\",\n info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n show=False,\n required=False,\n ),\n MultilineInput(\n name=\"system_prompt\",\n display_name=\"Agent Instructions\",\n info=(\n \"System Prompt: Initial instructions and context provided to guide the agent's behavior. \"\n \"Supports dynamic placeholders: {current_date}, {model_name}, {optional_user_context}.\"\n ),\n value=DEFAULT_SYSTEM_PROMPT_TEMPLATE,\n advanced=False,\n ),\n MessageTextInput(\n name=\"context_id\",\n display_name=\"Context ID\",\n info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n value=\"\",\n advanced=True,\n ),\n IntInput(\n name=\"n_messages\",\n display_name=\"Number of Chat History Messages\",\n value=100,\n info=\"Number of chat history messages to retrieve.\",\n advanced=True,\n show=True,\n ),\n IntInput(\n name=\"max_tokens\",\n display_name=\"Max Tokens\",\n info=\"Maximum number of tokens to generate. Field name varies by provider.\",\n advanced=True,\n range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n ),\n MultilineInput(\n name=\"format_instructions\",\n display_name=\"Output Format Instructions\",\n info=\"Generic Template for structured output formatting. Valid only with Structured response.\",\n value=(\n \"You are an AI that extracts structured JSON objects from unstructured text. \"\n \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n \"Fill missing or ambiguous values with defaults: null for missing values. \"\n \"Remove exact duplicates but keep variations that have different field values. \"\n \"Always return valid JSON in the expected format, never throw errors. \"\n \"If multiple objects can be extracted, return them all in the structured format.\"\n ),\n advanced=True,\n ),\n TableInput(\n name=\"output_schema\",\n display_name=\"Output Schema\",\n info=(\n \"Schema Validation: Define the structure and data types for structured output. \"\n \"No validation if no output schema.\"\n ),\n advanced=True,\n required=False,\n value=[],\n table_schema=[\n {\n \"name\": \"name\",\n \"display_name\": \"Name\",\n \"type\": \"str\",\n \"description\": \"Specify the name of the output field.\",\n \"default\": \"field\",\n \"edit_mode\": EditMode.INLINE,\n },\n {\n \"name\": \"description\",\n \"display_name\": \"Description\",\n \"type\": \"str\",\n \"description\": \"Describe the purpose of the output field.\",\n \"default\": \"description of field\",\n \"edit_mode\": EditMode.POPOVER,\n },\n {\n \"name\": \"type\",\n \"display_name\": \"Type\",\n \"type\": \"str\",\n \"edit_mode\": EditMode.INLINE,\n \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n \"default\": \"str\",\n },\n {\n \"name\": \"multiple\",\n \"display_name\": \"As List\",\n \"type\": \"boolean\",\n \"description\": \"Set to True if this output field should be a list of the specified type.\",\n \"default\": \"False\",\n \"edit_mode\": EditMode.INLINE,\n },\n ],\n ),\n *LCToolsAgentComponent.get_base_inputs(),\n # removed memory inputs from agent component\n # *memory_inputs,\n BoolInput(\n name=\"stream\",\n display_name=\"Stream\",\n info=STREAM_INFO_TEXT,\n value=False,\n advanced=True,\n ),\n BoolInput(\n name=\"add_current_date_tool\",\n display_name=\"Current Date\",\n advanced=True,\n info=\"If true, will add a tool to the agent that returns the current date.\",\n value=True,\n ),\n BoolInput(\n name=\"add_calculator_tool\",\n display_name=\"Calculator\",\n advanced=True,\n info=(\n \"If true, adds a zero-config arithmetic calculator tool to the agent \"\n \"(safe: only +, -, *, /, ** operators via AST).\"\n ),\n value=True,\n ),\n ]\n outputs = [\n Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n Output(\n name=\"structured_response\",\n display_name=\"Structured Response\",\n method=\"json_response\",\n types=[\"Data\"],\n ),\n ]\n\n def _resolve_selected_model(self):\n \"\"\"Resolve the selected model, including legacy agent_llm/model_name inputs.\"\"\"\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n if isinstance(self.model, BaseLanguageModel):\n return self.model\n except ImportError:\n pass\n\n if isinstance(self.model, list) and self.model:\n return self.model\n\n legacy_provider = getattr(self, \"agent_llm\", None)\n legacy_model_name = getattr(self, \"model_name\", None)\n if not legacy_provider or not legacy_model_name:\n return self.model\n\n options = get_language_model_options(user_id=self.user_id)\n for option in options:\n if option.get(\"provider\") == legacy_provider and option.get(\"name\") == legacy_model_name:\n return [option]\n\n return [\n {\n \"name\": legacy_model_name,\n \"provider\": legacy_provider,\n \"metadata\": {},\n }\n ]\n\n def _get_max_tokens_value(self):\n \"\"\"Return the user-supplied max_tokens or None when unset/zero.\"\"\"\n val = getattr(self, \"max_tokens\", None)\n if val in {\"\", 0}:\n return None\n return val\n\n def _get_llm(self):\n \"\"\"Override parent to include max_tokens from the Agent's input field.\"\"\"\n return get_llm(\n model=self.model,\n user_id=self.user_id,\n api_key=getattr(self, \"api_key\", None),\n stream=bool(getattr(self, \"stream\", False)),\n max_tokens=self._get_max_tokens_value(),\n watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n watsonx_project_id=getattr(self, \"project_id\", None),\n )\n\n async def get_agent_requirements(self):\n \"\"\"Get the agent requirements for the agent.\"\"\"\n from langchain_core.tools import StructuredTool\n\n selected_model = self._resolve_selected_model()\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n is_connected_model = isinstance(selected_model, BaseLanguageModel)\n except ImportError:\n is_connected_model = False\n\n if not is_connected_model:\n validate_model_selection(selected_model)\n\n # Ensure _get_llm() uses the resolved model (e.g. from legacy agent_llm/model_name)\n self.model = selected_model\n llm_model = self._get_llm()\n if llm_model is None:\n msg = \"No language model selected. Please choose a model to proceed.\"\n raise ValueError(msg)\n\n # Get memory data\n self.chat_history = await self.get_memory_data()\n await logger.adebug(f\"Retrieved {len(self.chat_history)} chat history messages\")\n if isinstance(self.chat_history, Message):\n self.chat_history = [self.chat_history]\n\n # Add current date tool if enabled\n if self.add_current_date_tool:\n if not isinstance(self.tools, list): # type: ignore[has-type]\n self.tools = []\n current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n if not isinstance(current_date_tool, StructuredTool):\n msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n raise TypeError(msg)\n # Skip if an externally-connected tool already provides the same name.\n # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n if not any(getattr(t, \"name\", None) == current_date_tool.name for t in self.tools):\n self.tools.append(current_date_tool)\n\n # Add calculator tool if enabled (zero-config arithmetic)\n if getattr(self, \"add_calculator_tool\", False):\n if not isinstance(self.tools, list): # type: ignore[has-type]\n self.tools = []\n calculator_tool = (await CalculatorComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n if not isinstance(calculator_tool, StructuredTool):\n msg = \"CalculatorComponent must be converted to a StructuredTool\"\n raise TypeError(msg)\n # Skip if an externally-connected tool already provides the same name.\n # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n if not any(getattr(t, \"name\", None) == calculator_tool.name for t in self.tools):\n self.tools.append(calculator_tool)\n\n # Set shared callbacks for tracing the tools used by the agent\n self.set_tools_callbacks(self.tools, self._get_shared_callbacks())\n\n return llm_model, self.chat_history, self.tools\n\n def _get_resolved_model_name(self) -> str:\n \"\"\"Best-effort human-readable model name for {model_name} injection.\"\"\"\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n if isinstance(self.model, BaseLanguageModel):\n return type(self.model).__name__\n except ImportError:\n pass\n\n if isinstance(self.model, list) and self.model:\n first = self.model[0]\n if isinstance(first, dict):\n name = first.get(\"name\")\n if isinstance(name, str) and name:\n return name\n\n legacy_model_name = getattr(self, \"model_name\", None)\n if isinstance(legacy_model_name, str) and legacy_model_name:\n return legacy_model_name\n return \"\"\n\n def _inject_dynamic_prompt_values(self, prompt: str | None) -> str | None:\n \"\"\"Replace known env placeholders in the system prompt.\n\n Handles {current_date}, {model_name}, and {optional_user_context} (the\n last one ships with the structured DEFAULT_SYSTEM_PROMPT_TEMPLATE and\n is currently unused at the AgentComponent layer, so it resolves to \"\").\n Uses str.replace (not str.format) so user prompts containing literal\n braces such as JSON examples ({\"key\": 1}) never break the agent.\n \"\"\"\n if not prompt:\n return prompt\n replacements = {\n \"{current_date}\": datetime.now(tz=timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S UTC\"),\n \"{model_name}\": self._get_resolved_model_name(),\n \"{optional_user_context}\": \"\",\n }\n for placeholder, value in replacements.items():\n prompt = prompt.replace(placeholder, value)\n return prompt\n\n async def message_response(self) -> Message:\n try:\n llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n # Set up and run agent\n self.set(\n llm=llm_model,\n tools=self.tools or [],\n chat_history=self.chat_history,\n input_value=self.input_value,\n system_prompt=self._inject_dynamic_prompt_values(self.system_prompt),\n )\n agent = self.create_agent_runnable()\n result = await self.run_agent(agent)\n\n # Store result for potential JSON output\n self._agent_result = result\n\n except (ValueError, TypeError, KeyError) as e:\n await logger.aerror(f\"{type(e).__name__}: {e!s}\")\n raise\n except ExceptionWithMessageError as e:\n await logger.aerror(f\"ExceptionWithMessageError occurred: {e}\")\n raise\n # Avoid catching blind Exception; let truly unexpected exceptions propagate\n except Exception as e:\n await logger.aerror(f\"Unexpected error: {e!s}\")\n raise\n else:\n return result\n\n async def json_response(self) -> Data:\n \"\"\"Produce structured Data via native LLM structured output, with prompt-based fallback.\n\n Native path (no tools, llm has with_structured_output) bypasses the agent loop and\n returns provider-validated JSON. When tools are attached, falls back to running the\n agent with a schema-augmented system prompt and parsing the final message content.\n \"\"\"\n from lfx.components.models_and_agents.structured_output.structured_output_orchestrator import (\n orchestrate_structured_output,\n )\n\n try:\n llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n except (ValueError, TypeError) as exc:\n await logger.aerror(f\"json_response.requirements_failed: {exc}\")\n return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n injected_system_prompt = self._inject_dynamic_prompt_values(getattr(self, \"system_prompt\", \"\") or \"\") or \"\"\n format_instructions = getattr(self, \"format_instructions\", \"\") or \"\"\n output_schema = getattr(self, \"output_schema\", None) or []\n has_tools = bool(self.tools)\n\n async def _run_agent_for_fallback(augmented_prompt: str) -> str:\n self.set(\n llm=llm_model,\n tools=self.tools or [],\n chat_history=self.chat_history,\n input_value=self.input_value,\n system_prompt=augmented_prompt,\n )\n agent_runnable = self.create_agent_runnable()\n with _suppress_send_message(self):\n result = await self.run_agent(agent_runnable)\n return _extract_text_content(result)\n\n try:\n return await orchestrate_structured_output(\n llm=llm_model,\n output_schema=output_schema,\n system_prompt=injected_system_prompt,\n format_instructions=format_instructions,\n input_value=_extract_text_content(self.input_value),\n run_prompt_fallback=_run_agent_for_fallback,\n prefer_native=not has_tools,\n )\n except (\n ExceptionWithMessageError,\n ValueError,\n TypeError,\n NotImplementedError,\n AttributeError,\n ) as exc:\n await logger.aerror(f\"json_response.orchestration_failed: {exc}\")\n return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n async def get_memory_data(self):\n # Scope by flow_id so default playground session names (e.g. \"New Session 0\")\n # cannot leak chat history across unrelated flows. See issue #13059.\n # The helper also returns [] when n_messages == 0, preserving the\n # explicit \"memory disabled\" contract from MemoryComponent.retrieve_messages.\n messages = await aget_agent_chat_history(\n session_id=self.graph.session_id,\n flow_id=getattr(self.graph, \"flow_id\", None),\n context_id=self.context_id,\n n_messages=self.n_messages,\n )\n return [\n message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n ]\n\n def update_input_types(self, build_config: dotdict) -> dotdict:\n \"\"\"Update input types for all fields in build_config.\"\"\"\n for key, value in build_config.items():\n if isinstance(value, dict):\n if value.get(\"input_types\") is None:\n build_config[key][\"input_types\"] = []\n elif hasattr(value, \"input_types\") and value.input_types is None:\n value.input_types = []\n return build_config\n\n async def update_build_config(\n self,\n build_config: dotdict,\n field_value: list[dict],\n field_name: str | None = None,\n ) -> dotdict:\n # Update model options with caching (for all field changes).\n # The tool-calling constraint lives on the ModelInput's ``filters``\n # field (declared above); ``handle_model_input_update`` reads it\n # and applies the filter to both the dropdown options and the\n # sticky-default re-injection path.\n build_config = handle_model_input_update(\n component=self,\n build_config=dict(build_config),\n field_value=field_value,\n field_name=field_name,\n )\n build_config = dotdict(build_config)\n\n if field_name == \"model\":\n build_config = self.update_input_types(build_config)\n\n # Validate required keys\n default_keys = [\n \"code\",\n \"_type\",\n \"model\",\n \"tools\",\n \"input_value\",\n \"add_current_date_tool\",\n \"add_calculator_tool\",\n \"system_prompt\",\n \"agent_description\",\n \"max_iterations\",\n \"handle_parsing_errors\",\n \"verbose\",\n ]\n missing_keys = [key for key in default_keys if key not in build_config]\n if missing_keys:\n msg = f\"Missing required keys in build_config: {missing_keys}\"\n raise ValueError(msg)\n return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n async def _get_tools(self) -> list[Tool]:\n component_toolkit = get_component_toolkit()\n tools_names = self._build_tools_names()\n agent_description = self.get_tool_description()\n # TODO: Agent Description Depreciated Feature to be removed\n description = f\"{agent_description}{tools_names}\"\n\n tools = component_toolkit(component=self).get_tools(\n tool_name=\"Call_Agent\",\n tool_description=description,\n # here we do not use the shared callbacks as we are exposing the agent as a tool\n callbacks=self.get_langchain_callbacks(),\n )\n if hasattr(self, \"tools_metadata\"):\n tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n\n return tools\n" }, "context_id": { "_input_type": "MessageTextInput", diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Research Agent.json b/src/backend/base/langflow/initial_setup/starter_projects/Research Agent.json index e260e87b47..7df866e36c 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Research Agent.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Research Agent.json @@ -2813,7 +2813,7 @@ "last_updated": "2025-12-11T21:41:48.407Z", "legacy": false, "metadata": { - "code_hash": "ebac74bfcae6", + "code_hash": "443159257161", "dependencies": { "dependencies": [ { @@ -3002,7 +3002,7 @@ "show": true, "title_case": false, "type": "code", - "value": "from __future__ import annotations\n\nfrom contextlib import contextmanager\nfrom datetime import datetime, timezone\nfrom typing import TYPE_CHECKING, Any\n\nfrom lfx.components.models_and_agents.memory import MemoryComponent\n\nif TYPE_CHECKING:\n from langchain_core.tools import Tool\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.agents.default_system_prompt import DEFAULT_SYSTEM_PROMPT_TEMPLATE\nfrom lfx.base.agents.events import ExceptionWithMessageError\nfrom lfx.base.constants import STREAM_INFO_TEXT\nfrom lfx.base.models.unified_models import (\n get_language_model_options,\n get_llm,\n handle_model_input_update,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.components.agentics.helpers.model_config import validate_model_selection\nfrom lfx.components.helpers import CalculatorComponent, CurrentDateComponent\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.custom.custom_component.component import get_component_toolkit\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, ModelInput, StrInput\nfrom lfx.io import IntInput, MessageTextInput, MultilineInput, Output, SecretStrInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.schema.data import Data\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.schema.table import EditMode\n\n\ndef set_advanced_true(component_input):\n component_input.advanced = True\n return component_input\n\n\ndef _extract_text_content(value) -> str:\n \"\"\"Pull a string payload from a Message-like, AIMessage-like, or string value.\"\"\"\n if isinstance(value, str):\n return value\n text = getattr(value, \"text\", None)\n if isinstance(text, str):\n return text\n content = getattr(value, \"content\", None)\n if isinstance(content, str):\n return content\n return str(value) if value is not None else \"\"\n\n\n@contextmanager\ndef _suppress_send_message(component: Any):\n \"\"\"Temporarily replace component.send_message with a no-op for the duration of the block.\n\n Used during the structured-output prompt fallback: run_agent streams the agent's\n final answer through self.send_message (correct for message_response), but in\n json_response the orchestrator parses that text into structured Data which the\n downstream Chat Output emits — leaving the original emission in place produces a\n duplicate message in the playground. The original method is always restored on exit,\n even when the wrapped call raises.\n \"\"\"\n original = component.send_message\n\n async def _noop(message, *_args, **_kwargs):\n return message\n\n component.send_message = _noop\n try:\n yield\n finally:\n component.send_message = original\n\n\nclass AgentComponent(ToolCallingAgentComponent):\n display_name: str = \"Agent\"\n description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n documentation: str = \"https://docs.langflow.org/agents\"\n icon = \"bot\"\n beta = False\n name = \"Agent\"\n\n memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n inputs = [\n ModelInput(\n name=\"model\",\n display_name=\"Language Model\",\n info=\"Select your model provider\",\n real_time_refresh=True,\n required=True,\n # Agents require tool calling — the filter is honored by\n # ``handle_model_input_update`` so models that can't run with\n # tools never reach the picker (and any saved selection that\n # no longer satisfies the constraint is auto-replaced).\n filters={\"tool_calling\": True},\n ),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"API Key\",\n info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n real_time_refresh=True,\n advanced=True,\n ),\n DropdownInput(\n name=\"base_url_ibm_watsonx\",\n display_name=\"watsonx API Endpoint\",\n info=\"The base URL of the API (IBM watsonx.ai only)\",\n options=IBM_WATSONX_URLS,\n value=IBM_WATSONX_URLS[0],\n combobox=True,\n show=False,\n real_time_refresh=True,\n ),\n StrInput(\n name=\"project_id\",\n display_name=\"watsonx Project ID\",\n info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n show=False,\n required=False,\n ),\n MultilineInput(\n name=\"system_prompt\",\n display_name=\"Agent Instructions\",\n info=(\n \"System Prompt: Initial instructions and context provided to guide the agent's behavior. \"\n \"Supports dynamic placeholders: {current_date}, {model_name}, {optional_user_context}.\"\n ),\n value=DEFAULT_SYSTEM_PROMPT_TEMPLATE,\n advanced=False,\n ),\n MessageTextInput(\n name=\"context_id\",\n display_name=\"Context ID\",\n info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n value=\"\",\n advanced=True,\n ),\n IntInput(\n name=\"n_messages\",\n display_name=\"Number of Chat History Messages\",\n value=100,\n info=\"Number of chat history messages to retrieve.\",\n advanced=True,\n show=True,\n ),\n IntInput(\n name=\"max_tokens\",\n display_name=\"Max Tokens\",\n info=\"Maximum number of tokens to generate. Field name varies by provider.\",\n advanced=True,\n range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n ),\n MultilineInput(\n name=\"format_instructions\",\n display_name=\"Output Format Instructions\",\n info=\"Generic Template for structured output formatting. Valid only with Structured response.\",\n value=(\n \"You are an AI that extracts structured JSON objects from unstructured text. \"\n \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n \"Fill missing or ambiguous values with defaults: null for missing values. \"\n \"Remove exact duplicates but keep variations that have different field values. \"\n \"Always return valid JSON in the expected format, never throw errors. \"\n \"If multiple objects can be extracted, return them all in the structured format.\"\n ),\n advanced=True,\n ),\n TableInput(\n name=\"output_schema\",\n display_name=\"Output Schema\",\n info=(\n \"Schema Validation: Define the structure and data types for structured output. \"\n \"No validation if no output schema.\"\n ),\n advanced=True,\n required=False,\n value=[],\n table_schema=[\n {\n \"name\": \"name\",\n \"display_name\": \"Name\",\n \"type\": \"str\",\n \"description\": \"Specify the name of the output field.\",\n \"default\": \"field\",\n \"edit_mode\": EditMode.INLINE,\n },\n {\n \"name\": \"description\",\n \"display_name\": \"Description\",\n \"type\": \"str\",\n \"description\": \"Describe the purpose of the output field.\",\n \"default\": \"description of field\",\n \"edit_mode\": EditMode.POPOVER,\n },\n {\n \"name\": \"type\",\n \"display_name\": \"Type\",\n \"type\": \"str\",\n \"edit_mode\": EditMode.INLINE,\n \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n \"default\": \"str\",\n },\n {\n \"name\": \"multiple\",\n \"display_name\": \"As List\",\n \"type\": \"boolean\",\n \"description\": \"Set to True if this output field should be a list of the specified type.\",\n \"default\": \"False\",\n \"edit_mode\": EditMode.INLINE,\n },\n ],\n ),\n *LCToolsAgentComponent.get_base_inputs(),\n # removed memory inputs from agent component\n # *memory_inputs,\n BoolInput(\n name=\"stream\",\n display_name=\"Stream\",\n info=STREAM_INFO_TEXT,\n value=False,\n advanced=True,\n ),\n BoolInput(\n name=\"add_current_date_tool\",\n display_name=\"Current Date\",\n advanced=True,\n info=\"If true, will add a tool to the agent that returns the current date.\",\n value=True,\n ),\n BoolInput(\n name=\"add_calculator_tool\",\n display_name=\"Calculator\",\n advanced=True,\n info=(\n \"If true, adds a zero-config arithmetic calculator tool to the agent \"\n \"(safe: only +, -, *, /, ** operators via AST).\"\n ),\n value=True,\n ),\n ]\n outputs = [\n Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n Output(\n name=\"structured_response\",\n display_name=\"Structured Response\",\n method=\"json_response\",\n types=[\"Data\"],\n ),\n ]\n\n def _resolve_selected_model(self):\n \"\"\"Resolve the selected model, including legacy agent_llm/model_name inputs.\"\"\"\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n if isinstance(self.model, BaseLanguageModel):\n return self.model\n except ImportError:\n pass\n\n if isinstance(self.model, list) and self.model:\n return self.model\n\n legacy_provider = getattr(self, \"agent_llm\", None)\n legacy_model_name = getattr(self, \"model_name\", None)\n if not legacy_provider or not legacy_model_name:\n return self.model\n\n options = get_language_model_options(user_id=self.user_id)\n for option in options:\n if option.get(\"provider\") == legacy_provider and option.get(\"name\") == legacy_model_name:\n return [option]\n\n return [\n {\n \"name\": legacy_model_name,\n \"provider\": legacy_provider,\n \"metadata\": {},\n }\n ]\n\n def _get_max_tokens_value(self):\n \"\"\"Return the user-supplied max_tokens or None when unset/zero.\"\"\"\n val = getattr(self, \"max_tokens\", None)\n if val in {\"\", 0}:\n return None\n return val\n\n def _get_llm(self):\n \"\"\"Override parent to include max_tokens from the Agent's input field.\"\"\"\n return get_llm(\n model=self.model,\n user_id=self.user_id,\n api_key=getattr(self, \"api_key\", None),\n stream=bool(getattr(self, \"stream\", False)),\n max_tokens=self._get_max_tokens_value(),\n watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n watsonx_project_id=getattr(self, \"project_id\", None),\n )\n\n async def get_agent_requirements(self):\n \"\"\"Get the agent requirements for the agent.\"\"\"\n from langchain_core.tools import StructuredTool\n\n selected_model = self._resolve_selected_model()\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n is_connected_model = isinstance(selected_model, BaseLanguageModel)\n except ImportError:\n is_connected_model = False\n\n if not is_connected_model:\n validate_model_selection(selected_model)\n\n # Ensure _get_llm() uses the resolved model (e.g. from legacy agent_llm/model_name)\n self.model = selected_model\n llm_model = self._get_llm()\n if llm_model is None:\n msg = \"No language model selected. Please choose a model to proceed.\"\n raise ValueError(msg)\n\n # Get memory data\n self.chat_history = await self.get_memory_data()\n await logger.adebug(f\"Retrieved {len(self.chat_history)} chat history messages\")\n if isinstance(self.chat_history, Message):\n self.chat_history = [self.chat_history]\n\n # Add current date tool if enabled\n if self.add_current_date_tool:\n if not isinstance(self.tools, list): # type: ignore[has-type]\n self.tools = []\n current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n if not isinstance(current_date_tool, StructuredTool):\n msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n raise TypeError(msg)\n # Skip if an externally-connected tool already provides the same name.\n # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n if not any(getattr(t, \"name\", None) == current_date_tool.name for t in self.tools):\n self.tools.append(current_date_tool)\n\n # Add calculator tool if enabled (zero-config arithmetic)\n if getattr(self, \"add_calculator_tool\", False):\n if not isinstance(self.tools, list): # type: ignore[has-type]\n self.tools = []\n calculator_tool = (await CalculatorComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n if not isinstance(calculator_tool, StructuredTool):\n msg = \"CalculatorComponent must be converted to a StructuredTool\"\n raise TypeError(msg)\n # Skip if an externally-connected tool already provides the same name.\n # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n if not any(getattr(t, \"name\", None) == calculator_tool.name for t in self.tools):\n self.tools.append(calculator_tool)\n\n # Set shared callbacks for tracing the tools used by the agent\n self.set_tools_callbacks(self.tools, self._get_shared_callbacks())\n\n return llm_model, self.chat_history, self.tools\n\n def _get_resolved_model_name(self) -> str:\n \"\"\"Best-effort human-readable model name for {model_name} injection.\"\"\"\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n if isinstance(self.model, BaseLanguageModel):\n return type(self.model).__name__\n except ImportError:\n pass\n\n if isinstance(self.model, list) and self.model:\n first = self.model[0]\n if isinstance(first, dict):\n name = first.get(\"name\")\n if isinstance(name, str) and name:\n return name\n\n legacy_model_name = getattr(self, \"model_name\", None)\n if isinstance(legacy_model_name, str) and legacy_model_name:\n return legacy_model_name\n return \"\"\n\n def _inject_dynamic_prompt_values(self, prompt: str | None) -> str | None:\n \"\"\"Replace known env placeholders in the system prompt.\n\n Handles {current_date}, {model_name}, and {optional_user_context} (the\n last one ships with the structured DEFAULT_SYSTEM_PROMPT_TEMPLATE and\n is currently unused at the AgentComponent layer, so it resolves to \"\").\n Uses str.replace (not str.format) so user prompts containing literal\n braces such as JSON examples ({\"key\": 1}) never break the agent.\n \"\"\"\n if not prompt:\n return prompt\n replacements = {\n \"{current_date}\": datetime.now(tz=timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S UTC\"),\n \"{model_name}\": self._get_resolved_model_name(),\n \"{optional_user_context}\": \"\",\n }\n for placeholder, value in replacements.items():\n prompt = prompt.replace(placeholder, value)\n return prompt\n\n async def message_response(self) -> Message:\n try:\n llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n # Set up and run agent\n self.set(\n llm=llm_model,\n tools=self.tools or [],\n chat_history=self.chat_history,\n input_value=self.input_value,\n system_prompt=self._inject_dynamic_prompt_values(self.system_prompt),\n )\n agent = self.create_agent_runnable()\n result = await self.run_agent(agent)\n\n # Store result for potential JSON output\n self._agent_result = result\n\n except (ValueError, TypeError, KeyError) as e:\n await logger.aerror(f\"{type(e).__name__}: {e!s}\")\n raise\n except ExceptionWithMessageError as e:\n await logger.aerror(f\"ExceptionWithMessageError occurred: {e}\")\n raise\n # Avoid catching blind Exception; let truly unexpected exceptions propagate\n except Exception as e:\n await logger.aerror(f\"Unexpected error: {e!s}\")\n raise\n else:\n return result\n\n async def json_response(self) -> Data:\n \"\"\"Produce structured Data via native LLM structured output, with prompt-based fallback.\n\n Native path (no tools, llm has with_structured_output) bypasses the agent loop and\n returns provider-validated JSON. When tools are attached, falls back to running the\n agent with a schema-augmented system prompt and parsing the final message content.\n \"\"\"\n from lfx.components.models_and_agents.structured_output.structured_output_orchestrator import (\n orchestrate_structured_output,\n )\n\n try:\n llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n except (ValueError, TypeError) as exc:\n await logger.aerror(f\"json_response.requirements_failed: {exc}\")\n return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n injected_system_prompt = self._inject_dynamic_prompt_values(getattr(self, \"system_prompt\", \"\") or \"\") or \"\"\n format_instructions = getattr(self, \"format_instructions\", \"\") or \"\"\n output_schema = getattr(self, \"output_schema\", None) or []\n has_tools = bool(self.tools)\n\n async def _run_agent_for_fallback(augmented_prompt: str) -> str:\n self.set(\n llm=llm_model,\n tools=self.tools or [],\n chat_history=self.chat_history,\n input_value=self.input_value,\n system_prompt=augmented_prompt,\n )\n agent_runnable = self.create_agent_runnable()\n with _suppress_send_message(self):\n result = await self.run_agent(agent_runnable)\n return _extract_text_content(result)\n\n try:\n return await orchestrate_structured_output(\n llm=llm_model,\n output_schema=output_schema,\n system_prompt=injected_system_prompt,\n format_instructions=format_instructions,\n input_value=_extract_text_content(self.input_value),\n run_prompt_fallback=_run_agent_for_fallback,\n prefer_native=not has_tools,\n )\n except (\n ExceptionWithMessageError,\n ValueError,\n TypeError,\n NotImplementedError,\n AttributeError,\n ) as exc:\n await logger.aerror(f\"json_response.orchestration_failed: {exc}\")\n return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n async def get_memory_data(self):\n # TODO: This is a temporary fix to avoid message duplication. We should develop a function for this.\n messages = (\n await MemoryComponent(**self.get_base_args())\n .set(\n session_id=self.graph.session_id,\n context_id=self.context_id,\n order=\"Ascending\",\n n_messages=self.n_messages,\n )\n .retrieve_messages()\n )\n return [\n message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n ]\n\n def update_input_types(self, build_config: dotdict) -> dotdict:\n \"\"\"Update input types for all fields in build_config.\"\"\"\n for key, value in build_config.items():\n if isinstance(value, dict):\n if value.get(\"input_types\") is None:\n build_config[key][\"input_types\"] = []\n elif hasattr(value, \"input_types\") and value.input_types is None:\n value.input_types = []\n return build_config\n\n async def update_build_config(\n self,\n build_config: dotdict,\n field_value: list[dict],\n field_name: str | None = None,\n ) -> dotdict:\n # Update model options with caching (for all field changes).\n # The tool-calling constraint lives on the ModelInput's ``filters``\n # field (declared above); ``handle_model_input_update`` reads it\n # and applies the filter to both the dropdown options and the\n # sticky-default re-injection path.\n build_config = handle_model_input_update(\n component=self,\n build_config=dict(build_config),\n field_value=field_value,\n field_name=field_name,\n )\n build_config = dotdict(build_config)\n\n if field_name == \"model\":\n build_config = self.update_input_types(build_config)\n\n # Validate required keys\n default_keys = [\n \"code\",\n \"_type\",\n \"model\",\n \"tools\",\n \"input_value\",\n \"add_current_date_tool\",\n \"add_calculator_tool\",\n \"system_prompt\",\n \"agent_description\",\n \"max_iterations\",\n \"handle_parsing_errors\",\n \"verbose\",\n ]\n missing_keys = [key for key in default_keys if key not in build_config]\n if missing_keys:\n msg = f\"Missing required keys in build_config: {missing_keys}\"\n raise ValueError(msg)\n return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n async def _get_tools(self) -> list[Tool]:\n component_toolkit = get_component_toolkit()\n tools_names = self._build_tools_names()\n agent_description = self.get_tool_description()\n # TODO: Agent Description Depreciated Feature to be removed\n description = f\"{agent_description}{tools_names}\"\n\n tools = component_toolkit(component=self).get_tools(\n tool_name=\"Call_Agent\",\n tool_description=description,\n # here we do not use the shared callbacks as we are exposing the agent as a tool\n callbacks=self.get_langchain_callbacks(),\n )\n if hasattr(self, \"tools_metadata\"):\n tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n\n return tools\n" + "value": "from __future__ import annotations\n\nfrom contextlib import contextmanager\nfrom datetime import datetime, timezone\nfrom typing import TYPE_CHECKING, Any\n\nfrom lfx.components.models_and_agents.memory import MemoryComponent, aget_agent_chat_history\n\nif TYPE_CHECKING:\n from langchain_core.tools import Tool\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.agents.default_system_prompt import DEFAULT_SYSTEM_PROMPT_TEMPLATE\nfrom lfx.base.agents.events import ExceptionWithMessageError\nfrom lfx.base.constants import STREAM_INFO_TEXT\nfrom lfx.base.models.unified_models import (\n get_language_model_options,\n get_llm,\n handle_model_input_update,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.components.agentics.helpers.model_config import validate_model_selection\nfrom lfx.components.helpers import CalculatorComponent, CurrentDateComponent\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.custom.custom_component.component import get_component_toolkit\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, ModelInput, StrInput\nfrom lfx.io import IntInput, MessageTextInput, MultilineInput, Output, SecretStrInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.schema.data import Data\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.schema.table import EditMode\n\n\ndef set_advanced_true(component_input):\n component_input.advanced = True\n return component_input\n\n\ndef _extract_text_content(value) -> str:\n \"\"\"Pull a string payload from a Message-like, AIMessage-like, or string value.\"\"\"\n if isinstance(value, str):\n return value\n text = getattr(value, \"text\", None)\n if isinstance(text, str):\n return text\n content = getattr(value, \"content\", None)\n if isinstance(content, str):\n return content\n return str(value) if value is not None else \"\"\n\n\n@contextmanager\ndef _suppress_send_message(component: Any):\n \"\"\"Temporarily replace component.send_message with a no-op for the duration of the block.\n\n Used during the structured-output prompt fallback: run_agent streams the agent's\n final answer through self.send_message (correct for message_response), but in\n json_response the orchestrator parses that text into structured Data which the\n downstream Chat Output emits — leaving the original emission in place produces a\n duplicate message in the playground. The original method is always restored on exit,\n even when the wrapped call raises.\n \"\"\"\n original = component.send_message\n\n async def _noop(message, *_args, **_kwargs):\n return message\n\n component.send_message = _noop\n try:\n yield\n finally:\n component.send_message = original\n\n\nclass AgentComponent(ToolCallingAgentComponent):\n display_name: str = \"Agent\"\n description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n documentation: str = \"https://docs.langflow.org/agents\"\n icon = \"bot\"\n beta = False\n name = \"Agent\"\n\n memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n inputs = [\n ModelInput(\n name=\"model\",\n display_name=\"Language Model\",\n info=\"Select your model provider\",\n real_time_refresh=True,\n required=True,\n # Agents require tool calling — the filter is honored by\n # ``handle_model_input_update`` so models that can't run with\n # tools never reach the picker (and any saved selection that\n # no longer satisfies the constraint is auto-replaced).\n filters={\"tool_calling\": True},\n ),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"API Key\",\n info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n real_time_refresh=True,\n advanced=True,\n ),\n DropdownInput(\n name=\"base_url_ibm_watsonx\",\n display_name=\"watsonx API Endpoint\",\n info=\"The base URL of the API (IBM watsonx.ai only)\",\n options=IBM_WATSONX_URLS,\n value=IBM_WATSONX_URLS[0],\n combobox=True,\n show=False,\n real_time_refresh=True,\n ),\n StrInput(\n name=\"project_id\",\n display_name=\"watsonx Project ID\",\n info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n show=False,\n required=False,\n ),\n MultilineInput(\n name=\"system_prompt\",\n display_name=\"Agent Instructions\",\n info=(\n \"System Prompt: Initial instructions and context provided to guide the agent's behavior. \"\n \"Supports dynamic placeholders: {current_date}, {model_name}, {optional_user_context}.\"\n ),\n value=DEFAULT_SYSTEM_PROMPT_TEMPLATE,\n advanced=False,\n ),\n MessageTextInput(\n name=\"context_id\",\n display_name=\"Context ID\",\n info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n value=\"\",\n advanced=True,\n ),\n IntInput(\n name=\"n_messages\",\n display_name=\"Number of Chat History Messages\",\n value=100,\n info=\"Number of chat history messages to retrieve.\",\n advanced=True,\n show=True,\n ),\n IntInput(\n name=\"max_tokens\",\n display_name=\"Max Tokens\",\n info=\"Maximum number of tokens to generate. Field name varies by provider.\",\n advanced=True,\n range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n ),\n MultilineInput(\n name=\"format_instructions\",\n display_name=\"Output Format Instructions\",\n info=\"Generic Template for structured output formatting. Valid only with Structured response.\",\n value=(\n \"You are an AI that extracts structured JSON objects from unstructured text. \"\n \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n \"Fill missing or ambiguous values with defaults: null for missing values. \"\n \"Remove exact duplicates but keep variations that have different field values. \"\n \"Always return valid JSON in the expected format, never throw errors. \"\n \"If multiple objects can be extracted, return them all in the structured format.\"\n ),\n advanced=True,\n ),\n TableInput(\n name=\"output_schema\",\n display_name=\"Output Schema\",\n info=(\n \"Schema Validation: Define the structure and data types for structured output. \"\n \"No validation if no output schema.\"\n ),\n advanced=True,\n required=False,\n value=[],\n table_schema=[\n {\n \"name\": \"name\",\n \"display_name\": \"Name\",\n \"type\": \"str\",\n \"description\": \"Specify the name of the output field.\",\n \"default\": \"field\",\n \"edit_mode\": EditMode.INLINE,\n },\n {\n \"name\": \"description\",\n \"display_name\": \"Description\",\n \"type\": \"str\",\n \"description\": \"Describe the purpose of the output field.\",\n \"default\": \"description of field\",\n \"edit_mode\": EditMode.POPOVER,\n },\n {\n \"name\": \"type\",\n \"display_name\": \"Type\",\n \"type\": \"str\",\n \"edit_mode\": EditMode.INLINE,\n \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n \"default\": \"str\",\n },\n {\n \"name\": \"multiple\",\n \"display_name\": \"As List\",\n \"type\": \"boolean\",\n \"description\": \"Set to True if this output field should be a list of the specified type.\",\n \"default\": \"False\",\n \"edit_mode\": EditMode.INLINE,\n },\n ],\n ),\n *LCToolsAgentComponent.get_base_inputs(),\n # removed memory inputs from agent component\n # *memory_inputs,\n BoolInput(\n name=\"stream\",\n display_name=\"Stream\",\n info=STREAM_INFO_TEXT,\n value=False,\n advanced=True,\n ),\n BoolInput(\n name=\"add_current_date_tool\",\n display_name=\"Current Date\",\n advanced=True,\n info=\"If true, will add a tool to the agent that returns the current date.\",\n value=True,\n ),\n BoolInput(\n name=\"add_calculator_tool\",\n display_name=\"Calculator\",\n advanced=True,\n info=(\n \"If true, adds a zero-config arithmetic calculator tool to the agent \"\n \"(safe: only +, -, *, /, ** operators via AST).\"\n ),\n value=True,\n ),\n ]\n outputs = [\n Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n Output(\n name=\"structured_response\",\n display_name=\"Structured Response\",\n method=\"json_response\",\n types=[\"Data\"],\n ),\n ]\n\n def _resolve_selected_model(self):\n \"\"\"Resolve the selected model, including legacy agent_llm/model_name inputs.\"\"\"\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n if isinstance(self.model, BaseLanguageModel):\n return self.model\n except ImportError:\n pass\n\n if isinstance(self.model, list) and self.model:\n return self.model\n\n legacy_provider = getattr(self, \"agent_llm\", None)\n legacy_model_name = getattr(self, \"model_name\", None)\n if not legacy_provider or not legacy_model_name:\n return self.model\n\n options = get_language_model_options(user_id=self.user_id)\n for option in options:\n if option.get(\"provider\") == legacy_provider and option.get(\"name\") == legacy_model_name:\n return [option]\n\n return [\n {\n \"name\": legacy_model_name,\n \"provider\": legacy_provider,\n \"metadata\": {},\n }\n ]\n\n def _get_max_tokens_value(self):\n \"\"\"Return the user-supplied max_tokens or None when unset/zero.\"\"\"\n val = getattr(self, \"max_tokens\", None)\n if val in {\"\", 0}:\n return None\n return val\n\n def _get_llm(self):\n \"\"\"Override parent to include max_tokens from the Agent's input field.\"\"\"\n return get_llm(\n model=self.model,\n user_id=self.user_id,\n api_key=getattr(self, \"api_key\", None),\n stream=bool(getattr(self, \"stream\", False)),\n max_tokens=self._get_max_tokens_value(),\n watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n watsonx_project_id=getattr(self, \"project_id\", None),\n )\n\n async def get_agent_requirements(self):\n \"\"\"Get the agent requirements for the agent.\"\"\"\n from langchain_core.tools import StructuredTool\n\n selected_model = self._resolve_selected_model()\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n is_connected_model = isinstance(selected_model, BaseLanguageModel)\n except ImportError:\n is_connected_model = False\n\n if not is_connected_model:\n validate_model_selection(selected_model)\n\n # Ensure _get_llm() uses the resolved model (e.g. from legacy agent_llm/model_name)\n self.model = selected_model\n llm_model = self._get_llm()\n if llm_model is None:\n msg = \"No language model selected. Please choose a model to proceed.\"\n raise ValueError(msg)\n\n # Get memory data\n self.chat_history = await self.get_memory_data()\n await logger.adebug(f\"Retrieved {len(self.chat_history)} chat history messages\")\n if isinstance(self.chat_history, Message):\n self.chat_history = [self.chat_history]\n\n # Add current date tool if enabled\n if self.add_current_date_tool:\n if not isinstance(self.tools, list): # type: ignore[has-type]\n self.tools = []\n current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n if not isinstance(current_date_tool, StructuredTool):\n msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n raise TypeError(msg)\n # Skip if an externally-connected tool already provides the same name.\n # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n if not any(getattr(t, \"name\", None) == current_date_tool.name for t in self.tools):\n self.tools.append(current_date_tool)\n\n # Add calculator tool if enabled (zero-config arithmetic)\n if getattr(self, \"add_calculator_tool\", False):\n if not isinstance(self.tools, list): # type: ignore[has-type]\n self.tools = []\n calculator_tool = (await CalculatorComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n if not isinstance(calculator_tool, StructuredTool):\n msg = \"CalculatorComponent must be converted to a StructuredTool\"\n raise TypeError(msg)\n # Skip if an externally-connected tool already provides the same name.\n # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n if not any(getattr(t, \"name\", None) == calculator_tool.name for t in self.tools):\n self.tools.append(calculator_tool)\n\n # Set shared callbacks for tracing the tools used by the agent\n self.set_tools_callbacks(self.tools, self._get_shared_callbacks())\n\n return llm_model, self.chat_history, self.tools\n\n def _get_resolved_model_name(self) -> str:\n \"\"\"Best-effort human-readable model name for {model_name} injection.\"\"\"\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n if isinstance(self.model, BaseLanguageModel):\n return type(self.model).__name__\n except ImportError:\n pass\n\n if isinstance(self.model, list) and self.model:\n first = self.model[0]\n if isinstance(first, dict):\n name = first.get(\"name\")\n if isinstance(name, str) and name:\n return name\n\n legacy_model_name = getattr(self, \"model_name\", None)\n if isinstance(legacy_model_name, str) and legacy_model_name:\n return legacy_model_name\n return \"\"\n\n def _inject_dynamic_prompt_values(self, prompt: str | None) -> str | None:\n \"\"\"Replace known env placeholders in the system prompt.\n\n Handles {current_date}, {model_name}, and {optional_user_context} (the\n last one ships with the structured DEFAULT_SYSTEM_PROMPT_TEMPLATE and\n is currently unused at the AgentComponent layer, so it resolves to \"\").\n Uses str.replace (not str.format) so user prompts containing literal\n braces such as JSON examples ({\"key\": 1}) never break the agent.\n \"\"\"\n if not prompt:\n return prompt\n replacements = {\n \"{current_date}\": datetime.now(tz=timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S UTC\"),\n \"{model_name}\": self._get_resolved_model_name(),\n \"{optional_user_context}\": \"\",\n }\n for placeholder, value in replacements.items():\n prompt = prompt.replace(placeholder, value)\n return prompt\n\n async def message_response(self) -> Message:\n try:\n llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n # Set up and run agent\n self.set(\n llm=llm_model,\n tools=self.tools or [],\n chat_history=self.chat_history,\n input_value=self.input_value,\n system_prompt=self._inject_dynamic_prompt_values(self.system_prompt),\n )\n agent = self.create_agent_runnable()\n result = await self.run_agent(agent)\n\n # Store result for potential JSON output\n self._agent_result = result\n\n except (ValueError, TypeError, KeyError) as e:\n await logger.aerror(f\"{type(e).__name__}: {e!s}\")\n raise\n except ExceptionWithMessageError as e:\n await logger.aerror(f\"ExceptionWithMessageError occurred: {e}\")\n raise\n # Avoid catching blind Exception; let truly unexpected exceptions propagate\n except Exception as e:\n await logger.aerror(f\"Unexpected error: {e!s}\")\n raise\n else:\n return result\n\n async def json_response(self) -> Data:\n \"\"\"Produce structured Data via native LLM structured output, with prompt-based fallback.\n\n Native path (no tools, llm has with_structured_output) bypasses the agent loop and\n returns provider-validated JSON. When tools are attached, falls back to running the\n agent with a schema-augmented system prompt and parsing the final message content.\n \"\"\"\n from lfx.components.models_and_agents.structured_output.structured_output_orchestrator import (\n orchestrate_structured_output,\n )\n\n try:\n llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n except (ValueError, TypeError) as exc:\n await logger.aerror(f\"json_response.requirements_failed: {exc}\")\n return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n injected_system_prompt = self._inject_dynamic_prompt_values(getattr(self, \"system_prompt\", \"\") or \"\") or \"\"\n format_instructions = getattr(self, \"format_instructions\", \"\") or \"\"\n output_schema = getattr(self, \"output_schema\", None) or []\n has_tools = bool(self.tools)\n\n async def _run_agent_for_fallback(augmented_prompt: str) -> str:\n self.set(\n llm=llm_model,\n tools=self.tools or [],\n chat_history=self.chat_history,\n input_value=self.input_value,\n system_prompt=augmented_prompt,\n )\n agent_runnable = self.create_agent_runnable()\n with _suppress_send_message(self):\n result = await self.run_agent(agent_runnable)\n return _extract_text_content(result)\n\n try:\n return await orchestrate_structured_output(\n llm=llm_model,\n output_schema=output_schema,\n system_prompt=injected_system_prompt,\n format_instructions=format_instructions,\n input_value=_extract_text_content(self.input_value),\n run_prompt_fallback=_run_agent_for_fallback,\n prefer_native=not has_tools,\n )\n except (\n ExceptionWithMessageError,\n ValueError,\n TypeError,\n NotImplementedError,\n AttributeError,\n ) as exc:\n await logger.aerror(f\"json_response.orchestration_failed: {exc}\")\n return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n async def get_memory_data(self):\n # Scope by flow_id so default playground session names (e.g. \"New Session 0\")\n # cannot leak chat history across unrelated flows. See issue #13059.\n # The helper also returns [] when n_messages == 0, preserving the\n # explicit \"memory disabled\" contract from MemoryComponent.retrieve_messages.\n messages = await aget_agent_chat_history(\n session_id=self.graph.session_id,\n flow_id=getattr(self.graph, \"flow_id\", None),\n context_id=self.context_id,\n n_messages=self.n_messages,\n )\n return [\n message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n ]\n\n def update_input_types(self, build_config: dotdict) -> dotdict:\n \"\"\"Update input types for all fields in build_config.\"\"\"\n for key, value in build_config.items():\n if isinstance(value, dict):\n if value.get(\"input_types\") is None:\n build_config[key][\"input_types\"] = []\n elif hasattr(value, \"input_types\") and value.input_types is None:\n value.input_types = []\n return build_config\n\n async def update_build_config(\n self,\n build_config: dotdict,\n field_value: list[dict],\n field_name: str | None = None,\n ) -> dotdict:\n # Update model options with caching (for all field changes).\n # The tool-calling constraint lives on the ModelInput's ``filters``\n # field (declared above); ``handle_model_input_update`` reads it\n # and applies the filter to both the dropdown options and the\n # sticky-default re-injection path.\n build_config = handle_model_input_update(\n component=self,\n build_config=dict(build_config),\n field_value=field_value,\n field_name=field_name,\n )\n build_config = dotdict(build_config)\n\n if field_name == \"model\":\n build_config = self.update_input_types(build_config)\n\n # Validate required keys\n default_keys = [\n \"code\",\n \"_type\",\n \"model\",\n \"tools\",\n \"input_value\",\n \"add_current_date_tool\",\n \"add_calculator_tool\",\n \"system_prompt\",\n \"agent_description\",\n \"max_iterations\",\n \"handle_parsing_errors\",\n \"verbose\",\n ]\n missing_keys = [key for key in default_keys if key not in build_config]\n if missing_keys:\n msg = f\"Missing required keys in build_config: {missing_keys}\"\n raise ValueError(msg)\n return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n async def _get_tools(self) -> list[Tool]:\n component_toolkit = get_component_toolkit()\n tools_names = self._build_tools_names()\n agent_description = self.get_tool_description()\n # TODO: Agent Description Depreciated Feature to be removed\n description = f\"{agent_description}{tools_names}\"\n\n tools = component_toolkit(component=self).get_tools(\n tool_name=\"Call_Agent\",\n tool_description=description,\n # here we do not use the shared callbacks as we are exposing the agent as a tool\n callbacks=self.get_langchain_callbacks(),\n )\n if hasattr(self, \"tools_metadata\"):\n tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n\n return tools\n" }, "context_id": { "_input_type": "MessageTextInput", diff --git a/src/backend/base/langflow/initial_setup/starter_projects/SaaS Pricing.json b/src/backend/base/langflow/initial_setup/starter_projects/SaaS Pricing.json index 17c4ed4fc9..183b952717 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/SaaS Pricing.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/SaaS Pricing.json @@ -895,7 +895,7 @@ "last_updated": "2025-12-11T21:41:48.407Z", "legacy": false, "metadata": { - "code_hash": "ebac74bfcae6", + "code_hash": "443159257161", "dependencies": { "dependencies": [ { @@ -1084,7 +1084,7 @@ "show": true, "title_case": false, "type": "code", - "value": "from __future__ import annotations\n\nfrom contextlib import contextmanager\nfrom datetime import datetime, timezone\nfrom typing import TYPE_CHECKING, Any\n\nfrom lfx.components.models_and_agents.memory import MemoryComponent\n\nif TYPE_CHECKING:\n from langchain_core.tools import Tool\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.agents.default_system_prompt import DEFAULT_SYSTEM_PROMPT_TEMPLATE\nfrom lfx.base.agents.events import ExceptionWithMessageError\nfrom lfx.base.constants import STREAM_INFO_TEXT\nfrom lfx.base.models.unified_models import (\n get_language_model_options,\n get_llm,\n handle_model_input_update,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.components.agentics.helpers.model_config import validate_model_selection\nfrom lfx.components.helpers import CalculatorComponent, CurrentDateComponent\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.custom.custom_component.component import get_component_toolkit\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, ModelInput, StrInput\nfrom lfx.io import IntInput, MessageTextInput, MultilineInput, Output, SecretStrInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.schema.data import Data\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.schema.table import EditMode\n\n\ndef set_advanced_true(component_input):\n component_input.advanced = True\n return component_input\n\n\ndef _extract_text_content(value) -> str:\n \"\"\"Pull a string payload from a Message-like, AIMessage-like, or string value.\"\"\"\n if isinstance(value, str):\n return value\n text = getattr(value, \"text\", None)\n if isinstance(text, str):\n return text\n content = getattr(value, \"content\", None)\n if isinstance(content, str):\n return content\n return str(value) if value is not None else \"\"\n\n\n@contextmanager\ndef _suppress_send_message(component: Any):\n \"\"\"Temporarily replace component.send_message with a no-op for the duration of the block.\n\n Used during the structured-output prompt fallback: run_agent streams the agent's\n final answer through self.send_message (correct for message_response), but in\n json_response the orchestrator parses that text into structured Data which the\n downstream Chat Output emits — leaving the original emission in place produces a\n duplicate message in the playground. The original method is always restored on exit,\n even when the wrapped call raises.\n \"\"\"\n original = component.send_message\n\n async def _noop(message, *_args, **_kwargs):\n return message\n\n component.send_message = _noop\n try:\n yield\n finally:\n component.send_message = original\n\n\nclass AgentComponent(ToolCallingAgentComponent):\n display_name: str = \"Agent\"\n description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n documentation: str = \"https://docs.langflow.org/agents\"\n icon = \"bot\"\n beta = False\n name = \"Agent\"\n\n memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n inputs = [\n ModelInput(\n name=\"model\",\n display_name=\"Language Model\",\n info=\"Select your model provider\",\n real_time_refresh=True,\n required=True,\n # Agents require tool calling — the filter is honored by\n # ``handle_model_input_update`` so models that can't run with\n # tools never reach the picker (and any saved selection that\n # no longer satisfies the constraint is auto-replaced).\n filters={\"tool_calling\": True},\n ),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"API Key\",\n info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n real_time_refresh=True,\n advanced=True,\n ),\n DropdownInput(\n name=\"base_url_ibm_watsonx\",\n display_name=\"watsonx API Endpoint\",\n info=\"The base URL of the API (IBM watsonx.ai only)\",\n options=IBM_WATSONX_URLS,\n value=IBM_WATSONX_URLS[0],\n combobox=True,\n show=False,\n real_time_refresh=True,\n ),\n StrInput(\n name=\"project_id\",\n display_name=\"watsonx Project ID\",\n info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n show=False,\n required=False,\n ),\n MultilineInput(\n name=\"system_prompt\",\n display_name=\"Agent Instructions\",\n info=(\n \"System Prompt: Initial instructions and context provided to guide the agent's behavior. \"\n \"Supports dynamic placeholders: {current_date}, {model_name}, {optional_user_context}.\"\n ),\n value=DEFAULT_SYSTEM_PROMPT_TEMPLATE,\n advanced=False,\n ),\n MessageTextInput(\n name=\"context_id\",\n display_name=\"Context ID\",\n info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n value=\"\",\n advanced=True,\n ),\n IntInput(\n name=\"n_messages\",\n display_name=\"Number of Chat History Messages\",\n value=100,\n info=\"Number of chat history messages to retrieve.\",\n advanced=True,\n show=True,\n ),\n IntInput(\n name=\"max_tokens\",\n display_name=\"Max Tokens\",\n info=\"Maximum number of tokens to generate. Field name varies by provider.\",\n advanced=True,\n range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n ),\n MultilineInput(\n name=\"format_instructions\",\n display_name=\"Output Format Instructions\",\n info=\"Generic Template for structured output formatting. Valid only with Structured response.\",\n value=(\n \"You are an AI that extracts structured JSON objects from unstructured text. \"\n \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n \"Fill missing or ambiguous values with defaults: null for missing values. \"\n \"Remove exact duplicates but keep variations that have different field values. \"\n \"Always return valid JSON in the expected format, never throw errors. \"\n \"If multiple objects can be extracted, return them all in the structured format.\"\n ),\n advanced=True,\n ),\n TableInput(\n name=\"output_schema\",\n display_name=\"Output Schema\",\n info=(\n \"Schema Validation: Define the structure and data types for structured output. \"\n \"No validation if no output schema.\"\n ),\n advanced=True,\n required=False,\n value=[],\n table_schema=[\n {\n \"name\": \"name\",\n \"display_name\": \"Name\",\n \"type\": \"str\",\n \"description\": \"Specify the name of the output field.\",\n \"default\": \"field\",\n \"edit_mode\": EditMode.INLINE,\n },\n {\n \"name\": \"description\",\n \"display_name\": \"Description\",\n \"type\": \"str\",\n \"description\": \"Describe the purpose of the output field.\",\n \"default\": \"description of field\",\n \"edit_mode\": EditMode.POPOVER,\n },\n {\n \"name\": \"type\",\n \"display_name\": \"Type\",\n \"type\": \"str\",\n \"edit_mode\": EditMode.INLINE,\n \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n \"default\": \"str\",\n },\n {\n \"name\": \"multiple\",\n \"display_name\": \"As List\",\n \"type\": \"boolean\",\n \"description\": \"Set to True if this output field should be a list of the specified type.\",\n \"default\": \"False\",\n \"edit_mode\": EditMode.INLINE,\n },\n ],\n ),\n *LCToolsAgentComponent.get_base_inputs(),\n # removed memory inputs from agent component\n # *memory_inputs,\n BoolInput(\n name=\"stream\",\n display_name=\"Stream\",\n info=STREAM_INFO_TEXT,\n value=False,\n advanced=True,\n ),\n BoolInput(\n name=\"add_current_date_tool\",\n display_name=\"Current Date\",\n advanced=True,\n info=\"If true, will add a tool to the agent that returns the current date.\",\n value=True,\n ),\n BoolInput(\n name=\"add_calculator_tool\",\n display_name=\"Calculator\",\n advanced=True,\n info=(\n \"If true, adds a zero-config arithmetic calculator tool to the agent \"\n \"(safe: only +, -, *, /, ** operators via AST).\"\n ),\n value=True,\n ),\n ]\n outputs = [\n Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n Output(\n name=\"structured_response\",\n display_name=\"Structured Response\",\n method=\"json_response\",\n types=[\"Data\"],\n ),\n ]\n\n def _resolve_selected_model(self):\n \"\"\"Resolve the selected model, including legacy agent_llm/model_name inputs.\"\"\"\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n if isinstance(self.model, BaseLanguageModel):\n return self.model\n except ImportError:\n pass\n\n if isinstance(self.model, list) and self.model:\n return self.model\n\n legacy_provider = getattr(self, \"agent_llm\", None)\n legacy_model_name = getattr(self, \"model_name\", None)\n if not legacy_provider or not legacy_model_name:\n return self.model\n\n options = get_language_model_options(user_id=self.user_id)\n for option in options:\n if option.get(\"provider\") == legacy_provider and option.get(\"name\") == legacy_model_name:\n return [option]\n\n return [\n {\n \"name\": legacy_model_name,\n \"provider\": legacy_provider,\n \"metadata\": {},\n }\n ]\n\n def _get_max_tokens_value(self):\n \"\"\"Return the user-supplied max_tokens or None when unset/zero.\"\"\"\n val = getattr(self, \"max_tokens\", None)\n if val in {\"\", 0}:\n return None\n return val\n\n def _get_llm(self):\n \"\"\"Override parent to include max_tokens from the Agent's input field.\"\"\"\n return get_llm(\n model=self.model,\n user_id=self.user_id,\n api_key=getattr(self, \"api_key\", None),\n stream=bool(getattr(self, \"stream\", False)),\n max_tokens=self._get_max_tokens_value(),\n watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n watsonx_project_id=getattr(self, \"project_id\", None),\n )\n\n async def get_agent_requirements(self):\n \"\"\"Get the agent requirements for the agent.\"\"\"\n from langchain_core.tools import StructuredTool\n\n selected_model = self._resolve_selected_model()\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n is_connected_model = isinstance(selected_model, BaseLanguageModel)\n except ImportError:\n is_connected_model = False\n\n if not is_connected_model:\n validate_model_selection(selected_model)\n\n # Ensure _get_llm() uses the resolved model (e.g. from legacy agent_llm/model_name)\n self.model = selected_model\n llm_model = self._get_llm()\n if llm_model is None:\n msg = \"No language model selected. Please choose a model to proceed.\"\n raise ValueError(msg)\n\n # Get memory data\n self.chat_history = await self.get_memory_data()\n await logger.adebug(f\"Retrieved {len(self.chat_history)} chat history messages\")\n if isinstance(self.chat_history, Message):\n self.chat_history = [self.chat_history]\n\n # Add current date tool if enabled\n if self.add_current_date_tool:\n if not isinstance(self.tools, list): # type: ignore[has-type]\n self.tools = []\n current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n if not isinstance(current_date_tool, StructuredTool):\n msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n raise TypeError(msg)\n # Skip if an externally-connected tool already provides the same name.\n # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n if not any(getattr(t, \"name\", None) == current_date_tool.name for t in self.tools):\n self.tools.append(current_date_tool)\n\n # Add calculator tool if enabled (zero-config arithmetic)\n if getattr(self, \"add_calculator_tool\", False):\n if not isinstance(self.tools, list): # type: ignore[has-type]\n self.tools = []\n calculator_tool = (await CalculatorComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n if not isinstance(calculator_tool, StructuredTool):\n msg = \"CalculatorComponent must be converted to a StructuredTool\"\n raise TypeError(msg)\n # Skip if an externally-connected tool already provides the same name.\n # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n if not any(getattr(t, \"name\", None) == calculator_tool.name for t in self.tools):\n self.tools.append(calculator_tool)\n\n # Set shared callbacks for tracing the tools used by the agent\n self.set_tools_callbacks(self.tools, self._get_shared_callbacks())\n\n return llm_model, self.chat_history, self.tools\n\n def _get_resolved_model_name(self) -> str:\n \"\"\"Best-effort human-readable model name for {model_name} injection.\"\"\"\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n if isinstance(self.model, BaseLanguageModel):\n return type(self.model).__name__\n except ImportError:\n pass\n\n if isinstance(self.model, list) and self.model:\n first = self.model[0]\n if isinstance(first, dict):\n name = first.get(\"name\")\n if isinstance(name, str) and name:\n return name\n\n legacy_model_name = getattr(self, \"model_name\", None)\n if isinstance(legacy_model_name, str) and legacy_model_name:\n return legacy_model_name\n return \"\"\n\n def _inject_dynamic_prompt_values(self, prompt: str | None) -> str | None:\n \"\"\"Replace known env placeholders in the system prompt.\n\n Handles {current_date}, {model_name}, and {optional_user_context} (the\n last one ships with the structured DEFAULT_SYSTEM_PROMPT_TEMPLATE and\n is currently unused at the AgentComponent layer, so it resolves to \"\").\n Uses str.replace (not str.format) so user prompts containing literal\n braces such as JSON examples ({\"key\": 1}) never break the agent.\n \"\"\"\n if not prompt:\n return prompt\n replacements = {\n \"{current_date}\": datetime.now(tz=timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S UTC\"),\n \"{model_name}\": self._get_resolved_model_name(),\n \"{optional_user_context}\": \"\",\n }\n for placeholder, value in replacements.items():\n prompt = prompt.replace(placeholder, value)\n return prompt\n\n async def message_response(self) -> Message:\n try:\n llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n # Set up and run agent\n self.set(\n llm=llm_model,\n tools=self.tools or [],\n chat_history=self.chat_history,\n input_value=self.input_value,\n system_prompt=self._inject_dynamic_prompt_values(self.system_prompt),\n )\n agent = self.create_agent_runnable()\n result = await self.run_agent(agent)\n\n # Store result for potential JSON output\n self._agent_result = result\n\n except (ValueError, TypeError, KeyError) as e:\n await logger.aerror(f\"{type(e).__name__}: {e!s}\")\n raise\n except ExceptionWithMessageError as e:\n await logger.aerror(f\"ExceptionWithMessageError occurred: {e}\")\n raise\n # Avoid catching blind Exception; let truly unexpected exceptions propagate\n except Exception as e:\n await logger.aerror(f\"Unexpected error: {e!s}\")\n raise\n else:\n return result\n\n async def json_response(self) -> Data:\n \"\"\"Produce structured Data via native LLM structured output, with prompt-based fallback.\n\n Native path (no tools, llm has with_structured_output) bypasses the agent loop and\n returns provider-validated JSON. When tools are attached, falls back to running the\n agent with a schema-augmented system prompt and parsing the final message content.\n \"\"\"\n from lfx.components.models_and_agents.structured_output.structured_output_orchestrator import (\n orchestrate_structured_output,\n )\n\n try:\n llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n except (ValueError, TypeError) as exc:\n await logger.aerror(f\"json_response.requirements_failed: {exc}\")\n return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n injected_system_prompt = self._inject_dynamic_prompt_values(getattr(self, \"system_prompt\", \"\") or \"\") or \"\"\n format_instructions = getattr(self, \"format_instructions\", \"\") or \"\"\n output_schema = getattr(self, \"output_schema\", None) or []\n has_tools = bool(self.tools)\n\n async def _run_agent_for_fallback(augmented_prompt: str) -> str:\n self.set(\n llm=llm_model,\n tools=self.tools or [],\n chat_history=self.chat_history,\n input_value=self.input_value,\n system_prompt=augmented_prompt,\n )\n agent_runnable = self.create_agent_runnable()\n with _suppress_send_message(self):\n result = await self.run_agent(agent_runnable)\n return _extract_text_content(result)\n\n try:\n return await orchestrate_structured_output(\n llm=llm_model,\n output_schema=output_schema,\n system_prompt=injected_system_prompt,\n format_instructions=format_instructions,\n input_value=_extract_text_content(self.input_value),\n run_prompt_fallback=_run_agent_for_fallback,\n prefer_native=not has_tools,\n )\n except (\n ExceptionWithMessageError,\n ValueError,\n TypeError,\n NotImplementedError,\n AttributeError,\n ) as exc:\n await logger.aerror(f\"json_response.orchestration_failed: {exc}\")\n return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n async def get_memory_data(self):\n # TODO: This is a temporary fix to avoid message duplication. We should develop a function for this.\n messages = (\n await MemoryComponent(**self.get_base_args())\n .set(\n session_id=self.graph.session_id,\n context_id=self.context_id,\n order=\"Ascending\",\n n_messages=self.n_messages,\n )\n .retrieve_messages()\n )\n return [\n message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n ]\n\n def update_input_types(self, build_config: dotdict) -> dotdict:\n \"\"\"Update input types for all fields in build_config.\"\"\"\n for key, value in build_config.items():\n if isinstance(value, dict):\n if value.get(\"input_types\") is None:\n build_config[key][\"input_types\"] = []\n elif hasattr(value, \"input_types\") and value.input_types is None:\n value.input_types = []\n return build_config\n\n async def update_build_config(\n self,\n build_config: dotdict,\n field_value: list[dict],\n field_name: str | None = None,\n ) -> dotdict:\n # Update model options with caching (for all field changes).\n # The tool-calling constraint lives on the ModelInput's ``filters``\n # field (declared above); ``handle_model_input_update`` reads it\n # and applies the filter to both the dropdown options and the\n # sticky-default re-injection path.\n build_config = handle_model_input_update(\n component=self,\n build_config=dict(build_config),\n field_value=field_value,\n field_name=field_name,\n )\n build_config = dotdict(build_config)\n\n if field_name == \"model\":\n build_config = self.update_input_types(build_config)\n\n # Validate required keys\n default_keys = [\n \"code\",\n \"_type\",\n \"model\",\n \"tools\",\n \"input_value\",\n \"add_current_date_tool\",\n \"add_calculator_tool\",\n \"system_prompt\",\n \"agent_description\",\n \"max_iterations\",\n \"handle_parsing_errors\",\n \"verbose\",\n ]\n missing_keys = [key for key in default_keys if key not in build_config]\n if missing_keys:\n msg = f\"Missing required keys in build_config: {missing_keys}\"\n raise ValueError(msg)\n return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n async def _get_tools(self) -> list[Tool]:\n component_toolkit = get_component_toolkit()\n tools_names = self._build_tools_names()\n agent_description = self.get_tool_description()\n # TODO: Agent Description Depreciated Feature to be removed\n description = f\"{agent_description}{tools_names}\"\n\n tools = component_toolkit(component=self).get_tools(\n tool_name=\"Call_Agent\",\n tool_description=description,\n # here we do not use the shared callbacks as we are exposing the agent as a tool\n callbacks=self.get_langchain_callbacks(),\n )\n if hasattr(self, \"tools_metadata\"):\n tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n\n return tools\n" + "value": "from __future__ import annotations\n\nfrom contextlib import contextmanager\nfrom datetime import datetime, timezone\nfrom typing import TYPE_CHECKING, Any\n\nfrom lfx.components.models_and_agents.memory import MemoryComponent, aget_agent_chat_history\n\nif TYPE_CHECKING:\n from langchain_core.tools import Tool\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.agents.default_system_prompt import DEFAULT_SYSTEM_PROMPT_TEMPLATE\nfrom lfx.base.agents.events import ExceptionWithMessageError\nfrom lfx.base.constants import STREAM_INFO_TEXT\nfrom lfx.base.models.unified_models import (\n get_language_model_options,\n get_llm,\n handle_model_input_update,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.components.agentics.helpers.model_config import validate_model_selection\nfrom lfx.components.helpers import CalculatorComponent, CurrentDateComponent\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.custom.custom_component.component import get_component_toolkit\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, ModelInput, StrInput\nfrom lfx.io import IntInput, MessageTextInput, MultilineInput, Output, SecretStrInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.schema.data import Data\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.schema.table import EditMode\n\n\ndef set_advanced_true(component_input):\n component_input.advanced = True\n return component_input\n\n\ndef _extract_text_content(value) -> str:\n \"\"\"Pull a string payload from a Message-like, AIMessage-like, or string value.\"\"\"\n if isinstance(value, str):\n return value\n text = getattr(value, \"text\", None)\n if isinstance(text, str):\n return text\n content = getattr(value, \"content\", None)\n if isinstance(content, str):\n return content\n return str(value) if value is not None else \"\"\n\n\n@contextmanager\ndef _suppress_send_message(component: Any):\n \"\"\"Temporarily replace component.send_message with a no-op for the duration of the block.\n\n Used during the structured-output prompt fallback: run_agent streams the agent's\n final answer through self.send_message (correct for message_response), but in\n json_response the orchestrator parses that text into structured Data which the\n downstream Chat Output emits — leaving the original emission in place produces a\n duplicate message in the playground. The original method is always restored on exit,\n even when the wrapped call raises.\n \"\"\"\n original = component.send_message\n\n async def _noop(message, *_args, **_kwargs):\n return message\n\n component.send_message = _noop\n try:\n yield\n finally:\n component.send_message = original\n\n\nclass AgentComponent(ToolCallingAgentComponent):\n display_name: str = \"Agent\"\n description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n documentation: str = \"https://docs.langflow.org/agents\"\n icon = \"bot\"\n beta = False\n name = \"Agent\"\n\n memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n inputs = [\n ModelInput(\n name=\"model\",\n display_name=\"Language Model\",\n info=\"Select your model provider\",\n real_time_refresh=True,\n required=True,\n # Agents require tool calling — the filter is honored by\n # ``handle_model_input_update`` so models that can't run with\n # tools never reach the picker (and any saved selection that\n # no longer satisfies the constraint is auto-replaced).\n filters={\"tool_calling\": True},\n ),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"API Key\",\n info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n real_time_refresh=True,\n advanced=True,\n ),\n DropdownInput(\n name=\"base_url_ibm_watsonx\",\n display_name=\"watsonx API Endpoint\",\n info=\"The base URL of the API (IBM watsonx.ai only)\",\n options=IBM_WATSONX_URLS,\n value=IBM_WATSONX_URLS[0],\n combobox=True,\n show=False,\n real_time_refresh=True,\n ),\n StrInput(\n name=\"project_id\",\n display_name=\"watsonx Project ID\",\n info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n show=False,\n required=False,\n ),\n MultilineInput(\n name=\"system_prompt\",\n display_name=\"Agent Instructions\",\n info=(\n \"System Prompt: Initial instructions and context provided to guide the agent's behavior. \"\n \"Supports dynamic placeholders: {current_date}, {model_name}, {optional_user_context}.\"\n ),\n value=DEFAULT_SYSTEM_PROMPT_TEMPLATE,\n advanced=False,\n ),\n MessageTextInput(\n name=\"context_id\",\n display_name=\"Context ID\",\n info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n value=\"\",\n advanced=True,\n ),\n IntInput(\n name=\"n_messages\",\n display_name=\"Number of Chat History Messages\",\n value=100,\n info=\"Number of chat history messages to retrieve.\",\n advanced=True,\n show=True,\n ),\n IntInput(\n name=\"max_tokens\",\n display_name=\"Max Tokens\",\n info=\"Maximum number of tokens to generate. Field name varies by provider.\",\n advanced=True,\n range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n ),\n MultilineInput(\n name=\"format_instructions\",\n display_name=\"Output Format Instructions\",\n info=\"Generic Template for structured output formatting. Valid only with Structured response.\",\n value=(\n \"You are an AI that extracts structured JSON objects from unstructured text. \"\n \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n \"Fill missing or ambiguous values with defaults: null for missing values. \"\n \"Remove exact duplicates but keep variations that have different field values. \"\n \"Always return valid JSON in the expected format, never throw errors. \"\n \"If multiple objects can be extracted, return them all in the structured format.\"\n ),\n advanced=True,\n ),\n TableInput(\n name=\"output_schema\",\n display_name=\"Output Schema\",\n info=(\n \"Schema Validation: Define the structure and data types for structured output. \"\n \"No validation if no output schema.\"\n ),\n advanced=True,\n required=False,\n value=[],\n table_schema=[\n {\n \"name\": \"name\",\n \"display_name\": \"Name\",\n \"type\": \"str\",\n \"description\": \"Specify the name of the output field.\",\n \"default\": \"field\",\n \"edit_mode\": EditMode.INLINE,\n },\n {\n \"name\": \"description\",\n \"display_name\": \"Description\",\n \"type\": \"str\",\n \"description\": \"Describe the purpose of the output field.\",\n \"default\": \"description of field\",\n \"edit_mode\": EditMode.POPOVER,\n },\n {\n \"name\": \"type\",\n \"display_name\": \"Type\",\n \"type\": \"str\",\n \"edit_mode\": EditMode.INLINE,\n \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n \"default\": \"str\",\n },\n {\n \"name\": \"multiple\",\n \"display_name\": \"As List\",\n \"type\": \"boolean\",\n \"description\": \"Set to True if this output field should be a list of the specified type.\",\n \"default\": \"False\",\n \"edit_mode\": EditMode.INLINE,\n },\n ],\n ),\n *LCToolsAgentComponent.get_base_inputs(),\n # removed memory inputs from agent component\n # *memory_inputs,\n BoolInput(\n name=\"stream\",\n display_name=\"Stream\",\n info=STREAM_INFO_TEXT,\n value=False,\n advanced=True,\n ),\n BoolInput(\n name=\"add_current_date_tool\",\n display_name=\"Current Date\",\n advanced=True,\n info=\"If true, will add a tool to the agent that returns the current date.\",\n value=True,\n ),\n BoolInput(\n name=\"add_calculator_tool\",\n display_name=\"Calculator\",\n advanced=True,\n info=(\n \"If true, adds a zero-config arithmetic calculator tool to the agent \"\n \"(safe: only +, -, *, /, ** operators via AST).\"\n ),\n value=True,\n ),\n ]\n outputs = [\n Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n Output(\n name=\"structured_response\",\n display_name=\"Structured Response\",\n method=\"json_response\",\n types=[\"Data\"],\n ),\n ]\n\n def _resolve_selected_model(self):\n \"\"\"Resolve the selected model, including legacy agent_llm/model_name inputs.\"\"\"\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n if isinstance(self.model, BaseLanguageModel):\n return self.model\n except ImportError:\n pass\n\n if isinstance(self.model, list) and self.model:\n return self.model\n\n legacy_provider = getattr(self, \"agent_llm\", None)\n legacy_model_name = getattr(self, \"model_name\", None)\n if not legacy_provider or not legacy_model_name:\n return self.model\n\n options = get_language_model_options(user_id=self.user_id)\n for option in options:\n if option.get(\"provider\") == legacy_provider and option.get(\"name\") == legacy_model_name:\n return [option]\n\n return [\n {\n \"name\": legacy_model_name,\n \"provider\": legacy_provider,\n \"metadata\": {},\n }\n ]\n\n def _get_max_tokens_value(self):\n \"\"\"Return the user-supplied max_tokens or None when unset/zero.\"\"\"\n val = getattr(self, \"max_tokens\", None)\n if val in {\"\", 0}:\n return None\n return val\n\n def _get_llm(self):\n \"\"\"Override parent to include max_tokens from the Agent's input field.\"\"\"\n return get_llm(\n model=self.model,\n user_id=self.user_id,\n api_key=getattr(self, \"api_key\", None),\n stream=bool(getattr(self, \"stream\", False)),\n max_tokens=self._get_max_tokens_value(),\n watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n watsonx_project_id=getattr(self, \"project_id\", None),\n )\n\n async def get_agent_requirements(self):\n \"\"\"Get the agent requirements for the agent.\"\"\"\n from langchain_core.tools import StructuredTool\n\n selected_model = self._resolve_selected_model()\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n is_connected_model = isinstance(selected_model, BaseLanguageModel)\n except ImportError:\n is_connected_model = False\n\n if not is_connected_model:\n validate_model_selection(selected_model)\n\n # Ensure _get_llm() uses the resolved model (e.g. from legacy agent_llm/model_name)\n self.model = selected_model\n llm_model = self._get_llm()\n if llm_model is None:\n msg = \"No language model selected. Please choose a model to proceed.\"\n raise ValueError(msg)\n\n # Get memory data\n self.chat_history = await self.get_memory_data()\n await logger.adebug(f\"Retrieved {len(self.chat_history)} chat history messages\")\n if isinstance(self.chat_history, Message):\n self.chat_history = [self.chat_history]\n\n # Add current date tool if enabled\n if self.add_current_date_tool:\n if not isinstance(self.tools, list): # type: ignore[has-type]\n self.tools = []\n current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n if not isinstance(current_date_tool, StructuredTool):\n msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n raise TypeError(msg)\n # Skip if an externally-connected tool already provides the same name.\n # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n if not any(getattr(t, \"name\", None) == current_date_tool.name for t in self.tools):\n self.tools.append(current_date_tool)\n\n # Add calculator tool if enabled (zero-config arithmetic)\n if getattr(self, \"add_calculator_tool\", False):\n if not isinstance(self.tools, list): # type: ignore[has-type]\n self.tools = []\n calculator_tool = (await CalculatorComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n if not isinstance(calculator_tool, StructuredTool):\n msg = \"CalculatorComponent must be converted to a StructuredTool\"\n raise TypeError(msg)\n # Skip if an externally-connected tool already provides the same name.\n # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n if not any(getattr(t, \"name\", None) == calculator_tool.name for t in self.tools):\n self.tools.append(calculator_tool)\n\n # Set shared callbacks for tracing the tools used by the agent\n self.set_tools_callbacks(self.tools, self._get_shared_callbacks())\n\n return llm_model, self.chat_history, self.tools\n\n def _get_resolved_model_name(self) -> str:\n \"\"\"Best-effort human-readable model name for {model_name} injection.\"\"\"\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n if isinstance(self.model, BaseLanguageModel):\n return type(self.model).__name__\n except ImportError:\n pass\n\n if isinstance(self.model, list) and self.model:\n first = self.model[0]\n if isinstance(first, dict):\n name = first.get(\"name\")\n if isinstance(name, str) and name:\n return name\n\n legacy_model_name = getattr(self, \"model_name\", None)\n if isinstance(legacy_model_name, str) and legacy_model_name:\n return legacy_model_name\n return \"\"\n\n def _inject_dynamic_prompt_values(self, prompt: str | None) -> str | None:\n \"\"\"Replace known env placeholders in the system prompt.\n\n Handles {current_date}, {model_name}, and {optional_user_context} (the\n last one ships with the structured DEFAULT_SYSTEM_PROMPT_TEMPLATE and\n is currently unused at the AgentComponent layer, so it resolves to \"\").\n Uses str.replace (not str.format) so user prompts containing literal\n braces such as JSON examples ({\"key\": 1}) never break the agent.\n \"\"\"\n if not prompt:\n return prompt\n replacements = {\n \"{current_date}\": datetime.now(tz=timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S UTC\"),\n \"{model_name}\": self._get_resolved_model_name(),\n \"{optional_user_context}\": \"\",\n }\n for placeholder, value in replacements.items():\n prompt = prompt.replace(placeholder, value)\n return prompt\n\n async def message_response(self) -> Message:\n try:\n llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n # Set up and run agent\n self.set(\n llm=llm_model,\n tools=self.tools or [],\n chat_history=self.chat_history,\n input_value=self.input_value,\n system_prompt=self._inject_dynamic_prompt_values(self.system_prompt),\n )\n agent = self.create_agent_runnable()\n result = await self.run_agent(agent)\n\n # Store result for potential JSON output\n self._agent_result = result\n\n except (ValueError, TypeError, KeyError) as e:\n await logger.aerror(f\"{type(e).__name__}: {e!s}\")\n raise\n except ExceptionWithMessageError as e:\n await logger.aerror(f\"ExceptionWithMessageError occurred: {e}\")\n raise\n # Avoid catching blind Exception; let truly unexpected exceptions propagate\n except Exception as e:\n await logger.aerror(f\"Unexpected error: {e!s}\")\n raise\n else:\n return result\n\n async def json_response(self) -> Data:\n \"\"\"Produce structured Data via native LLM structured output, with prompt-based fallback.\n\n Native path (no tools, llm has with_structured_output) bypasses the agent loop and\n returns provider-validated JSON. When tools are attached, falls back to running the\n agent with a schema-augmented system prompt and parsing the final message content.\n \"\"\"\n from lfx.components.models_and_agents.structured_output.structured_output_orchestrator import (\n orchestrate_structured_output,\n )\n\n try:\n llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n except (ValueError, TypeError) as exc:\n await logger.aerror(f\"json_response.requirements_failed: {exc}\")\n return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n injected_system_prompt = self._inject_dynamic_prompt_values(getattr(self, \"system_prompt\", \"\") or \"\") or \"\"\n format_instructions = getattr(self, \"format_instructions\", \"\") or \"\"\n output_schema = getattr(self, \"output_schema\", None) or []\n has_tools = bool(self.tools)\n\n async def _run_agent_for_fallback(augmented_prompt: str) -> str:\n self.set(\n llm=llm_model,\n tools=self.tools or [],\n chat_history=self.chat_history,\n input_value=self.input_value,\n system_prompt=augmented_prompt,\n )\n agent_runnable = self.create_agent_runnable()\n with _suppress_send_message(self):\n result = await self.run_agent(agent_runnable)\n return _extract_text_content(result)\n\n try:\n return await orchestrate_structured_output(\n llm=llm_model,\n output_schema=output_schema,\n system_prompt=injected_system_prompt,\n format_instructions=format_instructions,\n input_value=_extract_text_content(self.input_value),\n run_prompt_fallback=_run_agent_for_fallback,\n prefer_native=not has_tools,\n )\n except (\n ExceptionWithMessageError,\n ValueError,\n TypeError,\n NotImplementedError,\n AttributeError,\n ) as exc:\n await logger.aerror(f\"json_response.orchestration_failed: {exc}\")\n return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n async def get_memory_data(self):\n # Scope by flow_id so default playground session names (e.g. \"New Session 0\")\n # cannot leak chat history across unrelated flows. See issue #13059.\n # The helper also returns [] when n_messages == 0, preserving the\n # explicit \"memory disabled\" contract from MemoryComponent.retrieve_messages.\n messages = await aget_agent_chat_history(\n session_id=self.graph.session_id,\n flow_id=getattr(self.graph, \"flow_id\", None),\n context_id=self.context_id,\n n_messages=self.n_messages,\n )\n return [\n message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n ]\n\n def update_input_types(self, build_config: dotdict) -> dotdict:\n \"\"\"Update input types for all fields in build_config.\"\"\"\n for key, value in build_config.items():\n if isinstance(value, dict):\n if value.get(\"input_types\") is None:\n build_config[key][\"input_types\"] = []\n elif hasattr(value, \"input_types\") and value.input_types is None:\n value.input_types = []\n return build_config\n\n async def update_build_config(\n self,\n build_config: dotdict,\n field_value: list[dict],\n field_name: str | None = None,\n ) -> dotdict:\n # Update model options with caching (for all field changes).\n # The tool-calling constraint lives on the ModelInput's ``filters``\n # field (declared above); ``handle_model_input_update`` reads it\n # and applies the filter to both the dropdown options and the\n # sticky-default re-injection path.\n build_config = handle_model_input_update(\n component=self,\n build_config=dict(build_config),\n field_value=field_value,\n field_name=field_name,\n )\n build_config = dotdict(build_config)\n\n if field_name == \"model\":\n build_config = self.update_input_types(build_config)\n\n # Validate required keys\n default_keys = [\n \"code\",\n \"_type\",\n \"model\",\n \"tools\",\n \"input_value\",\n \"add_current_date_tool\",\n \"add_calculator_tool\",\n \"system_prompt\",\n \"agent_description\",\n \"max_iterations\",\n \"handle_parsing_errors\",\n \"verbose\",\n ]\n missing_keys = [key for key in default_keys if key not in build_config]\n if missing_keys:\n msg = f\"Missing required keys in build_config: {missing_keys}\"\n raise ValueError(msg)\n return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n async def _get_tools(self) -> list[Tool]:\n component_toolkit = get_component_toolkit()\n tools_names = self._build_tools_names()\n agent_description = self.get_tool_description()\n # TODO: Agent Description Depreciated Feature to be removed\n description = f\"{agent_description}{tools_names}\"\n\n tools = component_toolkit(component=self).get_tools(\n tool_name=\"Call_Agent\",\n tool_description=description,\n # here we do not use the shared callbacks as we are exposing the agent as a tool\n callbacks=self.get_langchain_callbacks(),\n )\n if hasattr(self, \"tools_metadata\"):\n tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n\n return tools\n" }, "context_id": { "_input_type": "MessageTextInput", diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Search agent.json b/src/backend/base/langflow/initial_setup/starter_projects/Search agent.json index 71473e8c16..2d72f09bc7 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Search agent.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Search agent.json @@ -944,7 +944,7 @@ "last_updated": "2025-12-11T21:41:48.407Z", "legacy": false, "metadata": { - "code_hash": "ebac74bfcae6", + "code_hash": "443159257161", "dependencies": { "dependencies": [ { @@ -1133,7 +1133,7 @@ "show": true, "title_case": false, "type": "code", - "value": "from __future__ import annotations\n\nfrom contextlib import contextmanager\nfrom datetime import datetime, timezone\nfrom typing import TYPE_CHECKING, Any\n\nfrom lfx.components.models_and_agents.memory import MemoryComponent\n\nif TYPE_CHECKING:\n from langchain_core.tools import Tool\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.agents.default_system_prompt import DEFAULT_SYSTEM_PROMPT_TEMPLATE\nfrom lfx.base.agents.events import ExceptionWithMessageError\nfrom lfx.base.constants import STREAM_INFO_TEXT\nfrom lfx.base.models.unified_models import (\n get_language_model_options,\n get_llm,\n handle_model_input_update,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.components.agentics.helpers.model_config import validate_model_selection\nfrom lfx.components.helpers import CalculatorComponent, CurrentDateComponent\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.custom.custom_component.component import get_component_toolkit\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, ModelInput, StrInput\nfrom lfx.io import IntInput, MessageTextInput, MultilineInput, Output, SecretStrInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.schema.data import Data\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.schema.table import EditMode\n\n\ndef set_advanced_true(component_input):\n component_input.advanced = True\n return component_input\n\n\ndef _extract_text_content(value) -> str:\n \"\"\"Pull a string payload from a Message-like, AIMessage-like, or string value.\"\"\"\n if isinstance(value, str):\n return value\n text = getattr(value, \"text\", None)\n if isinstance(text, str):\n return text\n content = getattr(value, \"content\", None)\n if isinstance(content, str):\n return content\n return str(value) if value is not None else \"\"\n\n\n@contextmanager\ndef _suppress_send_message(component: Any):\n \"\"\"Temporarily replace component.send_message with a no-op for the duration of the block.\n\n Used during the structured-output prompt fallback: run_agent streams the agent's\n final answer through self.send_message (correct for message_response), but in\n json_response the orchestrator parses that text into structured Data which the\n downstream Chat Output emits — leaving the original emission in place produces a\n duplicate message in the playground. The original method is always restored on exit,\n even when the wrapped call raises.\n \"\"\"\n original = component.send_message\n\n async def _noop(message, *_args, **_kwargs):\n return message\n\n component.send_message = _noop\n try:\n yield\n finally:\n component.send_message = original\n\n\nclass AgentComponent(ToolCallingAgentComponent):\n display_name: str = \"Agent\"\n description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n documentation: str = \"https://docs.langflow.org/agents\"\n icon = \"bot\"\n beta = False\n name = \"Agent\"\n\n memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n inputs = [\n ModelInput(\n name=\"model\",\n display_name=\"Language Model\",\n info=\"Select your model provider\",\n real_time_refresh=True,\n required=True,\n # Agents require tool calling — the filter is honored by\n # ``handle_model_input_update`` so models that can't run with\n # tools never reach the picker (and any saved selection that\n # no longer satisfies the constraint is auto-replaced).\n filters={\"tool_calling\": True},\n ),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"API Key\",\n info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n real_time_refresh=True,\n advanced=True,\n ),\n DropdownInput(\n name=\"base_url_ibm_watsonx\",\n display_name=\"watsonx API Endpoint\",\n info=\"The base URL of the API (IBM watsonx.ai only)\",\n options=IBM_WATSONX_URLS,\n value=IBM_WATSONX_URLS[0],\n combobox=True,\n show=False,\n real_time_refresh=True,\n ),\n StrInput(\n name=\"project_id\",\n display_name=\"watsonx Project ID\",\n info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n show=False,\n required=False,\n ),\n MultilineInput(\n name=\"system_prompt\",\n display_name=\"Agent Instructions\",\n info=(\n \"System Prompt: Initial instructions and context provided to guide the agent's behavior. \"\n \"Supports dynamic placeholders: {current_date}, {model_name}, {optional_user_context}.\"\n ),\n value=DEFAULT_SYSTEM_PROMPT_TEMPLATE,\n advanced=False,\n ),\n MessageTextInput(\n name=\"context_id\",\n display_name=\"Context ID\",\n info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n value=\"\",\n advanced=True,\n ),\n IntInput(\n name=\"n_messages\",\n display_name=\"Number of Chat History Messages\",\n value=100,\n info=\"Number of chat history messages to retrieve.\",\n advanced=True,\n show=True,\n ),\n IntInput(\n name=\"max_tokens\",\n display_name=\"Max Tokens\",\n info=\"Maximum number of tokens to generate. Field name varies by provider.\",\n advanced=True,\n range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n ),\n MultilineInput(\n name=\"format_instructions\",\n display_name=\"Output Format Instructions\",\n info=\"Generic Template for structured output formatting. Valid only with Structured response.\",\n value=(\n \"You are an AI that extracts structured JSON objects from unstructured text. \"\n \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n \"Fill missing or ambiguous values with defaults: null for missing values. \"\n \"Remove exact duplicates but keep variations that have different field values. \"\n \"Always return valid JSON in the expected format, never throw errors. \"\n \"If multiple objects can be extracted, return them all in the structured format.\"\n ),\n advanced=True,\n ),\n TableInput(\n name=\"output_schema\",\n display_name=\"Output Schema\",\n info=(\n \"Schema Validation: Define the structure and data types for structured output. \"\n \"No validation if no output schema.\"\n ),\n advanced=True,\n required=False,\n value=[],\n table_schema=[\n {\n \"name\": \"name\",\n \"display_name\": \"Name\",\n \"type\": \"str\",\n \"description\": \"Specify the name of the output field.\",\n \"default\": \"field\",\n \"edit_mode\": EditMode.INLINE,\n },\n {\n \"name\": \"description\",\n \"display_name\": \"Description\",\n \"type\": \"str\",\n \"description\": \"Describe the purpose of the output field.\",\n \"default\": \"description of field\",\n \"edit_mode\": EditMode.POPOVER,\n },\n {\n \"name\": \"type\",\n \"display_name\": \"Type\",\n \"type\": \"str\",\n \"edit_mode\": EditMode.INLINE,\n \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n \"default\": \"str\",\n },\n {\n \"name\": \"multiple\",\n \"display_name\": \"As List\",\n \"type\": \"boolean\",\n \"description\": \"Set to True if this output field should be a list of the specified type.\",\n \"default\": \"False\",\n \"edit_mode\": EditMode.INLINE,\n },\n ],\n ),\n *LCToolsAgentComponent.get_base_inputs(),\n # removed memory inputs from agent component\n # *memory_inputs,\n BoolInput(\n name=\"stream\",\n display_name=\"Stream\",\n info=STREAM_INFO_TEXT,\n value=False,\n advanced=True,\n ),\n BoolInput(\n name=\"add_current_date_tool\",\n display_name=\"Current Date\",\n advanced=True,\n info=\"If true, will add a tool to the agent that returns the current date.\",\n value=True,\n ),\n BoolInput(\n name=\"add_calculator_tool\",\n display_name=\"Calculator\",\n advanced=True,\n info=(\n \"If true, adds a zero-config arithmetic calculator tool to the agent \"\n \"(safe: only +, -, *, /, ** operators via AST).\"\n ),\n value=True,\n ),\n ]\n outputs = [\n Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n Output(\n name=\"structured_response\",\n display_name=\"Structured Response\",\n method=\"json_response\",\n types=[\"Data\"],\n ),\n ]\n\n def _resolve_selected_model(self):\n \"\"\"Resolve the selected model, including legacy agent_llm/model_name inputs.\"\"\"\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n if isinstance(self.model, BaseLanguageModel):\n return self.model\n except ImportError:\n pass\n\n if isinstance(self.model, list) and self.model:\n return self.model\n\n legacy_provider = getattr(self, \"agent_llm\", None)\n legacy_model_name = getattr(self, \"model_name\", None)\n if not legacy_provider or not legacy_model_name:\n return self.model\n\n options = get_language_model_options(user_id=self.user_id)\n for option in options:\n if option.get(\"provider\") == legacy_provider and option.get(\"name\") == legacy_model_name:\n return [option]\n\n return [\n {\n \"name\": legacy_model_name,\n \"provider\": legacy_provider,\n \"metadata\": {},\n }\n ]\n\n def _get_max_tokens_value(self):\n \"\"\"Return the user-supplied max_tokens or None when unset/zero.\"\"\"\n val = getattr(self, \"max_tokens\", None)\n if val in {\"\", 0}:\n return None\n return val\n\n def _get_llm(self):\n \"\"\"Override parent to include max_tokens from the Agent's input field.\"\"\"\n return get_llm(\n model=self.model,\n user_id=self.user_id,\n api_key=getattr(self, \"api_key\", None),\n stream=bool(getattr(self, \"stream\", False)),\n max_tokens=self._get_max_tokens_value(),\n watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n watsonx_project_id=getattr(self, \"project_id\", None),\n )\n\n async def get_agent_requirements(self):\n \"\"\"Get the agent requirements for the agent.\"\"\"\n from langchain_core.tools import StructuredTool\n\n selected_model = self._resolve_selected_model()\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n is_connected_model = isinstance(selected_model, BaseLanguageModel)\n except ImportError:\n is_connected_model = False\n\n if not is_connected_model:\n validate_model_selection(selected_model)\n\n # Ensure _get_llm() uses the resolved model (e.g. from legacy agent_llm/model_name)\n self.model = selected_model\n llm_model = self._get_llm()\n if llm_model is None:\n msg = \"No language model selected. Please choose a model to proceed.\"\n raise ValueError(msg)\n\n # Get memory data\n self.chat_history = await self.get_memory_data()\n await logger.adebug(f\"Retrieved {len(self.chat_history)} chat history messages\")\n if isinstance(self.chat_history, Message):\n self.chat_history = [self.chat_history]\n\n # Add current date tool if enabled\n if self.add_current_date_tool:\n if not isinstance(self.tools, list): # type: ignore[has-type]\n self.tools = []\n current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n if not isinstance(current_date_tool, StructuredTool):\n msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n raise TypeError(msg)\n # Skip if an externally-connected tool already provides the same name.\n # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n if not any(getattr(t, \"name\", None) == current_date_tool.name for t in self.tools):\n self.tools.append(current_date_tool)\n\n # Add calculator tool if enabled (zero-config arithmetic)\n if getattr(self, \"add_calculator_tool\", False):\n if not isinstance(self.tools, list): # type: ignore[has-type]\n self.tools = []\n calculator_tool = (await CalculatorComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n if not isinstance(calculator_tool, StructuredTool):\n msg = \"CalculatorComponent must be converted to a StructuredTool\"\n raise TypeError(msg)\n # Skip if an externally-connected tool already provides the same name.\n # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n if not any(getattr(t, \"name\", None) == calculator_tool.name for t in self.tools):\n self.tools.append(calculator_tool)\n\n # Set shared callbacks for tracing the tools used by the agent\n self.set_tools_callbacks(self.tools, self._get_shared_callbacks())\n\n return llm_model, self.chat_history, self.tools\n\n def _get_resolved_model_name(self) -> str:\n \"\"\"Best-effort human-readable model name for {model_name} injection.\"\"\"\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n if isinstance(self.model, BaseLanguageModel):\n return type(self.model).__name__\n except ImportError:\n pass\n\n if isinstance(self.model, list) and self.model:\n first = self.model[0]\n if isinstance(first, dict):\n name = first.get(\"name\")\n if isinstance(name, str) and name:\n return name\n\n legacy_model_name = getattr(self, \"model_name\", None)\n if isinstance(legacy_model_name, str) and legacy_model_name:\n return legacy_model_name\n return \"\"\n\n def _inject_dynamic_prompt_values(self, prompt: str | None) -> str | None:\n \"\"\"Replace known env placeholders in the system prompt.\n\n Handles {current_date}, {model_name}, and {optional_user_context} (the\n last one ships with the structured DEFAULT_SYSTEM_PROMPT_TEMPLATE and\n is currently unused at the AgentComponent layer, so it resolves to \"\").\n Uses str.replace (not str.format) so user prompts containing literal\n braces such as JSON examples ({\"key\": 1}) never break the agent.\n \"\"\"\n if not prompt:\n return prompt\n replacements = {\n \"{current_date}\": datetime.now(tz=timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S UTC\"),\n \"{model_name}\": self._get_resolved_model_name(),\n \"{optional_user_context}\": \"\",\n }\n for placeholder, value in replacements.items():\n prompt = prompt.replace(placeholder, value)\n return prompt\n\n async def message_response(self) -> Message:\n try:\n llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n # Set up and run agent\n self.set(\n llm=llm_model,\n tools=self.tools or [],\n chat_history=self.chat_history,\n input_value=self.input_value,\n system_prompt=self._inject_dynamic_prompt_values(self.system_prompt),\n )\n agent = self.create_agent_runnable()\n result = await self.run_agent(agent)\n\n # Store result for potential JSON output\n self._agent_result = result\n\n except (ValueError, TypeError, KeyError) as e:\n await logger.aerror(f\"{type(e).__name__}: {e!s}\")\n raise\n except ExceptionWithMessageError as e:\n await logger.aerror(f\"ExceptionWithMessageError occurred: {e}\")\n raise\n # Avoid catching blind Exception; let truly unexpected exceptions propagate\n except Exception as e:\n await logger.aerror(f\"Unexpected error: {e!s}\")\n raise\n else:\n return result\n\n async def json_response(self) -> Data:\n \"\"\"Produce structured Data via native LLM structured output, with prompt-based fallback.\n\n Native path (no tools, llm has with_structured_output) bypasses the agent loop and\n returns provider-validated JSON. When tools are attached, falls back to running the\n agent with a schema-augmented system prompt and parsing the final message content.\n \"\"\"\n from lfx.components.models_and_agents.structured_output.structured_output_orchestrator import (\n orchestrate_structured_output,\n )\n\n try:\n llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n except (ValueError, TypeError) as exc:\n await logger.aerror(f\"json_response.requirements_failed: {exc}\")\n return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n injected_system_prompt = self._inject_dynamic_prompt_values(getattr(self, \"system_prompt\", \"\") or \"\") or \"\"\n format_instructions = getattr(self, \"format_instructions\", \"\") or \"\"\n output_schema = getattr(self, \"output_schema\", None) or []\n has_tools = bool(self.tools)\n\n async def _run_agent_for_fallback(augmented_prompt: str) -> str:\n self.set(\n llm=llm_model,\n tools=self.tools or [],\n chat_history=self.chat_history,\n input_value=self.input_value,\n system_prompt=augmented_prompt,\n )\n agent_runnable = self.create_agent_runnable()\n with _suppress_send_message(self):\n result = await self.run_agent(agent_runnable)\n return _extract_text_content(result)\n\n try:\n return await orchestrate_structured_output(\n llm=llm_model,\n output_schema=output_schema,\n system_prompt=injected_system_prompt,\n format_instructions=format_instructions,\n input_value=_extract_text_content(self.input_value),\n run_prompt_fallback=_run_agent_for_fallback,\n prefer_native=not has_tools,\n )\n except (\n ExceptionWithMessageError,\n ValueError,\n TypeError,\n NotImplementedError,\n AttributeError,\n ) as exc:\n await logger.aerror(f\"json_response.orchestration_failed: {exc}\")\n return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n async def get_memory_data(self):\n # TODO: This is a temporary fix to avoid message duplication. We should develop a function for this.\n messages = (\n await MemoryComponent(**self.get_base_args())\n .set(\n session_id=self.graph.session_id,\n context_id=self.context_id,\n order=\"Ascending\",\n n_messages=self.n_messages,\n )\n .retrieve_messages()\n )\n return [\n message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n ]\n\n def update_input_types(self, build_config: dotdict) -> dotdict:\n \"\"\"Update input types for all fields in build_config.\"\"\"\n for key, value in build_config.items():\n if isinstance(value, dict):\n if value.get(\"input_types\") is None:\n build_config[key][\"input_types\"] = []\n elif hasattr(value, \"input_types\") and value.input_types is None:\n value.input_types = []\n return build_config\n\n async def update_build_config(\n self,\n build_config: dotdict,\n field_value: list[dict],\n field_name: str | None = None,\n ) -> dotdict:\n # Update model options with caching (for all field changes).\n # The tool-calling constraint lives on the ModelInput's ``filters``\n # field (declared above); ``handle_model_input_update`` reads it\n # and applies the filter to both the dropdown options and the\n # sticky-default re-injection path.\n build_config = handle_model_input_update(\n component=self,\n build_config=dict(build_config),\n field_value=field_value,\n field_name=field_name,\n )\n build_config = dotdict(build_config)\n\n if field_name == \"model\":\n build_config = self.update_input_types(build_config)\n\n # Validate required keys\n default_keys = [\n \"code\",\n \"_type\",\n \"model\",\n \"tools\",\n \"input_value\",\n \"add_current_date_tool\",\n \"add_calculator_tool\",\n \"system_prompt\",\n \"agent_description\",\n \"max_iterations\",\n \"handle_parsing_errors\",\n \"verbose\",\n ]\n missing_keys = [key for key in default_keys if key not in build_config]\n if missing_keys:\n msg = f\"Missing required keys in build_config: {missing_keys}\"\n raise ValueError(msg)\n return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n async def _get_tools(self) -> list[Tool]:\n component_toolkit = get_component_toolkit()\n tools_names = self._build_tools_names()\n agent_description = self.get_tool_description()\n # TODO: Agent Description Depreciated Feature to be removed\n description = f\"{agent_description}{tools_names}\"\n\n tools = component_toolkit(component=self).get_tools(\n tool_name=\"Call_Agent\",\n tool_description=description,\n # here we do not use the shared callbacks as we are exposing the agent as a tool\n callbacks=self.get_langchain_callbacks(),\n )\n if hasattr(self, \"tools_metadata\"):\n tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n\n return tools\n" + "value": "from __future__ import annotations\n\nfrom contextlib import contextmanager\nfrom datetime import datetime, timezone\nfrom typing import TYPE_CHECKING, Any\n\nfrom lfx.components.models_and_agents.memory import MemoryComponent, aget_agent_chat_history\n\nif TYPE_CHECKING:\n from langchain_core.tools import Tool\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.agents.default_system_prompt import DEFAULT_SYSTEM_PROMPT_TEMPLATE\nfrom lfx.base.agents.events import ExceptionWithMessageError\nfrom lfx.base.constants import STREAM_INFO_TEXT\nfrom lfx.base.models.unified_models import (\n get_language_model_options,\n get_llm,\n handle_model_input_update,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.components.agentics.helpers.model_config import validate_model_selection\nfrom lfx.components.helpers import CalculatorComponent, CurrentDateComponent\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.custom.custom_component.component import get_component_toolkit\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, ModelInput, StrInput\nfrom lfx.io import IntInput, MessageTextInput, MultilineInput, Output, SecretStrInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.schema.data import Data\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.schema.table import EditMode\n\n\ndef set_advanced_true(component_input):\n component_input.advanced = True\n return component_input\n\n\ndef _extract_text_content(value) -> str:\n \"\"\"Pull a string payload from a Message-like, AIMessage-like, or string value.\"\"\"\n if isinstance(value, str):\n return value\n text = getattr(value, \"text\", None)\n if isinstance(text, str):\n return text\n content = getattr(value, \"content\", None)\n if isinstance(content, str):\n return content\n return str(value) if value is not None else \"\"\n\n\n@contextmanager\ndef _suppress_send_message(component: Any):\n \"\"\"Temporarily replace component.send_message with a no-op for the duration of the block.\n\n Used during the structured-output prompt fallback: run_agent streams the agent's\n final answer through self.send_message (correct for message_response), but in\n json_response the orchestrator parses that text into structured Data which the\n downstream Chat Output emits — leaving the original emission in place produces a\n duplicate message in the playground. The original method is always restored on exit,\n even when the wrapped call raises.\n \"\"\"\n original = component.send_message\n\n async def _noop(message, *_args, **_kwargs):\n return message\n\n component.send_message = _noop\n try:\n yield\n finally:\n component.send_message = original\n\n\nclass AgentComponent(ToolCallingAgentComponent):\n display_name: str = \"Agent\"\n description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n documentation: str = \"https://docs.langflow.org/agents\"\n icon = \"bot\"\n beta = False\n name = \"Agent\"\n\n memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n inputs = [\n ModelInput(\n name=\"model\",\n display_name=\"Language Model\",\n info=\"Select your model provider\",\n real_time_refresh=True,\n required=True,\n # Agents require tool calling — the filter is honored by\n # ``handle_model_input_update`` so models that can't run with\n # tools never reach the picker (and any saved selection that\n # no longer satisfies the constraint is auto-replaced).\n filters={\"tool_calling\": True},\n ),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"API Key\",\n info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n real_time_refresh=True,\n advanced=True,\n ),\n DropdownInput(\n name=\"base_url_ibm_watsonx\",\n display_name=\"watsonx API Endpoint\",\n info=\"The base URL of the API (IBM watsonx.ai only)\",\n options=IBM_WATSONX_URLS,\n value=IBM_WATSONX_URLS[0],\n combobox=True,\n show=False,\n real_time_refresh=True,\n ),\n StrInput(\n name=\"project_id\",\n display_name=\"watsonx Project ID\",\n info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n show=False,\n required=False,\n ),\n MultilineInput(\n name=\"system_prompt\",\n display_name=\"Agent Instructions\",\n info=(\n \"System Prompt: Initial instructions and context provided to guide the agent's behavior. \"\n \"Supports dynamic placeholders: {current_date}, {model_name}, {optional_user_context}.\"\n ),\n value=DEFAULT_SYSTEM_PROMPT_TEMPLATE,\n advanced=False,\n ),\n MessageTextInput(\n name=\"context_id\",\n display_name=\"Context ID\",\n info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n value=\"\",\n advanced=True,\n ),\n IntInput(\n name=\"n_messages\",\n display_name=\"Number of Chat History Messages\",\n value=100,\n info=\"Number of chat history messages to retrieve.\",\n advanced=True,\n show=True,\n ),\n IntInput(\n name=\"max_tokens\",\n display_name=\"Max Tokens\",\n info=\"Maximum number of tokens to generate. Field name varies by provider.\",\n advanced=True,\n range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n ),\n MultilineInput(\n name=\"format_instructions\",\n display_name=\"Output Format Instructions\",\n info=\"Generic Template for structured output formatting. Valid only with Structured response.\",\n value=(\n \"You are an AI that extracts structured JSON objects from unstructured text. \"\n \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n \"Fill missing or ambiguous values with defaults: null for missing values. \"\n \"Remove exact duplicates but keep variations that have different field values. \"\n \"Always return valid JSON in the expected format, never throw errors. \"\n \"If multiple objects can be extracted, return them all in the structured format.\"\n ),\n advanced=True,\n ),\n TableInput(\n name=\"output_schema\",\n display_name=\"Output Schema\",\n info=(\n \"Schema Validation: Define the structure and data types for structured output. \"\n \"No validation if no output schema.\"\n ),\n advanced=True,\n required=False,\n value=[],\n table_schema=[\n {\n \"name\": \"name\",\n \"display_name\": \"Name\",\n \"type\": \"str\",\n \"description\": \"Specify the name of the output field.\",\n \"default\": \"field\",\n \"edit_mode\": EditMode.INLINE,\n },\n {\n \"name\": \"description\",\n \"display_name\": \"Description\",\n \"type\": \"str\",\n \"description\": \"Describe the purpose of the output field.\",\n \"default\": \"description of field\",\n \"edit_mode\": EditMode.POPOVER,\n },\n {\n \"name\": \"type\",\n \"display_name\": \"Type\",\n \"type\": \"str\",\n \"edit_mode\": EditMode.INLINE,\n \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n \"default\": \"str\",\n },\n {\n \"name\": \"multiple\",\n \"display_name\": \"As List\",\n \"type\": \"boolean\",\n \"description\": \"Set to True if this output field should be a list of the specified type.\",\n \"default\": \"False\",\n \"edit_mode\": EditMode.INLINE,\n },\n ],\n ),\n *LCToolsAgentComponent.get_base_inputs(),\n # removed memory inputs from agent component\n # *memory_inputs,\n BoolInput(\n name=\"stream\",\n display_name=\"Stream\",\n info=STREAM_INFO_TEXT,\n value=False,\n advanced=True,\n ),\n BoolInput(\n name=\"add_current_date_tool\",\n display_name=\"Current Date\",\n advanced=True,\n info=\"If true, will add a tool to the agent that returns the current date.\",\n value=True,\n ),\n BoolInput(\n name=\"add_calculator_tool\",\n display_name=\"Calculator\",\n advanced=True,\n info=(\n \"If true, adds a zero-config arithmetic calculator tool to the agent \"\n \"(safe: only +, -, *, /, ** operators via AST).\"\n ),\n value=True,\n ),\n ]\n outputs = [\n Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n Output(\n name=\"structured_response\",\n display_name=\"Structured Response\",\n method=\"json_response\",\n types=[\"Data\"],\n ),\n ]\n\n def _resolve_selected_model(self):\n \"\"\"Resolve the selected model, including legacy agent_llm/model_name inputs.\"\"\"\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n if isinstance(self.model, BaseLanguageModel):\n return self.model\n except ImportError:\n pass\n\n if isinstance(self.model, list) and self.model:\n return self.model\n\n legacy_provider = getattr(self, \"agent_llm\", None)\n legacy_model_name = getattr(self, \"model_name\", None)\n if not legacy_provider or not legacy_model_name:\n return self.model\n\n options = get_language_model_options(user_id=self.user_id)\n for option in options:\n if option.get(\"provider\") == legacy_provider and option.get(\"name\") == legacy_model_name:\n return [option]\n\n return [\n {\n \"name\": legacy_model_name,\n \"provider\": legacy_provider,\n \"metadata\": {},\n }\n ]\n\n def _get_max_tokens_value(self):\n \"\"\"Return the user-supplied max_tokens or None when unset/zero.\"\"\"\n val = getattr(self, \"max_tokens\", None)\n if val in {\"\", 0}:\n return None\n return val\n\n def _get_llm(self):\n \"\"\"Override parent to include max_tokens from the Agent's input field.\"\"\"\n return get_llm(\n model=self.model,\n user_id=self.user_id,\n api_key=getattr(self, \"api_key\", None),\n stream=bool(getattr(self, \"stream\", False)),\n max_tokens=self._get_max_tokens_value(),\n watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n watsonx_project_id=getattr(self, \"project_id\", None),\n )\n\n async def get_agent_requirements(self):\n \"\"\"Get the agent requirements for the agent.\"\"\"\n from langchain_core.tools import StructuredTool\n\n selected_model = self._resolve_selected_model()\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n is_connected_model = isinstance(selected_model, BaseLanguageModel)\n except ImportError:\n is_connected_model = False\n\n if not is_connected_model:\n validate_model_selection(selected_model)\n\n # Ensure _get_llm() uses the resolved model (e.g. from legacy agent_llm/model_name)\n self.model = selected_model\n llm_model = self._get_llm()\n if llm_model is None:\n msg = \"No language model selected. Please choose a model to proceed.\"\n raise ValueError(msg)\n\n # Get memory data\n self.chat_history = await self.get_memory_data()\n await logger.adebug(f\"Retrieved {len(self.chat_history)} chat history messages\")\n if isinstance(self.chat_history, Message):\n self.chat_history = [self.chat_history]\n\n # Add current date tool if enabled\n if self.add_current_date_tool:\n if not isinstance(self.tools, list): # type: ignore[has-type]\n self.tools = []\n current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n if not isinstance(current_date_tool, StructuredTool):\n msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n raise TypeError(msg)\n # Skip if an externally-connected tool already provides the same name.\n # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n if not any(getattr(t, \"name\", None) == current_date_tool.name for t in self.tools):\n self.tools.append(current_date_tool)\n\n # Add calculator tool if enabled (zero-config arithmetic)\n if getattr(self, \"add_calculator_tool\", False):\n if not isinstance(self.tools, list): # type: ignore[has-type]\n self.tools = []\n calculator_tool = (await CalculatorComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n if not isinstance(calculator_tool, StructuredTool):\n msg = \"CalculatorComponent must be converted to a StructuredTool\"\n raise TypeError(msg)\n # Skip if an externally-connected tool already provides the same name.\n # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n if not any(getattr(t, \"name\", None) == calculator_tool.name for t in self.tools):\n self.tools.append(calculator_tool)\n\n # Set shared callbacks for tracing the tools used by the agent\n self.set_tools_callbacks(self.tools, self._get_shared_callbacks())\n\n return llm_model, self.chat_history, self.tools\n\n def _get_resolved_model_name(self) -> str:\n \"\"\"Best-effort human-readable model name for {model_name} injection.\"\"\"\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n if isinstance(self.model, BaseLanguageModel):\n return type(self.model).__name__\n except ImportError:\n pass\n\n if isinstance(self.model, list) and self.model:\n first = self.model[0]\n if isinstance(first, dict):\n name = first.get(\"name\")\n if isinstance(name, str) and name:\n return name\n\n legacy_model_name = getattr(self, \"model_name\", None)\n if isinstance(legacy_model_name, str) and legacy_model_name:\n return legacy_model_name\n return \"\"\n\n def _inject_dynamic_prompt_values(self, prompt: str | None) -> str | None:\n \"\"\"Replace known env placeholders in the system prompt.\n\n Handles {current_date}, {model_name}, and {optional_user_context} (the\n last one ships with the structured DEFAULT_SYSTEM_PROMPT_TEMPLATE and\n is currently unused at the AgentComponent layer, so it resolves to \"\").\n Uses str.replace (not str.format) so user prompts containing literal\n braces such as JSON examples ({\"key\": 1}) never break the agent.\n \"\"\"\n if not prompt:\n return prompt\n replacements = {\n \"{current_date}\": datetime.now(tz=timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S UTC\"),\n \"{model_name}\": self._get_resolved_model_name(),\n \"{optional_user_context}\": \"\",\n }\n for placeholder, value in replacements.items():\n prompt = prompt.replace(placeholder, value)\n return prompt\n\n async def message_response(self) -> Message:\n try:\n llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n # Set up and run agent\n self.set(\n llm=llm_model,\n tools=self.tools or [],\n chat_history=self.chat_history,\n input_value=self.input_value,\n system_prompt=self._inject_dynamic_prompt_values(self.system_prompt),\n )\n agent = self.create_agent_runnable()\n result = await self.run_agent(agent)\n\n # Store result for potential JSON output\n self._agent_result = result\n\n except (ValueError, TypeError, KeyError) as e:\n await logger.aerror(f\"{type(e).__name__}: {e!s}\")\n raise\n except ExceptionWithMessageError as e:\n await logger.aerror(f\"ExceptionWithMessageError occurred: {e}\")\n raise\n # Avoid catching blind Exception; let truly unexpected exceptions propagate\n except Exception as e:\n await logger.aerror(f\"Unexpected error: {e!s}\")\n raise\n else:\n return result\n\n async def json_response(self) -> Data:\n \"\"\"Produce structured Data via native LLM structured output, with prompt-based fallback.\n\n Native path (no tools, llm has with_structured_output) bypasses the agent loop and\n returns provider-validated JSON. When tools are attached, falls back to running the\n agent with a schema-augmented system prompt and parsing the final message content.\n \"\"\"\n from lfx.components.models_and_agents.structured_output.structured_output_orchestrator import (\n orchestrate_structured_output,\n )\n\n try:\n llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n except (ValueError, TypeError) as exc:\n await logger.aerror(f\"json_response.requirements_failed: {exc}\")\n return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n injected_system_prompt = self._inject_dynamic_prompt_values(getattr(self, \"system_prompt\", \"\") or \"\") or \"\"\n format_instructions = getattr(self, \"format_instructions\", \"\") or \"\"\n output_schema = getattr(self, \"output_schema\", None) or []\n has_tools = bool(self.tools)\n\n async def _run_agent_for_fallback(augmented_prompt: str) -> str:\n self.set(\n llm=llm_model,\n tools=self.tools or [],\n chat_history=self.chat_history,\n input_value=self.input_value,\n system_prompt=augmented_prompt,\n )\n agent_runnable = self.create_agent_runnable()\n with _suppress_send_message(self):\n result = await self.run_agent(agent_runnable)\n return _extract_text_content(result)\n\n try:\n return await orchestrate_structured_output(\n llm=llm_model,\n output_schema=output_schema,\n system_prompt=injected_system_prompt,\n format_instructions=format_instructions,\n input_value=_extract_text_content(self.input_value),\n run_prompt_fallback=_run_agent_for_fallback,\n prefer_native=not has_tools,\n )\n except (\n ExceptionWithMessageError,\n ValueError,\n TypeError,\n NotImplementedError,\n AttributeError,\n ) as exc:\n await logger.aerror(f\"json_response.orchestration_failed: {exc}\")\n return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n async def get_memory_data(self):\n # Scope by flow_id so default playground session names (e.g. \"New Session 0\")\n # cannot leak chat history across unrelated flows. See issue #13059.\n # The helper also returns [] when n_messages == 0, preserving the\n # explicit \"memory disabled\" contract from MemoryComponent.retrieve_messages.\n messages = await aget_agent_chat_history(\n session_id=self.graph.session_id,\n flow_id=getattr(self.graph, \"flow_id\", None),\n context_id=self.context_id,\n n_messages=self.n_messages,\n )\n return [\n message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n ]\n\n def update_input_types(self, build_config: dotdict) -> dotdict:\n \"\"\"Update input types for all fields in build_config.\"\"\"\n for key, value in build_config.items():\n if isinstance(value, dict):\n if value.get(\"input_types\") is None:\n build_config[key][\"input_types\"] = []\n elif hasattr(value, \"input_types\") and value.input_types is None:\n value.input_types = []\n return build_config\n\n async def update_build_config(\n self,\n build_config: dotdict,\n field_value: list[dict],\n field_name: str | None = None,\n ) -> dotdict:\n # Update model options with caching (for all field changes).\n # The tool-calling constraint lives on the ModelInput's ``filters``\n # field (declared above); ``handle_model_input_update`` reads it\n # and applies the filter to both the dropdown options and the\n # sticky-default re-injection path.\n build_config = handle_model_input_update(\n component=self,\n build_config=dict(build_config),\n field_value=field_value,\n field_name=field_name,\n )\n build_config = dotdict(build_config)\n\n if field_name == \"model\":\n build_config = self.update_input_types(build_config)\n\n # Validate required keys\n default_keys = [\n \"code\",\n \"_type\",\n \"model\",\n \"tools\",\n \"input_value\",\n \"add_current_date_tool\",\n \"add_calculator_tool\",\n \"system_prompt\",\n \"agent_description\",\n \"max_iterations\",\n \"handle_parsing_errors\",\n \"verbose\",\n ]\n missing_keys = [key for key in default_keys if key not in build_config]\n if missing_keys:\n msg = f\"Missing required keys in build_config: {missing_keys}\"\n raise ValueError(msg)\n return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n async def _get_tools(self) -> list[Tool]:\n component_toolkit = get_component_toolkit()\n tools_names = self._build_tools_names()\n agent_description = self.get_tool_description()\n # TODO: Agent Description Depreciated Feature to be removed\n description = f\"{agent_description}{tools_names}\"\n\n tools = component_toolkit(component=self).get_tools(\n tool_name=\"Call_Agent\",\n tool_description=description,\n # here we do not use the shared callbacks as we are exposing the agent as a tool\n callbacks=self.get_langchain_callbacks(),\n )\n if hasattr(self, \"tools_metadata\"):\n tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n\n return tools\n" }, "context_id": { "_input_type": "MessageTextInput", diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Sequential Tasks Agents.json b/src/backend/base/langflow/initial_setup/starter_projects/Sequential Tasks Agents.json index f90c18f87b..cf1ea06a03 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Sequential Tasks Agents.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Sequential Tasks Agents.json @@ -359,7 +359,7 @@ "last_updated": "2025-12-11T21:41:48.407Z", "legacy": false, "metadata": { - "code_hash": "ebac74bfcae6", + "code_hash": "443159257161", "dependencies": { "dependencies": [ { @@ -548,7 +548,7 @@ "show": true, "title_case": false, "type": "code", - "value": "from __future__ import annotations\n\nfrom contextlib import contextmanager\nfrom datetime import datetime, timezone\nfrom typing import TYPE_CHECKING, Any\n\nfrom lfx.components.models_and_agents.memory import MemoryComponent\n\nif TYPE_CHECKING:\n from langchain_core.tools import Tool\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.agents.default_system_prompt import DEFAULT_SYSTEM_PROMPT_TEMPLATE\nfrom lfx.base.agents.events import ExceptionWithMessageError\nfrom lfx.base.constants import STREAM_INFO_TEXT\nfrom lfx.base.models.unified_models import (\n get_language_model_options,\n get_llm,\n handle_model_input_update,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.components.agentics.helpers.model_config import validate_model_selection\nfrom lfx.components.helpers import CalculatorComponent, CurrentDateComponent\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.custom.custom_component.component import get_component_toolkit\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, ModelInput, StrInput\nfrom lfx.io import IntInput, MessageTextInput, MultilineInput, Output, SecretStrInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.schema.data import Data\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.schema.table import EditMode\n\n\ndef set_advanced_true(component_input):\n component_input.advanced = True\n return component_input\n\n\ndef _extract_text_content(value) -> str:\n \"\"\"Pull a string payload from a Message-like, AIMessage-like, or string value.\"\"\"\n if isinstance(value, str):\n return value\n text = getattr(value, \"text\", None)\n if isinstance(text, str):\n return text\n content = getattr(value, \"content\", None)\n if isinstance(content, str):\n return content\n return str(value) if value is not None else \"\"\n\n\n@contextmanager\ndef _suppress_send_message(component: Any):\n \"\"\"Temporarily replace component.send_message with a no-op for the duration of the block.\n\n Used during the structured-output prompt fallback: run_agent streams the agent's\n final answer through self.send_message (correct for message_response), but in\n json_response the orchestrator parses that text into structured Data which the\n downstream Chat Output emits — leaving the original emission in place produces a\n duplicate message in the playground. The original method is always restored on exit,\n even when the wrapped call raises.\n \"\"\"\n original = component.send_message\n\n async def _noop(message, *_args, **_kwargs):\n return message\n\n component.send_message = _noop\n try:\n yield\n finally:\n component.send_message = original\n\n\nclass AgentComponent(ToolCallingAgentComponent):\n display_name: str = \"Agent\"\n description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n documentation: str = \"https://docs.langflow.org/agents\"\n icon = \"bot\"\n beta = False\n name = \"Agent\"\n\n memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n inputs = [\n ModelInput(\n name=\"model\",\n display_name=\"Language Model\",\n info=\"Select your model provider\",\n real_time_refresh=True,\n required=True,\n # Agents require tool calling — the filter is honored by\n # ``handle_model_input_update`` so models that can't run with\n # tools never reach the picker (and any saved selection that\n # no longer satisfies the constraint is auto-replaced).\n filters={\"tool_calling\": True},\n ),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"API Key\",\n info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n real_time_refresh=True,\n advanced=True,\n ),\n DropdownInput(\n name=\"base_url_ibm_watsonx\",\n display_name=\"watsonx API Endpoint\",\n info=\"The base URL of the API (IBM watsonx.ai only)\",\n options=IBM_WATSONX_URLS,\n value=IBM_WATSONX_URLS[0],\n combobox=True,\n show=False,\n real_time_refresh=True,\n ),\n StrInput(\n name=\"project_id\",\n display_name=\"watsonx Project ID\",\n info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n show=False,\n required=False,\n ),\n MultilineInput(\n name=\"system_prompt\",\n display_name=\"Agent Instructions\",\n info=(\n \"System Prompt: Initial instructions and context provided to guide the agent's behavior. \"\n \"Supports dynamic placeholders: {current_date}, {model_name}, {optional_user_context}.\"\n ),\n value=DEFAULT_SYSTEM_PROMPT_TEMPLATE,\n advanced=False,\n ),\n MessageTextInput(\n name=\"context_id\",\n display_name=\"Context ID\",\n info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n value=\"\",\n advanced=True,\n ),\n IntInput(\n name=\"n_messages\",\n display_name=\"Number of Chat History Messages\",\n value=100,\n info=\"Number of chat history messages to retrieve.\",\n advanced=True,\n show=True,\n ),\n IntInput(\n name=\"max_tokens\",\n display_name=\"Max Tokens\",\n info=\"Maximum number of tokens to generate. Field name varies by provider.\",\n advanced=True,\n range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n ),\n MultilineInput(\n name=\"format_instructions\",\n display_name=\"Output Format Instructions\",\n info=\"Generic Template for structured output formatting. Valid only with Structured response.\",\n value=(\n \"You are an AI that extracts structured JSON objects from unstructured text. \"\n \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n \"Fill missing or ambiguous values with defaults: null for missing values. \"\n \"Remove exact duplicates but keep variations that have different field values. \"\n \"Always return valid JSON in the expected format, never throw errors. \"\n \"If multiple objects can be extracted, return them all in the structured format.\"\n ),\n advanced=True,\n ),\n TableInput(\n name=\"output_schema\",\n display_name=\"Output Schema\",\n info=(\n \"Schema Validation: Define the structure and data types for structured output. \"\n \"No validation if no output schema.\"\n ),\n advanced=True,\n required=False,\n value=[],\n table_schema=[\n {\n \"name\": \"name\",\n \"display_name\": \"Name\",\n \"type\": \"str\",\n \"description\": \"Specify the name of the output field.\",\n \"default\": \"field\",\n \"edit_mode\": EditMode.INLINE,\n },\n {\n \"name\": \"description\",\n \"display_name\": \"Description\",\n \"type\": \"str\",\n \"description\": \"Describe the purpose of the output field.\",\n \"default\": \"description of field\",\n \"edit_mode\": EditMode.POPOVER,\n },\n {\n \"name\": \"type\",\n \"display_name\": \"Type\",\n \"type\": \"str\",\n \"edit_mode\": EditMode.INLINE,\n \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n \"default\": \"str\",\n },\n {\n \"name\": \"multiple\",\n \"display_name\": \"As List\",\n \"type\": \"boolean\",\n \"description\": \"Set to True if this output field should be a list of the specified type.\",\n \"default\": \"False\",\n \"edit_mode\": EditMode.INLINE,\n },\n ],\n ),\n *LCToolsAgentComponent.get_base_inputs(),\n # removed memory inputs from agent component\n # *memory_inputs,\n BoolInput(\n name=\"stream\",\n display_name=\"Stream\",\n info=STREAM_INFO_TEXT,\n value=False,\n advanced=True,\n ),\n BoolInput(\n name=\"add_current_date_tool\",\n display_name=\"Current Date\",\n advanced=True,\n info=\"If true, will add a tool to the agent that returns the current date.\",\n value=True,\n ),\n BoolInput(\n name=\"add_calculator_tool\",\n display_name=\"Calculator\",\n advanced=True,\n info=(\n \"If true, adds a zero-config arithmetic calculator tool to the agent \"\n \"(safe: only +, -, *, /, ** operators via AST).\"\n ),\n value=True,\n ),\n ]\n outputs = [\n Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n Output(\n name=\"structured_response\",\n display_name=\"Structured Response\",\n method=\"json_response\",\n types=[\"Data\"],\n ),\n ]\n\n def _resolve_selected_model(self):\n \"\"\"Resolve the selected model, including legacy agent_llm/model_name inputs.\"\"\"\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n if isinstance(self.model, BaseLanguageModel):\n return self.model\n except ImportError:\n pass\n\n if isinstance(self.model, list) and self.model:\n return self.model\n\n legacy_provider = getattr(self, \"agent_llm\", None)\n legacy_model_name = getattr(self, \"model_name\", None)\n if not legacy_provider or not legacy_model_name:\n return self.model\n\n options = get_language_model_options(user_id=self.user_id)\n for option in options:\n if option.get(\"provider\") == legacy_provider and option.get(\"name\") == legacy_model_name:\n return [option]\n\n return [\n {\n \"name\": legacy_model_name,\n \"provider\": legacy_provider,\n \"metadata\": {},\n }\n ]\n\n def _get_max_tokens_value(self):\n \"\"\"Return the user-supplied max_tokens or None when unset/zero.\"\"\"\n val = getattr(self, \"max_tokens\", None)\n if val in {\"\", 0}:\n return None\n return val\n\n def _get_llm(self):\n \"\"\"Override parent to include max_tokens from the Agent's input field.\"\"\"\n return get_llm(\n model=self.model,\n user_id=self.user_id,\n api_key=getattr(self, \"api_key\", None),\n stream=bool(getattr(self, \"stream\", False)),\n max_tokens=self._get_max_tokens_value(),\n watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n watsonx_project_id=getattr(self, \"project_id\", None),\n )\n\n async def get_agent_requirements(self):\n \"\"\"Get the agent requirements for the agent.\"\"\"\n from langchain_core.tools import StructuredTool\n\n selected_model = self._resolve_selected_model()\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n is_connected_model = isinstance(selected_model, BaseLanguageModel)\n except ImportError:\n is_connected_model = False\n\n if not is_connected_model:\n validate_model_selection(selected_model)\n\n # Ensure _get_llm() uses the resolved model (e.g. from legacy agent_llm/model_name)\n self.model = selected_model\n llm_model = self._get_llm()\n if llm_model is None:\n msg = \"No language model selected. Please choose a model to proceed.\"\n raise ValueError(msg)\n\n # Get memory data\n self.chat_history = await self.get_memory_data()\n await logger.adebug(f\"Retrieved {len(self.chat_history)} chat history messages\")\n if isinstance(self.chat_history, Message):\n self.chat_history = [self.chat_history]\n\n # Add current date tool if enabled\n if self.add_current_date_tool:\n if not isinstance(self.tools, list): # type: ignore[has-type]\n self.tools = []\n current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n if not isinstance(current_date_tool, StructuredTool):\n msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n raise TypeError(msg)\n # Skip if an externally-connected tool already provides the same name.\n # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n if not any(getattr(t, \"name\", None) == current_date_tool.name for t in self.tools):\n self.tools.append(current_date_tool)\n\n # Add calculator tool if enabled (zero-config arithmetic)\n if getattr(self, \"add_calculator_tool\", False):\n if not isinstance(self.tools, list): # type: ignore[has-type]\n self.tools = []\n calculator_tool = (await CalculatorComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n if not isinstance(calculator_tool, StructuredTool):\n msg = \"CalculatorComponent must be converted to a StructuredTool\"\n raise TypeError(msg)\n # Skip if an externally-connected tool already provides the same name.\n # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n if not any(getattr(t, \"name\", None) == calculator_tool.name for t in self.tools):\n self.tools.append(calculator_tool)\n\n # Set shared callbacks for tracing the tools used by the agent\n self.set_tools_callbacks(self.tools, self._get_shared_callbacks())\n\n return llm_model, self.chat_history, self.tools\n\n def _get_resolved_model_name(self) -> str:\n \"\"\"Best-effort human-readable model name for {model_name} injection.\"\"\"\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n if isinstance(self.model, BaseLanguageModel):\n return type(self.model).__name__\n except ImportError:\n pass\n\n if isinstance(self.model, list) and self.model:\n first = self.model[0]\n if isinstance(first, dict):\n name = first.get(\"name\")\n if isinstance(name, str) and name:\n return name\n\n legacy_model_name = getattr(self, \"model_name\", None)\n if isinstance(legacy_model_name, str) and legacy_model_name:\n return legacy_model_name\n return \"\"\n\n def _inject_dynamic_prompt_values(self, prompt: str | None) -> str | None:\n \"\"\"Replace known env placeholders in the system prompt.\n\n Handles {current_date}, {model_name}, and {optional_user_context} (the\n last one ships with the structured DEFAULT_SYSTEM_PROMPT_TEMPLATE and\n is currently unused at the AgentComponent layer, so it resolves to \"\").\n Uses str.replace (not str.format) so user prompts containing literal\n braces such as JSON examples ({\"key\": 1}) never break the agent.\n \"\"\"\n if not prompt:\n return prompt\n replacements = {\n \"{current_date}\": datetime.now(tz=timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S UTC\"),\n \"{model_name}\": self._get_resolved_model_name(),\n \"{optional_user_context}\": \"\",\n }\n for placeholder, value in replacements.items():\n prompt = prompt.replace(placeholder, value)\n return prompt\n\n async def message_response(self) -> Message:\n try:\n llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n # Set up and run agent\n self.set(\n llm=llm_model,\n tools=self.tools or [],\n chat_history=self.chat_history,\n input_value=self.input_value,\n system_prompt=self._inject_dynamic_prompt_values(self.system_prompt),\n )\n agent = self.create_agent_runnable()\n result = await self.run_agent(agent)\n\n # Store result for potential JSON output\n self._agent_result = result\n\n except (ValueError, TypeError, KeyError) as e:\n await logger.aerror(f\"{type(e).__name__}: {e!s}\")\n raise\n except ExceptionWithMessageError as e:\n await logger.aerror(f\"ExceptionWithMessageError occurred: {e}\")\n raise\n # Avoid catching blind Exception; let truly unexpected exceptions propagate\n except Exception as e:\n await logger.aerror(f\"Unexpected error: {e!s}\")\n raise\n else:\n return result\n\n async def json_response(self) -> Data:\n \"\"\"Produce structured Data via native LLM structured output, with prompt-based fallback.\n\n Native path (no tools, llm has with_structured_output) bypasses the agent loop and\n returns provider-validated JSON. When tools are attached, falls back to running the\n agent with a schema-augmented system prompt and parsing the final message content.\n \"\"\"\n from lfx.components.models_and_agents.structured_output.structured_output_orchestrator import (\n orchestrate_structured_output,\n )\n\n try:\n llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n except (ValueError, TypeError) as exc:\n await logger.aerror(f\"json_response.requirements_failed: {exc}\")\n return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n injected_system_prompt = self._inject_dynamic_prompt_values(getattr(self, \"system_prompt\", \"\") or \"\") or \"\"\n format_instructions = getattr(self, \"format_instructions\", \"\") or \"\"\n output_schema = getattr(self, \"output_schema\", None) or []\n has_tools = bool(self.tools)\n\n async def _run_agent_for_fallback(augmented_prompt: str) -> str:\n self.set(\n llm=llm_model,\n tools=self.tools or [],\n chat_history=self.chat_history,\n input_value=self.input_value,\n system_prompt=augmented_prompt,\n )\n agent_runnable = self.create_agent_runnable()\n with _suppress_send_message(self):\n result = await self.run_agent(agent_runnable)\n return _extract_text_content(result)\n\n try:\n return await orchestrate_structured_output(\n llm=llm_model,\n output_schema=output_schema,\n system_prompt=injected_system_prompt,\n format_instructions=format_instructions,\n input_value=_extract_text_content(self.input_value),\n run_prompt_fallback=_run_agent_for_fallback,\n prefer_native=not has_tools,\n )\n except (\n ExceptionWithMessageError,\n ValueError,\n TypeError,\n NotImplementedError,\n AttributeError,\n ) as exc:\n await logger.aerror(f\"json_response.orchestration_failed: {exc}\")\n return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n async def get_memory_data(self):\n # TODO: This is a temporary fix to avoid message duplication. We should develop a function for this.\n messages = (\n await MemoryComponent(**self.get_base_args())\n .set(\n session_id=self.graph.session_id,\n context_id=self.context_id,\n order=\"Ascending\",\n n_messages=self.n_messages,\n )\n .retrieve_messages()\n )\n return [\n message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n ]\n\n def update_input_types(self, build_config: dotdict) -> dotdict:\n \"\"\"Update input types for all fields in build_config.\"\"\"\n for key, value in build_config.items():\n if isinstance(value, dict):\n if value.get(\"input_types\") is None:\n build_config[key][\"input_types\"] = []\n elif hasattr(value, \"input_types\") and value.input_types is None:\n value.input_types = []\n return build_config\n\n async def update_build_config(\n self,\n build_config: dotdict,\n field_value: list[dict],\n field_name: str | None = None,\n ) -> dotdict:\n # Update model options with caching (for all field changes).\n # The tool-calling constraint lives on the ModelInput's ``filters``\n # field (declared above); ``handle_model_input_update`` reads it\n # and applies the filter to both the dropdown options and the\n # sticky-default re-injection path.\n build_config = handle_model_input_update(\n component=self,\n build_config=dict(build_config),\n field_value=field_value,\n field_name=field_name,\n )\n build_config = dotdict(build_config)\n\n if field_name == \"model\":\n build_config = self.update_input_types(build_config)\n\n # Validate required keys\n default_keys = [\n \"code\",\n \"_type\",\n \"model\",\n \"tools\",\n \"input_value\",\n \"add_current_date_tool\",\n \"add_calculator_tool\",\n \"system_prompt\",\n \"agent_description\",\n \"max_iterations\",\n \"handle_parsing_errors\",\n \"verbose\",\n ]\n missing_keys = [key for key in default_keys if key not in build_config]\n if missing_keys:\n msg = f\"Missing required keys in build_config: {missing_keys}\"\n raise ValueError(msg)\n return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n async def _get_tools(self) -> list[Tool]:\n component_toolkit = get_component_toolkit()\n tools_names = self._build_tools_names()\n agent_description = self.get_tool_description()\n # TODO: Agent Description Depreciated Feature to be removed\n description = f\"{agent_description}{tools_names}\"\n\n tools = component_toolkit(component=self).get_tools(\n tool_name=\"Call_Agent\",\n tool_description=description,\n # here we do not use the shared callbacks as we are exposing the agent as a tool\n callbacks=self.get_langchain_callbacks(),\n )\n if hasattr(self, \"tools_metadata\"):\n tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n\n return tools\n" + "value": "from __future__ import annotations\n\nfrom contextlib import contextmanager\nfrom datetime import datetime, timezone\nfrom typing import TYPE_CHECKING, Any\n\nfrom lfx.components.models_and_agents.memory import MemoryComponent, aget_agent_chat_history\n\nif TYPE_CHECKING:\n from langchain_core.tools import Tool\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.agents.default_system_prompt import DEFAULT_SYSTEM_PROMPT_TEMPLATE\nfrom lfx.base.agents.events import ExceptionWithMessageError\nfrom lfx.base.constants import STREAM_INFO_TEXT\nfrom lfx.base.models.unified_models import (\n get_language_model_options,\n get_llm,\n handle_model_input_update,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.components.agentics.helpers.model_config import validate_model_selection\nfrom lfx.components.helpers import CalculatorComponent, CurrentDateComponent\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.custom.custom_component.component import get_component_toolkit\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, ModelInput, StrInput\nfrom lfx.io import IntInput, MessageTextInput, MultilineInput, Output, SecretStrInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.schema.data import Data\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.schema.table import EditMode\n\n\ndef set_advanced_true(component_input):\n component_input.advanced = True\n return component_input\n\n\ndef _extract_text_content(value) -> str:\n \"\"\"Pull a string payload from a Message-like, AIMessage-like, or string value.\"\"\"\n if isinstance(value, str):\n return value\n text = getattr(value, \"text\", None)\n if isinstance(text, str):\n return text\n content = getattr(value, \"content\", None)\n if isinstance(content, str):\n return content\n return str(value) if value is not None else \"\"\n\n\n@contextmanager\ndef _suppress_send_message(component: Any):\n \"\"\"Temporarily replace component.send_message with a no-op for the duration of the block.\n\n Used during the structured-output prompt fallback: run_agent streams the agent's\n final answer through self.send_message (correct for message_response), but in\n json_response the orchestrator parses that text into structured Data which the\n downstream Chat Output emits — leaving the original emission in place produces a\n duplicate message in the playground. The original method is always restored on exit,\n even when the wrapped call raises.\n \"\"\"\n original = component.send_message\n\n async def _noop(message, *_args, **_kwargs):\n return message\n\n component.send_message = _noop\n try:\n yield\n finally:\n component.send_message = original\n\n\nclass AgentComponent(ToolCallingAgentComponent):\n display_name: str = \"Agent\"\n description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n documentation: str = \"https://docs.langflow.org/agents\"\n icon = \"bot\"\n beta = False\n name = \"Agent\"\n\n memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n inputs = [\n ModelInput(\n name=\"model\",\n display_name=\"Language Model\",\n info=\"Select your model provider\",\n real_time_refresh=True,\n required=True,\n # Agents require tool calling — the filter is honored by\n # ``handle_model_input_update`` so models that can't run with\n # tools never reach the picker (and any saved selection that\n # no longer satisfies the constraint is auto-replaced).\n filters={\"tool_calling\": True},\n ),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"API Key\",\n info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n real_time_refresh=True,\n advanced=True,\n ),\n DropdownInput(\n name=\"base_url_ibm_watsonx\",\n display_name=\"watsonx API Endpoint\",\n info=\"The base URL of the API (IBM watsonx.ai only)\",\n options=IBM_WATSONX_URLS,\n value=IBM_WATSONX_URLS[0],\n combobox=True,\n show=False,\n real_time_refresh=True,\n ),\n StrInput(\n name=\"project_id\",\n display_name=\"watsonx Project ID\",\n info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n show=False,\n required=False,\n ),\n MultilineInput(\n name=\"system_prompt\",\n display_name=\"Agent Instructions\",\n info=(\n \"System Prompt: Initial instructions and context provided to guide the agent's behavior. \"\n \"Supports dynamic placeholders: {current_date}, {model_name}, {optional_user_context}.\"\n ),\n value=DEFAULT_SYSTEM_PROMPT_TEMPLATE,\n advanced=False,\n ),\n MessageTextInput(\n name=\"context_id\",\n display_name=\"Context ID\",\n info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n value=\"\",\n advanced=True,\n ),\n IntInput(\n name=\"n_messages\",\n display_name=\"Number of Chat History Messages\",\n value=100,\n info=\"Number of chat history messages to retrieve.\",\n advanced=True,\n show=True,\n ),\n IntInput(\n name=\"max_tokens\",\n display_name=\"Max Tokens\",\n info=\"Maximum number of tokens to generate. Field name varies by provider.\",\n advanced=True,\n range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n ),\n MultilineInput(\n name=\"format_instructions\",\n display_name=\"Output Format Instructions\",\n info=\"Generic Template for structured output formatting. Valid only with Structured response.\",\n value=(\n \"You are an AI that extracts structured JSON objects from unstructured text. \"\n \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n \"Fill missing or ambiguous values with defaults: null for missing values. \"\n \"Remove exact duplicates but keep variations that have different field values. \"\n \"Always return valid JSON in the expected format, never throw errors. \"\n \"If multiple objects can be extracted, return them all in the structured format.\"\n ),\n advanced=True,\n ),\n TableInput(\n name=\"output_schema\",\n display_name=\"Output Schema\",\n info=(\n \"Schema Validation: Define the structure and data types for structured output. \"\n \"No validation if no output schema.\"\n ),\n advanced=True,\n required=False,\n value=[],\n table_schema=[\n {\n \"name\": \"name\",\n \"display_name\": \"Name\",\n \"type\": \"str\",\n \"description\": \"Specify the name of the output field.\",\n \"default\": \"field\",\n \"edit_mode\": EditMode.INLINE,\n },\n {\n \"name\": \"description\",\n \"display_name\": \"Description\",\n \"type\": \"str\",\n \"description\": \"Describe the purpose of the output field.\",\n \"default\": \"description of field\",\n \"edit_mode\": EditMode.POPOVER,\n },\n {\n \"name\": \"type\",\n \"display_name\": \"Type\",\n \"type\": \"str\",\n \"edit_mode\": EditMode.INLINE,\n \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n \"default\": \"str\",\n },\n {\n \"name\": \"multiple\",\n \"display_name\": \"As List\",\n \"type\": \"boolean\",\n \"description\": \"Set to True if this output field should be a list of the specified type.\",\n \"default\": \"False\",\n \"edit_mode\": EditMode.INLINE,\n },\n ],\n ),\n *LCToolsAgentComponent.get_base_inputs(),\n # removed memory inputs from agent component\n # *memory_inputs,\n BoolInput(\n name=\"stream\",\n display_name=\"Stream\",\n info=STREAM_INFO_TEXT,\n value=False,\n advanced=True,\n ),\n BoolInput(\n name=\"add_current_date_tool\",\n display_name=\"Current Date\",\n advanced=True,\n info=\"If true, will add a tool to the agent that returns the current date.\",\n value=True,\n ),\n BoolInput(\n name=\"add_calculator_tool\",\n display_name=\"Calculator\",\n advanced=True,\n info=(\n \"If true, adds a zero-config arithmetic calculator tool to the agent \"\n \"(safe: only +, -, *, /, ** operators via AST).\"\n ),\n value=True,\n ),\n ]\n outputs = [\n Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n Output(\n name=\"structured_response\",\n display_name=\"Structured Response\",\n method=\"json_response\",\n types=[\"Data\"],\n ),\n ]\n\n def _resolve_selected_model(self):\n \"\"\"Resolve the selected model, including legacy agent_llm/model_name inputs.\"\"\"\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n if isinstance(self.model, BaseLanguageModel):\n return self.model\n except ImportError:\n pass\n\n if isinstance(self.model, list) and self.model:\n return self.model\n\n legacy_provider = getattr(self, \"agent_llm\", None)\n legacy_model_name = getattr(self, \"model_name\", None)\n if not legacy_provider or not legacy_model_name:\n return self.model\n\n options = get_language_model_options(user_id=self.user_id)\n for option in options:\n if option.get(\"provider\") == legacy_provider and option.get(\"name\") == legacy_model_name:\n return [option]\n\n return [\n {\n \"name\": legacy_model_name,\n \"provider\": legacy_provider,\n \"metadata\": {},\n }\n ]\n\n def _get_max_tokens_value(self):\n \"\"\"Return the user-supplied max_tokens or None when unset/zero.\"\"\"\n val = getattr(self, \"max_tokens\", None)\n if val in {\"\", 0}:\n return None\n return val\n\n def _get_llm(self):\n \"\"\"Override parent to include max_tokens from the Agent's input field.\"\"\"\n return get_llm(\n model=self.model,\n user_id=self.user_id,\n api_key=getattr(self, \"api_key\", None),\n stream=bool(getattr(self, \"stream\", False)),\n max_tokens=self._get_max_tokens_value(),\n watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n watsonx_project_id=getattr(self, \"project_id\", None),\n )\n\n async def get_agent_requirements(self):\n \"\"\"Get the agent requirements for the agent.\"\"\"\n from langchain_core.tools import StructuredTool\n\n selected_model = self._resolve_selected_model()\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n is_connected_model = isinstance(selected_model, BaseLanguageModel)\n except ImportError:\n is_connected_model = False\n\n if not is_connected_model:\n validate_model_selection(selected_model)\n\n # Ensure _get_llm() uses the resolved model (e.g. from legacy agent_llm/model_name)\n self.model = selected_model\n llm_model = self._get_llm()\n if llm_model is None:\n msg = \"No language model selected. Please choose a model to proceed.\"\n raise ValueError(msg)\n\n # Get memory data\n self.chat_history = await self.get_memory_data()\n await logger.adebug(f\"Retrieved {len(self.chat_history)} chat history messages\")\n if isinstance(self.chat_history, Message):\n self.chat_history = [self.chat_history]\n\n # Add current date tool if enabled\n if self.add_current_date_tool:\n if not isinstance(self.tools, list): # type: ignore[has-type]\n self.tools = []\n current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n if not isinstance(current_date_tool, StructuredTool):\n msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n raise TypeError(msg)\n # Skip if an externally-connected tool already provides the same name.\n # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n if not any(getattr(t, \"name\", None) == current_date_tool.name for t in self.tools):\n self.tools.append(current_date_tool)\n\n # Add calculator tool if enabled (zero-config arithmetic)\n if getattr(self, \"add_calculator_tool\", False):\n if not isinstance(self.tools, list): # type: ignore[has-type]\n self.tools = []\n calculator_tool = (await CalculatorComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n if not isinstance(calculator_tool, StructuredTool):\n msg = \"CalculatorComponent must be converted to a StructuredTool\"\n raise TypeError(msg)\n # Skip if an externally-connected tool already provides the same name.\n # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n if not any(getattr(t, \"name\", None) == calculator_tool.name for t in self.tools):\n self.tools.append(calculator_tool)\n\n # Set shared callbacks for tracing the tools used by the agent\n self.set_tools_callbacks(self.tools, self._get_shared_callbacks())\n\n return llm_model, self.chat_history, self.tools\n\n def _get_resolved_model_name(self) -> str:\n \"\"\"Best-effort human-readable model name for {model_name} injection.\"\"\"\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n if isinstance(self.model, BaseLanguageModel):\n return type(self.model).__name__\n except ImportError:\n pass\n\n if isinstance(self.model, list) and self.model:\n first = self.model[0]\n if isinstance(first, dict):\n name = first.get(\"name\")\n if isinstance(name, str) and name:\n return name\n\n legacy_model_name = getattr(self, \"model_name\", None)\n if isinstance(legacy_model_name, str) and legacy_model_name:\n return legacy_model_name\n return \"\"\n\n def _inject_dynamic_prompt_values(self, prompt: str | None) -> str | None:\n \"\"\"Replace known env placeholders in the system prompt.\n\n Handles {current_date}, {model_name}, and {optional_user_context} (the\n last one ships with the structured DEFAULT_SYSTEM_PROMPT_TEMPLATE and\n is currently unused at the AgentComponent layer, so it resolves to \"\").\n Uses str.replace (not str.format) so user prompts containing literal\n braces such as JSON examples ({\"key\": 1}) never break the agent.\n \"\"\"\n if not prompt:\n return prompt\n replacements = {\n \"{current_date}\": datetime.now(tz=timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S UTC\"),\n \"{model_name}\": self._get_resolved_model_name(),\n \"{optional_user_context}\": \"\",\n }\n for placeholder, value in replacements.items():\n prompt = prompt.replace(placeholder, value)\n return prompt\n\n async def message_response(self) -> Message:\n try:\n llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n # Set up and run agent\n self.set(\n llm=llm_model,\n tools=self.tools or [],\n chat_history=self.chat_history,\n input_value=self.input_value,\n system_prompt=self._inject_dynamic_prompt_values(self.system_prompt),\n )\n agent = self.create_agent_runnable()\n result = await self.run_agent(agent)\n\n # Store result for potential JSON output\n self._agent_result = result\n\n except (ValueError, TypeError, KeyError) as e:\n await logger.aerror(f\"{type(e).__name__}: {e!s}\")\n raise\n except ExceptionWithMessageError as e:\n await logger.aerror(f\"ExceptionWithMessageError occurred: {e}\")\n raise\n # Avoid catching blind Exception; let truly unexpected exceptions propagate\n except Exception as e:\n await logger.aerror(f\"Unexpected error: {e!s}\")\n raise\n else:\n return result\n\n async def json_response(self) -> Data:\n \"\"\"Produce structured Data via native LLM structured output, with prompt-based fallback.\n\n Native path (no tools, llm has with_structured_output) bypasses the agent loop and\n returns provider-validated JSON. When tools are attached, falls back to running the\n agent with a schema-augmented system prompt and parsing the final message content.\n \"\"\"\n from lfx.components.models_and_agents.structured_output.structured_output_orchestrator import (\n orchestrate_structured_output,\n )\n\n try:\n llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n except (ValueError, TypeError) as exc:\n await logger.aerror(f\"json_response.requirements_failed: {exc}\")\n return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n injected_system_prompt = self._inject_dynamic_prompt_values(getattr(self, \"system_prompt\", \"\") or \"\") or \"\"\n format_instructions = getattr(self, \"format_instructions\", \"\") or \"\"\n output_schema = getattr(self, \"output_schema\", None) or []\n has_tools = bool(self.tools)\n\n async def _run_agent_for_fallback(augmented_prompt: str) -> str:\n self.set(\n llm=llm_model,\n tools=self.tools or [],\n chat_history=self.chat_history,\n input_value=self.input_value,\n system_prompt=augmented_prompt,\n )\n agent_runnable = self.create_agent_runnable()\n with _suppress_send_message(self):\n result = await self.run_agent(agent_runnable)\n return _extract_text_content(result)\n\n try:\n return await orchestrate_structured_output(\n llm=llm_model,\n output_schema=output_schema,\n system_prompt=injected_system_prompt,\n format_instructions=format_instructions,\n input_value=_extract_text_content(self.input_value),\n run_prompt_fallback=_run_agent_for_fallback,\n prefer_native=not has_tools,\n )\n except (\n ExceptionWithMessageError,\n ValueError,\n TypeError,\n NotImplementedError,\n AttributeError,\n ) as exc:\n await logger.aerror(f\"json_response.orchestration_failed: {exc}\")\n return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n async def get_memory_data(self):\n # Scope by flow_id so default playground session names (e.g. \"New Session 0\")\n # cannot leak chat history across unrelated flows. See issue #13059.\n # The helper also returns [] when n_messages == 0, preserving the\n # explicit \"memory disabled\" contract from MemoryComponent.retrieve_messages.\n messages = await aget_agent_chat_history(\n session_id=self.graph.session_id,\n flow_id=getattr(self.graph, \"flow_id\", None),\n context_id=self.context_id,\n n_messages=self.n_messages,\n )\n return [\n message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n ]\n\n def update_input_types(self, build_config: dotdict) -> dotdict:\n \"\"\"Update input types for all fields in build_config.\"\"\"\n for key, value in build_config.items():\n if isinstance(value, dict):\n if value.get(\"input_types\") is None:\n build_config[key][\"input_types\"] = []\n elif hasattr(value, \"input_types\") and value.input_types is None:\n value.input_types = []\n return build_config\n\n async def update_build_config(\n self,\n build_config: dotdict,\n field_value: list[dict],\n field_name: str | None = None,\n ) -> dotdict:\n # Update model options with caching (for all field changes).\n # The tool-calling constraint lives on the ModelInput's ``filters``\n # field (declared above); ``handle_model_input_update`` reads it\n # and applies the filter to both the dropdown options and the\n # sticky-default re-injection path.\n build_config = handle_model_input_update(\n component=self,\n build_config=dict(build_config),\n field_value=field_value,\n field_name=field_name,\n )\n build_config = dotdict(build_config)\n\n if field_name == \"model\":\n build_config = self.update_input_types(build_config)\n\n # Validate required keys\n default_keys = [\n \"code\",\n \"_type\",\n \"model\",\n \"tools\",\n \"input_value\",\n \"add_current_date_tool\",\n \"add_calculator_tool\",\n \"system_prompt\",\n \"agent_description\",\n \"max_iterations\",\n \"handle_parsing_errors\",\n \"verbose\",\n ]\n missing_keys = [key for key in default_keys if key not in build_config]\n if missing_keys:\n msg = f\"Missing required keys in build_config: {missing_keys}\"\n raise ValueError(msg)\n return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n async def _get_tools(self) -> list[Tool]:\n component_toolkit = get_component_toolkit()\n tools_names = self._build_tools_names()\n agent_description = self.get_tool_description()\n # TODO: Agent Description Depreciated Feature to be removed\n description = f\"{agent_description}{tools_names}\"\n\n tools = component_toolkit(component=self).get_tools(\n tool_name=\"Call_Agent\",\n tool_description=description,\n # here we do not use the shared callbacks as we are exposing the agent as a tool\n callbacks=self.get_langchain_callbacks(),\n )\n if hasattr(self, \"tools_metadata\"):\n tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n\n return tools\n" }, "context_id": { "_input_type": "MessageTextInput", @@ -1000,7 +1000,7 @@ "last_updated": "2025-12-11T21:41:48.407Z", "legacy": false, "metadata": { - "code_hash": "ebac74bfcae6", + "code_hash": "443159257161", "dependencies": { "dependencies": [ { @@ -1189,7 +1189,7 @@ "show": true, "title_case": false, "type": "code", - "value": "from __future__ import annotations\n\nfrom contextlib import contextmanager\nfrom datetime import datetime, timezone\nfrom typing import TYPE_CHECKING, Any\n\nfrom lfx.components.models_and_agents.memory import MemoryComponent\n\nif TYPE_CHECKING:\n from langchain_core.tools import Tool\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.agents.default_system_prompt import DEFAULT_SYSTEM_PROMPT_TEMPLATE\nfrom lfx.base.agents.events import ExceptionWithMessageError\nfrom lfx.base.constants import STREAM_INFO_TEXT\nfrom lfx.base.models.unified_models import (\n get_language_model_options,\n get_llm,\n handle_model_input_update,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.components.agentics.helpers.model_config import validate_model_selection\nfrom lfx.components.helpers import CalculatorComponent, CurrentDateComponent\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.custom.custom_component.component import get_component_toolkit\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, ModelInput, StrInput\nfrom lfx.io import IntInput, MessageTextInput, MultilineInput, Output, SecretStrInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.schema.data import Data\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.schema.table import EditMode\n\n\ndef set_advanced_true(component_input):\n component_input.advanced = True\n return component_input\n\n\ndef _extract_text_content(value) -> str:\n \"\"\"Pull a string payload from a Message-like, AIMessage-like, or string value.\"\"\"\n if isinstance(value, str):\n return value\n text = getattr(value, \"text\", None)\n if isinstance(text, str):\n return text\n content = getattr(value, \"content\", None)\n if isinstance(content, str):\n return content\n return str(value) if value is not None else \"\"\n\n\n@contextmanager\ndef _suppress_send_message(component: Any):\n \"\"\"Temporarily replace component.send_message with a no-op for the duration of the block.\n\n Used during the structured-output prompt fallback: run_agent streams the agent's\n final answer through self.send_message (correct for message_response), but in\n json_response the orchestrator parses that text into structured Data which the\n downstream Chat Output emits — leaving the original emission in place produces a\n duplicate message in the playground. The original method is always restored on exit,\n even when the wrapped call raises.\n \"\"\"\n original = component.send_message\n\n async def _noop(message, *_args, **_kwargs):\n return message\n\n component.send_message = _noop\n try:\n yield\n finally:\n component.send_message = original\n\n\nclass AgentComponent(ToolCallingAgentComponent):\n display_name: str = \"Agent\"\n description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n documentation: str = \"https://docs.langflow.org/agents\"\n icon = \"bot\"\n beta = False\n name = \"Agent\"\n\n memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n inputs = [\n ModelInput(\n name=\"model\",\n display_name=\"Language Model\",\n info=\"Select your model provider\",\n real_time_refresh=True,\n required=True,\n # Agents require tool calling — the filter is honored by\n # ``handle_model_input_update`` so models that can't run with\n # tools never reach the picker (and any saved selection that\n # no longer satisfies the constraint is auto-replaced).\n filters={\"tool_calling\": True},\n ),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"API Key\",\n info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n real_time_refresh=True,\n advanced=True,\n ),\n DropdownInput(\n name=\"base_url_ibm_watsonx\",\n display_name=\"watsonx API Endpoint\",\n info=\"The base URL of the API (IBM watsonx.ai only)\",\n options=IBM_WATSONX_URLS,\n value=IBM_WATSONX_URLS[0],\n combobox=True,\n show=False,\n real_time_refresh=True,\n ),\n StrInput(\n name=\"project_id\",\n display_name=\"watsonx Project ID\",\n info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n show=False,\n required=False,\n ),\n MultilineInput(\n name=\"system_prompt\",\n display_name=\"Agent Instructions\",\n info=(\n \"System Prompt: Initial instructions and context provided to guide the agent's behavior. \"\n \"Supports dynamic placeholders: {current_date}, {model_name}, {optional_user_context}.\"\n ),\n value=DEFAULT_SYSTEM_PROMPT_TEMPLATE,\n advanced=False,\n ),\n MessageTextInput(\n name=\"context_id\",\n display_name=\"Context ID\",\n info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n value=\"\",\n advanced=True,\n ),\n IntInput(\n name=\"n_messages\",\n display_name=\"Number of Chat History Messages\",\n value=100,\n info=\"Number of chat history messages to retrieve.\",\n advanced=True,\n show=True,\n ),\n IntInput(\n name=\"max_tokens\",\n display_name=\"Max Tokens\",\n info=\"Maximum number of tokens to generate. Field name varies by provider.\",\n advanced=True,\n range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n ),\n MultilineInput(\n name=\"format_instructions\",\n display_name=\"Output Format Instructions\",\n info=\"Generic Template for structured output formatting. Valid only with Structured response.\",\n value=(\n \"You are an AI that extracts structured JSON objects from unstructured text. \"\n \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n \"Fill missing or ambiguous values with defaults: null for missing values. \"\n \"Remove exact duplicates but keep variations that have different field values. \"\n \"Always return valid JSON in the expected format, never throw errors. \"\n \"If multiple objects can be extracted, return them all in the structured format.\"\n ),\n advanced=True,\n ),\n TableInput(\n name=\"output_schema\",\n display_name=\"Output Schema\",\n info=(\n \"Schema Validation: Define the structure and data types for structured output. \"\n \"No validation if no output schema.\"\n ),\n advanced=True,\n required=False,\n value=[],\n table_schema=[\n {\n \"name\": \"name\",\n \"display_name\": \"Name\",\n \"type\": \"str\",\n \"description\": \"Specify the name of the output field.\",\n \"default\": \"field\",\n \"edit_mode\": EditMode.INLINE,\n },\n {\n \"name\": \"description\",\n \"display_name\": \"Description\",\n \"type\": \"str\",\n \"description\": \"Describe the purpose of the output field.\",\n \"default\": \"description of field\",\n \"edit_mode\": EditMode.POPOVER,\n },\n {\n \"name\": \"type\",\n \"display_name\": \"Type\",\n \"type\": \"str\",\n \"edit_mode\": EditMode.INLINE,\n \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n \"default\": \"str\",\n },\n {\n \"name\": \"multiple\",\n \"display_name\": \"As List\",\n \"type\": \"boolean\",\n \"description\": \"Set to True if this output field should be a list of the specified type.\",\n \"default\": \"False\",\n \"edit_mode\": EditMode.INLINE,\n },\n ],\n ),\n *LCToolsAgentComponent.get_base_inputs(),\n # removed memory inputs from agent component\n # *memory_inputs,\n BoolInput(\n name=\"stream\",\n display_name=\"Stream\",\n info=STREAM_INFO_TEXT,\n value=False,\n advanced=True,\n ),\n BoolInput(\n name=\"add_current_date_tool\",\n display_name=\"Current Date\",\n advanced=True,\n info=\"If true, will add a tool to the agent that returns the current date.\",\n value=True,\n ),\n BoolInput(\n name=\"add_calculator_tool\",\n display_name=\"Calculator\",\n advanced=True,\n info=(\n \"If true, adds a zero-config arithmetic calculator tool to the agent \"\n \"(safe: only +, -, *, /, ** operators via AST).\"\n ),\n value=True,\n ),\n ]\n outputs = [\n Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n Output(\n name=\"structured_response\",\n display_name=\"Structured Response\",\n method=\"json_response\",\n types=[\"Data\"],\n ),\n ]\n\n def _resolve_selected_model(self):\n \"\"\"Resolve the selected model, including legacy agent_llm/model_name inputs.\"\"\"\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n if isinstance(self.model, BaseLanguageModel):\n return self.model\n except ImportError:\n pass\n\n if isinstance(self.model, list) and self.model:\n return self.model\n\n legacy_provider = getattr(self, \"agent_llm\", None)\n legacy_model_name = getattr(self, \"model_name\", None)\n if not legacy_provider or not legacy_model_name:\n return self.model\n\n options = get_language_model_options(user_id=self.user_id)\n for option in options:\n if option.get(\"provider\") == legacy_provider and option.get(\"name\") == legacy_model_name:\n return [option]\n\n return [\n {\n \"name\": legacy_model_name,\n \"provider\": legacy_provider,\n \"metadata\": {},\n }\n ]\n\n def _get_max_tokens_value(self):\n \"\"\"Return the user-supplied max_tokens or None when unset/zero.\"\"\"\n val = getattr(self, \"max_tokens\", None)\n if val in {\"\", 0}:\n return None\n return val\n\n def _get_llm(self):\n \"\"\"Override parent to include max_tokens from the Agent's input field.\"\"\"\n return get_llm(\n model=self.model,\n user_id=self.user_id,\n api_key=getattr(self, \"api_key\", None),\n stream=bool(getattr(self, \"stream\", False)),\n max_tokens=self._get_max_tokens_value(),\n watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n watsonx_project_id=getattr(self, \"project_id\", None),\n )\n\n async def get_agent_requirements(self):\n \"\"\"Get the agent requirements for the agent.\"\"\"\n from langchain_core.tools import StructuredTool\n\n selected_model = self._resolve_selected_model()\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n is_connected_model = isinstance(selected_model, BaseLanguageModel)\n except ImportError:\n is_connected_model = False\n\n if not is_connected_model:\n validate_model_selection(selected_model)\n\n # Ensure _get_llm() uses the resolved model (e.g. from legacy agent_llm/model_name)\n self.model = selected_model\n llm_model = self._get_llm()\n if llm_model is None:\n msg = \"No language model selected. Please choose a model to proceed.\"\n raise ValueError(msg)\n\n # Get memory data\n self.chat_history = await self.get_memory_data()\n await logger.adebug(f\"Retrieved {len(self.chat_history)} chat history messages\")\n if isinstance(self.chat_history, Message):\n self.chat_history = [self.chat_history]\n\n # Add current date tool if enabled\n if self.add_current_date_tool:\n if not isinstance(self.tools, list): # type: ignore[has-type]\n self.tools = []\n current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n if not isinstance(current_date_tool, StructuredTool):\n msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n raise TypeError(msg)\n # Skip if an externally-connected tool already provides the same name.\n # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n if not any(getattr(t, \"name\", None) == current_date_tool.name for t in self.tools):\n self.tools.append(current_date_tool)\n\n # Add calculator tool if enabled (zero-config arithmetic)\n if getattr(self, \"add_calculator_tool\", False):\n if not isinstance(self.tools, list): # type: ignore[has-type]\n self.tools = []\n calculator_tool = (await CalculatorComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n if not isinstance(calculator_tool, StructuredTool):\n msg = \"CalculatorComponent must be converted to a StructuredTool\"\n raise TypeError(msg)\n # Skip if an externally-connected tool already provides the same name.\n # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n if not any(getattr(t, \"name\", None) == calculator_tool.name for t in self.tools):\n self.tools.append(calculator_tool)\n\n # Set shared callbacks for tracing the tools used by the agent\n self.set_tools_callbacks(self.tools, self._get_shared_callbacks())\n\n return llm_model, self.chat_history, self.tools\n\n def _get_resolved_model_name(self) -> str:\n \"\"\"Best-effort human-readable model name for {model_name} injection.\"\"\"\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n if isinstance(self.model, BaseLanguageModel):\n return type(self.model).__name__\n except ImportError:\n pass\n\n if isinstance(self.model, list) and self.model:\n first = self.model[0]\n if isinstance(first, dict):\n name = first.get(\"name\")\n if isinstance(name, str) and name:\n return name\n\n legacy_model_name = getattr(self, \"model_name\", None)\n if isinstance(legacy_model_name, str) and legacy_model_name:\n return legacy_model_name\n return \"\"\n\n def _inject_dynamic_prompt_values(self, prompt: str | None) -> str | None:\n \"\"\"Replace known env placeholders in the system prompt.\n\n Handles {current_date}, {model_name}, and {optional_user_context} (the\n last one ships with the structured DEFAULT_SYSTEM_PROMPT_TEMPLATE and\n is currently unused at the AgentComponent layer, so it resolves to \"\").\n Uses str.replace (not str.format) so user prompts containing literal\n braces such as JSON examples ({\"key\": 1}) never break the agent.\n \"\"\"\n if not prompt:\n return prompt\n replacements = {\n \"{current_date}\": datetime.now(tz=timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S UTC\"),\n \"{model_name}\": self._get_resolved_model_name(),\n \"{optional_user_context}\": \"\",\n }\n for placeholder, value in replacements.items():\n prompt = prompt.replace(placeholder, value)\n return prompt\n\n async def message_response(self) -> Message:\n try:\n llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n # Set up and run agent\n self.set(\n llm=llm_model,\n tools=self.tools or [],\n chat_history=self.chat_history,\n input_value=self.input_value,\n system_prompt=self._inject_dynamic_prompt_values(self.system_prompt),\n )\n agent = self.create_agent_runnable()\n result = await self.run_agent(agent)\n\n # Store result for potential JSON output\n self._agent_result = result\n\n except (ValueError, TypeError, KeyError) as e:\n await logger.aerror(f\"{type(e).__name__}: {e!s}\")\n raise\n except ExceptionWithMessageError as e:\n await logger.aerror(f\"ExceptionWithMessageError occurred: {e}\")\n raise\n # Avoid catching blind Exception; let truly unexpected exceptions propagate\n except Exception as e:\n await logger.aerror(f\"Unexpected error: {e!s}\")\n raise\n else:\n return result\n\n async def json_response(self) -> Data:\n \"\"\"Produce structured Data via native LLM structured output, with prompt-based fallback.\n\n Native path (no tools, llm has with_structured_output) bypasses the agent loop and\n returns provider-validated JSON. When tools are attached, falls back to running the\n agent with a schema-augmented system prompt and parsing the final message content.\n \"\"\"\n from lfx.components.models_and_agents.structured_output.structured_output_orchestrator import (\n orchestrate_structured_output,\n )\n\n try:\n llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n except (ValueError, TypeError) as exc:\n await logger.aerror(f\"json_response.requirements_failed: {exc}\")\n return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n injected_system_prompt = self._inject_dynamic_prompt_values(getattr(self, \"system_prompt\", \"\") or \"\") or \"\"\n format_instructions = getattr(self, \"format_instructions\", \"\") or \"\"\n output_schema = getattr(self, \"output_schema\", None) or []\n has_tools = bool(self.tools)\n\n async def _run_agent_for_fallback(augmented_prompt: str) -> str:\n self.set(\n llm=llm_model,\n tools=self.tools or [],\n chat_history=self.chat_history,\n input_value=self.input_value,\n system_prompt=augmented_prompt,\n )\n agent_runnable = self.create_agent_runnable()\n with _suppress_send_message(self):\n result = await self.run_agent(agent_runnable)\n return _extract_text_content(result)\n\n try:\n return await orchestrate_structured_output(\n llm=llm_model,\n output_schema=output_schema,\n system_prompt=injected_system_prompt,\n format_instructions=format_instructions,\n input_value=_extract_text_content(self.input_value),\n run_prompt_fallback=_run_agent_for_fallback,\n prefer_native=not has_tools,\n )\n except (\n ExceptionWithMessageError,\n ValueError,\n TypeError,\n NotImplementedError,\n AttributeError,\n ) as exc:\n await logger.aerror(f\"json_response.orchestration_failed: {exc}\")\n return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n async def get_memory_data(self):\n # TODO: This is a temporary fix to avoid message duplication. We should develop a function for this.\n messages = (\n await MemoryComponent(**self.get_base_args())\n .set(\n session_id=self.graph.session_id,\n context_id=self.context_id,\n order=\"Ascending\",\n n_messages=self.n_messages,\n )\n .retrieve_messages()\n )\n return [\n message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n ]\n\n def update_input_types(self, build_config: dotdict) -> dotdict:\n \"\"\"Update input types for all fields in build_config.\"\"\"\n for key, value in build_config.items():\n if isinstance(value, dict):\n if value.get(\"input_types\") is None:\n build_config[key][\"input_types\"] = []\n elif hasattr(value, \"input_types\") and value.input_types is None:\n value.input_types = []\n return build_config\n\n async def update_build_config(\n self,\n build_config: dotdict,\n field_value: list[dict],\n field_name: str | None = None,\n ) -> dotdict:\n # Update model options with caching (for all field changes).\n # The tool-calling constraint lives on the ModelInput's ``filters``\n # field (declared above); ``handle_model_input_update`` reads it\n # and applies the filter to both the dropdown options and the\n # sticky-default re-injection path.\n build_config = handle_model_input_update(\n component=self,\n build_config=dict(build_config),\n field_value=field_value,\n field_name=field_name,\n )\n build_config = dotdict(build_config)\n\n if field_name == \"model\":\n build_config = self.update_input_types(build_config)\n\n # Validate required keys\n default_keys = [\n \"code\",\n \"_type\",\n \"model\",\n \"tools\",\n \"input_value\",\n \"add_current_date_tool\",\n \"add_calculator_tool\",\n \"system_prompt\",\n \"agent_description\",\n \"max_iterations\",\n \"handle_parsing_errors\",\n \"verbose\",\n ]\n missing_keys = [key for key in default_keys if key not in build_config]\n if missing_keys:\n msg = f\"Missing required keys in build_config: {missing_keys}\"\n raise ValueError(msg)\n return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n async def _get_tools(self) -> list[Tool]:\n component_toolkit = get_component_toolkit()\n tools_names = self._build_tools_names()\n agent_description = self.get_tool_description()\n # TODO: Agent Description Depreciated Feature to be removed\n description = f\"{agent_description}{tools_names}\"\n\n tools = component_toolkit(component=self).get_tools(\n tool_name=\"Call_Agent\",\n tool_description=description,\n # here we do not use the shared callbacks as we are exposing the agent as a tool\n callbacks=self.get_langchain_callbacks(),\n )\n if hasattr(self, \"tools_metadata\"):\n tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n\n return tools\n" + "value": "from __future__ import annotations\n\nfrom contextlib import contextmanager\nfrom datetime import datetime, timezone\nfrom typing import TYPE_CHECKING, Any\n\nfrom lfx.components.models_and_agents.memory import MemoryComponent, aget_agent_chat_history\n\nif TYPE_CHECKING:\n from langchain_core.tools import Tool\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.agents.default_system_prompt import DEFAULT_SYSTEM_PROMPT_TEMPLATE\nfrom lfx.base.agents.events import ExceptionWithMessageError\nfrom lfx.base.constants import STREAM_INFO_TEXT\nfrom lfx.base.models.unified_models import (\n get_language_model_options,\n get_llm,\n handle_model_input_update,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.components.agentics.helpers.model_config import validate_model_selection\nfrom lfx.components.helpers import CalculatorComponent, CurrentDateComponent\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.custom.custom_component.component import get_component_toolkit\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, ModelInput, StrInput\nfrom lfx.io import IntInput, MessageTextInput, MultilineInput, Output, SecretStrInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.schema.data import Data\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.schema.table import EditMode\n\n\ndef set_advanced_true(component_input):\n component_input.advanced = True\n return component_input\n\n\ndef _extract_text_content(value) -> str:\n \"\"\"Pull a string payload from a Message-like, AIMessage-like, or string value.\"\"\"\n if isinstance(value, str):\n return value\n text = getattr(value, \"text\", None)\n if isinstance(text, str):\n return text\n content = getattr(value, \"content\", None)\n if isinstance(content, str):\n return content\n return str(value) if value is not None else \"\"\n\n\n@contextmanager\ndef _suppress_send_message(component: Any):\n \"\"\"Temporarily replace component.send_message with a no-op for the duration of the block.\n\n Used during the structured-output prompt fallback: run_agent streams the agent's\n final answer through self.send_message (correct for message_response), but in\n json_response the orchestrator parses that text into structured Data which the\n downstream Chat Output emits — leaving the original emission in place produces a\n duplicate message in the playground. The original method is always restored on exit,\n even when the wrapped call raises.\n \"\"\"\n original = component.send_message\n\n async def _noop(message, *_args, **_kwargs):\n return message\n\n component.send_message = _noop\n try:\n yield\n finally:\n component.send_message = original\n\n\nclass AgentComponent(ToolCallingAgentComponent):\n display_name: str = \"Agent\"\n description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n documentation: str = \"https://docs.langflow.org/agents\"\n icon = \"bot\"\n beta = False\n name = \"Agent\"\n\n memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n inputs = [\n ModelInput(\n name=\"model\",\n display_name=\"Language Model\",\n info=\"Select your model provider\",\n real_time_refresh=True,\n required=True,\n # Agents require tool calling — the filter is honored by\n # ``handle_model_input_update`` so models that can't run with\n # tools never reach the picker (and any saved selection that\n # no longer satisfies the constraint is auto-replaced).\n filters={\"tool_calling\": True},\n ),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"API Key\",\n info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n real_time_refresh=True,\n advanced=True,\n ),\n DropdownInput(\n name=\"base_url_ibm_watsonx\",\n display_name=\"watsonx API Endpoint\",\n info=\"The base URL of the API (IBM watsonx.ai only)\",\n options=IBM_WATSONX_URLS,\n value=IBM_WATSONX_URLS[0],\n combobox=True,\n show=False,\n real_time_refresh=True,\n ),\n StrInput(\n name=\"project_id\",\n display_name=\"watsonx Project ID\",\n info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n show=False,\n required=False,\n ),\n MultilineInput(\n name=\"system_prompt\",\n display_name=\"Agent Instructions\",\n info=(\n \"System Prompt: Initial instructions and context provided to guide the agent's behavior. \"\n \"Supports dynamic placeholders: {current_date}, {model_name}, {optional_user_context}.\"\n ),\n value=DEFAULT_SYSTEM_PROMPT_TEMPLATE,\n advanced=False,\n ),\n MessageTextInput(\n name=\"context_id\",\n display_name=\"Context ID\",\n info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n value=\"\",\n advanced=True,\n ),\n IntInput(\n name=\"n_messages\",\n display_name=\"Number of Chat History Messages\",\n value=100,\n info=\"Number of chat history messages to retrieve.\",\n advanced=True,\n show=True,\n ),\n IntInput(\n name=\"max_tokens\",\n display_name=\"Max Tokens\",\n info=\"Maximum number of tokens to generate. Field name varies by provider.\",\n advanced=True,\n range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n ),\n MultilineInput(\n name=\"format_instructions\",\n display_name=\"Output Format Instructions\",\n info=\"Generic Template for structured output formatting. Valid only with Structured response.\",\n value=(\n \"You are an AI that extracts structured JSON objects from unstructured text. \"\n \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n \"Fill missing or ambiguous values with defaults: null for missing values. \"\n \"Remove exact duplicates but keep variations that have different field values. \"\n \"Always return valid JSON in the expected format, never throw errors. \"\n \"If multiple objects can be extracted, return them all in the structured format.\"\n ),\n advanced=True,\n ),\n TableInput(\n name=\"output_schema\",\n display_name=\"Output Schema\",\n info=(\n \"Schema Validation: Define the structure and data types for structured output. \"\n \"No validation if no output schema.\"\n ),\n advanced=True,\n required=False,\n value=[],\n table_schema=[\n {\n \"name\": \"name\",\n \"display_name\": \"Name\",\n \"type\": \"str\",\n \"description\": \"Specify the name of the output field.\",\n \"default\": \"field\",\n \"edit_mode\": EditMode.INLINE,\n },\n {\n \"name\": \"description\",\n \"display_name\": \"Description\",\n \"type\": \"str\",\n \"description\": \"Describe the purpose of the output field.\",\n \"default\": \"description of field\",\n \"edit_mode\": EditMode.POPOVER,\n },\n {\n \"name\": \"type\",\n \"display_name\": \"Type\",\n \"type\": \"str\",\n \"edit_mode\": EditMode.INLINE,\n \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n \"default\": \"str\",\n },\n {\n \"name\": \"multiple\",\n \"display_name\": \"As List\",\n \"type\": \"boolean\",\n \"description\": \"Set to True if this output field should be a list of the specified type.\",\n \"default\": \"False\",\n \"edit_mode\": EditMode.INLINE,\n },\n ],\n ),\n *LCToolsAgentComponent.get_base_inputs(),\n # removed memory inputs from agent component\n # *memory_inputs,\n BoolInput(\n name=\"stream\",\n display_name=\"Stream\",\n info=STREAM_INFO_TEXT,\n value=False,\n advanced=True,\n ),\n BoolInput(\n name=\"add_current_date_tool\",\n display_name=\"Current Date\",\n advanced=True,\n info=\"If true, will add a tool to the agent that returns the current date.\",\n value=True,\n ),\n BoolInput(\n name=\"add_calculator_tool\",\n display_name=\"Calculator\",\n advanced=True,\n info=(\n \"If true, adds a zero-config arithmetic calculator tool to the agent \"\n \"(safe: only +, -, *, /, ** operators via AST).\"\n ),\n value=True,\n ),\n ]\n outputs = [\n Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n Output(\n name=\"structured_response\",\n display_name=\"Structured Response\",\n method=\"json_response\",\n types=[\"Data\"],\n ),\n ]\n\n def _resolve_selected_model(self):\n \"\"\"Resolve the selected model, including legacy agent_llm/model_name inputs.\"\"\"\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n if isinstance(self.model, BaseLanguageModel):\n return self.model\n except ImportError:\n pass\n\n if isinstance(self.model, list) and self.model:\n return self.model\n\n legacy_provider = getattr(self, \"agent_llm\", None)\n legacy_model_name = getattr(self, \"model_name\", None)\n if not legacy_provider or not legacy_model_name:\n return self.model\n\n options = get_language_model_options(user_id=self.user_id)\n for option in options:\n if option.get(\"provider\") == legacy_provider and option.get(\"name\") == legacy_model_name:\n return [option]\n\n return [\n {\n \"name\": legacy_model_name,\n \"provider\": legacy_provider,\n \"metadata\": {},\n }\n ]\n\n def _get_max_tokens_value(self):\n \"\"\"Return the user-supplied max_tokens or None when unset/zero.\"\"\"\n val = getattr(self, \"max_tokens\", None)\n if val in {\"\", 0}:\n return None\n return val\n\n def _get_llm(self):\n \"\"\"Override parent to include max_tokens from the Agent's input field.\"\"\"\n return get_llm(\n model=self.model,\n user_id=self.user_id,\n api_key=getattr(self, \"api_key\", None),\n stream=bool(getattr(self, \"stream\", False)),\n max_tokens=self._get_max_tokens_value(),\n watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n watsonx_project_id=getattr(self, \"project_id\", None),\n )\n\n async def get_agent_requirements(self):\n \"\"\"Get the agent requirements for the agent.\"\"\"\n from langchain_core.tools import StructuredTool\n\n selected_model = self._resolve_selected_model()\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n is_connected_model = isinstance(selected_model, BaseLanguageModel)\n except ImportError:\n is_connected_model = False\n\n if not is_connected_model:\n validate_model_selection(selected_model)\n\n # Ensure _get_llm() uses the resolved model (e.g. from legacy agent_llm/model_name)\n self.model = selected_model\n llm_model = self._get_llm()\n if llm_model is None:\n msg = \"No language model selected. Please choose a model to proceed.\"\n raise ValueError(msg)\n\n # Get memory data\n self.chat_history = await self.get_memory_data()\n await logger.adebug(f\"Retrieved {len(self.chat_history)} chat history messages\")\n if isinstance(self.chat_history, Message):\n self.chat_history = [self.chat_history]\n\n # Add current date tool if enabled\n if self.add_current_date_tool:\n if not isinstance(self.tools, list): # type: ignore[has-type]\n self.tools = []\n current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n if not isinstance(current_date_tool, StructuredTool):\n msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n raise TypeError(msg)\n # Skip if an externally-connected tool already provides the same name.\n # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n if not any(getattr(t, \"name\", None) == current_date_tool.name for t in self.tools):\n self.tools.append(current_date_tool)\n\n # Add calculator tool if enabled (zero-config arithmetic)\n if getattr(self, \"add_calculator_tool\", False):\n if not isinstance(self.tools, list): # type: ignore[has-type]\n self.tools = []\n calculator_tool = (await CalculatorComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n if not isinstance(calculator_tool, StructuredTool):\n msg = \"CalculatorComponent must be converted to a StructuredTool\"\n raise TypeError(msg)\n # Skip if an externally-connected tool already provides the same name.\n # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n if not any(getattr(t, \"name\", None) == calculator_tool.name for t in self.tools):\n self.tools.append(calculator_tool)\n\n # Set shared callbacks for tracing the tools used by the agent\n self.set_tools_callbacks(self.tools, self._get_shared_callbacks())\n\n return llm_model, self.chat_history, self.tools\n\n def _get_resolved_model_name(self) -> str:\n \"\"\"Best-effort human-readable model name for {model_name} injection.\"\"\"\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n if isinstance(self.model, BaseLanguageModel):\n return type(self.model).__name__\n except ImportError:\n pass\n\n if isinstance(self.model, list) and self.model:\n first = self.model[0]\n if isinstance(first, dict):\n name = first.get(\"name\")\n if isinstance(name, str) and name:\n return name\n\n legacy_model_name = getattr(self, \"model_name\", None)\n if isinstance(legacy_model_name, str) and legacy_model_name:\n return legacy_model_name\n return \"\"\n\n def _inject_dynamic_prompt_values(self, prompt: str | None) -> str | None:\n \"\"\"Replace known env placeholders in the system prompt.\n\n Handles {current_date}, {model_name}, and {optional_user_context} (the\n last one ships with the structured DEFAULT_SYSTEM_PROMPT_TEMPLATE and\n is currently unused at the AgentComponent layer, so it resolves to \"\").\n Uses str.replace (not str.format) so user prompts containing literal\n braces such as JSON examples ({\"key\": 1}) never break the agent.\n \"\"\"\n if not prompt:\n return prompt\n replacements = {\n \"{current_date}\": datetime.now(tz=timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S UTC\"),\n \"{model_name}\": self._get_resolved_model_name(),\n \"{optional_user_context}\": \"\",\n }\n for placeholder, value in replacements.items():\n prompt = prompt.replace(placeholder, value)\n return prompt\n\n async def message_response(self) -> Message:\n try:\n llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n # Set up and run agent\n self.set(\n llm=llm_model,\n tools=self.tools or [],\n chat_history=self.chat_history,\n input_value=self.input_value,\n system_prompt=self._inject_dynamic_prompt_values(self.system_prompt),\n )\n agent = self.create_agent_runnable()\n result = await self.run_agent(agent)\n\n # Store result for potential JSON output\n self._agent_result = result\n\n except (ValueError, TypeError, KeyError) as e:\n await logger.aerror(f\"{type(e).__name__}: {e!s}\")\n raise\n except ExceptionWithMessageError as e:\n await logger.aerror(f\"ExceptionWithMessageError occurred: {e}\")\n raise\n # Avoid catching blind Exception; let truly unexpected exceptions propagate\n except Exception as e:\n await logger.aerror(f\"Unexpected error: {e!s}\")\n raise\n else:\n return result\n\n async def json_response(self) -> Data:\n \"\"\"Produce structured Data via native LLM structured output, with prompt-based fallback.\n\n Native path (no tools, llm has with_structured_output) bypasses the agent loop and\n returns provider-validated JSON. When tools are attached, falls back to running the\n agent with a schema-augmented system prompt and parsing the final message content.\n \"\"\"\n from lfx.components.models_and_agents.structured_output.structured_output_orchestrator import (\n orchestrate_structured_output,\n )\n\n try:\n llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n except (ValueError, TypeError) as exc:\n await logger.aerror(f\"json_response.requirements_failed: {exc}\")\n return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n injected_system_prompt = self._inject_dynamic_prompt_values(getattr(self, \"system_prompt\", \"\") or \"\") or \"\"\n format_instructions = getattr(self, \"format_instructions\", \"\") or \"\"\n output_schema = getattr(self, \"output_schema\", None) or []\n has_tools = bool(self.tools)\n\n async def _run_agent_for_fallback(augmented_prompt: str) -> str:\n self.set(\n llm=llm_model,\n tools=self.tools or [],\n chat_history=self.chat_history,\n input_value=self.input_value,\n system_prompt=augmented_prompt,\n )\n agent_runnable = self.create_agent_runnable()\n with _suppress_send_message(self):\n result = await self.run_agent(agent_runnable)\n return _extract_text_content(result)\n\n try:\n return await orchestrate_structured_output(\n llm=llm_model,\n output_schema=output_schema,\n system_prompt=injected_system_prompt,\n format_instructions=format_instructions,\n input_value=_extract_text_content(self.input_value),\n run_prompt_fallback=_run_agent_for_fallback,\n prefer_native=not has_tools,\n )\n except (\n ExceptionWithMessageError,\n ValueError,\n TypeError,\n NotImplementedError,\n AttributeError,\n ) as exc:\n await logger.aerror(f\"json_response.orchestration_failed: {exc}\")\n return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n async def get_memory_data(self):\n # Scope by flow_id so default playground session names (e.g. \"New Session 0\")\n # cannot leak chat history across unrelated flows. See issue #13059.\n # The helper also returns [] when n_messages == 0, preserving the\n # explicit \"memory disabled\" contract from MemoryComponent.retrieve_messages.\n messages = await aget_agent_chat_history(\n session_id=self.graph.session_id,\n flow_id=getattr(self.graph, \"flow_id\", None),\n context_id=self.context_id,\n n_messages=self.n_messages,\n )\n return [\n message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n ]\n\n def update_input_types(self, build_config: dotdict) -> dotdict:\n \"\"\"Update input types for all fields in build_config.\"\"\"\n for key, value in build_config.items():\n if isinstance(value, dict):\n if value.get(\"input_types\") is None:\n build_config[key][\"input_types\"] = []\n elif hasattr(value, \"input_types\") and value.input_types is None:\n value.input_types = []\n return build_config\n\n async def update_build_config(\n self,\n build_config: dotdict,\n field_value: list[dict],\n field_name: str | None = None,\n ) -> dotdict:\n # Update model options with caching (for all field changes).\n # The tool-calling constraint lives on the ModelInput's ``filters``\n # field (declared above); ``handle_model_input_update`` reads it\n # and applies the filter to both the dropdown options and the\n # sticky-default re-injection path.\n build_config = handle_model_input_update(\n component=self,\n build_config=dict(build_config),\n field_value=field_value,\n field_name=field_name,\n )\n build_config = dotdict(build_config)\n\n if field_name == \"model\":\n build_config = self.update_input_types(build_config)\n\n # Validate required keys\n default_keys = [\n \"code\",\n \"_type\",\n \"model\",\n \"tools\",\n \"input_value\",\n \"add_current_date_tool\",\n \"add_calculator_tool\",\n \"system_prompt\",\n \"agent_description\",\n \"max_iterations\",\n \"handle_parsing_errors\",\n \"verbose\",\n ]\n missing_keys = [key for key in default_keys if key not in build_config]\n if missing_keys:\n msg = f\"Missing required keys in build_config: {missing_keys}\"\n raise ValueError(msg)\n return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n async def _get_tools(self) -> list[Tool]:\n component_toolkit = get_component_toolkit()\n tools_names = self._build_tools_names()\n agent_description = self.get_tool_description()\n # TODO: Agent Description Depreciated Feature to be removed\n description = f\"{agent_description}{tools_names}\"\n\n tools = component_toolkit(component=self).get_tools(\n tool_name=\"Call_Agent\",\n tool_description=description,\n # here we do not use the shared callbacks as we are exposing the agent as a tool\n callbacks=self.get_langchain_callbacks(),\n )\n if hasattr(self, \"tools_metadata\"):\n tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n\n return tools\n" }, "context_id": { "_input_type": "MessageTextInput", @@ -2499,7 +2499,7 @@ "last_updated": "2025-12-11T21:41:48.407Z", "legacy": false, "metadata": { - "code_hash": "ebac74bfcae6", + "code_hash": "443159257161", "dependencies": { "dependencies": [ { @@ -2688,7 +2688,7 @@ "show": true, "title_case": false, "type": "code", - "value": "from __future__ import annotations\n\nfrom contextlib import contextmanager\nfrom datetime import datetime, timezone\nfrom typing import TYPE_CHECKING, Any\n\nfrom lfx.components.models_and_agents.memory import MemoryComponent\n\nif TYPE_CHECKING:\n from langchain_core.tools import Tool\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.agents.default_system_prompt import DEFAULT_SYSTEM_PROMPT_TEMPLATE\nfrom lfx.base.agents.events import ExceptionWithMessageError\nfrom lfx.base.constants import STREAM_INFO_TEXT\nfrom lfx.base.models.unified_models import (\n get_language_model_options,\n get_llm,\n handle_model_input_update,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.components.agentics.helpers.model_config import validate_model_selection\nfrom lfx.components.helpers import CalculatorComponent, CurrentDateComponent\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.custom.custom_component.component import get_component_toolkit\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, ModelInput, StrInput\nfrom lfx.io import IntInput, MessageTextInput, MultilineInput, Output, SecretStrInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.schema.data import Data\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.schema.table import EditMode\n\n\ndef set_advanced_true(component_input):\n component_input.advanced = True\n return component_input\n\n\ndef _extract_text_content(value) -> str:\n \"\"\"Pull a string payload from a Message-like, AIMessage-like, or string value.\"\"\"\n if isinstance(value, str):\n return value\n text = getattr(value, \"text\", None)\n if isinstance(text, str):\n return text\n content = getattr(value, \"content\", None)\n if isinstance(content, str):\n return content\n return str(value) if value is not None else \"\"\n\n\n@contextmanager\ndef _suppress_send_message(component: Any):\n \"\"\"Temporarily replace component.send_message with a no-op for the duration of the block.\n\n Used during the structured-output prompt fallback: run_agent streams the agent's\n final answer through self.send_message (correct for message_response), but in\n json_response the orchestrator parses that text into structured Data which the\n downstream Chat Output emits — leaving the original emission in place produces a\n duplicate message in the playground. The original method is always restored on exit,\n even when the wrapped call raises.\n \"\"\"\n original = component.send_message\n\n async def _noop(message, *_args, **_kwargs):\n return message\n\n component.send_message = _noop\n try:\n yield\n finally:\n component.send_message = original\n\n\nclass AgentComponent(ToolCallingAgentComponent):\n display_name: str = \"Agent\"\n description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n documentation: str = \"https://docs.langflow.org/agents\"\n icon = \"bot\"\n beta = False\n name = \"Agent\"\n\n memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n inputs = [\n ModelInput(\n name=\"model\",\n display_name=\"Language Model\",\n info=\"Select your model provider\",\n real_time_refresh=True,\n required=True,\n # Agents require tool calling — the filter is honored by\n # ``handle_model_input_update`` so models that can't run with\n # tools never reach the picker (and any saved selection that\n # no longer satisfies the constraint is auto-replaced).\n filters={\"tool_calling\": True},\n ),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"API Key\",\n info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n real_time_refresh=True,\n advanced=True,\n ),\n DropdownInput(\n name=\"base_url_ibm_watsonx\",\n display_name=\"watsonx API Endpoint\",\n info=\"The base URL of the API (IBM watsonx.ai only)\",\n options=IBM_WATSONX_URLS,\n value=IBM_WATSONX_URLS[0],\n combobox=True,\n show=False,\n real_time_refresh=True,\n ),\n StrInput(\n name=\"project_id\",\n display_name=\"watsonx Project ID\",\n info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n show=False,\n required=False,\n ),\n MultilineInput(\n name=\"system_prompt\",\n display_name=\"Agent Instructions\",\n info=(\n \"System Prompt: Initial instructions and context provided to guide the agent's behavior. \"\n \"Supports dynamic placeholders: {current_date}, {model_name}, {optional_user_context}.\"\n ),\n value=DEFAULT_SYSTEM_PROMPT_TEMPLATE,\n advanced=False,\n ),\n MessageTextInput(\n name=\"context_id\",\n display_name=\"Context ID\",\n info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n value=\"\",\n advanced=True,\n ),\n IntInput(\n name=\"n_messages\",\n display_name=\"Number of Chat History Messages\",\n value=100,\n info=\"Number of chat history messages to retrieve.\",\n advanced=True,\n show=True,\n ),\n IntInput(\n name=\"max_tokens\",\n display_name=\"Max Tokens\",\n info=\"Maximum number of tokens to generate. Field name varies by provider.\",\n advanced=True,\n range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n ),\n MultilineInput(\n name=\"format_instructions\",\n display_name=\"Output Format Instructions\",\n info=\"Generic Template for structured output formatting. Valid only with Structured response.\",\n value=(\n \"You are an AI that extracts structured JSON objects from unstructured text. \"\n \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n \"Fill missing or ambiguous values with defaults: null for missing values. \"\n \"Remove exact duplicates but keep variations that have different field values. \"\n \"Always return valid JSON in the expected format, never throw errors. \"\n \"If multiple objects can be extracted, return them all in the structured format.\"\n ),\n advanced=True,\n ),\n TableInput(\n name=\"output_schema\",\n display_name=\"Output Schema\",\n info=(\n \"Schema Validation: Define the structure and data types for structured output. \"\n \"No validation if no output schema.\"\n ),\n advanced=True,\n required=False,\n value=[],\n table_schema=[\n {\n \"name\": \"name\",\n \"display_name\": \"Name\",\n \"type\": \"str\",\n \"description\": \"Specify the name of the output field.\",\n \"default\": \"field\",\n \"edit_mode\": EditMode.INLINE,\n },\n {\n \"name\": \"description\",\n \"display_name\": \"Description\",\n \"type\": \"str\",\n \"description\": \"Describe the purpose of the output field.\",\n \"default\": \"description of field\",\n \"edit_mode\": EditMode.POPOVER,\n },\n {\n \"name\": \"type\",\n \"display_name\": \"Type\",\n \"type\": \"str\",\n \"edit_mode\": EditMode.INLINE,\n \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n \"default\": \"str\",\n },\n {\n \"name\": \"multiple\",\n \"display_name\": \"As List\",\n \"type\": \"boolean\",\n \"description\": \"Set to True if this output field should be a list of the specified type.\",\n \"default\": \"False\",\n \"edit_mode\": EditMode.INLINE,\n },\n ],\n ),\n *LCToolsAgentComponent.get_base_inputs(),\n # removed memory inputs from agent component\n # *memory_inputs,\n BoolInput(\n name=\"stream\",\n display_name=\"Stream\",\n info=STREAM_INFO_TEXT,\n value=False,\n advanced=True,\n ),\n BoolInput(\n name=\"add_current_date_tool\",\n display_name=\"Current Date\",\n advanced=True,\n info=\"If true, will add a tool to the agent that returns the current date.\",\n value=True,\n ),\n BoolInput(\n name=\"add_calculator_tool\",\n display_name=\"Calculator\",\n advanced=True,\n info=(\n \"If true, adds a zero-config arithmetic calculator tool to the agent \"\n \"(safe: only +, -, *, /, ** operators via AST).\"\n ),\n value=True,\n ),\n ]\n outputs = [\n Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n Output(\n name=\"structured_response\",\n display_name=\"Structured Response\",\n method=\"json_response\",\n types=[\"Data\"],\n ),\n ]\n\n def _resolve_selected_model(self):\n \"\"\"Resolve the selected model, including legacy agent_llm/model_name inputs.\"\"\"\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n if isinstance(self.model, BaseLanguageModel):\n return self.model\n except ImportError:\n pass\n\n if isinstance(self.model, list) and self.model:\n return self.model\n\n legacy_provider = getattr(self, \"agent_llm\", None)\n legacy_model_name = getattr(self, \"model_name\", None)\n if not legacy_provider or not legacy_model_name:\n return self.model\n\n options = get_language_model_options(user_id=self.user_id)\n for option in options:\n if option.get(\"provider\") == legacy_provider and option.get(\"name\") == legacy_model_name:\n return [option]\n\n return [\n {\n \"name\": legacy_model_name,\n \"provider\": legacy_provider,\n \"metadata\": {},\n }\n ]\n\n def _get_max_tokens_value(self):\n \"\"\"Return the user-supplied max_tokens or None when unset/zero.\"\"\"\n val = getattr(self, \"max_tokens\", None)\n if val in {\"\", 0}:\n return None\n return val\n\n def _get_llm(self):\n \"\"\"Override parent to include max_tokens from the Agent's input field.\"\"\"\n return get_llm(\n model=self.model,\n user_id=self.user_id,\n api_key=getattr(self, \"api_key\", None),\n stream=bool(getattr(self, \"stream\", False)),\n max_tokens=self._get_max_tokens_value(),\n watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n watsonx_project_id=getattr(self, \"project_id\", None),\n )\n\n async def get_agent_requirements(self):\n \"\"\"Get the agent requirements for the agent.\"\"\"\n from langchain_core.tools import StructuredTool\n\n selected_model = self._resolve_selected_model()\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n is_connected_model = isinstance(selected_model, BaseLanguageModel)\n except ImportError:\n is_connected_model = False\n\n if not is_connected_model:\n validate_model_selection(selected_model)\n\n # Ensure _get_llm() uses the resolved model (e.g. from legacy agent_llm/model_name)\n self.model = selected_model\n llm_model = self._get_llm()\n if llm_model is None:\n msg = \"No language model selected. Please choose a model to proceed.\"\n raise ValueError(msg)\n\n # Get memory data\n self.chat_history = await self.get_memory_data()\n await logger.adebug(f\"Retrieved {len(self.chat_history)} chat history messages\")\n if isinstance(self.chat_history, Message):\n self.chat_history = [self.chat_history]\n\n # Add current date tool if enabled\n if self.add_current_date_tool:\n if not isinstance(self.tools, list): # type: ignore[has-type]\n self.tools = []\n current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n if not isinstance(current_date_tool, StructuredTool):\n msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n raise TypeError(msg)\n # Skip if an externally-connected tool already provides the same name.\n # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n if not any(getattr(t, \"name\", None) == current_date_tool.name for t in self.tools):\n self.tools.append(current_date_tool)\n\n # Add calculator tool if enabled (zero-config arithmetic)\n if getattr(self, \"add_calculator_tool\", False):\n if not isinstance(self.tools, list): # type: ignore[has-type]\n self.tools = []\n calculator_tool = (await CalculatorComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n if not isinstance(calculator_tool, StructuredTool):\n msg = \"CalculatorComponent must be converted to a StructuredTool\"\n raise TypeError(msg)\n # Skip if an externally-connected tool already provides the same name.\n # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n if not any(getattr(t, \"name\", None) == calculator_tool.name for t in self.tools):\n self.tools.append(calculator_tool)\n\n # Set shared callbacks for tracing the tools used by the agent\n self.set_tools_callbacks(self.tools, self._get_shared_callbacks())\n\n return llm_model, self.chat_history, self.tools\n\n def _get_resolved_model_name(self) -> str:\n \"\"\"Best-effort human-readable model name for {model_name} injection.\"\"\"\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n if isinstance(self.model, BaseLanguageModel):\n return type(self.model).__name__\n except ImportError:\n pass\n\n if isinstance(self.model, list) and self.model:\n first = self.model[0]\n if isinstance(first, dict):\n name = first.get(\"name\")\n if isinstance(name, str) and name:\n return name\n\n legacy_model_name = getattr(self, \"model_name\", None)\n if isinstance(legacy_model_name, str) and legacy_model_name:\n return legacy_model_name\n return \"\"\n\n def _inject_dynamic_prompt_values(self, prompt: str | None) -> str | None:\n \"\"\"Replace known env placeholders in the system prompt.\n\n Handles {current_date}, {model_name}, and {optional_user_context} (the\n last one ships with the structured DEFAULT_SYSTEM_PROMPT_TEMPLATE and\n is currently unused at the AgentComponent layer, so it resolves to \"\").\n Uses str.replace (not str.format) so user prompts containing literal\n braces such as JSON examples ({\"key\": 1}) never break the agent.\n \"\"\"\n if not prompt:\n return prompt\n replacements = {\n \"{current_date}\": datetime.now(tz=timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S UTC\"),\n \"{model_name}\": self._get_resolved_model_name(),\n \"{optional_user_context}\": \"\",\n }\n for placeholder, value in replacements.items():\n prompt = prompt.replace(placeholder, value)\n return prompt\n\n async def message_response(self) -> Message:\n try:\n llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n # Set up and run agent\n self.set(\n llm=llm_model,\n tools=self.tools or [],\n chat_history=self.chat_history,\n input_value=self.input_value,\n system_prompt=self._inject_dynamic_prompt_values(self.system_prompt),\n )\n agent = self.create_agent_runnable()\n result = await self.run_agent(agent)\n\n # Store result for potential JSON output\n self._agent_result = result\n\n except (ValueError, TypeError, KeyError) as e:\n await logger.aerror(f\"{type(e).__name__}: {e!s}\")\n raise\n except ExceptionWithMessageError as e:\n await logger.aerror(f\"ExceptionWithMessageError occurred: {e}\")\n raise\n # Avoid catching blind Exception; let truly unexpected exceptions propagate\n except Exception as e:\n await logger.aerror(f\"Unexpected error: {e!s}\")\n raise\n else:\n return result\n\n async def json_response(self) -> Data:\n \"\"\"Produce structured Data via native LLM structured output, with prompt-based fallback.\n\n Native path (no tools, llm has with_structured_output) bypasses the agent loop and\n returns provider-validated JSON. When tools are attached, falls back to running the\n agent with a schema-augmented system prompt and parsing the final message content.\n \"\"\"\n from lfx.components.models_and_agents.structured_output.structured_output_orchestrator import (\n orchestrate_structured_output,\n )\n\n try:\n llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n except (ValueError, TypeError) as exc:\n await logger.aerror(f\"json_response.requirements_failed: {exc}\")\n return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n injected_system_prompt = self._inject_dynamic_prompt_values(getattr(self, \"system_prompt\", \"\") or \"\") or \"\"\n format_instructions = getattr(self, \"format_instructions\", \"\") or \"\"\n output_schema = getattr(self, \"output_schema\", None) or []\n has_tools = bool(self.tools)\n\n async def _run_agent_for_fallback(augmented_prompt: str) -> str:\n self.set(\n llm=llm_model,\n tools=self.tools or [],\n chat_history=self.chat_history,\n input_value=self.input_value,\n system_prompt=augmented_prompt,\n )\n agent_runnable = self.create_agent_runnable()\n with _suppress_send_message(self):\n result = await self.run_agent(agent_runnable)\n return _extract_text_content(result)\n\n try:\n return await orchestrate_structured_output(\n llm=llm_model,\n output_schema=output_schema,\n system_prompt=injected_system_prompt,\n format_instructions=format_instructions,\n input_value=_extract_text_content(self.input_value),\n run_prompt_fallback=_run_agent_for_fallback,\n prefer_native=not has_tools,\n )\n except (\n ExceptionWithMessageError,\n ValueError,\n TypeError,\n NotImplementedError,\n AttributeError,\n ) as exc:\n await logger.aerror(f\"json_response.orchestration_failed: {exc}\")\n return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n async def get_memory_data(self):\n # TODO: This is a temporary fix to avoid message duplication. We should develop a function for this.\n messages = (\n await MemoryComponent(**self.get_base_args())\n .set(\n session_id=self.graph.session_id,\n context_id=self.context_id,\n order=\"Ascending\",\n n_messages=self.n_messages,\n )\n .retrieve_messages()\n )\n return [\n message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n ]\n\n def update_input_types(self, build_config: dotdict) -> dotdict:\n \"\"\"Update input types for all fields in build_config.\"\"\"\n for key, value in build_config.items():\n if isinstance(value, dict):\n if value.get(\"input_types\") is None:\n build_config[key][\"input_types\"] = []\n elif hasattr(value, \"input_types\") and value.input_types is None:\n value.input_types = []\n return build_config\n\n async def update_build_config(\n self,\n build_config: dotdict,\n field_value: list[dict],\n field_name: str | None = None,\n ) -> dotdict:\n # Update model options with caching (for all field changes).\n # The tool-calling constraint lives on the ModelInput's ``filters``\n # field (declared above); ``handle_model_input_update`` reads it\n # and applies the filter to both the dropdown options and the\n # sticky-default re-injection path.\n build_config = handle_model_input_update(\n component=self,\n build_config=dict(build_config),\n field_value=field_value,\n field_name=field_name,\n )\n build_config = dotdict(build_config)\n\n if field_name == \"model\":\n build_config = self.update_input_types(build_config)\n\n # Validate required keys\n default_keys = [\n \"code\",\n \"_type\",\n \"model\",\n \"tools\",\n \"input_value\",\n \"add_current_date_tool\",\n \"add_calculator_tool\",\n \"system_prompt\",\n \"agent_description\",\n \"max_iterations\",\n \"handle_parsing_errors\",\n \"verbose\",\n ]\n missing_keys = [key for key in default_keys if key not in build_config]\n if missing_keys:\n msg = f\"Missing required keys in build_config: {missing_keys}\"\n raise ValueError(msg)\n return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n async def _get_tools(self) -> list[Tool]:\n component_toolkit = get_component_toolkit()\n tools_names = self._build_tools_names()\n agent_description = self.get_tool_description()\n # TODO: Agent Description Depreciated Feature to be removed\n description = f\"{agent_description}{tools_names}\"\n\n tools = component_toolkit(component=self).get_tools(\n tool_name=\"Call_Agent\",\n tool_description=description,\n # here we do not use the shared callbacks as we are exposing the agent as a tool\n callbacks=self.get_langchain_callbacks(),\n )\n if hasattr(self, \"tools_metadata\"):\n tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n\n return tools\n" + "value": "from __future__ import annotations\n\nfrom contextlib import contextmanager\nfrom datetime import datetime, timezone\nfrom typing import TYPE_CHECKING, Any\n\nfrom lfx.components.models_and_agents.memory import MemoryComponent, aget_agent_chat_history\n\nif TYPE_CHECKING:\n from langchain_core.tools import Tool\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.agents.default_system_prompt import DEFAULT_SYSTEM_PROMPT_TEMPLATE\nfrom lfx.base.agents.events import ExceptionWithMessageError\nfrom lfx.base.constants import STREAM_INFO_TEXT\nfrom lfx.base.models.unified_models import (\n get_language_model_options,\n get_llm,\n handle_model_input_update,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.components.agentics.helpers.model_config import validate_model_selection\nfrom lfx.components.helpers import CalculatorComponent, CurrentDateComponent\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.custom.custom_component.component import get_component_toolkit\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, ModelInput, StrInput\nfrom lfx.io import IntInput, MessageTextInput, MultilineInput, Output, SecretStrInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.schema.data import Data\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.schema.table import EditMode\n\n\ndef set_advanced_true(component_input):\n component_input.advanced = True\n return component_input\n\n\ndef _extract_text_content(value) -> str:\n \"\"\"Pull a string payload from a Message-like, AIMessage-like, or string value.\"\"\"\n if isinstance(value, str):\n return value\n text = getattr(value, \"text\", None)\n if isinstance(text, str):\n return text\n content = getattr(value, \"content\", None)\n if isinstance(content, str):\n return content\n return str(value) if value is not None else \"\"\n\n\n@contextmanager\ndef _suppress_send_message(component: Any):\n \"\"\"Temporarily replace component.send_message with a no-op for the duration of the block.\n\n Used during the structured-output prompt fallback: run_agent streams the agent's\n final answer through self.send_message (correct for message_response), but in\n json_response the orchestrator parses that text into structured Data which the\n downstream Chat Output emits — leaving the original emission in place produces a\n duplicate message in the playground. The original method is always restored on exit,\n even when the wrapped call raises.\n \"\"\"\n original = component.send_message\n\n async def _noop(message, *_args, **_kwargs):\n return message\n\n component.send_message = _noop\n try:\n yield\n finally:\n component.send_message = original\n\n\nclass AgentComponent(ToolCallingAgentComponent):\n display_name: str = \"Agent\"\n description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n documentation: str = \"https://docs.langflow.org/agents\"\n icon = \"bot\"\n beta = False\n name = \"Agent\"\n\n memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n inputs = [\n ModelInput(\n name=\"model\",\n display_name=\"Language Model\",\n info=\"Select your model provider\",\n real_time_refresh=True,\n required=True,\n # Agents require tool calling — the filter is honored by\n # ``handle_model_input_update`` so models that can't run with\n # tools never reach the picker (and any saved selection that\n # no longer satisfies the constraint is auto-replaced).\n filters={\"tool_calling\": True},\n ),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"API Key\",\n info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n real_time_refresh=True,\n advanced=True,\n ),\n DropdownInput(\n name=\"base_url_ibm_watsonx\",\n display_name=\"watsonx API Endpoint\",\n info=\"The base URL of the API (IBM watsonx.ai only)\",\n options=IBM_WATSONX_URLS,\n value=IBM_WATSONX_URLS[0],\n combobox=True,\n show=False,\n real_time_refresh=True,\n ),\n StrInput(\n name=\"project_id\",\n display_name=\"watsonx Project ID\",\n info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n show=False,\n required=False,\n ),\n MultilineInput(\n name=\"system_prompt\",\n display_name=\"Agent Instructions\",\n info=(\n \"System Prompt: Initial instructions and context provided to guide the agent's behavior. \"\n \"Supports dynamic placeholders: {current_date}, {model_name}, {optional_user_context}.\"\n ),\n value=DEFAULT_SYSTEM_PROMPT_TEMPLATE,\n advanced=False,\n ),\n MessageTextInput(\n name=\"context_id\",\n display_name=\"Context ID\",\n info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n value=\"\",\n advanced=True,\n ),\n IntInput(\n name=\"n_messages\",\n display_name=\"Number of Chat History Messages\",\n value=100,\n info=\"Number of chat history messages to retrieve.\",\n advanced=True,\n show=True,\n ),\n IntInput(\n name=\"max_tokens\",\n display_name=\"Max Tokens\",\n info=\"Maximum number of tokens to generate. Field name varies by provider.\",\n advanced=True,\n range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n ),\n MultilineInput(\n name=\"format_instructions\",\n display_name=\"Output Format Instructions\",\n info=\"Generic Template for structured output formatting. Valid only with Structured response.\",\n value=(\n \"You are an AI that extracts structured JSON objects from unstructured text. \"\n \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n \"Fill missing or ambiguous values with defaults: null for missing values. \"\n \"Remove exact duplicates but keep variations that have different field values. \"\n \"Always return valid JSON in the expected format, never throw errors. \"\n \"If multiple objects can be extracted, return them all in the structured format.\"\n ),\n advanced=True,\n ),\n TableInput(\n name=\"output_schema\",\n display_name=\"Output Schema\",\n info=(\n \"Schema Validation: Define the structure and data types for structured output. \"\n \"No validation if no output schema.\"\n ),\n advanced=True,\n required=False,\n value=[],\n table_schema=[\n {\n \"name\": \"name\",\n \"display_name\": \"Name\",\n \"type\": \"str\",\n \"description\": \"Specify the name of the output field.\",\n \"default\": \"field\",\n \"edit_mode\": EditMode.INLINE,\n },\n {\n \"name\": \"description\",\n \"display_name\": \"Description\",\n \"type\": \"str\",\n \"description\": \"Describe the purpose of the output field.\",\n \"default\": \"description of field\",\n \"edit_mode\": EditMode.POPOVER,\n },\n {\n \"name\": \"type\",\n \"display_name\": \"Type\",\n \"type\": \"str\",\n \"edit_mode\": EditMode.INLINE,\n \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n \"default\": \"str\",\n },\n {\n \"name\": \"multiple\",\n \"display_name\": \"As List\",\n \"type\": \"boolean\",\n \"description\": \"Set to True if this output field should be a list of the specified type.\",\n \"default\": \"False\",\n \"edit_mode\": EditMode.INLINE,\n },\n ],\n ),\n *LCToolsAgentComponent.get_base_inputs(),\n # removed memory inputs from agent component\n # *memory_inputs,\n BoolInput(\n name=\"stream\",\n display_name=\"Stream\",\n info=STREAM_INFO_TEXT,\n value=False,\n advanced=True,\n ),\n BoolInput(\n name=\"add_current_date_tool\",\n display_name=\"Current Date\",\n advanced=True,\n info=\"If true, will add a tool to the agent that returns the current date.\",\n value=True,\n ),\n BoolInput(\n name=\"add_calculator_tool\",\n display_name=\"Calculator\",\n advanced=True,\n info=(\n \"If true, adds a zero-config arithmetic calculator tool to the agent \"\n \"(safe: only +, -, *, /, ** operators via AST).\"\n ),\n value=True,\n ),\n ]\n outputs = [\n Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n Output(\n name=\"structured_response\",\n display_name=\"Structured Response\",\n method=\"json_response\",\n types=[\"Data\"],\n ),\n ]\n\n def _resolve_selected_model(self):\n \"\"\"Resolve the selected model, including legacy agent_llm/model_name inputs.\"\"\"\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n if isinstance(self.model, BaseLanguageModel):\n return self.model\n except ImportError:\n pass\n\n if isinstance(self.model, list) and self.model:\n return self.model\n\n legacy_provider = getattr(self, \"agent_llm\", None)\n legacy_model_name = getattr(self, \"model_name\", None)\n if not legacy_provider or not legacy_model_name:\n return self.model\n\n options = get_language_model_options(user_id=self.user_id)\n for option in options:\n if option.get(\"provider\") == legacy_provider and option.get(\"name\") == legacy_model_name:\n return [option]\n\n return [\n {\n \"name\": legacy_model_name,\n \"provider\": legacy_provider,\n \"metadata\": {},\n }\n ]\n\n def _get_max_tokens_value(self):\n \"\"\"Return the user-supplied max_tokens or None when unset/zero.\"\"\"\n val = getattr(self, \"max_tokens\", None)\n if val in {\"\", 0}:\n return None\n return val\n\n def _get_llm(self):\n \"\"\"Override parent to include max_tokens from the Agent's input field.\"\"\"\n return get_llm(\n model=self.model,\n user_id=self.user_id,\n api_key=getattr(self, \"api_key\", None),\n stream=bool(getattr(self, \"stream\", False)),\n max_tokens=self._get_max_tokens_value(),\n watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n watsonx_project_id=getattr(self, \"project_id\", None),\n )\n\n async def get_agent_requirements(self):\n \"\"\"Get the agent requirements for the agent.\"\"\"\n from langchain_core.tools import StructuredTool\n\n selected_model = self._resolve_selected_model()\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n is_connected_model = isinstance(selected_model, BaseLanguageModel)\n except ImportError:\n is_connected_model = False\n\n if not is_connected_model:\n validate_model_selection(selected_model)\n\n # Ensure _get_llm() uses the resolved model (e.g. from legacy agent_llm/model_name)\n self.model = selected_model\n llm_model = self._get_llm()\n if llm_model is None:\n msg = \"No language model selected. Please choose a model to proceed.\"\n raise ValueError(msg)\n\n # Get memory data\n self.chat_history = await self.get_memory_data()\n await logger.adebug(f\"Retrieved {len(self.chat_history)} chat history messages\")\n if isinstance(self.chat_history, Message):\n self.chat_history = [self.chat_history]\n\n # Add current date tool if enabled\n if self.add_current_date_tool:\n if not isinstance(self.tools, list): # type: ignore[has-type]\n self.tools = []\n current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n if not isinstance(current_date_tool, StructuredTool):\n msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n raise TypeError(msg)\n # Skip if an externally-connected tool already provides the same name.\n # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n if not any(getattr(t, \"name\", None) == current_date_tool.name for t in self.tools):\n self.tools.append(current_date_tool)\n\n # Add calculator tool if enabled (zero-config arithmetic)\n if getattr(self, \"add_calculator_tool\", False):\n if not isinstance(self.tools, list): # type: ignore[has-type]\n self.tools = []\n calculator_tool = (await CalculatorComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n if not isinstance(calculator_tool, StructuredTool):\n msg = \"CalculatorComponent must be converted to a StructuredTool\"\n raise TypeError(msg)\n # Skip if an externally-connected tool already provides the same name.\n # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n if not any(getattr(t, \"name\", None) == calculator_tool.name for t in self.tools):\n self.tools.append(calculator_tool)\n\n # Set shared callbacks for tracing the tools used by the agent\n self.set_tools_callbacks(self.tools, self._get_shared_callbacks())\n\n return llm_model, self.chat_history, self.tools\n\n def _get_resolved_model_name(self) -> str:\n \"\"\"Best-effort human-readable model name for {model_name} injection.\"\"\"\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n if isinstance(self.model, BaseLanguageModel):\n return type(self.model).__name__\n except ImportError:\n pass\n\n if isinstance(self.model, list) and self.model:\n first = self.model[0]\n if isinstance(first, dict):\n name = first.get(\"name\")\n if isinstance(name, str) and name:\n return name\n\n legacy_model_name = getattr(self, \"model_name\", None)\n if isinstance(legacy_model_name, str) and legacy_model_name:\n return legacy_model_name\n return \"\"\n\n def _inject_dynamic_prompt_values(self, prompt: str | None) -> str | None:\n \"\"\"Replace known env placeholders in the system prompt.\n\n Handles {current_date}, {model_name}, and {optional_user_context} (the\n last one ships with the structured DEFAULT_SYSTEM_PROMPT_TEMPLATE and\n is currently unused at the AgentComponent layer, so it resolves to \"\").\n Uses str.replace (not str.format) so user prompts containing literal\n braces such as JSON examples ({\"key\": 1}) never break the agent.\n \"\"\"\n if not prompt:\n return prompt\n replacements = {\n \"{current_date}\": datetime.now(tz=timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S UTC\"),\n \"{model_name}\": self._get_resolved_model_name(),\n \"{optional_user_context}\": \"\",\n }\n for placeholder, value in replacements.items():\n prompt = prompt.replace(placeholder, value)\n return prompt\n\n async def message_response(self) -> Message:\n try:\n llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n # Set up and run agent\n self.set(\n llm=llm_model,\n tools=self.tools or [],\n chat_history=self.chat_history,\n input_value=self.input_value,\n system_prompt=self._inject_dynamic_prompt_values(self.system_prompt),\n )\n agent = self.create_agent_runnable()\n result = await self.run_agent(agent)\n\n # Store result for potential JSON output\n self._agent_result = result\n\n except (ValueError, TypeError, KeyError) as e:\n await logger.aerror(f\"{type(e).__name__}: {e!s}\")\n raise\n except ExceptionWithMessageError as e:\n await logger.aerror(f\"ExceptionWithMessageError occurred: {e}\")\n raise\n # Avoid catching blind Exception; let truly unexpected exceptions propagate\n except Exception as e:\n await logger.aerror(f\"Unexpected error: {e!s}\")\n raise\n else:\n return result\n\n async def json_response(self) -> Data:\n \"\"\"Produce structured Data via native LLM structured output, with prompt-based fallback.\n\n Native path (no tools, llm has with_structured_output) bypasses the agent loop and\n returns provider-validated JSON. When tools are attached, falls back to running the\n agent with a schema-augmented system prompt and parsing the final message content.\n \"\"\"\n from lfx.components.models_and_agents.structured_output.structured_output_orchestrator import (\n orchestrate_structured_output,\n )\n\n try:\n llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n except (ValueError, TypeError) as exc:\n await logger.aerror(f\"json_response.requirements_failed: {exc}\")\n return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n injected_system_prompt = self._inject_dynamic_prompt_values(getattr(self, \"system_prompt\", \"\") or \"\") or \"\"\n format_instructions = getattr(self, \"format_instructions\", \"\") or \"\"\n output_schema = getattr(self, \"output_schema\", None) or []\n has_tools = bool(self.tools)\n\n async def _run_agent_for_fallback(augmented_prompt: str) -> str:\n self.set(\n llm=llm_model,\n tools=self.tools or [],\n chat_history=self.chat_history,\n input_value=self.input_value,\n system_prompt=augmented_prompt,\n )\n agent_runnable = self.create_agent_runnable()\n with _suppress_send_message(self):\n result = await self.run_agent(agent_runnable)\n return _extract_text_content(result)\n\n try:\n return await orchestrate_structured_output(\n llm=llm_model,\n output_schema=output_schema,\n system_prompt=injected_system_prompt,\n format_instructions=format_instructions,\n input_value=_extract_text_content(self.input_value),\n run_prompt_fallback=_run_agent_for_fallback,\n prefer_native=not has_tools,\n )\n except (\n ExceptionWithMessageError,\n ValueError,\n TypeError,\n NotImplementedError,\n AttributeError,\n ) as exc:\n await logger.aerror(f\"json_response.orchestration_failed: {exc}\")\n return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n async def get_memory_data(self):\n # Scope by flow_id so default playground session names (e.g. \"New Session 0\")\n # cannot leak chat history across unrelated flows. See issue #13059.\n # The helper also returns [] when n_messages == 0, preserving the\n # explicit \"memory disabled\" contract from MemoryComponent.retrieve_messages.\n messages = await aget_agent_chat_history(\n session_id=self.graph.session_id,\n flow_id=getattr(self.graph, \"flow_id\", None),\n context_id=self.context_id,\n n_messages=self.n_messages,\n )\n return [\n message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n ]\n\n def update_input_types(self, build_config: dotdict) -> dotdict:\n \"\"\"Update input types for all fields in build_config.\"\"\"\n for key, value in build_config.items():\n if isinstance(value, dict):\n if value.get(\"input_types\") is None:\n build_config[key][\"input_types\"] = []\n elif hasattr(value, \"input_types\") and value.input_types is None:\n value.input_types = []\n return build_config\n\n async def update_build_config(\n self,\n build_config: dotdict,\n field_value: list[dict],\n field_name: str | None = None,\n ) -> dotdict:\n # Update model options with caching (for all field changes).\n # The tool-calling constraint lives on the ModelInput's ``filters``\n # field (declared above); ``handle_model_input_update`` reads it\n # and applies the filter to both the dropdown options and the\n # sticky-default re-injection path.\n build_config = handle_model_input_update(\n component=self,\n build_config=dict(build_config),\n field_value=field_value,\n field_name=field_name,\n )\n build_config = dotdict(build_config)\n\n if field_name == \"model\":\n build_config = self.update_input_types(build_config)\n\n # Validate required keys\n default_keys = [\n \"code\",\n \"_type\",\n \"model\",\n \"tools\",\n \"input_value\",\n \"add_current_date_tool\",\n \"add_calculator_tool\",\n \"system_prompt\",\n \"agent_description\",\n \"max_iterations\",\n \"handle_parsing_errors\",\n \"verbose\",\n ]\n missing_keys = [key for key in default_keys if key not in build_config]\n if missing_keys:\n msg = f\"Missing required keys in build_config: {missing_keys}\"\n raise ValueError(msg)\n return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n async def _get_tools(self) -> list[Tool]:\n component_toolkit = get_component_toolkit()\n tools_names = self._build_tools_names()\n agent_description = self.get_tool_description()\n # TODO: Agent Description Depreciated Feature to be removed\n description = f\"{agent_description}{tools_names}\"\n\n tools = component_toolkit(component=self).get_tools(\n tool_name=\"Call_Agent\",\n tool_description=description,\n # here we do not use the shared callbacks as we are exposing the agent as a tool\n callbacks=self.get_langchain_callbacks(),\n )\n if hasattr(self, \"tools_metadata\"):\n tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n\n return tools\n" }, "context_id": { "_input_type": "MessageTextInput", diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Simple Agent.json b/src/backend/base/langflow/initial_setup/starter_projects/Simple Agent.json index fd1070dea6..a2ca3037b1 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Simple Agent.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Simple Agent.json @@ -942,7 +942,7 @@ "last_updated": "2026-02-12T20:48:13.965Z", "legacy": false, "metadata": { - "code_hash": "ebac74bfcae6", + "code_hash": "443159257161", "dependencies": { "dependencies": [ { @@ -1132,7 +1132,7 @@ "show": true, "title_case": false, "type": "code", - "value": "from __future__ import annotations\n\nfrom contextlib import contextmanager\nfrom datetime import datetime, timezone\nfrom typing import TYPE_CHECKING, Any\n\nfrom lfx.components.models_and_agents.memory import MemoryComponent\n\nif TYPE_CHECKING:\n from langchain_core.tools import Tool\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.agents.default_system_prompt import DEFAULT_SYSTEM_PROMPT_TEMPLATE\nfrom lfx.base.agents.events import ExceptionWithMessageError\nfrom lfx.base.constants import STREAM_INFO_TEXT\nfrom lfx.base.models.unified_models import (\n get_language_model_options,\n get_llm,\n handle_model_input_update,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.components.agentics.helpers.model_config import validate_model_selection\nfrom lfx.components.helpers import CalculatorComponent, CurrentDateComponent\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.custom.custom_component.component import get_component_toolkit\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, ModelInput, StrInput\nfrom lfx.io import IntInput, MessageTextInput, MultilineInput, Output, SecretStrInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.schema.data import Data\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.schema.table import EditMode\n\n\ndef set_advanced_true(component_input):\n component_input.advanced = True\n return component_input\n\n\ndef _extract_text_content(value) -> str:\n \"\"\"Pull a string payload from a Message-like, AIMessage-like, or string value.\"\"\"\n if isinstance(value, str):\n return value\n text = getattr(value, \"text\", None)\n if isinstance(text, str):\n return text\n content = getattr(value, \"content\", None)\n if isinstance(content, str):\n return content\n return str(value) if value is not None else \"\"\n\n\n@contextmanager\ndef _suppress_send_message(component: Any):\n \"\"\"Temporarily replace component.send_message with a no-op for the duration of the block.\n\n Used during the structured-output prompt fallback: run_agent streams the agent's\n final answer through self.send_message (correct for message_response), but in\n json_response the orchestrator parses that text into structured Data which the\n downstream Chat Output emits — leaving the original emission in place produces a\n duplicate message in the playground. The original method is always restored on exit,\n even when the wrapped call raises.\n \"\"\"\n original = component.send_message\n\n async def _noop(message, *_args, **_kwargs):\n return message\n\n component.send_message = _noop\n try:\n yield\n finally:\n component.send_message = original\n\n\nclass AgentComponent(ToolCallingAgentComponent):\n display_name: str = \"Agent\"\n description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n documentation: str = \"https://docs.langflow.org/agents\"\n icon = \"bot\"\n beta = False\n name = \"Agent\"\n\n memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n inputs = [\n ModelInput(\n name=\"model\",\n display_name=\"Language Model\",\n info=\"Select your model provider\",\n real_time_refresh=True,\n required=True,\n # Agents require tool calling — the filter is honored by\n # ``handle_model_input_update`` so models that can't run with\n # tools never reach the picker (and any saved selection that\n # no longer satisfies the constraint is auto-replaced).\n filters={\"tool_calling\": True},\n ),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"API Key\",\n info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n real_time_refresh=True,\n advanced=True,\n ),\n DropdownInput(\n name=\"base_url_ibm_watsonx\",\n display_name=\"watsonx API Endpoint\",\n info=\"The base URL of the API (IBM watsonx.ai only)\",\n options=IBM_WATSONX_URLS,\n value=IBM_WATSONX_URLS[0],\n combobox=True,\n show=False,\n real_time_refresh=True,\n ),\n StrInput(\n name=\"project_id\",\n display_name=\"watsonx Project ID\",\n info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n show=False,\n required=False,\n ),\n MultilineInput(\n name=\"system_prompt\",\n display_name=\"Agent Instructions\",\n info=(\n \"System Prompt: Initial instructions and context provided to guide the agent's behavior. \"\n \"Supports dynamic placeholders: {current_date}, {model_name}, {optional_user_context}.\"\n ),\n value=DEFAULT_SYSTEM_PROMPT_TEMPLATE,\n advanced=False,\n ),\n MessageTextInput(\n name=\"context_id\",\n display_name=\"Context ID\",\n info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n value=\"\",\n advanced=True,\n ),\n IntInput(\n name=\"n_messages\",\n display_name=\"Number of Chat History Messages\",\n value=100,\n info=\"Number of chat history messages to retrieve.\",\n advanced=True,\n show=True,\n ),\n IntInput(\n name=\"max_tokens\",\n display_name=\"Max Tokens\",\n info=\"Maximum number of tokens to generate. Field name varies by provider.\",\n advanced=True,\n range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n ),\n MultilineInput(\n name=\"format_instructions\",\n display_name=\"Output Format Instructions\",\n info=\"Generic Template for structured output formatting. Valid only with Structured response.\",\n value=(\n \"You are an AI that extracts structured JSON objects from unstructured text. \"\n \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n \"Fill missing or ambiguous values with defaults: null for missing values. \"\n \"Remove exact duplicates but keep variations that have different field values. \"\n \"Always return valid JSON in the expected format, never throw errors. \"\n \"If multiple objects can be extracted, return them all in the structured format.\"\n ),\n advanced=True,\n ),\n TableInput(\n name=\"output_schema\",\n display_name=\"Output Schema\",\n info=(\n \"Schema Validation: Define the structure and data types for structured output. \"\n \"No validation if no output schema.\"\n ),\n advanced=True,\n required=False,\n value=[],\n table_schema=[\n {\n \"name\": \"name\",\n \"display_name\": \"Name\",\n \"type\": \"str\",\n \"description\": \"Specify the name of the output field.\",\n \"default\": \"field\",\n \"edit_mode\": EditMode.INLINE,\n },\n {\n \"name\": \"description\",\n \"display_name\": \"Description\",\n \"type\": \"str\",\n \"description\": \"Describe the purpose of the output field.\",\n \"default\": \"description of field\",\n \"edit_mode\": EditMode.POPOVER,\n },\n {\n \"name\": \"type\",\n \"display_name\": \"Type\",\n \"type\": \"str\",\n \"edit_mode\": EditMode.INLINE,\n \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n \"default\": \"str\",\n },\n {\n \"name\": \"multiple\",\n \"display_name\": \"As List\",\n \"type\": \"boolean\",\n \"description\": \"Set to True if this output field should be a list of the specified type.\",\n \"default\": \"False\",\n \"edit_mode\": EditMode.INLINE,\n },\n ],\n ),\n *LCToolsAgentComponent.get_base_inputs(),\n # removed memory inputs from agent component\n # *memory_inputs,\n BoolInput(\n name=\"stream\",\n display_name=\"Stream\",\n info=STREAM_INFO_TEXT,\n value=False,\n advanced=True,\n ),\n BoolInput(\n name=\"add_current_date_tool\",\n display_name=\"Current Date\",\n advanced=True,\n info=\"If true, will add a tool to the agent that returns the current date.\",\n value=True,\n ),\n BoolInput(\n name=\"add_calculator_tool\",\n display_name=\"Calculator\",\n advanced=True,\n info=(\n \"If true, adds a zero-config arithmetic calculator tool to the agent \"\n \"(safe: only +, -, *, /, ** operators via AST).\"\n ),\n value=True,\n ),\n ]\n outputs = [\n Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n Output(\n name=\"structured_response\",\n display_name=\"Structured Response\",\n method=\"json_response\",\n types=[\"Data\"],\n ),\n ]\n\n def _resolve_selected_model(self):\n \"\"\"Resolve the selected model, including legacy agent_llm/model_name inputs.\"\"\"\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n if isinstance(self.model, BaseLanguageModel):\n return self.model\n except ImportError:\n pass\n\n if isinstance(self.model, list) and self.model:\n return self.model\n\n legacy_provider = getattr(self, \"agent_llm\", None)\n legacy_model_name = getattr(self, \"model_name\", None)\n if not legacy_provider or not legacy_model_name:\n return self.model\n\n options = get_language_model_options(user_id=self.user_id)\n for option in options:\n if option.get(\"provider\") == legacy_provider and option.get(\"name\") == legacy_model_name:\n return [option]\n\n return [\n {\n \"name\": legacy_model_name,\n \"provider\": legacy_provider,\n \"metadata\": {},\n }\n ]\n\n def _get_max_tokens_value(self):\n \"\"\"Return the user-supplied max_tokens or None when unset/zero.\"\"\"\n val = getattr(self, \"max_tokens\", None)\n if val in {\"\", 0}:\n return None\n return val\n\n def _get_llm(self):\n \"\"\"Override parent to include max_tokens from the Agent's input field.\"\"\"\n return get_llm(\n model=self.model,\n user_id=self.user_id,\n api_key=getattr(self, \"api_key\", None),\n stream=bool(getattr(self, \"stream\", False)),\n max_tokens=self._get_max_tokens_value(),\n watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n watsonx_project_id=getattr(self, \"project_id\", None),\n )\n\n async def get_agent_requirements(self):\n \"\"\"Get the agent requirements for the agent.\"\"\"\n from langchain_core.tools import StructuredTool\n\n selected_model = self._resolve_selected_model()\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n is_connected_model = isinstance(selected_model, BaseLanguageModel)\n except ImportError:\n is_connected_model = False\n\n if not is_connected_model:\n validate_model_selection(selected_model)\n\n # Ensure _get_llm() uses the resolved model (e.g. from legacy agent_llm/model_name)\n self.model = selected_model\n llm_model = self._get_llm()\n if llm_model is None:\n msg = \"No language model selected. Please choose a model to proceed.\"\n raise ValueError(msg)\n\n # Get memory data\n self.chat_history = await self.get_memory_data()\n await logger.adebug(f\"Retrieved {len(self.chat_history)} chat history messages\")\n if isinstance(self.chat_history, Message):\n self.chat_history = [self.chat_history]\n\n # Add current date tool if enabled\n if self.add_current_date_tool:\n if not isinstance(self.tools, list): # type: ignore[has-type]\n self.tools = []\n current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n if not isinstance(current_date_tool, StructuredTool):\n msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n raise TypeError(msg)\n # Skip if an externally-connected tool already provides the same name.\n # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n if not any(getattr(t, \"name\", None) == current_date_tool.name for t in self.tools):\n self.tools.append(current_date_tool)\n\n # Add calculator tool if enabled (zero-config arithmetic)\n if getattr(self, \"add_calculator_tool\", False):\n if not isinstance(self.tools, list): # type: ignore[has-type]\n self.tools = []\n calculator_tool = (await CalculatorComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n if not isinstance(calculator_tool, StructuredTool):\n msg = \"CalculatorComponent must be converted to a StructuredTool\"\n raise TypeError(msg)\n # Skip if an externally-connected tool already provides the same name.\n # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n if not any(getattr(t, \"name\", None) == calculator_tool.name for t in self.tools):\n self.tools.append(calculator_tool)\n\n # Set shared callbacks for tracing the tools used by the agent\n self.set_tools_callbacks(self.tools, self._get_shared_callbacks())\n\n return llm_model, self.chat_history, self.tools\n\n def _get_resolved_model_name(self) -> str:\n \"\"\"Best-effort human-readable model name for {model_name} injection.\"\"\"\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n if isinstance(self.model, BaseLanguageModel):\n return type(self.model).__name__\n except ImportError:\n pass\n\n if isinstance(self.model, list) and self.model:\n first = self.model[0]\n if isinstance(first, dict):\n name = first.get(\"name\")\n if isinstance(name, str) and name:\n return name\n\n legacy_model_name = getattr(self, \"model_name\", None)\n if isinstance(legacy_model_name, str) and legacy_model_name:\n return legacy_model_name\n return \"\"\n\n def _inject_dynamic_prompt_values(self, prompt: str | None) -> str | None:\n \"\"\"Replace known env placeholders in the system prompt.\n\n Handles {current_date}, {model_name}, and {optional_user_context} (the\n last one ships with the structured DEFAULT_SYSTEM_PROMPT_TEMPLATE and\n is currently unused at the AgentComponent layer, so it resolves to \"\").\n Uses str.replace (not str.format) so user prompts containing literal\n braces such as JSON examples ({\"key\": 1}) never break the agent.\n \"\"\"\n if not prompt:\n return prompt\n replacements = {\n \"{current_date}\": datetime.now(tz=timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S UTC\"),\n \"{model_name}\": self._get_resolved_model_name(),\n \"{optional_user_context}\": \"\",\n }\n for placeholder, value in replacements.items():\n prompt = prompt.replace(placeholder, value)\n return prompt\n\n async def message_response(self) -> Message:\n try:\n llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n # Set up and run agent\n self.set(\n llm=llm_model,\n tools=self.tools or [],\n chat_history=self.chat_history,\n input_value=self.input_value,\n system_prompt=self._inject_dynamic_prompt_values(self.system_prompt),\n )\n agent = self.create_agent_runnable()\n result = await self.run_agent(agent)\n\n # Store result for potential JSON output\n self._agent_result = result\n\n except (ValueError, TypeError, KeyError) as e:\n await logger.aerror(f\"{type(e).__name__}: {e!s}\")\n raise\n except ExceptionWithMessageError as e:\n await logger.aerror(f\"ExceptionWithMessageError occurred: {e}\")\n raise\n # Avoid catching blind Exception; let truly unexpected exceptions propagate\n except Exception as e:\n await logger.aerror(f\"Unexpected error: {e!s}\")\n raise\n else:\n return result\n\n async def json_response(self) -> Data:\n \"\"\"Produce structured Data via native LLM structured output, with prompt-based fallback.\n\n Native path (no tools, llm has with_structured_output) bypasses the agent loop and\n returns provider-validated JSON. When tools are attached, falls back to running the\n agent with a schema-augmented system prompt and parsing the final message content.\n \"\"\"\n from lfx.components.models_and_agents.structured_output.structured_output_orchestrator import (\n orchestrate_structured_output,\n )\n\n try:\n llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n except (ValueError, TypeError) as exc:\n await logger.aerror(f\"json_response.requirements_failed: {exc}\")\n return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n injected_system_prompt = self._inject_dynamic_prompt_values(getattr(self, \"system_prompt\", \"\") or \"\") or \"\"\n format_instructions = getattr(self, \"format_instructions\", \"\") or \"\"\n output_schema = getattr(self, \"output_schema\", None) or []\n has_tools = bool(self.tools)\n\n async def _run_agent_for_fallback(augmented_prompt: str) -> str:\n self.set(\n llm=llm_model,\n tools=self.tools or [],\n chat_history=self.chat_history,\n input_value=self.input_value,\n system_prompt=augmented_prompt,\n )\n agent_runnable = self.create_agent_runnable()\n with _suppress_send_message(self):\n result = await self.run_agent(agent_runnable)\n return _extract_text_content(result)\n\n try:\n return await orchestrate_structured_output(\n llm=llm_model,\n output_schema=output_schema,\n system_prompt=injected_system_prompt,\n format_instructions=format_instructions,\n input_value=_extract_text_content(self.input_value),\n run_prompt_fallback=_run_agent_for_fallback,\n prefer_native=not has_tools,\n )\n except (\n ExceptionWithMessageError,\n ValueError,\n TypeError,\n NotImplementedError,\n AttributeError,\n ) as exc:\n await logger.aerror(f\"json_response.orchestration_failed: {exc}\")\n return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n async def get_memory_data(self):\n # TODO: This is a temporary fix to avoid message duplication. We should develop a function for this.\n messages = (\n await MemoryComponent(**self.get_base_args())\n .set(\n session_id=self.graph.session_id,\n context_id=self.context_id,\n order=\"Ascending\",\n n_messages=self.n_messages,\n )\n .retrieve_messages()\n )\n return [\n message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n ]\n\n def update_input_types(self, build_config: dotdict) -> dotdict:\n \"\"\"Update input types for all fields in build_config.\"\"\"\n for key, value in build_config.items():\n if isinstance(value, dict):\n if value.get(\"input_types\") is None:\n build_config[key][\"input_types\"] = []\n elif hasattr(value, \"input_types\") and value.input_types is None:\n value.input_types = []\n return build_config\n\n async def update_build_config(\n self,\n build_config: dotdict,\n field_value: list[dict],\n field_name: str | None = None,\n ) -> dotdict:\n # Update model options with caching (for all field changes).\n # The tool-calling constraint lives on the ModelInput's ``filters``\n # field (declared above); ``handle_model_input_update`` reads it\n # and applies the filter to both the dropdown options and the\n # sticky-default re-injection path.\n build_config = handle_model_input_update(\n component=self,\n build_config=dict(build_config),\n field_value=field_value,\n field_name=field_name,\n )\n build_config = dotdict(build_config)\n\n if field_name == \"model\":\n build_config = self.update_input_types(build_config)\n\n # Validate required keys\n default_keys = [\n \"code\",\n \"_type\",\n \"model\",\n \"tools\",\n \"input_value\",\n \"add_current_date_tool\",\n \"add_calculator_tool\",\n \"system_prompt\",\n \"agent_description\",\n \"max_iterations\",\n \"handle_parsing_errors\",\n \"verbose\",\n ]\n missing_keys = [key for key in default_keys if key not in build_config]\n if missing_keys:\n msg = f\"Missing required keys in build_config: {missing_keys}\"\n raise ValueError(msg)\n return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n async def _get_tools(self) -> list[Tool]:\n component_toolkit = get_component_toolkit()\n tools_names = self._build_tools_names()\n agent_description = self.get_tool_description()\n # TODO: Agent Description Depreciated Feature to be removed\n description = f\"{agent_description}{tools_names}\"\n\n tools = component_toolkit(component=self).get_tools(\n tool_name=\"Call_Agent\",\n tool_description=description,\n # here we do not use the shared callbacks as we are exposing the agent as a tool\n callbacks=self.get_langchain_callbacks(),\n )\n if hasattr(self, \"tools_metadata\"):\n tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n\n return tools\n" + "value": "from __future__ import annotations\n\nfrom contextlib import contextmanager\nfrom datetime import datetime, timezone\nfrom typing import TYPE_CHECKING, Any\n\nfrom lfx.components.models_and_agents.memory import MemoryComponent, aget_agent_chat_history\n\nif TYPE_CHECKING:\n from langchain_core.tools import Tool\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.agents.default_system_prompt import DEFAULT_SYSTEM_PROMPT_TEMPLATE\nfrom lfx.base.agents.events import ExceptionWithMessageError\nfrom lfx.base.constants import STREAM_INFO_TEXT\nfrom lfx.base.models.unified_models import (\n get_language_model_options,\n get_llm,\n handle_model_input_update,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.components.agentics.helpers.model_config import validate_model_selection\nfrom lfx.components.helpers import CalculatorComponent, CurrentDateComponent\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.custom.custom_component.component import get_component_toolkit\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, ModelInput, StrInput\nfrom lfx.io import IntInput, MessageTextInput, MultilineInput, Output, SecretStrInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.schema.data import Data\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.schema.table import EditMode\n\n\ndef set_advanced_true(component_input):\n component_input.advanced = True\n return component_input\n\n\ndef _extract_text_content(value) -> str:\n \"\"\"Pull a string payload from a Message-like, AIMessage-like, or string value.\"\"\"\n if isinstance(value, str):\n return value\n text = getattr(value, \"text\", None)\n if isinstance(text, str):\n return text\n content = getattr(value, \"content\", None)\n if isinstance(content, str):\n return content\n return str(value) if value is not None else \"\"\n\n\n@contextmanager\ndef _suppress_send_message(component: Any):\n \"\"\"Temporarily replace component.send_message with a no-op for the duration of the block.\n\n Used during the structured-output prompt fallback: run_agent streams the agent's\n final answer through self.send_message (correct for message_response), but in\n json_response the orchestrator parses that text into structured Data which the\n downstream Chat Output emits — leaving the original emission in place produces a\n duplicate message in the playground. The original method is always restored on exit,\n even when the wrapped call raises.\n \"\"\"\n original = component.send_message\n\n async def _noop(message, *_args, **_kwargs):\n return message\n\n component.send_message = _noop\n try:\n yield\n finally:\n component.send_message = original\n\n\nclass AgentComponent(ToolCallingAgentComponent):\n display_name: str = \"Agent\"\n description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n documentation: str = \"https://docs.langflow.org/agents\"\n icon = \"bot\"\n beta = False\n name = \"Agent\"\n\n memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n inputs = [\n ModelInput(\n name=\"model\",\n display_name=\"Language Model\",\n info=\"Select your model provider\",\n real_time_refresh=True,\n required=True,\n # Agents require tool calling — the filter is honored by\n # ``handle_model_input_update`` so models that can't run with\n # tools never reach the picker (and any saved selection that\n # no longer satisfies the constraint is auto-replaced).\n filters={\"tool_calling\": True},\n ),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"API Key\",\n info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n real_time_refresh=True,\n advanced=True,\n ),\n DropdownInput(\n name=\"base_url_ibm_watsonx\",\n display_name=\"watsonx API Endpoint\",\n info=\"The base URL of the API (IBM watsonx.ai only)\",\n options=IBM_WATSONX_URLS,\n value=IBM_WATSONX_URLS[0],\n combobox=True,\n show=False,\n real_time_refresh=True,\n ),\n StrInput(\n name=\"project_id\",\n display_name=\"watsonx Project ID\",\n info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n show=False,\n required=False,\n ),\n MultilineInput(\n name=\"system_prompt\",\n display_name=\"Agent Instructions\",\n info=(\n \"System Prompt: Initial instructions and context provided to guide the agent's behavior. \"\n \"Supports dynamic placeholders: {current_date}, {model_name}, {optional_user_context}.\"\n ),\n value=DEFAULT_SYSTEM_PROMPT_TEMPLATE,\n advanced=False,\n ),\n MessageTextInput(\n name=\"context_id\",\n display_name=\"Context ID\",\n info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n value=\"\",\n advanced=True,\n ),\n IntInput(\n name=\"n_messages\",\n display_name=\"Number of Chat History Messages\",\n value=100,\n info=\"Number of chat history messages to retrieve.\",\n advanced=True,\n show=True,\n ),\n IntInput(\n name=\"max_tokens\",\n display_name=\"Max Tokens\",\n info=\"Maximum number of tokens to generate. Field name varies by provider.\",\n advanced=True,\n range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n ),\n MultilineInput(\n name=\"format_instructions\",\n display_name=\"Output Format Instructions\",\n info=\"Generic Template for structured output formatting. Valid only with Structured response.\",\n value=(\n \"You are an AI that extracts structured JSON objects from unstructured text. \"\n \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n \"Fill missing or ambiguous values with defaults: null for missing values. \"\n \"Remove exact duplicates but keep variations that have different field values. \"\n \"Always return valid JSON in the expected format, never throw errors. \"\n \"If multiple objects can be extracted, return them all in the structured format.\"\n ),\n advanced=True,\n ),\n TableInput(\n name=\"output_schema\",\n display_name=\"Output Schema\",\n info=(\n \"Schema Validation: Define the structure and data types for structured output. \"\n \"No validation if no output schema.\"\n ),\n advanced=True,\n required=False,\n value=[],\n table_schema=[\n {\n \"name\": \"name\",\n \"display_name\": \"Name\",\n \"type\": \"str\",\n \"description\": \"Specify the name of the output field.\",\n \"default\": \"field\",\n \"edit_mode\": EditMode.INLINE,\n },\n {\n \"name\": \"description\",\n \"display_name\": \"Description\",\n \"type\": \"str\",\n \"description\": \"Describe the purpose of the output field.\",\n \"default\": \"description of field\",\n \"edit_mode\": EditMode.POPOVER,\n },\n {\n \"name\": \"type\",\n \"display_name\": \"Type\",\n \"type\": \"str\",\n \"edit_mode\": EditMode.INLINE,\n \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n \"default\": \"str\",\n },\n {\n \"name\": \"multiple\",\n \"display_name\": \"As List\",\n \"type\": \"boolean\",\n \"description\": \"Set to True if this output field should be a list of the specified type.\",\n \"default\": \"False\",\n \"edit_mode\": EditMode.INLINE,\n },\n ],\n ),\n *LCToolsAgentComponent.get_base_inputs(),\n # removed memory inputs from agent component\n # *memory_inputs,\n BoolInput(\n name=\"stream\",\n display_name=\"Stream\",\n info=STREAM_INFO_TEXT,\n value=False,\n advanced=True,\n ),\n BoolInput(\n name=\"add_current_date_tool\",\n display_name=\"Current Date\",\n advanced=True,\n info=\"If true, will add a tool to the agent that returns the current date.\",\n value=True,\n ),\n BoolInput(\n name=\"add_calculator_tool\",\n display_name=\"Calculator\",\n advanced=True,\n info=(\n \"If true, adds a zero-config arithmetic calculator tool to the agent \"\n \"(safe: only +, -, *, /, ** operators via AST).\"\n ),\n value=True,\n ),\n ]\n outputs = [\n Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n Output(\n name=\"structured_response\",\n display_name=\"Structured Response\",\n method=\"json_response\",\n types=[\"Data\"],\n ),\n ]\n\n def _resolve_selected_model(self):\n \"\"\"Resolve the selected model, including legacy agent_llm/model_name inputs.\"\"\"\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n if isinstance(self.model, BaseLanguageModel):\n return self.model\n except ImportError:\n pass\n\n if isinstance(self.model, list) and self.model:\n return self.model\n\n legacy_provider = getattr(self, \"agent_llm\", None)\n legacy_model_name = getattr(self, \"model_name\", None)\n if not legacy_provider or not legacy_model_name:\n return self.model\n\n options = get_language_model_options(user_id=self.user_id)\n for option in options:\n if option.get(\"provider\") == legacy_provider and option.get(\"name\") == legacy_model_name:\n return [option]\n\n return [\n {\n \"name\": legacy_model_name,\n \"provider\": legacy_provider,\n \"metadata\": {},\n }\n ]\n\n def _get_max_tokens_value(self):\n \"\"\"Return the user-supplied max_tokens or None when unset/zero.\"\"\"\n val = getattr(self, \"max_tokens\", None)\n if val in {\"\", 0}:\n return None\n return val\n\n def _get_llm(self):\n \"\"\"Override parent to include max_tokens from the Agent's input field.\"\"\"\n return get_llm(\n model=self.model,\n user_id=self.user_id,\n api_key=getattr(self, \"api_key\", None),\n stream=bool(getattr(self, \"stream\", False)),\n max_tokens=self._get_max_tokens_value(),\n watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n watsonx_project_id=getattr(self, \"project_id\", None),\n )\n\n async def get_agent_requirements(self):\n \"\"\"Get the agent requirements for the agent.\"\"\"\n from langchain_core.tools import StructuredTool\n\n selected_model = self._resolve_selected_model()\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n is_connected_model = isinstance(selected_model, BaseLanguageModel)\n except ImportError:\n is_connected_model = False\n\n if not is_connected_model:\n validate_model_selection(selected_model)\n\n # Ensure _get_llm() uses the resolved model (e.g. from legacy agent_llm/model_name)\n self.model = selected_model\n llm_model = self._get_llm()\n if llm_model is None:\n msg = \"No language model selected. Please choose a model to proceed.\"\n raise ValueError(msg)\n\n # Get memory data\n self.chat_history = await self.get_memory_data()\n await logger.adebug(f\"Retrieved {len(self.chat_history)} chat history messages\")\n if isinstance(self.chat_history, Message):\n self.chat_history = [self.chat_history]\n\n # Add current date tool if enabled\n if self.add_current_date_tool:\n if not isinstance(self.tools, list): # type: ignore[has-type]\n self.tools = []\n current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n if not isinstance(current_date_tool, StructuredTool):\n msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n raise TypeError(msg)\n # Skip if an externally-connected tool already provides the same name.\n # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n if not any(getattr(t, \"name\", None) == current_date_tool.name for t in self.tools):\n self.tools.append(current_date_tool)\n\n # Add calculator tool if enabled (zero-config arithmetic)\n if getattr(self, \"add_calculator_tool\", False):\n if not isinstance(self.tools, list): # type: ignore[has-type]\n self.tools = []\n calculator_tool = (await CalculatorComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n if not isinstance(calculator_tool, StructuredTool):\n msg = \"CalculatorComponent must be converted to a StructuredTool\"\n raise TypeError(msg)\n # Skip if an externally-connected tool already provides the same name.\n # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n if not any(getattr(t, \"name\", None) == calculator_tool.name for t in self.tools):\n self.tools.append(calculator_tool)\n\n # Set shared callbacks for tracing the tools used by the agent\n self.set_tools_callbacks(self.tools, self._get_shared_callbacks())\n\n return llm_model, self.chat_history, self.tools\n\n def _get_resolved_model_name(self) -> str:\n \"\"\"Best-effort human-readable model name for {model_name} injection.\"\"\"\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n if isinstance(self.model, BaseLanguageModel):\n return type(self.model).__name__\n except ImportError:\n pass\n\n if isinstance(self.model, list) and self.model:\n first = self.model[0]\n if isinstance(first, dict):\n name = first.get(\"name\")\n if isinstance(name, str) and name:\n return name\n\n legacy_model_name = getattr(self, \"model_name\", None)\n if isinstance(legacy_model_name, str) and legacy_model_name:\n return legacy_model_name\n return \"\"\n\n def _inject_dynamic_prompt_values(self, prompt: str | None) -> str | None:\n \"\"\"Replace known env placeholders in the system prompt.\n\n Handles {current_date}, {model_name}, and {optional_user_context} (the\n last one ships with the structured DEFAULT_SYSTEM_PROMPT_TEMPLATE and\n is currently unused at the AgentComponent layer, so it resolves to \"\").\n Uses str.replace (not str.format) so user prompts containing literal\n braces such as JSON examples ({\"key\": 1}) never break the agent.\n \"\"\"\n if not prompt:\n return prompt\n replacements = {\n \"{current_date}\": datetime.now(tz=timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S UTC\"),\n \"{model_name}\": self._get_resolved_model_name(),\n \"{optional_user_context}\": \"\",\n }\n for placeholder, value in replacements.items():\n prompt = prompt.replace(placeholder, value)\n return prompt\n\n async def message_response(self) -> Message:\n try:\n llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n # Set up and run agent\n self.set(\n llm=llm_model,\n tools=self.tools or [],\n chat_history=self.chat_history,\n input_value=self.input_value,\n system_prompt=self._inject_dynamic_prompt_values(self.system_prompt),\n )\n agent = self.create_agent_runnable()\n result = await self.run_agent(agent)\n\n # Store result for potential JSON output\n self._agent_result = result\n\n except (ValueError, TypeError, KeyError) as e:\n await logger.aerror(f\"{type(e).__name__}: {e!s}\")\n raise\n except ExceptionWithMessageError as e:\n await logger.aerror(f\"ExceptionWithMessageError occurred: {e}\")\n raise\n # Avoid catching blind Exception; let truly unexpected exceptions propagate\n except Exception as e:\n await logger.aerror(f\"Unexpected error: {e!s}\")\n raise\n else:\n return result\n\n async def json_response(self) -> Data:\n \"\"\"Produce structured Data via native LLM structured output, with prompt-based fallback.\n\n Native path (no tools, llm has with_structured_output) bypasses the agent loop and\n returns provider-validated JSON. When tools are attached, falls back to running the\n agent with a schema-augmented system prompt and parsing the final message content.\n \"\"\"\n from lfx.components.models_and_agents.structured_output.structured_output_orchestrator import (\n orchestrate_structured_output,\n )\n\n try:\n llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n except (ValueError, TypeError) as exc:\n await logger.aerror(f\"json_response.requirements_failed: {exc}\")\n return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n injected_system_prompt = self._inject_dynamic_prompt_values(getattr(self, \"system_prompt\", \"\") or \"\") or \"\"\n format_instructions = getattr(self, \"format_instructions\", \"\") or \"\"\n output_schema = getattr(self, \"output_schema\", None) or []\n has_tools = bool(self.tools)\n\n async def _run_agent_for_fallback(augmented_prompt: str) -> str:\n self.set(\n llm=llm_model,\n tools=self.tools or [],\n chat_history=self.chat_history,\n input_value=self.input_value,\n system_prompt=augmented_prompt,\n )\n agent_runnable = self.create_agent_runnable()\n with _suppress_send_message(self):\n result = await self.run_agent(agent_runnable)\n return _extract_text_content(result)\n\n try:\n return await orchestrate_structured_output(\n llm=llm_model,\n output_schema=output_schema,\n system_prompt=injected_system_prompt,\n format_instructions=format_instructions,\n input_value=_extract_text_content(self.input_value),\n run_prompt_fallback=_run_agent_for_fallback,\n prefer_native=not has_tools,\n )\n except (\n ExceptionWithMessageError,\n ValueError,\n TypeError,\n NotImplementedError,\n AttributeError,\n ) as exc:\n await logger.aerror(f\"json_response.orchestration_failed: {exc}\")\n return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n async def get_memory_data(self):\n # Scope by flow_id so default playground session names (e.g. \"New Session 0\")\n # cannot leak chat history across unrelated flows. See issue #13059.\n # The helper also returns [] when n_messages == 0, preserving the\n # explicit \"memory disabled\" contract from MemoryComponent.retrieve_messages.\n messages = await aget_agent_chat_history(\n session_id=self.graph.session_id,\n flow_id=getattr(self.graph, \"flow_id\", None),\n context_id=self.context_id,\n n_messages=self.n_messages,\n )\n return [\n message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n ]\n\n def update_input_types(self, build_config: dotdict) -> dotdict:\n \"\"\"Update input types for all fields in build_config.\"\"\"\n for key, value in build_config.items():\n if isinstance(value, dict):\n if value.get(\"input_types\") is None:\n build_config[key][\"input_types\"] = []\n elif hasattr(value, \"input_types\") and value.input_types is None:\n value.input_types = []\n return build_config\n\n async def update_build_config(\n self,\n build_config: dotdict,\n field_value: list[dict],\n field_name: str | None = None,\n ) -> dotdict:\n # Update model options with caching (for all field changes).\n # The tool-calling constraint lives on the ModelInput's ``filters``\n # field (declared above); ``handle_model_input_update`` reads it\n # and applies the filter to both the dropdown options and the\n # sticky-default re-injection path.\n build_config = handle_model_input_update(\n component=self,\n build_config=dict(build_config),\n field_value=field_value,\n field_name=field_name,\n )\n build_config = dotdict(build_config)\n\n if field_name == \"model\":\n build_config = self.update_input_types(build_config)\n\n # Validate required keys\n default_keys = [\n \"code\",\n \"_type\",\n \"model\",\n \"tools\",\n \"input_value\",\n \"add_current_date_tool\",\n \"add_calculator_tool\",\n \"system_prompt\",\n \"agent_description\",\n \"max_iterations\",\n \"handle_parsing_errors\",\n \"verbose\",\n ]\n missing_keys = [key for key in default_keys if key not in build_config]\n if missing_keys:\n msg = f\"Missing required keys in build_config: {missing_keys}\"\n raise ValueError(msg)\n return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n async def _get_tools(self) -> list[Tool]:\n component_toolkit = get_component_toolkit()\n tools_names = self._build_tools_names()\n agent_description = self.get_tool_description()\n # TODO: Agent Description Depreciated Feature to be removed\n description = f\"{agent_description}{tools_names}\"\n\n tools = component_toolkit(component=self).get_tools(\n tool_name=\"Call_Agent\",\n tool_description=description,\n # here we do not use the shared callbacks as we are exposing the agent as a tool\n callbacks=self.get_langchain_callbacks(),\n )\n if hasattr(self, \"tools_metadata\"):\n tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n\n return tools\n" }, "context_id": { "_input_type": "MessageTextInput", diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Social Media Agent.json b/src/backend/base/langflow/initial_setup/starter_projects/Social Media Agent.json index 61af71fa22..aae02a5c14 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Social Media Agent.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Social Media Agent.json @@ -1294,7 +1294,7 @@ "last_updated": "2025-12-11T21:41:48.407Z", "legacy": false, "metadata": { - "code_hash": "ebac74bfcae6", + "code_hash": "443159257161", "dependencies": { "dependencies": [ { @@ -1483,7 +1483,7 @@ "show": true, "title_case": false, "type": "code", - "value": "from __future__ import annotations\n\nfrom contextlib import contextmanager\nfrom datetime import datetime, timezone\nfrom typing import TYPE_CHECKING, Any\n\nfrom lfx.components.models_and_agents.memory import MemoryComponent\n\nif TYPE_CHECKING:\n from langchain_core.tools import Tool\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.agents.default_system_prompt import DEFAULT_SYSTEM_PROMPT_TEMPLATE\nfrom lfx.base.agents.events import ExceptionWithMessageError\nfrom lfx.base.constants import STREAM_INFO_TEXT\nfrom lfx.base.models.unified_models import (\n get_language_model_options,\n get_llm,\n handle_model_input_update,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.components.agentics.helpers.model_config import validate_model_selection\nfrom lfx.components.helpers import CalculatorComponent, CurrentDateComponent\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.custom.custom_component.component import get_component_toolkit\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, ModelInput, StrInput\nfrom lfx.io import IntInput, MessageTextInput, MultilineInput, Output, SecretStrInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.schema.data import Data\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.schema.table import EditMode\n\n\ndef set_advanced_true(component_input):\n component_input.advanced = True\n return component_input\n\n\ndef _extract_text_content(value) -> str:\n \"\"\"Pull a string payload from a Message-like, AIMessage-like, or string value.\"\"\"\n if isinstance(value, str):\n return value\n text = getattr(value, \"text\", None)\n if isinstance(text, str):\n return text\n content = getattr(value, \"content\", None)\n if isinstance(content, str):\n return content\n return str(value) if value is not None else \"\"\n\n\n@contextmanager\ndef _suppress_send_message(component: Any):\n \"\"\"Temporarily replace component.send_message with a no-op for the duration of the block.\n\n Used during the structured-output prompt fallback: run_agent streams the agent's\n final answer through self.send_message (correct for message_response), but in\n json_response the orchestrator parses that text into structured Data which the\n downstream Chat Output emits — leaving the original emission in place produces a\n duplicate message in the playground. The original method is always restored on exit,\n even when the wrapped call raises.\n \"\"\"\n original = component.send_message\n\n async def _noop(message, *_args, **_kwargs):\n return message\n\n component.send_message = _noop\n try:\n yield\n finally:\n component.send_message = original\n\n\nclass AgentComponent(ToolCallingAgentComponent):\n display_name: str = \"Agent\"\n description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n documentation: str = \"https://docs.langflow.org/agents\"\n icon = \"bot\"\n beta = False\n name = \"Agent\"\n\n memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n inputs = [\n ModelInput(\n name=\"model\",\n display_name=\"Language Model\",\n info=\"Select your model provider\",\n real_time_refresh=True,\n required=True,\n # Agents require tool calling — the filter is honored by\n # ``handle_model_input_update`` so models that can't run with\n # tools never reach the picker (and any saved selection that\n # no longer satisfies the constraint is auto-replaced).\n filters={\"tool_calling\": True},\n ),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"API Key\",\n info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n real_time_refresh=True,\n advanced=True,\n ),\n DropdownInput(\n name=\"base_url_ibm_watsonx\",\n display_name=\"watsonx API Endpoint\",\n info=\"The base URL of the API (IBM watsonx.ai only)\",\n options=IBM_WATSONX_URLS,\n value=IBM_WATSONX_URLS[0],\n combobox=True,\n show=False,\n real_time_refresh=True,\n ),\n StrInput(\n name=\"project_id\",\n display_name=\"watsonx Project ID\",\n info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n show=False,\n required=False,\n ),\n MultilineInput(\n name=\"system_prompt\",\n display_name=\"Agent Instructions\",\n info=(\n \"System Prompt: Initial instructions and context provided to guide the agent's behavior. \"\n \"Supports dynamic placeholders: {current_date}, {model_name}, {optional_user_context}.\"\n ),\n value=DEFAULT_SYSTEM_PROMPT_TEMPLATE,\n advanced=False,\n ),\n MessageTextInput(\n name=\"context_id\",\n display_name=\"Context ID\",\n info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n value=\"\",\n advanced=True,\n ),\n IntInput(\n name=\"n_messages\",\n display_name=\"Number of Chat History Messages\",\n value=100,\n info=\"Number of chat history messages to retrieve.\",\n advanced=True,\n show=True,\n ),\n IntInput(\n name=\"max_tokens\",\n display_name=\"Max Tokens\",\n info=\"Maximum number of tokens to generate. Field name varies by provider.\",\n advanced=True,\n range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n ),\n MultilineInput(\n name=\"format_instructions\",\n display_name=\"Output Format Instructions\",\n info=\"Generic Template for structured output formatting. Valid only with Structured response.\",\n value=(\n \"You are an AI that extracts structured JSON objects from unstructured text. \"\n \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n \"Fill missing or ambiguous values with defaults: null for missing values. \"\n \"Remove exact duplicates but keep variations that have different field values. \"\n \"Always return valid JSON in the expected format, never throw errors. \"\n \"If multiple objects can be extracted, return them all in the structured format.\"\n ),\n advanced=True,\n ),\n TableInput(\n name=\"output_schema\",\n display_name=\"Output Schema\",\n info=(\n \"Schema Validation: Define the structure and data types for structured output. \"\n \"No validation if no output schema.\"\n ),\n advanced=True,\n required=False,\n value=[],\n table_schema=[\n {\n \"name\": \"name\",\n \"display_name\": \"Name\",\n \"type\": \"str\",\n \"description\": \"Specify the name of the output field.\",\n \"default\": \"field\",\n \"edit_mode\": EditMode.INLINE,\n },\n {\n \"name\": \"description\",\n \"display_name\": \"Description\",\n \"type\": \"str\",\n \"description\": \"Describe the purpose of the output field.\",\n \"default\": \"description of field\",\n \"edit_mode\": EditMode.POPOVER,\n },\n {\n \"name\": \"type\",\n \"display_name\": \"Type\",\n \"type\": \"str\",\n \"edit_mode\": EditMode.INLINE,\n \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n \"default\": \"str\",\n },\n {\n \"name\": \"multiple\",\n \"display_name\": \"As List\",\n \"type\": \"boolean\",\n \"description\": \"Set to True if this output field should be a list of the specified type.\",\n \"default\": \"False\",\n \"edit_mode\": EditMode.INLINE,\n },\n ],\n ),\n *LCToolsAgentComponent.get_base_inputs(),\n # removed memory inputs from agent component\n # *memory_inputs,\n BoolInput(\n name=\"stream\",\n display_name=\"Stream\",\n info=STREAM_INFO_TEXT,\n value=False,\n advanced=True,\n ),\n BoolInput(\n name=\"add_current_date_tool\",\n display_name=\"Current Date\",\n advanced=True,\n info=\"If true, will add a tool to the agent that returns the current date.\",\n value=True,\n ),\n BoolInput(\n name=\"add_calculator_tool\",\n display_name=\"Calculator\",\n advanced=True,\n info=(\n \"If true, adds a zero-config arithmetic calculator tool to the agent \"\n \"(safe: only +, -, *, /, ** operators via AST).\"\n ),\n value=True,\n ),\n ]\n outputs = [\n Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n Output(\n name=\"structured_response\",\n display_name=\"Structured Response\",\n method=\"json_response\",\n types=[\"Data\"],\n ),\n ]\n\n def _resolve_selected_model(self):\n \"\"\"Resolve the selected model, including legacy agent_llm/model_name inputs.\"\"\"\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n if isinstance(self.model, BaseLanguageModel):\n return self.model\n except ImportError:\n pass\n\n if isinstance(self.model, list) and self.model:\n return self.model\n\n legacy_provider = getattr(self, \"agent_llm\", None)\n legacy_model_name = getattr(self, \"model_name\", None)\n if not legacy_provider or not legacy_model_name:\n return self.model\n\n options = get_language_model_options(user_id=self.user_id)\n for option in options:\n if option.get(\"provider\") == legacy_provider and option.get(\"name\") == legacy_model_name:\n return [option]\n\n return [\n {\n \"name\": legacy_model_name,\n \"provider\": legacy_provider,\n \"metadata\": {},\n }\n ]\n\n def _get_max_tokens_value(self):\n \"\"\"Return the user-supplied max_tokens or None when unset/zero.\"\"\"\n val = getattr(self, \"max_tokens\", None)\n if val in {\"\", 0}:\n return None\n return val\n\n def _get_llm(self):\n \"\"\"Override parent to include max_tokens from the Agent's input field.\"\"\"\n return get_llm(\n model=self.model,\n user_id=self.user_id,\n api_key=getattr(self, \"api_key\", None),\n stream=bool(getattr(self, \"stream\", False)),\n max_tokens=self._get_max_tokens_value(),\n watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n watsonx_project_id=getattr(self, \"project_id\", None),\n )\n\n async def get_agent_requirements(self):\n \"\"\"Get the agent requirements for the agent.\"\"\"\n from langchain_core.tools import StructuredTool\n\n selected_model = self._resolve_selected_model()\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n is_connected_model = isinstance(selected_model, BaseLanguageModel)\n except ImportError:\n is_connected_model = False\n\n if not is_connected_model:\n validate_model_selection(selected_model)\n\n # Ensure _get_llm() uses the resolved model (e.g. from legacy agent_llm/model_name)\n self.model = selected_model\n llm_model = self._get_llm()\n if llm_model is None:\n msg = \"No language model selected. Please choose a model to proceed.\"\n raise ValueError(msg)\n\n # Get memory data\n self.chat_history = await self.get_memory_data()\n await logger.adebug(f\"Retrieved {len(self.chat_history)} chat history messages\")\n if isinstance(self.chat_history, Message):\n self.chat_history = [self.chat_history]\n\n # Add current date tool if enabled\n if self.add_current_date_tool:\n if not isinstance(self.tools, list): # type: ignore[has-type]\n self.tools = []\n current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n if not isinstance(current_date_tool, StructuredTool):\n msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n raise TypeError(msg)\n # Skip if an externally-connected tool already provides the same name.\n # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n if not any(getattr(t, \"name\", None) == current_date_tool.name for t in self.tools):\n self.tools.append(current_date_tool)\n\n # Add calculator tool if enabled (zero-config arithmetic)\n if getattr(self, \"add_calculator_tool\", False):\n if not isinstance(self.tools, list): # type: ignore[has-type]\n self.tools = []\n calculator_tool = (await CalculatorComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n if not isinstance(calculator_tool, StructuredTool):\n msg = \"CalculatorComponent must be converted to a StructuredTool\"\n raise TypeError(msg)\n # Skip if an externally-connected tool already provides the same name.\n # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n if not any(getattr(t, \"name\", None) == calculator_tool.name for t in self.tools):\n self.tools.append(calculator_tool)\n\n # Set shared callbacks for tracing the tools used by the agent\n self.set_tools_callbacks(self.tools, self._get_shared_callbacks())\n\n return llm_model, self.chat_history, self.tools\n\n def _get_resolved_model_name(self) -> str:\n \"\"\"Best-effort human-readable model name for {model_name} injection.\"\"\"\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n if isinstance(self.model, BaseLanguageModel):\n return type(self.model).__name__\n except ImportError:\n pass\n\n if isinstance(self.model, list) and self.model:\n first = self.model[0]\n if isinstance(first, dict):\n name = first.get(\"name\")\n if isinstance(name, str) and name:\n return name\n\n legacy_model_name = getattr(self, \"model_name\", None)\n if isinstance(legacy_model_name, str) and legacy_model_name:\n return legacy_model_name\n return \"\"\n\n def _inject_dynamic_prompt_values(self, prompt: str | None) -> str | None:\n \"\"\"Replace known env placeholders in the system prompt.\n\n Handles {current_date}, {model_name}, and {optional_user_context} (the\n last one ships with the structured DEFAULT_SYSTEM_PROMPT_TEMPLATE and\n is currently unused at the AgentComponent layer, so it resolves to \"\").\n Uses str.replace (not str.format) so user prompts containing literal\n braces such as JSON examples ({\"key\": 1}) never break the agent.\n \"\"\"\n if not prompt:\n return prompt\n replacements = {\n \"{current_date}\": datetime.now(tz=timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S UTC\"),\n \"{model_name}\": self._get_resolved_model_name(),\n \"{optional_user_context}\": \"\",\n }\n for placeholder, value in replacements.items():\n prompt = prompt.replace(placeholder, value)\n return prompt\n\n async def message_response(self) -> Message:\n try:\n llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n # Set up and run agent\n self.set(\n llm=llm_model,\n tools=self.tools or [],\n chat_history=self.chat_history,\n input_value=self.input_value,\n system_prompt=self._inject_dynamic_prompt_values(self.system_prompt),\n )\n agent = self.create_agent_runnable()\n result = await self.run_agent(agent)\n\n # Store result for potential JSON output\n self._agent_result = result\n\n except (ValueError, TypeError, KeyError) as e:\n await logger.aerror(f\"{type(e).__name__}: {e!s}\")\n raise\n except ExceptionWithMessageError as e:\n await logger.aerror(f\"ExceptionWithMessageError occurred: {e}\")\n raise\n # Avoid catching blind Exception; let truly unexpected exceptions propagate\n except Exception as e:\n await logger.aerror(f\"Unexpected error: {e!s}\")\n raise\n else:\n return result\n\n async def json_response(self) -> Data:\n \"\"\"Produce structured Data via native LLM structured output, with prompt-based fallback.\n\n Native path (no tools, llm has with_structured_output) bypasses the agent loop and\n returns provider-validated JSON. When tools are attached, falls back to running the\n agent with a schema-augmented system prompt and parsing the final message content.\n \"\"\"\n from lfx.components.models_and_agents.structured_output.structured_output_orchestrator import (\n orchestrate_structured_output,\n )\n\n try:\n llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n except (ValueError, TypeError) as exc:\n await logger.aerror(f\"json_response.requirements_failed: {exc}\")\n return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n injected_system_prompt = self._inject_dynamic_prompt_values(getattr(self, \"system_prompt\", \"\") or \"\") or \"\"\n format_instructions = getattr(self, \"format_instructions\", \"\") or \"\"\n output_schema = getattr(self, \"output_schema\", None) or []\n has_tools = bool(self.tools)\n\n async def _run_agent_for_fallback(augmented_prompt: str) -> str:\n self.set(\n llm=llm_model,\n tools=self.tools or [],\n chat_history=self.chat_history,\n input_value=self.input_value,\n system_prompt=augmented_prompt,\n )\n agent_runnable = self.create_agent_runnable()\n with _suppress_send_message(self):\n result = await self.run_agent(agent_runnable)\n return _extract_text_content(result)\n\n try:\n return await orchestrate_structured_output(\n llm=llm_model,\n output_schema=output_schema,\n system_prompt=injected_system_prompt,\n format_instructions=format_instructions,\n input_value=_extract_text_content(self.input_value),\n run_prompt_fallback=_run_agent_for_fallback,\n prefer_native=not has_tools,\n )\n except (\n ExceptionWithMessageError,\n ValueError,\n TypeError,\n NotImplementedError,\n AttributeError,\n ) as exc:\n await logger.aerror(f\"json_response.orchestration_failed: {exc}\")\n return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n async def get_memory_data(self):\n # TODO: This is a temporary fix to avoid message duplication. We should develop a function for this.\n messages = (\n await MemoryComponent(**self.get_base_args())\n .set(\n session_id=self.graph.session_id,\n context_id=self.context_id,\n order=\"Ascending\",\n n_messages=self.n_messages,\n )\n .retrieve_messages()\n )\n return [\n message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n ]\n\n def update_input_types(self, build_config: dotdict) -> dotdict:\n \"\"\"Update input types for all fields in build_config.\"\"\"\n for key, value in build_config.items():\n if isinstance(value, dict):\n if value.get(\"input_types\") is None:\n build_config[key][\"input_types\"] = []\n elif hasattr(value, \"input_types\") and value.input_types is None:\n value.input_types = []\n return build_config\n\n async def update_build_config(\n self,\n build_config: dotdict,\n field_value: list[dict],\n field_name: str | None = None,\n ) -> dotdict:\n # Update model options with caching (for all field changes).\n # The tool-calling constraint lives on the ModelInput's ``filters``\n # field (declared above); ``handle_model_input_update`` reads it\n # and applies the filter to both the dropdown options and the\n # sticky-default re-injection path.\n build_config = handle_model_input_update(\n component=self,\n build_config=dict(build_config),\n field_value=field_value,\n field_name=field_name,\n )\n build_config = dotdict(build_config)\n\n if field_name == \"model\":\n build_config = self.update_input_types(build_config)\n\n # Validate required keys\n default_keys = [\n \"code\",\n \"_type\",\n \"model\",\n \"tools\",\n \"input_value\",\n \"add_current_date_tool\",\n \"add_calculator_tool\",\n \"system_prompt\",\n \"agent_description\",\n \"max_iterations\",\n \"handle_parsing_errors\",\n \"verbose\",\n ]\n missing_keys = [key for key in default_keys if key not in build_config]\n if missing_keys:\n msg = f\"Missing required keys in build_config: {missing_keys}\"\n raise ValueError(msg)\n return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n async def _get_tools(self) -> list[Tool]:\n component_toolkit = get_component_toolkit()\n tools_names = self._build_tools_names()\n agent_description = self.get_tool_description()\n # TODO: Agent Description Depreciated Feature to be removed\n description = f\"{agent_description}{tools_names}\"\n\n tools = component_toolkit(component=self).get_tools(\n tool_name=\"Call_Agent\",\n tool_description=description,\n # here we do not use the shared callbacks as we are exposing the agent as a tool\n callbacks=self.get_langchain_callbacks(),\n )\n if hasattr(self, \"tools_metadata\"):\n tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n\n return tools\n" + "value": "from __future__ import annotations\n\nfrom contextlib import contextmanager\nfrom datetime import datetime, timezone\nfrom typing import TYPE_CHECKING, Any\n\nfrom lfx.components.models_and_agents.memory import MemoryComponent, aget_agent_chat_history\n\nif TYPE_CHECKING:\n from langchain_core.tools import Tool\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.agents.default_system_prompt import DEFAULT_SYSTEM_PROMPT_TEMPLATE\nfrom lfx.base.agents.events import ExceptionWithMessageError\nfrom lfx.base.constants import STREAM_INFO_TEXT\nfrom lfx.base.models.unified_models import (\n get_language_model_options,\n get_llm,\n handle_model_input_update,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.components.agentics.helpers.model_config import validate_model_selection\nfrom lfx.components.helpers import CalculatorComponent, CurrentDateComponent\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.custom.custom_component.component import get_component_toolkit\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, ModelInput, StrInput\nfrom lfx.io import IntInput, MessageTextInput, MultilineInput, Output, SecretStrInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.schema.data import Data\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.schema.table import EditMode\n\n\ndef set_advanced_true(component_input):\n component_input.advanced = True\n return component_input\n\n\ndef _extract_text_content(value) -> str:\n \"\"\"Pull a string payload from a Message-like, AIMessage-like, or string value.\"\"\"\n if isinstance(value, str):\n return value\n text = getattr(value, \"text\", None)\n if isinstance(text, str):\n return text\n content = getattr(value, \"content\", None)\n if isinstance(content, str):\n return content\n return str(value) if value is not None else \"\"\n\n\n@contextmanager\ndef _suppress_send_message(component: Any):\n \"\"\"Temporarily replace component.send_message with a no-op for the duration of the block.\n\n Used during the structured-output prompt fallback: run_agent streams the agent's\n final answer through self.send_message (correct for message_response), but in\n json_response the orchestrator parses that text into structured Data which the\n downstream Chat Output emits — leaving the original emission in place produces a\n duplicate message in the playground. The original method is always restored on exit,\n even when the wrapped call raises.\n \"\"\"\n original = component.send_message\n\n async def _noop(message, *_args, **_kwargs):\n return message\n\n component.send_message = _noop\n try:\n yield\n finally:\n component.send_message = original\n\n\nclass AgentComponent(ToolCallingAgentComponent):\n display_name: str = \"Agent\"\n description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n documentation: str = \"https://docs.langflow.org/agents\"\n icon = \"bot\"\n beta = False\n name = \"Agent\"\n\n memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n inputs = [\n ModelInput(\n name=\"model\",\n display_name=\"Language Model\",\n info=\"Select your model provider\",\n real_time_refresh=True,\n required=True,\n # Agents require tool calling — the filter is honored by\n # ``handle_model_input_update`` so models that can't run with\n # tools never reach the picker (and any saved selection that\n # no longer satisfies the constraint is auto-replaced).\n filters={\"tool_calling\": True},\n ),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"API Key\",\n info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n real_time_refresh=True,\n advanced=True,\n ),\n DropdownInput(\n name=\"base_url_ibm_watsonx\",\n display_name=\"watsonx API Endpoint\",\n info=\"The base URL of the API (IBM watsonx.ai only)\",\n options=IBM_WATSONX_URLS,\n value=IBM_WATSONX_URLS[0],\n combobox=True,\n show=False,\n real_time_refresh=True,\n ),\n StrInput(\n name=\"project_id\",\n display_name=\"watsonx Project ID\",\n info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n show=False,\n required=False,\n ),\n MultilineInput(\n name=\"system_prompt\",\n display_name=\"Agent Instructions\",\n info=(\n \"System Prompt: Initial instructions and context provided to guide the agent's behavior. \"\n \"Supports dynamic placeholders: {current_date}, {model_name}, {optional_user_context}.\"\n ),\n value=DEFAULT_SYSTEM_PROMPT_TEMPLATE,\n advanced=False,\n ),\n MessageTextInput(\n name=\"context_id\",\n display_name=\"Context ID\",\n info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n value=\"\",\n advanced=True,\n ),\n IntInput(\n name=\"n_messages\",\n display_name=\"Number of Chat History Messages\",\n value=100,\n info=\"Number of chat history messages to retrieve.\",\n advanced=True,\n show=True,\n ),\n IntInput(\n name=\"max_tokens\",\n display_name=\"Max Tokens\",\n info=\"Maximum number of tokens to generate. Field name varies by provider.\",\n advanced=True,\n range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n ),\n MultilineInput(\n name=\"format_instructions\",\n display_name=\"Output Format Instructions\",\n info=\"Generic Template for structured output formatting. Valid only with Structured response.\",\n value=(\n \"You are an AI that extracts structured JSON objects from unstructured text. \"\n \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n \"Fill missing or ambiguous values with defaults: null for missing values. \"\n \"Remove exact duplicates but keep variations that have different field values. \"\n \"Always return valid JSON in the expected format, never throw errors. \"\n \"If multiple objects can be extracted, return them all in the structured format.\"\n ),\n advanced=True,\n ),\n TableInput(\n name=\"output_schema\",\n display_name=\"Output Schema\",\n info=(\n \"Schema Validation: Define the structure and data types for structured output. \"\n \"No validation if no output schema.\"\n ),\n advanced=True,\n required=False,\n value=[],\n table_schema=[\n {\n \"name\": \"name\",\n \"display_name\": \"Name\",\n \"type\": \"str\",\n \"description\": \"Specify the name of the output field.\",\n \"default\": \"field\",\n \"edit_mode\": EditMode.INLINE,\n },\n {\n \"name\": \"description\",\n \"display_name\": \"Description\",\n \"type\": \"str\",\n \"description\": \"Describe the purpose of the output field.\",\n \"default\": \"description of field\",\n \"edit_mode\": EditMode.POPOVER,\n },\n {\n \"name\": \"type\",\n \"display_name\": \"Type\",\n \"type\": \"str\",\n \"edit_mode\": EditMode.INLINE,\n \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n \"default\": \"str\",\n },\n {\n \"name\": \"multiple\",\n \"display_name\": \"As List\",\n \"type\": \"boolean\",\n \"description\": \"Set to True if this output field should be a list of the specified type.\",\n \"default\": \"False\",\n \"edit_mode\": EditMode.INLINE,\n },\n ],\n ),\n *LCToolsAgentComponent.get_base_inputs(),\n # removed memory inputs from agent component\n # *memory_inputs,\n BoolInput(\n name=\"stream\",\n display_name=\"Stream\",\n info=STREAM_INFO_TEXT,\n value=False,\n advanced=True,\n ),\n BoolInput(\n name=\"add_current_date_tool\",\n display_name=\"Current Date\",\n advanced=True,\n info=\"If true, will add a tool to the agent that returns the current date.\",\n value=True,\n ),\n BoolInput(\n name=\"add_calculator_tool\",\n display_name=\"Calculator\",\n advanced=True,\n info=(\n \"If true, adds a zero-config arithmetic calculator tool to the agent \"\n \"(safe: only +, -, *, /, ** operators via AST).\"\n ),\n value=True,\n ),\n ]\n outputs = [\n Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n Output(\n name=\"structured_response\",\n display_name=\"Structured Response\",\n method=\"json_response\",\n types=[\"Data\"],\n ),\n ]\n\n def _resolve_selected_model(self):\n \"\"\"Resolve the selected model, including legacy agent_llm/model_name inputs.\"\"\"\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n if isinstance(self.model, BaseLanguageModel):\n return self.model\n except ImportError:\n pass\n\n if isinstance(self.model, list) and self.model:\n return self.model\n\n legacy_provider = getattr(self, \"agent_llm\", None)\n legacy_model_name = getattr(self, \"model_name\", None)\n if not legacy_provider or not legacy_model_name:\n return self.model\n\n options = get_language_model_options(user_id=self.user_id)\n for option in options:\n if option.get(\"provider\") == legacy_provider and option.get(\"name\") == legacy_model_name:\n return [option]\n\n return [\n {\n \"name\": legacy_model_name,\n \"provider\": legacy_provider,\n \"metadata\": {},\n }\n ]\n\n def _get_max_tokens_value(self):\n \"\"\"Return the user-supplied max_tokens or None when unset/zero.\"\"\"\n val = getattr(self, \"max_tokens\", None)\n if val in {\"\", 0}:\n return None\n return val\n\n def _get_llm(self):\n \"\"\"Override parent to include max_tokens from the Agent's input field.\"\"\"\n return get_llm(\n model=self.model,\n user_id=self.user_id,\n api_key=getattr(self, \"api_key\", None),\n stream=bool(getattr(self, \"stream\", False)),\n max_tokens=self._get_max_tokens_value(),\n watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n watsonx_project_id=getattr(self, \"project_id\", None),\n )\n\n async def get_agent_requirements(self):\n \"\"\"Get the agent requirements for the agent.\"\"\"\n from langchain_core.tools import StructuredTool\n\n selected_model = self._resolve_selected_model()\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n is_connected_model = isinstance(selected_model, BaseLanguageModel)\n except ImportError:\n is_connected_model = False\n\n if not is_connected_model:\n validate_model_selection(selected_model)\n\n # Ensure _get_llm() uses the resolved model (e.g. from legacy agent_llm/model_name)\n self.model = selected_model\n llm_model = self._get_llm()\n if llm_model is None:\n msg = \"No language model selected. Please choose a model to proceed.\"\n raise ValueError(msg)\n\n # Get memory data\n self.chat_history = await self.get_memory_data()\n await logger.adebug(f\"Retrieved {len(self.chat_history)} chat history messages\")\n if isinstance(self.chat_history, Message):\n self.chat_history = [self.chat_history]\n\n # Add current date tool if enabled\n if self.add_current_date_tool:\n if not isinstance(self.tools, list): # type: ignore[has-type]\n self.tools = []\n current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n if not isinstance(current_date_tool, StructuredTool):\n msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n raise TypeError(msg)\n # Skip if an externally-connected tool already provides the same name.\n # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n if not any(getattr(t, \"name\", None) == current_date_tool.name for t in self.tools):\n self.tools.append(current_date_tool)\n\n # Add calculator tool if enabled (zero-config arithmetic)\n if getattr(self, \"add_calculator_tool\", False):\n if not isinstance(self.tools, list): # type: ignore[has-type]\n self.tools = []\n calculator_tool = (await CalculatorComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n if not isinstance(calculator_tool, StructuredTool):\n msg = \"CalculatorComponent must be converted to a StructuredTool\"\n raise TypeError(msg)\n # Skip if an externally-connected tool already provides the same name.\n # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n if not any(getattr(t, \"name\", None) == calculator_tool.name for t in self.tools):\n self.tools.append(calculator_tool)\n\n # Set shared callbacks for tracing the tools used by the agent\n self.set_tools_callbacks(self.tools, self._get_shared_callbacks())\n\n return llm_model, self.chat_history, self.tools\n\n def _get_resolved_model_name(self) -> str:\n \"\"\"Best-effort human-readable model name for {model_name} injection.\"\"\"\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n if isinstance(self.model, BaseLanguageModel):\n return type(self.model).__name__\n except ImportError:\n pass\n\n if isinstance(self.model, list) and self.model:\n first = self.model[0]\n if isinstance(first, dict):\n name = first.get(\"name\")\n if isinstance(name, str) and name:\n return name\n\n legacy_model_name = getattr(self, \"model_name\", None)\n if isinstance(legacy_model_name, str) and legacy_model_name:\n return legacy_model_name\n return \"\"\n\n def _inject_dynamic_prompt_values(self, prompt: str | None) -> str | None:\n \"\"\"Replace known env placeholders in the system prompt.\n\n Handles {current_date}, {model_name}, and {optional_user_context} (the\n last one ships with the structured DEFAULT_SYSTEM_PROMPT_TEMPLATE and\n is currently unused at the AgentComponent layer, so it resolves to \"\").\n Uses str.replace (not str.format) so user prompts containing literal\n braces such as JSON examples ({\"key\": 1}) never break the agent.\n \"\"\"\n if not prompt:\n return prompt\n replacements = {\n \"{current_date}\": datetime.now(tz=timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S UTC\"),\n \"{model_name}\": self._get_resolved_model_name(),\n \"{optional_user_context}\": \"\",\n }\n for placeholder, value in replacements.items():\n prompt = prompt.replace(placeholder, value)\n return prompt\n\n async def message_response(self) -> Message:\n try:\n llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n # Set up and run agent\n self.set(\n llm=llm_model,\n tools=self.tools or [],\n chat_history=self.chat_history,\n input_value=self.input_value,\n system_prompt=self._inject_dynamic_prompt_values(self.system_prompt),\n )\n agent = self.create_agent_runnable()\n result = await self.run_agent(agent)\n\n # Store result for potential JSON output\n self._agent_result = result\n\n except (ValueError, TypeError, KeyError) as e:\n await logger.aerror(f\"{type(e).__name__}: {e!s}\")\n raise\n except ExceptionWithMessageError as e:\n await logger.aerror(f\"ExceptionWithMessageError occurred: {e}\")\n raise\n # Avoid catching blind Exception; let truly unexpected exceptions propagate\n except Exception as e:\n await logger.aerror(f\"Unexpected error: {e!s}\")\n raise\n else:\n return result\n\n async def json_response(self) -> Data:\n \"\"\"Produce structured Data via native LLM structured output, with prompt-based fallback.\n\n Native path (no tools, llm has with_structured_output) bypasses the agent loop and\n returns provider-validated JSON. When tools are attached, falls back to running the\n agent with a schema-augmented system prompt and parsing the final message content.\n \"\"\"\n from lfx.components.models_and_agents.structured_output.structured_output_orchestrator import (\n orchestrate_structured_output,\n )\n\n try:\n llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n except (ValueError, TypeError) as exc:\n await logger.aerror(f\"json_response.requirements_failed: {exc}\")\n return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n injected_system_prompt = self._inject_dynamic_prompt_values(getattr(self, \"system_prompt\", \"\") or \"\") or \"\"\n format_instructions = getattr(self, \"format_instructions\", \"\") or \"\"\n output_schema = getattr(self, \"output_schema\", None) or []\n has_tools = bool(self.tools)\n\n async def _run_agent_for_fallback(augmented_prompt: str) -> str:\n self.set(\n llm=llm_model,\n tools=self.tools or [],\n chat_history=self.chat_history,\n input_value=self.input_value,\n system_prompt=augmented_prompt,\n )\n agent_runnable = self.create_agent_runnable()\n with _suppress_send_message(self):\n result = await self.run_agent(agent_runnable)\n return _extract_text_content(result)\n\n try:\n return await orchestrate_structured_output(\n llm=llm_model,\n output_schema=output_schema,\n system_prompt=injected_system_prompt,\n format_instructions=format_instructions,\n input_value=_extract_text_content(self.input_value),\n run_prompt_fallback=_run_agent_for_fallback,\n prefer_native=not has_tools,\n )\n except (\n ExceptionWithMessageError,\n ValueError,\n TypeError,\n NotImplementedError,\n AttributeError,\n ) as exc:\n await logger.aerror(f\"json_response.orchestration_failed: {exc}\")\n return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n async def get_memory_data(self):\n # Scope by flow_id so default playground session names (e.g. \"New Session 0\")\n # cannot leak chat history across unrelated flows. See issue #13059.\n # The helper also returns [] when n_messages == 0, preserving the\n # explicit \"memory disabled\" contract from MemoryComponent.retrieve_messages.\n messages = await aget_agent_chat_history(\n session_id=self.graph.session_id,\n flow_id=getattr(self.graph, \"flow_id\", None),\n context_id=self.context_id,\n n_messages=self.n_messages,\n )\n return [\n message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n ]\n\n def update_input_types(self, build_config: dotdict) -> dotdict:\n \"\"\"Update input types for all fields in build_config.\"\"\"\n for key, value in build_config.items():\n if isinstance(value, dict):\n if value.get(\"input_types\") is None:\n build_config[key][\"input_types\"] = []\n elif hasattr(value, \"input_types\") and value.input_types is None:\n value.input_types = []\n return build_config\n\n async def update_build_config(\n self,\n build_config: dotdict,\n field_value: list[dict],\n field_name: str | None = None,\n ) -> dotdict:\n # Update model options with caching (for all field changes).\n # The tool-calling constraint lives on the ModelInput's ``filters``\n # field (declared above); ``handle_model_input_update`` reads it\n # and applies the filter to both the dropdown options and the\n # sticky-default re-injection path.\n build_config = handle_model_input_update(\n component=self,\n build_config=dict(build_config),\n field_value=field_value,\n field_name=field_name,\n )\n build_config = dotdict(build_config)\n\n if field_name == \"model\":\n build_config = self.update_input_types(build_config)\n\n # Validate required keys\n default_keys = [\n \"code\",\n \"_type\",\n \"model\",\n \"tools\",\n \"input_value\",\n \"add_current_date_tool\",\n \"add_calculator_tool\",\n \"system_prompt\",\n \"agent_description\",\n \"max_iterations\",\n \"handle_parsing_errors\",\n \"verbose\",\n ]\n missing_keys = [key for key in default_keys if key not in build_config]\n if missing_keys:\n msg = f\"Missing required keys in build_config: {missing_keys}\"\n raise ValueError(msg)\n return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n async def _get_tools(self) -> list[Tool]:\n component_toolkit = get_component_toolkit()\n tools_names = self._build_tools_names()\n agent_description = self.get_tool_description()\n # TODO: Agent Description Depreciated Feature to be removed\n description = f\"{agent_description}{tools_names}\"\n\n tools = component_toolkit(component=self).get_tools(\n tool_name=\"Call_Agent\",\n tool_description=description,\n # here we do not use the shared callbacks as we are exposing the agent as a tool\n callbacks=self.get_langchain_callbacks(),\n )\n if hasattr(self, \"tools_metadata\"):\n tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n\n return tools\n" }, "context_id": { "_input_type": "MessageTextInput", diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Travel Planning Agents.json b/src/backend/base/langflow/initial_setup/starter_projects/Travel Planning Agents.json index 1a0c7237de..4832400faa 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Travel Planning Agents.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Travel Planning Agents.json @@ -1707,7 +1707,7 @@ "last_updated": "2025-12-11T21:41:48.407Z", "legacy": false, "metadata": { - "code_hash": "ebac74bfcae6", + "code_hash": "443159257161", "dependencies": { "dependencies": [ { @@ -1896,7 +1896,7 @@ "show": true, "title_case": false, "type": "code", - "value": "from __future__ import annotations\n\nfrom contextlib import contextmanager\nfrom datetime import datetime, timezone\nfrom typing import TYPE_CHECKING, Any\n\nfrom lfx.components.models_and_agents.memory import MemoryComponent\n\nif TYPE_CHECKING:\n from langchain_core.tools import Tool\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.agents.default_system_prompt import DEFAULT_SYSTEM_PROMPT_TEMPLATE\nfrom lfx.base.agents.events import ExceptionWithMessageError\nfrom lfx.base.constants import STREAM_INFO_TEXT\nfrom lfx.base.models.unified_models import (\n get_language_model_options,\n get_llm,\n handle_model_input_update,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.components.agentics.helpers.model_config import validate_model_selection\nfrom lfx.components.helpers import CalculatorComponent, CurrentDateComponent\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.custom.custom_component.component import get_component_toolkit\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, ModelInput, StrInput\nfrom lfx.io import IntInput, MessageTextInput, MultilineInput, Output, SecretStrInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.schema.data import Data\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.schema.table import EditMode\n\n\ndef set_advanced_true(component_input):\n component_input.advanced = True\n return component_input\n\n\ndef _extract_text_content(value) -> str:\n \"\"\"Pull a string payload from a Message-like, AIMessage-like, or string value.\"\"\"\n if isinstance(value, str):\n return value\n text = getattr(value, \"text\", None)\n if isinstance(text, str):\n return text\n content = getattr(value, \"content\", None)\n if isinstance(content, str):\n return content\n return str(value) if value is not None else \"\"\n\n\n@contextmanager\ndef _suppress_send_message(component: Any):\n \"\"\"Temporarily replace component.send_message with a no-op for the duration of the block.\n\n Used during the structured-output prompt fallback: run_agent streams the agent's\n final answer through self.send_message (correct for message_response), but in\n json_response the orchestrator parses that text into structured Data which the\n downstream Chat Output emits — leaving the original emission in place produces a\n duplicate message in the playground. The original method is always restored on exit,\n even when the wrapped call raises.\n \"\"\"\n original = component.send_message\n\n async def _noop(message, *_args, **_kwargs):\n return message\n\n component.send_message = _noop\n try:\n yield\n finally:\n component.send_message = original\n\n\nclass AgentComponent(ToolCallingAgentComponent):\n display_name: str = \"Agent\"\n description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n documentation: str = \"https://docs.langflow.org/agents\"\n icon = \"bot\"\n beta = False\n name = \"Agent\"\n\n memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n inputs = [\n ModelInput(\n name=\"model\",\n display_name=\"Language Model\",\n info=\"Select your model provider\",\n real_time_refresh=True,\n required=True,\n # Agents require tool calling — the filter is honored by\n # ``handle_model_input_update`` so models that can't run with\n # tools never reach the picker (and any saved selection that\n # no longer satisfies the constraint is auto-replaced).\n filters={\"tool_calling\": True},\n ),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"API Key\",\n info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n real_time_refresh=True,\n advanced=True,\n ),\n DropdownInput(\n name=\"base_url_ibm_watsonx\",\n display_name=\"watsonx API Endpoint\",\n info=\"The base URL of the API (IBM watsonx.ai only)\",\n options=IBM_WATSONX_URLS,\n value=IBM_WATSONX_URLS[0],\n combobox=True,\n show=False,\n real_time_refresh=True,\n ),\n StrInput(\n name=\"project_id\",\n display_name=\"watsonx Project ID\",\n info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n show=False,\n required=False,\n ),\n MultilineInput(\n name=\"system_prompt\",\n display_name=\"Agent Instructions\",\n info=(\n \"System Prompt: Initial instructions and context provided to guide the agent's behavior. \"\n \"Supports dynamic placeholders: {current_date}, {model_name}, {optional_user_context}.\"\n ),\n value=DEFAULT_SYSTEM_PROMPT_TEMPLATE,\n advanced=False,\n ),\n MessageTextInput(\n name=\"context_id\",\n display_name=\"Context ID\",\n info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n value=\"\",\n advanced=True,\n ),\n IntInput(\n name=\"n_messages\",\n display_name=\"Number of Chat History Messages\",\n value=100,\n info=\"Number of chat history messages to retrieve.\",\n advanced=True,\n show=True,\n ),\n IntInput(\n name=\"max_tokens\",\n display_name=\"Max Tokens\",\n info=\"Maximum number of tokens to generate. Field name varies by provider.\",\n advanced=True,\n range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n ),\n MultilineInput(\n name=\"format_instructions\",\n display_name=\"Output Format Instructions\",\n info=\"Generic Template for structured output formatting. Valid only with Structured response.\",\n value=(\n \"You are an AI that extracts structured JSON objects from unstructured text. \"\n \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n \"Fill missing or ambiguous values with defaults: null for missing values. \"\n \"Remove exact duplicates but keep variations that have different field values. \"\n \"Always return valid JSON in the expected format, never throw errors. \"\n \"If multiple objects can be extracted, return them all in the structured format.\"\n ),\n advanced=True,\n ),\n TableInput(\n name=\"output_schema\",\n display_name=\"Output Schema\",\n info=(\n \"Schema Validation: Define the structure and data types for structured output. \"\n \"No validation if no output schema.\"\n ),\n advanced=True,\n required=False,\n value=[],\n table_schema=[\n {\n \"name\": \"name\",\n \"display_name\": \"Name\",\n \"type\": \"str\",\n \"description\": \"Specify the name of the output field.\",\n \"default\": \"field\",\n \"edit_mode\": EditMode.INLINE,\n },\n {\n \"name\": \"description\",\n \"display_name\": \"Description\",\n \"type\": \"str\",\n \"description\": \"Describe the purpose of the output field.\",\n \"default\": \"description of field\",\n \"edit_mode\": EditMode.POPOVER,\n },\n {\n \"name\": \"type\",\n \"display_name\": \"Type\",\n \"type\": \"str\",\n \"edit_mode\": EditMode.INLINE,\n \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n \"default\": \"str\",\n },\n {\n \"name\": \"multiple\",\n \"display_name\": \"As List\",\n \"type\": \"boolean\",\n \"description\": \"Set to True if this output field should be a list of the specified type.\",\n \"default\": \"False\",\n \"edit_mode\": EditMode.INLINE,\n },\n ],\n ),\n *LCToolsAgentComponent.get_base_inputs(),\n # removed memory inputs from agent component\n # *memory_inputs,\n BoolInput(\n name=\"stream\",\n display_name=\"Stream\",\n info=STREAM_INFO_TEXT,\n value=False,\n advanced=True,\n ),\n BoolInput(\n name=\"add_current_date_tool\",\n display_name=\"Current Date\",\n advanced=True,\n info=\"If true, will add a tool to the agent that returns the current date.\",\n value=True,\n ),\n BoolInput(\n name=\"add_calculator_tool\",\n display_name=\"Calculator\",\n advanced=True,\n info=(\n \"If true, adds a zero-config arithmetic calculator tool to the agent \"\n \"(safe: only +, -, *, /, ** operators via AST).\"\n ),\n value=True,\n ),\n ]\n outputs = [\n Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n Output(\n name=\"structured_response\",\n display_name=\"Structured Response\",\n method=\"json_response\",\n types=[\"Data\"],\n ),\n ]\n\n def _resolve_selected_model(self):\n \"\"\"Resolve the selected model, including legacy agent_llm/model_name inputs.\"\"\"\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n if isinstance(self.model, BaseLanguageModel):\n return self.model\n except ImportError:\n pass\n\n if isinstance(self.model, list) and self.model:\n return self.model\n\n legacy_provider = getattr(self, \"agent_llm\", None)\n legacy_model_name = getattr(self, \"model_name\", None)\n if not legacy_provider or not legacy_model_name:\n return self.model\n\n options = get_language_model_options(user_id=self.user_id)\n for option in options:\n if option.get(\"provider\") == legacy_provider and option.get(\"name\") == legacy_model_name:\n return [option]\n\n return [\n {\n \"name\": legacy_model_name,\n \"provider\": legacy_provider,\n \"metadata\": {},\n }\n ]\n\n def _get_max_tokens_value(self):\n \"\"\"Return the user-supplied max_tokens or None when unset/zero.\"\"\"\n val = getattr(self, \"max_tokens\", None)\n if val in {\"\", 0}:\n return None\n return val\n\n def _get_llm(self):\n \"\"\"Override parent to include max_tokens from the Agent's input field.\"\"\"\n return get_llm(\n model=self.model,\n user_id=self.user_id,\n api_key=getattr(self, \"api_key\", None),\n stream=bool(getattr(self, \"stream\", False)),\n max_tokens=self._get_max_tokens_value(),\n watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n watsonx_project_id=getattr(self, \"project_id\", None),\n )\n\n async def get_agent_requirements(self):\n \"\"\"Get the agent requirements for the agent.\"\"\"\n from langchain_core.tools import StructuredTool\n\n selected_model = self._resolve_selected_model()\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n is_connected_model = isinstance(selected_model, BaseLanguageModel)\n except ImportError:\n is_connected_model = False\n\n if not is_connected_model:\n validate_model_selection(selected_model)\n\n # Ensure _get_llm() uses the resolved model (e.g. from legacy agent_llm/model_name)\n self.model = selected_model\n llm_model = self._get_llm()\n if llm_model is None:\n msg = \"No language model selected. Please choose a model to proceed.\"\n raise ValueError(msg)\n\n # Get memory data\n self.chat_history = await self.get_memory_data()\n await logger.adebug(f\"Retrieved {len(self.chat_history)} chat history messages\")\n if isinstance(self.chat_history, Message):\n self.chat_history = [self.chat_history]\n\n # Add current date tool if enabled\n if self.add_current_date_tool:\n if not isinstance(self.tools, list): # type: ignore[has-type]\n self.tools = []\n current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n if not isinstance(current_date_tool, StructuredTool):\n msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n raise TypeError(msg)\n # Skip if an externally-connected tool already provides the same name.\n # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n if not any(getattr(t, \"name\", None) == current_date_tool.name for t in self.tools):\n self.tools.append(current_date_tool)\n\n # Add calculator tool if enabled (zero-config arithmetic)\n if getattr(self, \"add_calculator_tool\", False):\n if not isinstance(self.tools, list): # type: ignore[has-type]\n self.tools = []\n calculator_tool = (await CalculatorComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n if not isinstance(calculator_tool, StructuredTool):\n msg = \"CalculatorComponent must be converted to a StructuredTool\"\n raise TypeError(msg)\n # Skip if an externally-connected tool already provides the same name.\n # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n if not any(getattr(t, \"name\", None) == calculator_tool.name for t in self.tools):\n self.tools.append(calculator_tool)\n\n # Set shared callbacks for tracing the tools used by the agent\n self.set_tools_callbacks(self.tools, self._get_shared_callbacks())\n\n return llm_model, self.chat_history, self.tools\n\n def _get_resolved_model_name(self) -> str:\n \"\"\"Best-effort human-readable model name for {model_name} injection.\"\"\"\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n if isinstance(self.model, BaseLanguageModel):\n return type(self.model).__name__\n except ImportError:\n pass\n\n if isinstance(self.model, list) and self.model:\n first = self.model[0]\n if isinstance(first, dict):\n name = first.get(\"name\")\n if isinstance(name, str) and name:\n return name\n\n legacy_model_name = getattr(self, \"model_name\", None)\n if isinstance(legacy_model_name, str) and legacy_model_name:\n return legacy_model_name\n return \"\"\n\n def _inject_dynamic_prompt_values(self, prompt: str | None) -> str | None:\n \"\"\"Replace known env placeholders in the system prompt.\n\n Handles {current_date}, {model_name}, and {optional_user_context} (the\n last one ships with the structured DEFAULT_SYSTEM_PROMPT_TEMPLATE and\n is currently unused at the AgentComponent layer, so it resolves to \"\").\n Uses str.replace (not str.format) so user prompts containing literal\n braces such as JSON examples ({\"key\": 1}) never break the agent.\n \"\"\"\n if not prompt:\n return prompt\n replacements = {\n \"{current_date}\": datetime.now(tz=timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S UTC\"),\n \"{model_name}\": self._get_resolved_model_name(),\n \"{optional_user_context}\": \"\",\n }\n for placeholder, value in replacements.items():\n prompt = prompt.replace(placeholder, value)\n return prompt\n\n async def message_response(self) -> Message:\n try:\n llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n # Set up and run agent\n self.set(\n llm=llm_model,\n tools=self.tools or [],\n chat_history=self.chat_history,\n input_value=self.input_value,\n system_prompt=self._inject_dynamic_prompt_values(self.system_prompt),\n )\n agent = self.create_agent_runnable()\n result = await self.run_agent(agent)\n\n # Store result for potential JSON output\n self._agent_result = result\n\n except (ValueError, TypeError, KeyError) as e:\n await logger.aerror(f\"{type(e).__name__}: {e!s}\")\n raise\n except ExceptionWithMessageError as e:\n await logger.aerror(f\"ExceptionWithMessageError occurred: {e}\")\n raise\n # Avoid catching blind Exception; let truly unexpected exceptions propagate\n except Exception as e:\n await logger.aerror(f\"Unexpected error: {e!s}\")\n raise\n else:\n return result\n\n async def json_response(self) -> Data:\n \"\"\"Produce structured Data via native LLM structured output, with prompt-based fallback.\n\n Native path (no tools, llm has with_structured_output) bypasses the agent loop and\n returns provider-validated JSON. When tools are attached, falls back to running the\n agent with a schema-augmented system prompt and parsing the final message content.\n \"\"\"\n from lfx.components.models_and_agents.structured_output.structured_output_orchestrator import (\n orchestrate_structured_output,\n )\n\n try:\n llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n except (ValueError, TypeError) as exc:\n await logger.aerror(f\"json_response.requirements_failed: {exc}\")\n return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n injected_system_prompt = self._inject_dynamic_prompt_values(getattr(self, \"system_prompt\", \"\") or \"\") or \"\"\n format_instructions = getattr(self, \"format_instructions\", \"\") or \"\"\n output_schema = getattr(self, \"output_schema\", None) or []\n has_tools = bool(self.tools)\n\n async def _run_agent_for_fallback(augmented_prompt: str) -> str:\n self.set(\n llm=llm_model,\n tools=self.tools or [],\n chat_history=self.chat_history,\n input_value=self.input_value,\n system_prompt=augmented_prompt,\n )\n agent_runnable = self.create_agent_runnable()\n with _suppress_send_message(self):\n result = await self.run_agent(agent_runnable)\n return _extract_text_content(result)\n\n try:\n return await orchestrate_structured_output(\n llm=llm_model,\n output_schema=output_schema,\n system_prompt=injected_system_prompt,\n format_instructions=format_instructions,\n input_value=_extract_text_content(self.input_value),\n run_prompt_fallback=_run_agent_for_fallback,\n prefer_native=not has_tools,\n )\n except (\n ExceptionWithMessageError,\n ValueError,\n TypeError,\n NotImplementedError,\n AttributeError,\n ) as exc:\n await logger.aerror(f\"json_response.orchestration_failed: {exc}\")\n return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n async def get_memory_data(self):\n # TODO: This is a temporary fix to avoid message duplication. We should develop a function for this.\n messages = (\n await MemoryComponent(**self.get_base_args())\n .set(\n session_id=self.graph.session_id,\n context_id=self.context_id,\n order=\"Ascending\",\n n_messages=self.n_messages,\n )\n .retrieve_messages()\n )\n return [\n message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n ]\n\n def update_input_types(self, build_config: dotdict) -> dotdict:\n \"\"\"Update input types for all fields in build_config.\"\"\"\n for key, value in build_config.items():\n if isinstance(value, dict):\n if value.get(\"input_types\") is None:\n build_config[key][\"input_types\"] = []\n elif hasattr(value, \"input_types\") and value.input_types is None:\n value.input_types = []\n return build_config\n\n async def update_build_config(\n self,\n build_config: dotdict,\n field_value: list[dict],\n field_name: str | None = None,\n ) -> dotdict:\n # Update model options with caching (for all field changes).\n # The tool-calling constraint lives on the ModelInput's ``filters``\n # field (declared above); ``handle_model_input_update`` reads it\n # and applies the filter to both the dropdown options and the\n # sticky-default re-injection path.\n build_config = handle_model_input_update(\n component=self,\n build_config=dict(build_config),\n field_value=field_value,\n field_name=field_name,\n )\n build_config = dotdict(build_config)\n\n if field_name == \"model\":\n build_config = self.update_input_types(build_config)\n\n # Validate required keys\n default_keys = [\n \"code\",\n \"_type\",\n \"model\",\n \"tools\",\n \"input_value\",\n \"add_current_date_tool\",\n \"add_calculator_tool\",\n \"system_prompt\",\n \"agent_description\",\n \"max_iterations\",\n \"handle_parsing_errors\",\n \"verbose\",\n ]\n missing_keys = [key for key in default_keys if key not in build_config]\n if missing_keys:\n msg = f\"Missing required keys in build_config: {missing_keys}\"\n raise ValueError(msg)\n return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n async def _get_tools(self) -> list[Tool]:\n component_toolkit = get_component_toolkit()\n tools_names = self._build_tools_names()\n agent_description = self.get_tool_description()\n # TODO: Agent Description Depreciated Feature to be removed\n description = f\"{agent_description}{tools_names}\"\n\n tools = component_toolkit(component=self).get_tools(\n tool_name=\"Call_Agent\",\n tool_description=description,\n # here we do not use the shared callbacks as we are exposing the agent as a tool\n callbacks=self.get_langchain_callbacks(),\n )\n if hasattr(self, \"tools_metadata\"):\n tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n\n return tools\n" + "value": "from __future__ import annotations\n\nfrom contextlib import contextmanager\nfrom datetime import datetime, timezone\nfrom typing import TYPE_CHECKING, Any\n\nfrom lfx.components.models_and_agents.memory import MemoryComponent, aget_agent_chat_history\n\nif TYPE_CHECKING:\n from langchain_core.tools import Tool\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.agents.default_system_prompt import DEFAULT_SYSTEM_PROMPT_TEMPLATE\nfrom lfx.base.agents.events import ExceptionWithMessageError\nfrom lfx.base.constants import STREAM_INFO_TEXT\nfrom lfx.base.models.unified_models import (\n get_language_model_options,\n get_llm,\n handle_model_input_update,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.components.agentics.helpers.model_config import validate_model_selection\nfrom lfx.components.helpers import CalculatorComponent, CurrentDateComponent\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.custom.custom_component.component import get_component_toolkit\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, ModelInput, StrInput\nfrom lfx.io import IntInput, MessageTextInput, MultilineInput, Output, SecretStrInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.schema.data import Data\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.schema.table import EditMode\n\n\ndef set_advanced_true(component_input):\n component_input.advanced = True\n return component_input\n\n\ndef _extract_text_content(value) -> str:\n \"\"\"Pull a string payload from a Message-like, AIMessage-like, or string value.\"\"\"\n if isinstance(value, str):\n return value\n text = getattr(value, \"text\", None)\n if isinstance(text, str):\n return text\n content = getattr(value, \"content\", None)\n if isinstance(content, str):\n return content\n return str(value) if value is not None else \"\"\n\n\n@contextmanager\ndef _suppress_send_message(component: Any):\n \"\"\"Temporarily replace component.send_message with a no-op for the duration of the block.\n\n Used during the structured-output prompt fallback: run_agent streams the agent's\n final answer through self.send_message (correct for message_response), but in\n json_response the orchestrator parses that text into structured Data which the\n downstream Chat Output emits — leaving the original emission in place produces a\n duplicate message in the playground. The original method is always restored on exit,\n even when the wrapped call raises.\n \"\"\"\n original = component.send_message\n\n async def _noop(message, *_args, **_kwargs):\n return message\n\n component.send_message = _noop\n try:\n yield\n finally:\n component.send_message = original\n\n\nclass AgentComponent(ToolCallingAgentComponent):\n display_name: str = \"Agent\"\n description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n documentation: str = \"https://docs.langflow.org/agents\"\n icon = \"bot\"\n beta = False\n name = \"Agent\"\n\n memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n inputs = [\n ModelInput(\n name=\"model\",\n display_name=\"Language Model\",\n info=\"Select your model provider\",\n real_time_refresh=True,\n required=True,\n # Agents require tool calling — the filter is honored by\n # ``handle_model_input_update`` so models that can't run with\n # tools never reach the picker (and any saved selection that\n # no longer satisfies the constraint is auto-replaced).\n filters={\"tool_calling\": True},\n ),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"API Key\",\n info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n real_time_refresh=True,\n advanced=True,\n ),\n DropdownInput(\n name=\"base_url_ibm_watsonx\",\n display_name=\"watsonx API Endpoint\",\n info=\"The base URL of the API (IBM watsonx.ai only)\",\n options=IBM_WATSONX_URLS,\n value=IBM_WATSONX_URLS[0],\n combobox=True,\n show=False,\n real_time_refresh=True,\n ),\n StrInput(\n name=\"project_id\",\n display_name=\"watsonx Project ID\",\n info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n show=False,\n required=False,\n ),\n MultilineInput(\n name=\"system_prompt\",\n display_name=\"Agent Instructions\",\n info=(\n \"System Prompt: Initial instructions and context provided to guide the agent's behavior. \"\n \"Supports dynamic placeholders: {current_date}, {model_name}, {optional_user_context}.\"\n ),\n value=DEFAULT_SYSTEM_PROMPT_TEMPLATE,\n advanced=False,\n ),\n MessageTextInput(\n name=\"context_id\",\n display_name=\"Context ID\",\n info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n value=\"\",\n advanced=True,\n ),\n IntInput(\n name=\"n_messages\",\n display_name=\"Number of Chat History Messages\",\n value=100,\n info=\"Number of chat history messages to retrieve.\",\n advanced=True,\n show=True,\n ),\n IntInput(\n name=\"max_tokens\",\n display_name=\"Max Tokens\",\n info=\"Maximum number of tokens to generate. Field name varies by provider.\",\n advanced=True,\n range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n ),\n MultilineInput(\n name=\"format_instructions\",\n display_name=\"Output Format Instructions\",\n info=\"Generic Template for structured output formatting. Valid only with Structured response.\",\n value=(\n \"You are an AI that extracts structured JSON objects from unstructured text. \"\n \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n \"Fill missing or ambiguous values with defaults: null for missing values. \"\n \"Remove exact duplicates but keep variations that have different field values. \"\n \"Always return valid JSON in the expected format, never throw errors. \"\n \"If multiple objects can be extracted, return them all in the structured format.\"\n ),\n advanced=True,\n ),\n TableInput(\n name=\"output_schema\",\n display_name=\"Output Schema\",\n info=(\n \"Schema Validation: Define the structure and data types for structured output. \"\n \"No validation if no output schema.\"\n ),\n advanced=True,\n required=False,\n value=[],\n table_schema=[\n {\n \"name\": \"name\",\n \"display_name\": \"Name\",\n \"type\": \"str\",\n \"description\": \"Specify the name of the output field.\",\n \"default\": \"field\",\n \"edit_mode\": EditMode.INLINE,\n },\n {\n \"name\": \"description\",\n \"display_name\": \"Description\",\n \"type\": \"str\",\n \"description\": \"Describe the purpose of the output field.\",\n \"default\": \"description of field\",\n \"edit_mode\": EditMode.POPOVER,\n },\n {\n \"name\": \"type\",\n \"display_name\": \"Type\",\n \"type\": \"str\",\n \"edit_mode\": EditMode.INLINE,\n \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n \"default\": \"str\",\n },\n {\n \"name\": \"multiple\",\n \"display_name\": \"As List\",\n \"type\": \"boolean\",\n \"description\": \"Set to True if this output field should be a list of the specified type.\",\n \"default\": \"False\",\n \"edit_mode\": EditMode.INLINE,\n },\n ],\n ),\n *LCToolsAgentComponent.get_base_inputs(),\n # removed memory inputs from agent component\n # *memory_inputs,\n BoolInput(\n name=\"stream\",\n display_name=\"Stream\",\n info=STREAM_INFO_TEXT,\n value=False,\n advanced=True,\n ),\n BoolInput(\n name=\"add_current_date_tool\",\n display_name=\"Current Date\",\n advanced=True,\n info=\"If true, will add a tool to the agent that returns the current date.\",\n value=True,\n ),\n BoolInput(\n name=\"add_calculator_tool\",\n display_name=\"Calculator\",\n advanced=True,\n info=(\n \"If true, adds a zero-config arithmetic calculator tool to the agent \"\n \"(safe: only +, -, *, /, ** operators via AST).\"\n ),\n value=True,\n ),\n ]\n outputs = [\n Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n Output(\n name=\"structured_response\",\n display_name=\"Structured Response\",\n method=\"json_response\",\n types=[\"Data\"],\n ),\n ]\n\n def _resolve_selected_model(self):\n \"\"\"Resolve the selected model, including legacy agent_llm/model_name inputs.\"\"\"\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n if isinstance(self.model, BaseLanguageModel):\n return self.model\n except ImportError:\n pass\n\n if isinstance(self.model, list) and self.model:\n return self.model\n\n legacy_provider = getattr(self, \"agent_llm\", None)\n legacy_model_name = getattr(self, \"model_name\", None)\n if not legacy_provider or not legacy_model_name:\n return self.model\n\n options = get_language_model_options(user_id=self.user_id)\n for option in options:\n if option.get(\"provider\") == legacy_provider and option.get(\"name\") == legacy_model_name:\n return [option]\n\n return [\n {\n \"name\": legacy_model_name,\n \"provider\": legacy_provider,\n \"metadata\": {},\n }\n ]\n\n def _get_max_tokens_value(self):\n \"\"\"Return the user-supplied max_tokens or None when unset/zero.\"\"\"\n val = getattr(self, \"max_tokens\", None)\n if val in {\"\", 0}:\n return None\n return val\n\n def _get_llm(self):\n \"\"\"Override parent to include max_tokens from the Agent's input field.\"\"\"\n return get_llm(\n model=self.model,\n user_id=self.user_id,\n api_key=getattr(self, \"api_key\", None),\n stream=bool(getattr(self, \"stream\", False)),\n max_tokens=self._get_max_tokens_value(),\n watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n watsonx_project_id=getattr(self, \"project_id\", None),\n )\n\n async def get_agent_requirements(self):\n \"\"\"Get the agent requirements for the agent.\"\"\"\n from langchain_core.tools import StructuredTool\n\n selected_model = self._resolve_selected_model()\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n is_connected_model = isinstance(selected_model, BaseLanguageModel)\n except ImportError:\n is_connected_model = False\n\n if not is_connected_model:\n validate_model_selection(selected_model)\n\n # Ensure _get_llm() uses the resolved model (e.g. from legacy agent_llm/model_name)\n self.model = selected_model\n llm_model = self._get_llm()\n if llm_model is None:\n msg = \"No language model selected. Please choose a model to proceed.\"\n raise ValueError(msg)\n\n # Get memory data\n self.chat_history = await self.get_memory_data()\n await logger.adebug(f\"Retrieved {len(self.chat_history)} chat history messages\")\n if isinstance(self.chat_history, Message):\n self.chat_history = [self.chat_history]\n\n # Add current date tool if enabled\n if self.add_current_date_tool:\n if not isinstance(self.tools, list): # type: ignore[has-type]\n self.tools = []\n current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n if not isinstance(current_date_tool, StructuredTool):\n msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n raise TypeError(msg)\n # Skip if an externally-connected tool already provides the same name.\n # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n if not any(getattr(t, \"name\", None) == current_date_tool.name for t in self.tools):\n self.tools.append(current_date_tool)\n\n # Add calculator tool if enabled (zero-config arithmetic)\n if getattr(self, \"add_calculator_tool\", False):\n if not isinstance(self.tools, list): # type: ignore[has-type]\n self.tools = []\n calculator_tool = (await CalculatorComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n if not isinstance(calculator_tool, StructuredTool):\n msg = \"CalculatorComponent must be converted to a StructuredTool\"\n raise TypeError(msg)\n # Skip if an externally-connected tool already provides the same name.\n # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n if not any(getattr(t, \"name\", None) == calculator_tool.name for t in self.tools):\n self.tools.append(calculator_tool)\n\n # Set shared callbacks for tracing the tools used by the agent\n self.set_tools_callbacks(self.tools, self._get_shared_callbacks())\n\n return llm_model, self.chat_history, self.tools\n\n def _get_resolved_model_name(self) -> str:\n \"\"\"Best-effort human-readable model name for {model_name} injection.\"\"\"\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n if isinstance(self.model, BaseLanguageModel):\n return type(self.model).__name__\n except ImportError:\n pass\n\n if isinstance(self.model, list) and self.model:\n first = self.model[0]\n if isinstance(first, dict):\n name = first.get(\"name\")\n if isinstance(name, str) and name:\n return name\n\n legacy_model_name = getattr(self, \"model_name\", None)\n if isinstance(legacy_model_name, str) and legacy_model_name:\n return legacy_model_name\n return \"\"\n\n def _inject_dynamic_prompt_values(self, prompt: str | None) -> str | None:\n \"\"\"Replace known env placeholders in the system prompt.\n\n Handles {current_date}, {model_name}, and {optional_user_context} (the\n last one ships with the structured DEFAULT_SYSTEM_PROMPT_TEMPLATE and\n is currently unused at the AgentComponent layer, so it resolves to \"\").\n Uses str.replace (not str.format) so user prompts containing literal\n braces such as JSON examples ({\"key\": 1}) never break the agent.\n \"\"\"\n if not prompt:\n return prompt\n replacements = {\n \"{current_date}\": datetime.now(tz=timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S UTC\"),\n \"{model_name}\": self._get_resolved_model_name(),\n \"{optional_user_context}\": \"\",\n }\n for placeholder, value in replacements.items():\n prompt = prompt.replace(placeholder, value)\n return prompt\n\n async def message_response(self) -> Message:\n try:\n llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n # Set up and run agent\n self.set(\n llm=llm_model,\n tools=self.tools or [],\n chat_history=self.chat_history,\n input_value=self.input_value,\n system_prompt=self._inject_dynamic_prompt_values(self.system_prompt),\n )\n agent = self.create_agent_runnable()\n result = await self.run_agent(agent)\n\n # Store result for potential JSON output\n self._agent_result = result\n\n except (ValueError, TypeError, KeyError) as e:\n await logger.aerror(f\"{type(e).__name__}: {e!s}\")\n raise\n except ExceptionWithMessageError as e:\n await logger.aerror(f\"ExceptionWithMessageError occurred: {e}\")\n raise\n # Avoid catching blind Exception; let truly unexpected exceptions propagate\n except Exception as e:\n await logger.aerror(f\"Unexpected error: {e!s}\")\n raise\n else:\n return result\n\n async def json_response(self) -> Data:\n \"\"\"Produce structured Data via native LLM structured output, with prompt-based fallback.\n\n Native path (no tools, llm has with_structured_output) bypasses the agent loop and\n returns provider-validated JSON. When tools are attached, falls back to running the\n agent with a schema-augmented system prompt and parsing the final message content.\n \"\"\"\n from lfx.components.models_and_agents.structured_output.structured_output_orchestrator import (\n orchestrate_structured_output,\n )\n\n try:\n llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n except (ValueError, TypeError) as exc:\n await logger.aerror(f\"json_response.requirements_failed: {exc}\")\n return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n injected_system_prompt = self._inject_dynamic_prompt_values(getattr(self, \"system_prompt\", \"\") or \"\") or \"\"\n format_instructions = getattr(self, \"format_instructions\", \"\") or \"\"\n output_schema = getattr(self, \"output_schema\", None) or []\n has_tools = bool(self.tools)\n\n async def _run_agent_for_fallback(augmented_prompt: str) -> str:\n self.set(\n llm=llm_model,\n tools=self.tools or [],\n chat_history=self.chat_history,\n input_value=self.input_value,\n system_prompt=augmented_prompt,\n )\n agent_runnable = self.create_agent_runnable()\n with _suppress_send_message(self):\n result = await self.run_agent(agent_runnable)\n return _extract_text_content(result)\n\n try:\n return await orchestrate_structured_output(\n llm=llm_model,\n output_schema=output_schema,\n system_prompt=injected_system_prompt,\n format_instructions=format_instructions,\n input_value=_extract_text_content(self.input_value),\n run_prompt_fallback=_run_agent_for_fallback,\n prefer_native=not has_tools,\n )\n except (\n ExceptionWithMessageError,\n ValueError,\n TypeError,\n NotImplementedError,\n AttributeError,\n ) as exc:\n await logger.aerror(f\"json_response.orchestration_failed: {exc}\")\n return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n async def get_memory_data(self):\n # Scope by flow_id so default playground session names (e.g. \"New Session 0\")\n # cannot leak chat history across unrelated flows. See issue #13059.\n # The helper also returns [] when n_messages == 0, preserving the\n # explicit \"memory disabled\" contract from MemoryComponent.retrieve_messages.\n messages = await aget_agent_chat_history(\n session_id=self.graph.session_id,\n flow_id=getattr(self.graph, \"flow_id\", None),\n context_id=self.context_id,\n n_messages=self.n_messages,\n )\n return [\n message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n ]\n\n def update_input_types(self, build_config: dotdict) -> dotdict:\n \"\"\"Update input types for all fields in build_config.\"\"\"\n for key, value in build_config.items():\n if isinstance(value, dict):\n if value.get(\"input_types\") is None:\n build_config[key][\"input_types\"] = []\n elif hasattr(value, \"input_types\") and value.input_types is None:\n value.input_types = []\n return build_config\n\n async def update_build_config(\n self,\n build_config: dotdict,\n field_value: list[dict],\n field_name: str | None = None,\n ) -> dotdict:\n # Update model options with caching (for all field changes).\n # The tool-calling constraint lives on the ModelInput's ``filters``\n # field (declared above); ``handle_model_input_update`` reads it\n # and applies the filter to both the dropdown options and the\n # sticky-default re-injection path.\n build_config = handle_model_input_update(\n component=self,\n build_config=dict(build_config),\n field_value=field_value,\n field_name=field_name,\n )\n build_config = dotdict(build_config)\n\n if field_name == \"model\":\n build_config = self.update_input_types(build_config)\n\n # Validate required keys\n default_keys = [\n \"code\",\n \"_type\",\n \"model\",\n \"tools\",\n \"input_value\",\n \"add_current_date_tool\",\n \"add_calculator_tool\",\n \"system_prompt\",\n \"agent_description\",\n \"max_iterations\",\n \"handle_parsing_errors\",\n \"verbose\",\n ]\n missing_keys = [key for key in default_keys if key not in build_config]\n if missing_keys:\n msg = f\"Missing required keys in build_config: {missing_keys}\"\n raise ValueError(msg)\n return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n async def _get_tools(self) -> list[Tool]:\n component_toolkit = get_component_toolkit()\n tools_names = self._build_tools_names()\n agent_description = self.get_tool_description()\n # TODO: Agent Description Depreciated Feature to be removed\n description = f\"{agent_description}{tools_names}\"\n\n tools = component_toolkit(component=self).get_tools(\n tool_name=\"Call_Agent\",\n tool_description=description,\n # here we do not use the shared callbacks as we are exposing the agent as a tool\n callbacks=self.get_langchain_callbacks(),\n )\n if hasattr(self, \"tools_metadata\"):\n tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n\n return tools\n" }, "context_id": { "_input_type": "MessageTextInput", @@ -2343,7 +2343,7 @@ "last_updated": "2025-12-11T21:41:48.407Z", "legacy": false, "metadata": { - "code_hash": "ebac74bfcae6", + "code_hash": "443159257161", "dependencies": { "dependencies": [ { @@ -2532,7 +2532,7 @@ "show": true, "title_case": false, "type": "code", - "value": "from __future__ import annotations\n\nfrom contextlib import contextmanager\nfrom datetime import datetime, timezone\nfrom typing import TYPE_CHECKING, Any\n\nfrom lfx.components.models_and_agents.memory import MemoryComponent\n\nif TYPE_CHECKING:\n from langchain_core.tools import Tool\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.agents.default_system_prompt import DEFAULT_SYSTEM_PROMPT_TEMPLATE\nfrom lfx.base.agents.events import ExceptionWithMessageError\nfrom lfx.base.constants import STREAM_INFO_TEXT\nfrom lfx.base.models.unified_models import (\n get_language_model_options,\n get_llm,\n handle_model_input_update,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.components.agentics.helpers.model_config import validate_model_selection\nfrom lfx.components.helpers import CalculatorComponent, CurrentDateComponent\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.custom.custom_component.component import get_component_toolkit\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, ModelInput, StrInput\nfrom lfx.io import IntInput, MessageTextInput, MultilineInput, Output, SecretStrInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.schema.data import Data\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.schema.table import EditMode\n\n\ndef set_advanced_true(component_input):\n component_input.advanced = True\n return component_input\n\n\ndef _extract_text_content(value) -> str:\n \"\"\"Pull a string payload from a Message-like, AIMessage-like, or string value.\"\"\"\n if isinstance(value, str):\n return value\n text = getattr(value, \"text\", None)\n if isinstance(text, str):\n return text\n content = getattr(value, \"content\", None)\n if isinstance(content, str):\n return content\n return str(value) if value is not None else \"\"\n\n\n@contextmanager\ndef _suppress_send_message(component: Any):\n \"\"\"Temporarily replace component.send_message with a no-op for the duration of the block.\n\n Used during the structured-output prompt fallback: run_agent streams the agent's\n final answer through self.send_message (correct for message_response), but in\n json_response the orchestrator parses that text into structured Data which the\n downstream Chat Output emits — leaving the original emission in place produces a\n duplicate message in the playground. The original method is always restored on exit,\n even when the wrapped call raises.\n \"\"\"\n original = component.send_message\n\n async def _noop(message, *_args, **_kwargs):\n return message\n\n component.send_message = _noop\n try:\n yield\n finally:\n component.send_message = original\n\n\nclass AgentComponent(ToolCallingAgentComponent):\n display_name: str = \"Agent\"\n description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n documentation: str = \"https://docs.langflow.org/agents\"\n icon = \"bot\"\n beta = False\n name = \"Agent\"\n\n memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n inputs = [\n ModelInput(\n name=\"model\",\n display_name=\"Language Model\",\n info=\"Select your model provider\",\n real_time_refresh=True,\n required=True,\n # Agents require tool calling — the filter is honored by\n # ``handle_model_input_update`` so models that can't run with\n # tools never reach the picker (and any saved selection that\n # no longer satisfies the constraint is auto-replaced).\n filters={\"tool_calling\": True},\n ),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"API Key\",\n info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n real_time_refresh=True,\n advanced=True,\n ),\n DropdownInput(\n name=\"base_url_ibm_watsonx\",\n display_name=\"watsonx API Endpoint\",\n info=\"The base URL of the API (IBM watsonx.ai only)\",\n options=IBM_WATSONX_URLS,\n value=IBM_WATSONX_URLS[0],\n combobox=True,\n show=False,\n real_time_refresh=True,\n ),\n StrInput(\n name=\"project_id\",\n display_name=\"watsonx Project ID\",\n info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n show=False,\n required=False,\n ),\n MultilineInput(\n name=\"system_prompt\",\n display_name=\"Agent Instructions\",\n info=(\n \"System Prompt: Initial instructions and context provided to guide the agent's behavior. \"\n \"Supports dynamic placeholders: {current_date}, {model_name}, {optional_user_context}.\"\n ),\n value=DEFAULT_SYSTEM_PROMPT_TEMPLATE,\n advanced=False,\n ),\n MessageTextInput(\n name=\"context_id\",\n display_name=\"Context ID\",\n info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n value=\"\",\n advanced=True,\n ),\n IntInput(\n name=\"n_messages\",\n display_name=\"Number of Chat History Messages\",\n value=100,\n info=\"Number of chat history messages to retrieve.\",\n advanced=True,\n show=True,\n ),\n IntInput(\n name=\"max_tokens\",\n display_name=\"Max Tokens\",\n info=\"Maximum number of tokens to generate. Field name varies by provider.\",\n advanced=True,\n range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n ),\n MultilineInput(\n name=\"format_instructions\",\n display_name=\"Output Format Instructions\",\n info=\"Generic Template for structured output formatting. Valid only with Structured response.\",\n value=(\n \"You are an AI that extracts structured JSON objects from unstructured text. \"\n \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n \"Fill missing or ambiguous values with defaults: null for missing values. \"\n \"Remove exact duplicates but keep variations that have different field values. \"\n \"Always return valid JSON in the expected format, never throw errors. \"\n \"If multiple objects can be extracted, return them all in the structured format.\"\n ),\n advanced=True,\n ),\n TableInput(\n name=\"output_schema\",\n display_name=\"Output Schema\",\n info=(\n \"Schema Validation: Define the structure and data types for structured output. \"\n \"No validation if no output schema.\"\n ),\n advanced=True,\n required=False,\n value=[],\n table_schema=[\n {\n \"name\": \"name\",\n \"display_name\": \"Name\",\n \"type\": \"str\",\n \"description\": \"Specify the name of the output field.\",\n \"default\": \"field\",\n \"edit_mode\": EditMode.INLINE,\n },\n {\n \"name\": \"description\",\n \"display_name\": \"Description\",\n \"type\": \"str\",\n \"description\": \"Describe the purpose of the output field.\",\n \"default\": \"description of field\",\n \"edit_mode\": EditMode.POPOVER,\n },\n {\n \"name\": \"type\",\n \"display_name\": \"Type\",\n \"type\": \"str\",\n \"edit_mode\": EditMode.INLINE,\n \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n \"default\": \"str\",\n },\n {\n \"name\": \"multiple\",\n \"display_name\": \"As List\",\n \"type\": \"boolean\",\n \"description\": \"Set to True if this output field should be a list of the specified type.\",\n \"default\": \"False\",\n \"edit_mode\": EditMode.INLINE,\n },\n ],\n ),\n *LCToolsAgentComponent.get_base_inputs(),\n # removed memory inputs from agent component\n # *memory_inputs,\n BoolInput(\n name=\"stream\",\n display_name=\"Stream\",\n info=STREAM_INFO_TEXT,\n value=False,\n advanced=True,\n ),\n BoolInput(\n name=\"add_current_date_tool\",\n display_name=\"Current Date\",\n advanced=True,\n info=\"If true, will add a tool to the agent that returns the current date.\",\n value=True,\n ),\n BoolInput(\n name=\"add_calculator_tool\",\n display_name=\"Calculator\",\n advanced=True,\n info=(\n \"If true, adds a zero-config arithmetic calculator tool to the agent \"\n \"(safe: only +, -, *, /, ** operators via AST).\"\n ),\n value=True,\n ),\n ]\n outputs = [\n Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n Output(\n name=\"structured_response\",\n display_name=\"Structured Response\",\n method=\"json_response\",\n types=[\"Data\"],\n ),\n ]\n\n def _resolve_selected_model(self):\n \"\"\"Resolve the selected model, including legacy agent_llm/model_name inputs.\"\"\"\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n if isinstance(self.model, BaseLanguageModel):\n return self.model\n except ImportError:\n pass\n\n if isinstance(self.model, list) and self.model:\n return self.model\n\n legacy_provider = getattr(self, \"agent_llm\", None)\n legacy_model_name = getattr(self, \"model_name\", None)\n if not legacy_provider or not legacy_model_name:\n return self.model\n\n options = get_language_model_options(user_id=self.user_id)\n for option in options:\n if option.get(\"provider\") == legacy_provider and option.get(\"name\") == legacy_model_name:\n return [option]\n\n return [\n {\n \"name\": legacy_model_name,\n \"provider\": legacy_provider,\n \"metadata\": {},\n }\n ]\n\n def _get_max_tokens_value(self):\n \"\"\"Return the user-supplied max_tokens or None when unset/zero.\"\"\"\n val = getattr(self, \"max_tokens\", None)\n if val in {\"\", 0}:\n return None\n return val\n\n def _get_llm(self):\n \"\"\"Override parent to include max_tokens from the Agent's input field.\"\"\"\n return get_llm(\n model=self.model,\n user_id=self.user_id,\n api_key=getattr(self, \"api_key\", None),\n stream=bool(getattr(self, \"stream\", False)),\n max_tokens=self._get_max_tokens_value(),\n watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n watsonx_project_id=getattr(self, \"project_id\", None),\n )\n\n async def get_agent_requirements(self):\n \"\"\"Get the agent requirements for the agent.\"\"\"\n from langchain_core.tools import StructuredTool\n\n selected_model = self._resolve_selected_model()\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n is_connected_model = isinstance(selected_model, BaseLanguageModel)\n except ImportError:\n is_connected_model = False\n\n if not is_connected_model:\n validate_model_selection(selected_model)\n\n # Ensure _get_llm() uses the resolved model (e.g. from legacy agent_llm/model_name)\n self.model = selected_model\n llm_model = self._get_llm()\n if llm_model is None:\n msg = \"No language model selected. Please choose a model to proceed.\"\n raise ValueError(msg)\n\n # Get memory data\n self.chat_history = await self.get_memory_data()\n await logger.adebug(f\"Retrieved {len(self.chat_history)} chat history messages\")\n if isinstance(self.chat_history, Message):\n self.chat_history = [self.chat_history]\n\n # Add current date tool if enabled\n if self.add_current_date_tool:\n if not isinstance(self.tools, list): # type: ignore[has-type]\n self.tools = []\n current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n if not isinstance(current_date_tool, StructuredTool):\n msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n raise TypeError(msg)\n # Skip if an externally-connected tool already provides the same name.\n # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n if not any(getattr(t, \"name\", None) == current_date_tool.name for t in self.tools):\n self.tools.append(current_date_tool)\n\n # Add calculator tool if enabled (zero-config arithmetic)\n if getattr(self, \"add_calculator_tool\", False):\n if not isinstance(self.tools, list): # type: ignore[has-type]\n self.tools = []\n calculator_tool = (await CalculatorComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n if not isinstance(calculator_tool, StructuredTool):\n msg = \"CalculatorComponent must be converted to a StructuredTool\"\n raise TypeError(msg)\n # Skip if an externally-connected tool already provides the same name.\n # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n if not any(getattr(t, \"name\", None) == calculator_tool.name for t in self.tools):\n self.tools.append(calculator_tool)\n\n # Set shared callbacks for tracing the tools used by the agent\n self.set_tools_callbacks(self.tools, self._get_shared_callbacks())\n\n return llm_model, self.chat_history, self.tools\n\n def _get_resolved_model_name(self) -> str:\n \"\"\"Best-effort human-readable model name for {model_name} injection.\"\"\"\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n if isinstance(self.model, BaseLanguageModel):\n return type(self.model).__name__\n except ImportError:\n pass\n\n if isinstance(self.model, list) and self.model:\n first = self.model[0]\n if isinstance(first, dict):\n name = first.get(\"name\")\n if isinstance(name, str) and name:\n return name\n\n legacy_model_name = getattr(self, \"model_name\", None)\n if isinstance(legacy_model_name, str) and legacy_model_name:\n return legacy_model_name\n return \"\"\n\n def _inject_dynamic_prompt_values(self, prompt: str | None) -> str | None:\n \"\"\"Replace known env placeholders in the system prompt.\n\n Handles {current_date}, {model_name}, and {optional_user_context} (the\n last one ships with the structured DEFAULT_SYSTEM_PROMPT_TEMPLATE and\n is currently unused at the AgentComponent layer, so it resolves to \"\").\n Uses str.replace (not str.format) so user prompts containing literal\n braces such as JSON examples ({\"key\": 1}) never break the agent.\n \"\"\"\n if not prompt:\n return prompt\n replacements = {\n \"{current_date}\": datetime.now(tz=timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S UTC\"),\n \"{model_name}\": self._get_resolved_model_name(),\n \"{optional_user_context}\": \"\",\n }\n for placeholder, value in replacements.items():\n prompt = prompt.replace(placeholder, value)\n return prompt\n\n async def message_response(self) -> Message:\n try:\n llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n # Set up and run agent\n self.set(\n llm=llm_model,\n tools=self.tools or [],\n chat_history=self.chat_history,\n input_value=self.input_value,\n system_prompt=self._inject_dynamic_prompt_values(self.system_prompt),\n )\n agent = self.create_agent_runnable()\n result = await self.run_agent(agent)\n\n # Store result for potential JSON output\n self._agent_result = result\n\n except (ValueError, TypeError, KeyError) as e:\n await logger.aerror(f\"{type(e).__name__}: {e!s}\")\n raise\n except ExceptionWithMessageError as e:\n await logger.aerror(f\"ExceptionWithMessageError occurred: {e}\")\n raise\n # Avoid catching blind Exception; let truly unexpected exceptions propagate\n except Exception as e:\n await logger.aerror(f\"Unexpected error: {e!s}\")\n raise\n else:\n return result\n\n async def json_response(self) -> Data:\n \"\"\"Produce structured Data via native LLM structured output, with prompt-based fallback.\n\n Native path (no tools, llm has with_structured_output) bypasses the agent loop and\n returns provider-validated JSON. When tools are attached, falls back to running the\n agent with a schema-augmented system prompt and parsing the final message content.\n \"\"\"\n from lfx.components.models_and_agents.structured_output.structured_output_orchestrator import (\n orchestrate_structured_output,\n )\n\n try:\n llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n except (ValueError, TypeError) as exc:\n await logger.aerror(f\"json_response.requirements_failed: {exc}\")\n return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n injected_system_prompt = self._inject_dynamic_prompt_values(getattr(self, \"system_prompt\", \"\") or \"\") or \"\"\n format_instructions = getattr(self, \"format_instructions\", \"\") or \"\"\n output_schema = getattr(self, \"output_schema\", None) or []\n has_tools = bool(self.tools)\n\n async def _run_agent_for_fallback(augmented_prompt: str) -> str:\n self.set(\n llm=llm_model,\n tools=self.tools or [],\n chat_history=self.chat_history,\n input_value=self.input_value,\n system_prompt=augmented_prompt,\n )\n agent_runnable = self.create_agent_runnable()\n with _suppress_send_message(self):\n result = await self.run_agent(agent_runnable)\n return _extract_text_content(result)\n\n try:\n return await orchestrate_structured_output(\n llm=llm_model,\n output_schema=output_schema,\n system_prompt=injected_system_prompt,\n format_instructions=format_instructions,\n input_value=_extract_text_content(self.input_value),\n run_prompt_fallback=_run_agent_for_fallback,\n prefer_native=not has_tools,\n )\n except (\n ExceptionWithMessageError,\n ValueError,\n TypeError,\n NotImplementedError,\n AttributeError,\n ) as exc:\n await logger.aerror(f\"json_response.orchestration_failed: {exc}\")\n return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n async def get_memory_data(self):\n # TODO: This is a temporary fix to avoid message duplication. We should develop a function for this.\n messages = (\n await MemoryComponent(**self.get_base_args())\n .set(\n session_id=self.graph.session_id,\n context_id=self.context_id,\n order=\"Ascending\",\n n_messages=self.n_messages,\n )\n .retrieve_messages()\n )\n return [\n message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n ]\n\n def update_input_types(self, build_config: dotdict) -> dotdict:\n \"\"\"Update input types for all fields in build_config.\"\"\"\n for key, value in build_config.items():\n if isinstance(value, dict):\n if value.get(\"input_types\") is None:\n build_config[key][\"input_types\"] = []\n elif hasattr(value, \"input_types\") and value.input_types is None:\n value.input_types = []\n return build_config\n\n async def update_build_config(\n self,\n build_config: dotdict,\n field_value: list[dict],\n field_name: str | None = None,\n ) -> dotdict:\n # Update model options with caching (for all field changes).\n # The tool-calling constraint lives on the ModelInput's ``filters``\n # field (declared above); ``handle_model_input_update`` reads it\n # and applies the filter to both the dropdown options and the\n # sticky-default re-injection path.\n build_config = handle_model_input_update(\n component=self,\n build_config=dict(build_config),\n field_value=field_value,\n field_name=field_name,\n )\n build_config = dotdict(build_config)\n\n if field_name == \"model\":\n build_config = self.update_input_types(build_config)\n\n # Validate required keys\n default_keys = [\n \"code\",\n \"_type\",\n \"model\",\n \"tools\",\n \"input_value\",\n \"add_current_date_tool\",\n \"add_calculator_tool\",\n \"system_prompt\",\n \"agent_description\",\n \"max_iterations\",\n \"handle_parsing_errors\",\n \"verbose\",\n ]\n missing_keys = [key for key in default_keys if key not in build_config]\n if missing_keys:\n msg = f\"Missing required keys in build_config: {missing_keys}\"\n raise ValueError(msg)\n return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n async def _get_tools(self) -> list[Tool]:\n component_toolkit = get_component_toolkit()\n tools_names = self._build_tools_names()\n agent_description = self.get_tool_description()\n # TODO: Agent Description Depreciated Feature to be removed\n description = f\"{agent_description}{tools_names}\"\n\n tools = component_toolkit(component=self).get_tools(\n tool_name=\"Call_Agent\",\n tool_description=description,\n # here we do not use the shared callbacks as we are exposing the agent as a tool\n callbacks=self.get_langchain_callbacks(),\n )\n if hasattr(self, \"tools_metadata\"):\n tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n\n return tools\n" + "value": "from __future__ import annotations\n\nfrom contextlib import contextmanager\nfrom datetime import datetime, timezone\nfrom typing import TYPE_CHECKING, Any\n\nfrom lfx.components.models_and_agents.memory import MemoryComponent, aget_agent_chat_history\n\nif TYPE_CHECKING:\n from langchain_core.tools import Tool\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.agents.default_system_prompt import DEFAULT_SYSTEM_PROMPT_TEMPLATE\nfrom lfx.base.agents.events import ExceptionWithMessageError\nfrom lfx.base.constants import STREAM_INFO_TEXT\nfrom lfx.base.models.unified_models import (\n get_language_model_options,\n get_llm,\n handle_model_input_update,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.components.agentics.helpers.model_config import validate_model_selection\nfrom lfx.components.helpers import CalculatorComponent, CurrentDateComponent\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.custom.custom_component.component import get_component_toolkit\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, ModelInput, StrInput\nfrom lfx.io import IntInput, MessageTextInput, MultilineInput, Output, SecretStrInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.schema.data import Data\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.schema.table import EditMode\n\n\ndef set_advanced_true(component_input):\n component_input.advanced = True\n return component_input\n\n\ndef _extract_text_content(value) -> str:\n \"\"\"Pull a string payload from a Message-like, AIMessage-like, or string value.\"\"\"\n if isinstance(value, str):\n return value\n text = getattr(value, \"text\", None)\n if isinstance(text, str):\n return text\n content = getattr(value, \"content\", None)\n if isinstance(content, str):\n return content\n return str(value) if value is not None else \"\"\n\n\n@contextmanager\ndef _suppress_send_message(component: Any):\n \"\"\"Temporarily replace component.send_message with a no-op for the duration of the block.\n\n Used during the structured-output prompt fallback: run_agent streams the agent's\n final answer through self.send_message (correct for message_response), but in\n json_response the orchestrator parses that text into structured Data which the\n downstream Chat Output emits — leaving the original emission in place produces a\n duplicate message in the playground. The original method is always restored on exit,\n even when the wrapped call raises.\n \"\"\"\n original = component.send_message\n\n async def _noop(message, *_args, **_kwargs):\n return message\n\n component.send_message = _noop\n try:\n yield\n finally:\n component.send_message = original\n\n\nclass AgentComponent(ToolCallingAgentComponent):\n display_name: str = \"Agent\"\n description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n documentation: str = \"https://docs.langflow.org/agents\"\n icon = \"bot\"\n beta = False\n name = \"Agent\"\n\n memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n inputs = [\n ModelInput(\n name=\"model\",\n display_name=\"Language Model\",\n info=\"Select your model provider\",\n real_time_refresh=True,\n required=True,\n # Agents require tool calling — the filter is honored by\n # ``handle_model_input_update`` so models that can't run with\n # tools never reach the picker (and any saved selection that\n # no longer satisfies the constraint is auto-replaced).\n filters={\"tool_calling\": True},\n ),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"API Key\",\n info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n real_time_refresh=True,\n advanced=True,\n ),\n DropdownInput(\n name=\"base_url_ibm_watsonx\",\n display_name=\"watsonx API Endpoint\",\n info=\"The base URL of the API (IBM watsonx.ai only)\",\n options=IBM_WATSONX_URLS,\n value=IBM_WATSONX_URLS[0],\n combobox=True,\n show=False,\n real_time_refresh=True,\n ),\n StrInput(\n name=\"project_id\",\n display_name=\"watsonx Project ID\",\n info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n show=False,\n required=False,\n ),\n MultilineInput(\n name=\"system_prompt\",\n display_name=\"Agent Instructions\",\n info=(\n \"System Prompt: Initial instructions and context provided to guide the agent's behavior. \"\n \"Supports dynamic placeholders: {current_date}, {model_name}, {optional_user_context}.\"\n ),\n value=DEFAULT_SYSTEM_PROMPT_TEMPLATE,\n advanced=False,\n ),\n MessageTextInput(\n name=\"context_id\",\n display_name=\"Context ID\",\n info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n value=\"\",\n advanced=True,\n ),\n IntInput(\n name=\"n_messages\",\n display_name=\"Number of Chat History Messages\",\n value=100,\n info=\"Number of chat history messages to retrieve.\",\n advanced=True,\n show=True,\n ),\n IntInput(\n name=\"max_tokens\",\n display_name=\"Max Tokens\",\n info=\"Maximum number of tokens to generate. Field name varies by provider.\",\n advanced=True,\n range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n ),\n MultilineInput(\n name=\"format_instructions\",\n display_name=\"Output Format Instructions\",\n info=\"Generic Template for structured output formatting. Valid only with Structured response.\",\n value=(\n \"You are an AI that extracts structured JSON objects from unstructured text. \"\n \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n \"Fill missing or ambiguous values with defaults: null for missing values. \"\n \"Remove exact duplicates but keep variations that have different field values. \"\n \"Always return valid JSON in the expected format, never throw errors. \"\n \"If multiple objects can be extracted, return them all in the structured format.\"\n ),\n advanced=True,\n ),\n TableInput(\n name=\"output_schema\",\n display_name=\"Output Schema\",\n info=(\n \"Schema Validation: Define the structure and data types for structured output. \"\n \"No validation if no output schema.\"\n ),\n advanced=True,\n required=False,\n value=[],\n table_schema=[\n {\n \"name\": \"name\",\n \"display_name\": \"Name\",\n \"type\": \"str\",\n \"description\": \"Specify the name of the output field.\",\n \"default\": \"field\",\n \"edit_mode\": EditMode.INLINE,\n },\n {\n \"name\": \"description\",\n \"display_name\": \"Description\",\n \"type\": \"str\",\n \"description\": \"Describe the purpose of the output field.\",\n \"default\": \"description of field\",\n \"edit_mode\": EditMode.POPOVER,\n },\n {\n \"name\": \"type\",\n \"display_name\": \"Type\",\n \"type\": \"str\",\n \"edit_mode\": EditMode.INLINE,\n \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n \"default\": \"str\",\n },\n {\n \"name\": \"multiple\",\n \"display_name\": \"As List\",\n \"type\": \"boolean\",\n \"description\": \"Set to True if this output field should be a list of the specified type.\",\n \"default\": \"False\",\n \"edit_mode\": EditMode.INLINE,\n },\n ],\n ),\n *LCToolsAgentComponent.get_base_inputs(),\n # removed memory inputs from agent component\n # *memory_inputs,\n BoolInput(\n name=\"stream\",\n display_name=\"Stream\",\n info=STREAM_INFO_TEXT,\n value=False,\n advanced=True,\n ),\n BoolInput(\n name=\"add_current_date_tool\",\n display_name=\"Current Date\",\n advanced=True,\n info=\"If true, will add a tool to the agent that returns the current date.\",\n value=True,\n ),\n BoolInput(\n name=\"add_calculator_tool\",\n display_name=\"Calculator\",\n advanced=True,\n info=(\n \"If true, adds a zero-config arithmetic calculator tool to the agent \"\n \"(safe: only +, -, *, /, ** operators via AST).\"\n ),\n value=True,\n ),\n ]\n outputs = [\n Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n Output(\n name=\"structured_response\",\n display_name=\"Structured Response\",\n method=\"json_response\",\n types=[\"Data\"],\n ),\n ]\n\n def _resolve_selected_model(self):\n \"\"\"Resolve the selected model, including legacy agent_llm/model_name inputs.\"\"\"\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n if isinstance(self.model, BaseLanguageModel):\n return self.model\n except ImportError:\n pass\n\n if isinstance(self.model, list) and self.model:\n return self.model\n\n legacy_provider = getattr(self, \"agent_llm\", None)\n legacy_model_name = getattr(self, \"model_name\", None)\n if not legacy_provider or not legacy_model_name:\n return self.model\n\n options = get_language_model_options(user_id=self.user_id)\n for option in options:\n if option.get(\"provider\") == legacy_provider and option.get(\"name\") == legacy_model_name:\n return [option]\n\n return [\n {\n \"name\": legacy_model_name,\n \"provider\": legacy_provider,\n \"metadata\": {},\n }\n ]\n\n def _get_max_tokens_value(self):\n \"\"\"Return the user-supplied max_tokens or None when unset/zero.\"\"\"\n val = getattr(self, \"max_tokens\", None)\n if val in {\"\", 0}:\n return None\n return val\n\n def _get_llm(self):\n \"\"\"Override parent to include max_tokens from the Agent's input field.\"\"\"\n return get_llm(\n model=self.model,\n user_id=self.user_id,\n api_key=getattr(self, \"api_key\", None),\n stream=bool(getattr(self, \"stream\", False)),\n max_tokens=self._get_max_tokens_value(),\n watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n watsonx_project_id=getattr(self, \"project_id\", None),\n )\n\n async def get_agent_requirements(self):\n \"\"\"Get the agent requirements for the agent.\"\"\"\n from langchain_core.tools import StructuredTool\n\n selected_model = self._resolve_selected_model()\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n is_connected_model = isinstance(selected_model, BaseLanguageModel)\n except ImportError:\n is_connected_model = False\n\n if not is_connected_model:\n validate_model_selection(selected_model)\n\n # Ensure _get_llm() uses the resolved model (e.g. from legacy agent_llm/model_name)\n self.model = selected_model\n llm_model = self._get_llm()\n if llm_model is None:\n msg = \"No language model selected. Please choose a model to proceed.\"\n raise ValueError(msg)\n\n # Get memory data\n self.chat_history = await self.get_memory_data()\n await logger.adebug(f\"Retrieved {len(self.chat_history)} chat history messages\")\n if isinstance(self.chat_history, Message):\n self.chat_history = [self.chat_history]\n\n # Add current date tool if enabled\n if self.add_current_date_tool:\n if not isinstance(self.tools, list): # type: ignore[has-type]\n self.tools = []\n current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n if not isinstance(current_date_tool, StructuredTool):\n msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n raise TypeError(msg)\n # Skip if an externally-connected tool already provides the same name.\n # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n if not any(getattr(t, \"name\", None) == current_date_tool.name for t in self.tools):\n self.tools.append(current_date_tool)\n\n # Add calculator tool if enabled (zero-config arithmetic)\n if getattr(self, \"add_calculator_tool\", False):\n if not isinstance(self.tools, list): # type: ignore[has-type]\n self.tools = []\n calculator_tool = (await CalculatorComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n if not isinstance(calculator_tool, StructuredTool):\n msg = \"CalculatorComponent must be converted to a StructuredTool\"\n raise TypeError(msg)\n # Skip if an externally-connected tool already provides the same name.\n # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n if not any(getattr(t, \"name\", None) == calculator_tool.name for t in self.tools):\n self.tools.append(calculator_tool)\n\n # Set shared callbacks for tracing the tools used by the agent\n self.set_tools_callbacks(self.tools, self._get_shared_callbacks())\n\n return llm_model, self.chat_history, self.tools\n\n def _get_resolved_model_name(self) -> str:\n \"\"\"Best-effort human-readable model name for {model_name} injection.\"\"\"\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n if isinstance(self.model, BaseLanguageModel):\n return type(self.model).__name__\n except ImportError:\n pass\n\n if isinstance(self.model, list) and self.model:\n first = self.model[0]\n if isinstance(first, dict):\n name = first.get(\"name\")\n if isinstance(name, str) and name:\n return name\n\n legacy_model_name = getattr(self, \"model_name\", None)\n if isinstance(legacy_model_name, str) and legacy_model_name:\n return legacy_model_name\n return \"\"\n\n def _inject_dynamic_prompt_values(self, prompt: str | None) -> str | None:\n \"\"\"Replace known env placeholders in the system prompt.\n\n Handles {current_date}, {model_name}, and {optional_user_context} (the\n last one ships with the structured DEFAULT_SYSTEM_PROMPT_TEMPLATE and\n is currently unused at the AgentComponent layer, so it resolves to \"\").\n Uses str.replace (not str.format) so user prompts containing literal\n braces such as JSON examples ({\"key\": 1}) never break the agent.\n \"\"\"\n if not prompt:\n return prompt\n replacements = {\n \"{current_date}\": datetime.now(tz=timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S UTC\"),\n \"{model_name}\": self._get_resolved_model_name(),\n \"{optional_user_context}\": \"\",\n }\n for placeholder, value in replacements.items():\n prompt = prompt.replace(placeholder, value)\n return prompt\n\n async def message_response(self) -> Message:\n try:\n llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n # Set up and run agent\n self.set(\n llm=llm_model,\n tools=self.tools or [],\n chat_history=self.chat_history,\n input_value=self.input_value,\n system_prompt=self._inject_dynamic_prompt_values(self.system_prompt),\n )\n agent = self.create_agent_runnable()\n result = await self.run_agent(agent)\n\n # Store result for potential JSON output\n self._agent_result = result\n\n except (ValueError, TypeError, KeyError) as e:\n await logger.aerror(f\"{type(e).__name__}: {e!s}\")\n raise\n except ExceptionWithMessageError as e:\n await logger.aerror(f\"ExceptionWithMessageError occurred: {e}\")\n raise\n # Avoid catching blind Exception; let truly unexpected exceptions propagate\n except Exception as e:\n await logger.aerror(f\"Unexpected error: {e!s}\")\n raise\n else:\n return result\n\n async def json_response(self) -> Data:\n \"\"\"Produce structured Data via native LLM structured output, with prompt-based fallback.\n\n Native path (no tools, llm has with_structured_output) bypasses the agent loop and\n returns provider-validated JSON. When tools are attached, falls back to running the\n agent with a schema-augmented system prompt and parsing the final message content.\n \"\"\"\n from lfx.components.models_and_agents.structured_output.structured_output_orchestrator import (\n orchestrate_structured_output,\n )\n\n try:\n llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n except (ValueError, TypeError) as exc:\n await logger.aerror(f\"json_response.requirements_failed: {exc}\")\n return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n injected_system_prompt = self._inject_dynamic_prompt_values(getattr(self, \"system_prompt\", \"\") or \"\") or \"\"\n format_instructions = getattr(self, \"format_instructions\", \"\") or \"\"\n output_schema = getattr(self, \"output_schema\", None) or []\n has_tools = bool(self.tools)\n\n async def _run_agent_for_fallback(augmented_prompt: str) -> str:\n self.set(\n llm=llm_model,\n tools=self.tools or [],\n chat_history=self.chat_history,\n input_value=self.input_value,\n system_prompt=augmented_prompt,\n )\n agent_runnable = self.create_agent_runnable()\n with _suppress_send_message(self):\n result = await self.run_agent(agent_runnable)\n return _extract_text_content(result)\n\n try:\n return await orchestrate_structured_output(\n llm=llm_model,\n output_schema=output_schema,\n system_prompt=injected_system_prompt,\n format_instructions=format_instructions,\n input_value=_extract_text_content(self.input_value),\n run_prompt_fallback=_run_agent_for_fallback,\n prefer_native=not has_tools,\n )\n except (\n ExceptionWithMessageError,\n ValueError,\n TypeError,\n NotImplementedError,\n AttributeError,\n ) as exc:\n await logger.aerror(f\"json_response.orchestration_failed: {exc}\")\n return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n async def get_memory_data(self):\n # Scope by flow_id so default playground session names (e.g. \"New Session 0\")\n # cannot leak chat history across unrelated flows. See issue #13059.\n # The helper also returns [] when n_messages == 0, preserving the\n # explicit \"memory disabled\" contract from MemoryComponent.retrieve_messages.\n messages = await aget_agent_chat_history(\n session_id=self.graph.session_id,\n flow_id=getattr(self.graph, \"flow_id\", None),\n context_id=self.context_id,\n n_messages=self.n_messages,\n )\n return [\n message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n ]\n\n def update_input_types(self, build_config: dotdict) -> dotdict:\n \"\"\"Update input types for all fields in build_config.\"\"\"\n for key, value in build_config.items():\n if isinstance(value, dict):\n if value.get(\"input_types\") is None:\n build_config[key][\"input_types\"] = []\n elif hasattr(value, \"input_types\") and value.input_types is None:\n value.input_types = []\n return build_config\n\n async def update_build_config(\n self,\n build_config: dotdict,\n field_value: list[dict],\n field_name: str | None = None,\n ) -> dotdict:\n # Update model options with caching (for all field changes).\n # The tool-calling constraint lives on the ModelInput's ``filters``\n # field (declared above); ``handle_model_input_update`` reads it\n # and applies the filter to both the dropdown options and the\n # sticky-default re-injection path.\n build_config = handle_model_input_update(\n component=self,\n build_config=dict(build_config),\n field_value=field_value,\n field_name=field_name,\n )\n build_config = dotdict(build_config)\n\n if field_name == \"model\":\n build_config = self.update_input_types(build_config)\n\n # Validate required keys\n default_keys = [\n \"code\",\n \"_type\",\n \"model\",\n \"tools\",\n \"input_value\",\n \"add_current_date_tool\",\n \"add_calculator_tool\",\n \"system_prompt\",\n \"agent_description\",\n \"max_iterations\",\n \"handle_parsing_errors\",\n \"verbose\",\n ]\n missing_keys = [key for key in default_keys if key not in build_config]\n if missing_keys:\n msg = f\"Missing required keys in build_config: {missing_keys}\"\n raise ValueError(msg)\n return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n async def _get_tools(self) -> list[Tool]:\n component_toolkit = get_component_toolkit()\n tools_names = self._build_tools_names()\n agent_description = self.get_tool_description()\n # TODO: Agent Description Depreciated Feature to be removed\n description = f\"{agent_description}{tools_names}\"\n\n tools = component_toolkit(component=self).get_tools(\n tool_name=\"Call_Agent\",\n tool_description=description,\n # here we do not use the shared callbacks as we are exposing the agent as a tool\n callbacks=self.get_langchain_callbacks(),\n )\n if hasattr(self, \"tools_metadata\"):\n tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n\n return tools\n" }, "context_id": { "_input_type": "MessageTextInput", @@ -2979,7 +2979,7 @@ "last_updated": "2025-12-11T21:41:48.407Z", "legacy": false, "metadata": { - "code_hash": "ebac74bfcae6", + "code_hash": "443159257161", "dependencies": { "dependencies": [ { @@ -3168,7 +3168,7 @@ "show": true, "title_case": false, "type": "code", - "value": "from __future__ import annotations\n\nfrom contextlib import contextmanager\nfrom datetime import datetime, timezone\nfrom typing import TYPE_CHECKING, Any\n\nfrom lfx.components.models_and_agents.memory import MemoryComponent\n\nif TYPE_CHECKING:\n from langchain_core.tools import Tool\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.agents.default_system_prompt import DEFAULT_SYSTEM_PROMPT_TEMPLATE\nfrom lfx.base.agents.events import ExceptionWithMessageError\nfrom lfx.base.constants import STREAM_INFO_TEXT\nfrom lfx.base.models.unified_models import (\n get_language_model_options,\n get_llm,\n handle_model_input_update,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.components.agentics.helpers.model_config import validate_model_selection\nfrom lfx.components.helpers import CalculatorComponent, CurrentDateComponent\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.custom.custom_component.component import get_component_toolkit\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, ModelInput, StrInput\nfrom lfx.io import IntInput, MessageTextInput, MultilineInput, Output, SecretStrInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.schema.data import Data\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.schema.table import EditMode\n\n\ndef set_advanced_true(component_input):\n component_input.advanced = True\n return component_input\n\n\ndef _extract_text_content(value) -> str:\n \"\"\"Pull a string payload from a Message-like, AIMessage-like, or string value.\"\"\"\n if isinstance(value, str):\n return value\n text = getattr(value, \"text\", None)\n if isinstance(text, str):\n return text\n content = getattr(value, \"content\", None)\n if isinstance(content, str):\n return content\n return str(value) if value is not None else \"\"\n\n\n@contextmanager\ndef _suppress_send_message(component: Any):\n \"\"\"Temporarily replace component.send_message with a no-op for the duration of the block.\n\n Used during the structured-output prompt fallback: run_agent streams the agent's\n final answer through self.send_message (correct for message_response), but in\n json_response the orchestrator parses that text into structured Data which the\n downstream Chat Output emits — leaving the original emission in place produces a\n duplicate message in the playground. The original method is always restored on exit,\n even when the wrapped call raises.\n \"\"\"\n original = component.send_message\n\n async def _noop(message, *_args, **_kwargs):\n return message\n\n component.send_message = _noop\n try:\n yield\n finally:\n component.send_message = original\n\n\nclass AgentComponent(ToolCallingAgentComponent):\n display_name: str = \"Agent\"\n description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n documentation: str = \"https://docs.langflow.org/agents\"\n icon = \"bot\"\n beta = False\n name = \"Agent\"\n\n memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n inputs = [\n ModelInput(\n name=\"model\",\n display_name=\"Language Model\",\n info=\"Select your model provider\",\n real_time_refresh=True,\n required=True,\n # Agents require tool calling — the filter is honored by\n # ``handle_model_input_update`` so models that can't run with\n # tools never reach the picker (and any saved selection that\n # no longer satisfies the constraint is auto-replaced).\n filters={\"tool_calling\": True},\n ),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"API Key\",\n info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n real_time_refresh=True,\n advanced=True,\n ),\n DropdownInput(\n name=\"base_url_ibm_watsonx\",\n display_name=\"watsonx API Endpoint\",\n info=\"The base URL of the API (IBM watsonx.ai only)\",\n options=IBM_WATSONX_URLS,\n value=IBM_WATSONX_URLS[0],\n combobox=True,\n show=False,\n real_time_refresh=True,\n ),\n StrInput(\n name=\"project_id\",\n display_name=\"watsonx Project ID\",\n info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n show=False,\n required=False,\n ),\n MultilineInput(\n name=\"system_prompt\",\n display_name=\"Agent Instructions\",\n info=(\n \"System Prompt: Initial instructions and context provided to guide the agent's behavior. \"\n \"Supports dynamic placeholders: {current_date}, {model_name}, {optional_user_context}.\"\n ),\n value=DEFAULT_SYSTEM_PROMPT_TEMPLATE,\n advanced=False,\n ),\n MessageTextInput(\n name=\"context_id\",\n display_name=\"Context ID\",\n info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n value=\"\",\n advanced=True,\n ),\n IntInput(\n name=\"n_messages\",\n display_name=\"Number of Chat History Messages\",\n value=100,\n info=\"Number of chat history messages to retrieve.\",\n advanced=True,\n show=True,\n ),\n IntInput(\n name=\"max_tokens\",\n display_name=\"Max Tokens\",\n info=\"Maximum number of tokens to generate. Field name varies by provider.\",\n advanced=True,\n range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n ),\n MultilineInput(\n name=\"format_instructions\",\n display_name=\"Output Format Instructions\",\n info=\"Generic Template for structured output formatting. Valid only with Structured response.\",\n value=(\n \"You are an AI that extracts structured JSON objects from unstructured text. \"\n \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n \"Fill missing or ambiguous values with defaults: null for missing values. \"\n \"Remove exact duplicates but keep variations that have different field values. \"\n \"Always return valid JSON in the expected format, never throw errors. \"\n \"If multiple objects can be extracted, return them all in the structured format.\"\n ),\n advanced=True,\n ),\n TableInput(\n name=\"output_schema\",\n display_name=\"Output Schema\",\n info=(\n \"Schema Validation: Define the structure and data types for structured output. \"\n \"No validation if no output schema.\"\n ),\n advanced=True,\n required=False,\n value=[],\n table_schema=[\n {\n \"name\": \"name\",\n \"display_name\": \"Name\",\n \"type\": \"str\",\n \"description\": \"Specify the name of the output field.\",\n \"default\": \"field\",\n \"edit_mode\": EditMode.INLINE,\n },\n {\n \"name\": \"description\",\n \"display_name\": \"Description\",\n \"type\": \"str\",\n \"description\": \"Describe the purpose of the output field.\",\n \"default\": \"description of field\",\n \"edit_mode\": EditMode.POPOVER,\n },\n {\n \"name\": \"type\",\n \"display_name\": \"Type\",\n \"type\": \"str\",\n \"edit_mode\": EditMode.INLINE,\n \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n \"default\": \"str\",\n },\n {\n \"name\": \"multiple\",\n \"display_name\": \"As List\",\n \"type\": \"boolean\",\n \"description\": \"Set to True if this output field should be a list of the specified type.\",\n \"default\": \"False\",\n \"edit_mode\": EditMode.INLINE,\n },\n ],\n ),\n *LCToolsAgentComponent.get_base_inputs(),\n # removed memory inputs from agent component\n # *memory_inputs,\n BoolInput(\n name=\"stream\",\n display_name=\"Stream\",\n info=STREAM_INFO_TEXT,\n value=False,\n advanced=True,\n ),\n BoolInput(\n name=\"add_current_date_tool\",\n display_name=\"Current Date\",\n advanced=True,\n info=\"If true, will add a tool to the agent that returns the current date.\",\n value=True,\n ),\n BoolInput(\n name=\"add_calculator_tool\",\n display_name=\"Calculator\",\n advanced=True,\n info=(\n \"If true, adds a zero-config arithmetic calculator tool to the agent \"\n \"(safe: only +, -, *, /, ** operators via AST).\"\n ),\n value=True,\n ),\n ]\n outputs = [\n Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n Output(\n name=\"structured_response\",\n display_name=\"Structured Response\",\n method=\"json_response\",\n types=[\"Data\"],\n ),\n ]\n\n def _resolve_selected_model(self):\n \"\"\"Resolve the selected model, including legacy agent_llm/model_name inputs.\"\"\"\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n if isinstance(self.model, BaseLanguageModel):\n return self.model\n except ImportError:\n pass\n\n if isinstance(self.model, list) and self.model:\n return self.model\n\n legacy_provider = getattr(self, \"agent_llm\", None)\n legacy_model_name = getattr(self, \"model_name\", None)\n if not legacy_provider or not legacy_model_name:\n return self.model\n\n options = get_language_model_options(user_id=self.user_id)\n for option in options:\n if option.get(\"provider\") == legacy_provider and option.get(\"name\") == legacy_model_name:\n return [option]\n\n return [\n {\n \"name\": legacy_model_name,\n \"provider\": legacy_provider,\n \"metadata\": {},\n }\n ]\n\n def _get_max_tokens_value(self):\n \"\"\"Return the user-supplied max_tokens or None when unset/zero.\"\"\"\n val = getattr(self, \"max_tokens\", None)\n if val in {\"\", 0}:\n return None\n return val\n\n def _get_llm(self):\n \"\"\"Override parent to include max_tokens from the Agent's input field.\"\"\"\n return get_llm(\n model=self.model,\n user_id=self.user_id,\n api_key=getattr(self, \"api_key\", None),\n stream=bool(getattr(self, \"stream\", False)),\n max_tokens=self._get_max_tokens_value(),\n watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n watsonx_project_id=getattr(self, \"project_id\", None),\n )\n\n async def get_agent_requirements(self):\n \"\"\"Get the agent requirements for the agent.\"\"\"\n from langchain_core.tools import StructuredTool\n\n selected_model = self._resolve_selected_model()\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n is_connected_model = isinstance(selected_model, BaseLanguageModel)\n except ImportError:\n is_connected_model = False\n\n if not is_connected_model:\n validate_model_selection(selected_model)\n\n # Ensure _get_llm() uses the resolved model (e.g. from legacy agent_llm/model_name)\n self.model = selected_model\n llm_model = self._get_llm()\n if llm_model is None:\n msg = \"No language model selected. Please choose a model to proceed.\"\n raise ValueError(msg)\n\n # Get memory data\n self.chat_history = await self.get_memory_data()\n await logger.adebug(f\"Retrieved {len(self.chat_history)} chat history messages\")\n if isinstance(self.chat_history, Message):\n self.chat_history = [self.chat_history]\n\n # Add current date tool if enabled\n if self.add_current_date_tool:\n if not isinstance(self.tools, list): # type: ignore[has-type]\n self.tools = []\n current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n if not isinstance(current_date_tool, StructuredTool):\n msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n raise TypeError(msg)\n # Skip if an externally-connected tool already provides the same name.\n # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n if not any(getattr(t, \"name\", None) == current_date_tool.name for t in self.tools):\n self.tools.append(current_date_tool)\n\n # Add calculator tool if enabled (zero-config arithmetic)\n if getattr(self, \"add_calculator_tool\", False):\n if not isinstance(self.tools, list): # type: ignore[has-type]\n self.tools = []\n calculator_tool = (await CalculatorComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n if not isinstance(calculator_tool, StructuredTool):\n msg = \"CalculatorComponent must be converted to a StructuredTool\"\n raise TypeError(msg)\n # Skip if an externally-connected tool already provides the same name.\n # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n if not any(getattr(t, \"name\", None) == calculator_tool.name for t in self.tools):\n self.tools.append(calculator_tool)\n\n # Set shared callbacks for tracing the tools used by the agent\n self.set_tools_callbacks(self.tools, self._get_shared_callbacks())\n\n return llm_model, self.chat_history, self.tools\n\n def _get_resolved_model_name(self) -> str:\n \"\"\"Best-effort human-readable model name for {model_name} injection.\"\"\"\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n if isinstance(self.model, BaseLanguageModel):\n return type(self.model).__name__\n except ImportError:\n pass\n\n if isinstance(self.model, list) and self.model:\n first = self.model[0]\n if isinstance(first, dict):\n name = first.get(\"name\")\n if isinstance(name, str) and name:\n return name\n\n legacy_model_name = getattr(self, \"model_name\", None)\n if isinstance(legacy_model_name, str) and legacy_model_name:\n return legacy_model_name\n return \"\"\n\n def _inject_dynamic_prompt_values(self, prompt: str | None) -> str | None:\n \"\"\"Replace known env placeholders in the system prompt.\n\n Handles {current_date}, {model_name}, and {optional_user_context} (the\n last one ships with the structured DEFAULT_SYSTEM_PROMPT_TEMPLATE and\n is currently unused at the AgentComponent layer, so it resolves to \"\").\n Uses str.replace (not str.format) so user prompts containing literal\n braces such as JSON examples ({\"key\": 1}) never break the agent.\n \"\"\"\n if not prompt:\n return prompt\n replacements = {\n \"{current_date}\": datetime.now(tz=timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S UTC\"),\n \"{model_name}\": self._get_resolved_model_name(),\n \"{optional_user_context}\": \"\",\n }\n for placeholder, value in replacements.items():\n prompt = prompt.replace(placeholder, value)\n return prompt\n\n async def message_response(self) -> Message:\n try:\n llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n # Set up and run agent\n self.set(\n llm=llm_model,\n tools=self.tools or [],\n chat_history=self.chat_history,\n input_value=self.input_value,\n system_prompt=self._inject_dynamic_prompt_values(self.system_prompt),\n )\n agent = self.create_agent_runnable()\n result = await self.run_agent(agent)\n\n # Store result for potential JSON output\n self._agent_result = result\n\n except (ValueError, TypeError, KeyError) as e:\n await logger.aerror(f\"{type(e).__name__}: {e!s}\")\n raise\n except ExceptionWithMessageError as e:\n await logger.aerror(f\"ExceptionWithMessageError occurred: {e}\")\n raise\n # Avoid catching blind Exception; let truly unexpected exceptions propagate\n except Exception as e:\n await logger.aerror(f\"Unexpected error: {e!s}\")\n raise\n else:\n return result\n\n async def json_response(self) -> Data:\n \"\"\"Produce structured Data via native LLM structured output, with prompt-based fallback.\n\n Native path (no tools, llm has with_structured_output) bypasses the agent loop and\n returns provider-validated JSON. When tools are attached, falls back to running the\n agent with a schema-augmented system prompt and parsing the final message content.\n \"\"\"\n from lfx.components.models_and_agents.structured_output.structured_output_orchestrator import (\n orchestrate_structured_output,\n )\n\n try:\n llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n except (ValueError, TypeError) as exc:\n await logger.aerror(f\"json_response.requirements_failed: {exc}\")\n return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n injected_system_prompt = self._inject_dynamic_prompt_values(getattr(self, \"system_prompt\", \"\") or \"\") or \"\"\n format_instructions = getattr(self, \"format_instructions\", \"\") or \"\"\n output_schema = getattr(self, \"output_schema\", None) or []\n has_tools = bool(self.tools)\n\n async def _run_agent_for_fallback(augmented_prompt: str) -> str:\n self.set(\n llm=llm_model,\n tools=self.tools or [],\n chat_history=self.chat_history,\n input_value=self.input_value,\n system_prompt=augmented_prompt,\n )\n agent_runnable = self.create_agent_runnable()\n with _suppress_send_message(self):\n result = await self.run_agent(agent_runnable)\n return _extract_text_content(result)\n\n try:\n return await orchestrate_structured_output(\n llm=llm_model,\n output_schema=output_schema,\n system_prompt=injected_system_prompt,\n format_instructions=format_instructions,\n input_value=_extract_text_content(self.input_value),\n run_prompt_fallback=_run_agent_for_fallback,\n prefer_native=not has_tools,\n )\n except (\n ExceptionWithMessageError,\n ValueError,\n TypeError,\n NotImplementedError,\n AttributeError,\n ) as exc:\n await logger.aerror(f\"json_response.orchestration_failed: {exc}\")\n return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n async def get_memory_data(self):\n # TODO: This is a temporary fix to avoid message duplication. We should develop a function for this.\n messages = (\n await MemoryComponent(**self.get_base_args())\n .set(\n session_id=self.graph.session_id,\n context_id=self.context_id,\n order=\"Ascending\",\n n_messages=self.n_messages,\n )\n .retrieve_messages()\n )\n return [\n message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n ]\n\n def update_input_types(self, build_config: dotdict) -> dotdict:\n \"\"\"Update input types for all fields in build_config.\"\"\"\n for key, value in build_config.items():\n if isinstance(value, dict):\n if value.get(\"input_types\") is None:\n build_config[key][\"input_types\"] = []\n elif hasattr(value, \"input_types\") and value.input_types is None:\n value.input_types = []\n return build_config\n\n async def update_build_config(\n self,\n build_config: dotdict,\n field_value: list[dict],\n field_name: str | None = None,\n ) -> dotdict:\n # Update model options with caching (for all field changes).\n # The tool-calling constraint lives on the ModelInput's ``filters``\n # field (declared above); ``handle_model_input_update`` reads it\n # and applies the filter to both the dropdown options and the\n # sticky-default re-injection path.\n build_config = handle_model_input_update(\n component=self,\n build_config=dict(build_config),\n field_value=field_value,\n field_name=field_name,\n )\n build_config = dotdict(build_config)\n\n if field_name == \"model\":\n build_config = self.update_input_types(build_config)\n\n # Validate required keys\n default_keys = [\n \"code\",\n \"_type\",\n \"model\",\n \"tools\",\n \"input_value\",\n \"add_current_date_tool\",\n \"add_calculator_tool\",\n \"system_prompt\",\n \"agent_description\",\n \"max_iterations\",\n \"handle_parsing_errors\",\n \"verbose\",\n ]\n missing_keys = [key for key in default_keys if key not in build_config]\n if missing_keys:\n msg = f\"Missing required keys in build_config: {missing_keys}\"\n raise ValueError(msg)\n return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n async def _get_tools(self) -> list[Tool]:\n component_toolkit = get_component_toolkit()\n tools_names = self._build_tools_names()\n agent_description = self.get_tool_description()\n # TODO: Agent Description Depreciated Feature to be removed\n description = f\"{agent_description}{tools_names}\"\n\n tools = component_toolkit(component=self).get_tools(\n tool_name=\"Call_Agent\",\n tool_description=description,\n # here we do not use the shared callbacks as we are exposing the agent as a tool\n callbacks=self.get_langchain_callbacks(),\n )\n if hasattr(self, \"tools_metadata\"):\n tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n\n return tools\n" + "value": "from __future__ import annotations\n\nfrom contextlib import contextmanager\nfrom datetime import datetime, timezone\nfrom typing import TYPE_CHECKING, Any\n\nfrom lfx.components.models_and_agents.memory import MemoryComponent, aget_agent_chat_history\n\nif TYPE_CHECKING:\n from langchain_core.tools import Tool\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.agents.default_system_prompt import DEFAULT_SYSTEM_PROMPT_TEMPLATE\nfrom lfx.base.agents.events import ExceptionWithMessageError\nfrom lfx.base.constants import STREAM_INFO_TEXT\nfrom lfx.base.models.unified_models import (\n get_language_model_options,\n get_llm,\n handle_model_input_update,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.components.agentics.helpers.model_config import validate_model_selection\nfrom lfx.components.helpers import CalculatorComponent, CurrentDateComponent\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.custom.custom_component.component import get_component_toolkit\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, ModelInput, StrInput\nfrom lfx.io import IntInput, MessageTextInput, MultilineInput, Output, SecretStrInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.schema.data import Data\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.schema.table import EditMode\n\n\ndef set_advanced_true(component_input):\n component_input.advanced = True\n return component_input\n\n\ndef _extract_text_content(value) -> str:\n \"\"\"Pull a string payload from a Message-like, AIMessage-like, or string value.\"\"\"\n if isinstance(value, str):\n return value\n text = getattr(value, \"text\", None)\n if isinstance(text, str):\n return text\n content = getattr(value, \"content\", None)\n if isinstance(content, str):\n return content\n return str(value) if value is not None else \"\"\n\n\n@contextmanager\ndef _suppress_send_message(component: Any):\n \"\"\"Temporarily replace component.send_message with a no-op for the duration of the block.\n\n Used during the structured-output prompt fallback: run_agent streams the agent's\n final answer through self.send_message (correct for message_response), but in\n json_response the orchestrator parses that text into structured Data which the\n downstream Chat Output emits — leaving the original emission in place produces a\n duplicate message in the playground. The original method is always restored on exit,\n even when the wrapped call raises.\n \"\"\"\n original = component.send_message\n\n async def _noop(message, *_args, **_kwargs):\n return message\n\n component.send_message = _noop\n try:\n yield\n finally:\n component.send_message = original\n\n\nclass AgentComponent(ToolCallingAgentComponent):\n display_name: str = \"Agent\"\n description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n documentation: str = \"https://docs.langflow.org/agents\"\n icon = \"bot\"\n beta = False\n name = \"Agent\"\n\n memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n inputs = [\n ModelInput(\n name=\"model\",\n display_name=\"Language Model\",\n info=\"Select your model provider\",\n real_time_refresh=True,\n required=True,\n # Agents require tool calling — the filter is honored by\n # ``handle_model_input_update`` so models that can't run with\n # tools never reach the picker (and any saved selection that\n # no longer satisfies the constraint is auto-replaced).\n filters={\"tool_calling\": True},\n ),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"API Key\",\n info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n real_time_refresh=True,\n advanced=True,\n ),\n DropdownInput(\n name=\"base_url_ibm_watsonx\",\n display_name=\"watsonx API Endpoint\",\n info=\"The base URL of the API (IBM watsonx.ai only)\",\n options=IBM_WATSONX_URLS,\n value=IBM_WATSONX_URLS[0],\n combobox=True,\n show=False,\n real_time_refresh=True,\n ),\n StrInput(\n name=\"project_id\",\n display_name=\"watsonx Project ID\",\n info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n show=False,\n required=False,\n ),\n MultilineInput(\n name=\"system_prompt\",\n display_name=\"Agent Instructions\",\n info=(\n \"System Prompt: Initial instructions and context provided to guide the agent's behavior. \"\n \"Supports dynamic placeholders: {current_date}, {model_name}, {optional_user_context}.\"\n ),\n value=DEFAULT_SYSTEM_PROMPT_TEMPLATE,\n advanced=False,\n ),\n MessageTextInput(\n name=\"context_id\",\n display_name=\"Context ID\",\n info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n value=\"\",\n advanced=True,\n ),\n IntInput(\n name=\"n_messages\",\n display_name=\"Number of Chat History Messages\",\n value=100,\n info=\"Number of chat history messages to retrieve.\",\n advanced=True,\n show=True,\n ),\n IntInput(\n name=\"max_tokens\",\n display_name=\"Max Tokens\",\n info=\"Maximum number of tokens to generate. Field name varies by provider.\",\n advanced=True,\n range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n ),\n MultilineInput(\n name=\"format_instructions\",\n display_name=\"Output Format Instructions\",\n info=\"Generic Template for structured output formatting. Valid only with Structured response.\",\n value=(\n \"You are an AI that extracts structured JSON objects from unstructured text. \"\n \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n \"Fill missing or ambiguous values with defaults: null for missing values. \"\n \"Remove exact duplicates but keep variations that have different field values. \"\n \"Always return valid JSON in the expected format, never throw errors. \"\n \"If multiple objects can be extracted, return them all in the structured format.\"\n ),\n advanced=True,\n ),\n TableInput(\n name=\"output_schema\",\n display_name=\"Output Schema\",\n info=(\n \"Schema Validation: Define the structure and data types for structured output. \"\n \"No validation if no output schema.\"\n ),\n advanced=True,\n required=False,\n value=[],\n table_schema=[\n {\n \"name\": \"name\",\n \"display_name\": \"Name\",\n \"type\": \"str\",\n \"description\": \"Specify the name of the output field.\",\n \"default\": \"field\",\n \"edit_mode\": EditMode.INLINE,\n },\n {\n \"name\": \"description\",\n \"display_name\": \"Description\",\n \"type\": \"str\",\n \"description\": \"Describe the purpose of the output field.\",\n \"default\": \"description of field\",\n \"edit_mode\": EditMode.POPOVER,\n },\n {\n \"name\": \"type\",\n \"display_name\": \"Type\",\n \"type\": \"str\",\n \"edit_mode\": EditMode.INLINE,\n \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n \"default\": \"str\",\n },\n {\n \"name\": \"multiple\",\n \"display_name\": \"As List\",\n \"type\": \"boolean\",\n \"description\": \"Set to True if this output field should be a list of the specified type.\",\n \"default\": \"False\",\n \"edit_mode\": EditMode.INLINE,\n },\n ],\n ),\n *LCToolsAgentComponent.get_base_inputs(),\n # removed memory inputs from agent component\n # *memory_inputs,\n BoolInput(\n name=\"stream\",\n display_name=\"Stream\",\n info=STREAM_INFO_TEXT,\n value=False,\n advanced=True,\n ),\n BoolInput(\n name=\"add_current_date_tool\",\n display_name=\"Current Date\",\n advanced=True,\n info=\"If true, will add a tool to the agent that returns the current date.\",\n value=True,\n ),\n BoolInput(\n name=\"add_calculator_tool\",\n display_name=\"Calculator\",\n advanced=True,\n info=(\n \"If true, adds a zero-config arithmetic calculator tool to the agent \"\n \"(safe: only +, -, *, /, ** operators via AST).\"\n ),\n value=True,\n ),\n ]\n outputs = [\n Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n Output(\n name=\"structured_response\",\n display_name=\"Structured Response\",\n method=\"json_response\",\n types=[\"Data\"],\n ),\n ]\n\n def _resolve_selected_model(self):\n \"\"\"Resolve the selected model, including legacy agent_llm/model_name inputs.\"\"\"\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n if isinstance(self.model, BaseLanguageModel):\n return self.model\n except ImportError:\n pass\n\n if isinstance(self.model, list) and self.model:\n return self.model\n\n legacy_provider = getattr(self, \"agent_llm\", None)\n legacy_model_name = getattr(self, \"model_name\", None)\n if not legacy_provider or not legacy_model_name:\n return self.model\n\n options = get_language_model_options(user_id=self.user_id)\n for option in options:\n if option.get(\"provider\") == legacy_provider and option.get(\"name\") == legacy_model_name:\n return [option]\n\n return [\n {\n \"name\": legacy_model_name,\n \"provider\": legacy_provider,\n \"metadata\": {},\n }\n ]\n\n def _get_max_tokens_value(self):\n \"\"\"Return the user-supplied max_tokens or None when unset/zero.\"\"\"\n val = getattr(self, \"max_tokens\", None)\n if val in {\"\", 0}:\n return None\n return val\n\n def _get_llm(self):\n \"\"\"Override parent to include max_tokens from the Agent's input field.\"\"\"\n return get_llm(\n model=self.model,\n user_id=self.user_id,\n api_key=getattr(self, \"api_key\", None),\n stream=bool(getattr(self, \"stream\", False)),\n max_tokens=self._get_max_tokens_value(),\n watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n watsonx_project_id=getattr(self, \"project_id\", None),\n )\n\n async def get_agent_requirements(self):\n \"\"\"Get the agent requirements for the agent.\"\"\"\n from langchain_core.tools import StructuredTool\n\n selected_model = self._resolve_selected_model()\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n is_connected_model = isinstance(selected_model, BaseLanguageModel)\n except ImportError:\n is_connected_model = False\n\n if not is_connected_model:\n validate_model_selection(selected_model)\n\n # Ensure _get_llm() uses the resolved model (e.g. from legacy agent_llm/model_name)\n self.model = selected_model\n llm_model = self._get_llm()\n if llm_model is None:\n msg = \"No language model selected. Please choose a model to proceed.\"\n raise ValueError(msg)\n\n # Get memory data\n self.chat_history = await self.get_memory_data()\n await logger.adebug(f\"Retrieved {len(self.chat_history)} chat history messages\")\n if isinstance(self.chat_history, Message):\n self.chat_history = [self.chat_history]\n\n # Add current date tool if enabled\n if self.add_current_date_tool:\n if not isinstance(self.tools, list): # type: ignore[has-type]\n self.tools = []\n current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n if not isinstance(current_date_tool, StructuredTool):\n msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n raise TypeError(msg)\n # Skip if an externally-connected tool already provides the same name.\n # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n if not any(getattr(t, \"name\", None) == current_date_tool.name for t in self.tools):\n self.tools.append(current_date_tool)\n\n # Add calculator tool if enabled (zero-config arithmetic)\n if getattr(self, \"add_calculator_tool\", False):\n if not isinstance(self.tools, list): # type: ignore[has-type]\n self.tools = []\n calculator_tool = (await CalculatorComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n if not isinstance(calculator_tool, StructuredTool):\n msg = \"CalculatorComponent must be converted to a StructuredTool\"\n raise TypeError(msg)\n # Skip if an externally-connected tool already provides the same name.\n # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n if not any(getattr(t, \"name\", None) == calculator_tool.name for t in self.tools):\n self.tools.append(calculator_tool)\n\n # Set shared callbacks for tracing the tools used by the agent\n self.set_tools_callbacks(self.tools, self._get_shared_callbacks())\n\n return llm_model, self.chat_history, self.tools\n\n def _get_resolved_model_name(self) -> str:\n \"\"\"Best-effort human-readable model name for {model_name} injection.\"\"\"\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n if isinstance(self.model, BaseLanguageModel):\n return type(self.model).__name__\n except ImportError:\n pass\n\n if isinstance(self.model, list) and self.model:\n first = self.model[0]\n if isinstance(first, dict):\n name = first.get(\"name\")\n if isinstance(name, str) and name:\n return name\n\n legacy_model_name = getattr(self, \"model_name\", None)\n if isinstance(legacy_model_name, str) and legacy_model_name:\n return legacy_model_name\n return \"\"\n\n def _inject_dynamic_prompt_values(self, prompt: str | None) -> str | None:\n \"\"\"Replace known env placeholders in the system prompt.\n\n Handles {current_date}, {model_name}, and {optional_user_context} (the\n last one ships with the structured DEFAULT_SYSTEM_PROMPT_TEMPLATE and\n is currently unused at the AgentComponent layer, so it resolves to \"\").\n Uses str.replace (not str.format) so user prompts containing literal\n braces such as JSON examples ({\"key\": 1}) never break the agent.\n \"\"\"\n if not prompt:\n return prompt\n replacements = {\n \"{current_date}\": datetime.now(tz=timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S UTC\"),\n \"{model_name}\": self._get_resolved_model_name(),\n \"{optional_user_context}\": \"\",\n }\n for placeholder, value in replacements.items():\n prompt = prompt.replace(placeholder, value)\n return prompt\n\n async def message_response(self) -> Message:\n try:\n llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n # Set up and run agent\n self.set(\n llm=llm_model,\n tools=self.tools or [],\n chat_history=self.chat_history,\n input_value=self.input_value,\n system_prompt=self._inject_dynamic_prompt_values(self.system_prompt),\n )\n agent = self.create_agent_runnable()\n result = await self.run_agent(agent)\n\n # Store result for potential JSON output\n self._agent_result = result\n\n except (ValueError, TypeError, KeyError) as e:\n await logger.aerror(f\"{type(e).__name__}: {e!s}\")\n raise\n except ExceptionWithMessageError as e:\n await logger.aerror(f\"ExceptionWithMessageError occurred: {e}\")\n raise\n # Avoid catching blind Exception; let truly unexpected exceptions propagate\n except Exception as e:\n await logger.aerror(f\"Unexpected error: {e!s}\")\n raise\n else:\n return result\n\n async def json_response(self) -> Data:\n \"\"\"Produce structured Data via native LLM structured output, with prompt-based fallback.\n\n Native path (no tools, llm has with_structured_output) bypasses the agent loop and\n returns provider-validated JSON. When tools are attached, falls back to running the\n agent with a schema-augmented system prompt and parsing the final message content.\n \"\"\"\n from lfx.components.models_and_agents.structured_output.structured_output_orchestrator import (\n orchestrate_structured_output,\n )\n\n try:\n llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n except (ValueError, TypeError) as exc:\n await logger.aerror(f\"json_response.requirements_failed: {exc}\")\n return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n injected_system_prompt = self._inject_dynamic_prompt_values(getattr(self, \"system_prompt\", \"\") or \"\") or \"\"\n format_instructions = getattr(self, \"format_instructions\", \"\") or \"\"\n output_schema = getattr(self, \"output_schema\", None) or []\n has_tools = bool(self.tools)\n\n async def _run_agent_for_fallback(augmented_prompt: str) -> str:\n self.set(\n llm=llm_model,\n tools=self.tools or [],\n chat_history=self.chat_history,\n input_value=self.input_value,\n system_prompt=augmented_prompt,\n )\n agent_runnable = self.create_agent_runnable()\n with _suppress_send_message(self):\n result = await self.run_agent(agent_runnable)\n return _extract_text_content(result)\n\n try:\n return await orchestrate_structured_output(\n llm=llm_model,\n output_schema=output_schema,\n system_prompt=injected_system_prompt,\n format_instructions=format_instructions,\n input_value=_extract_text_content(self.input_value),\n run_prompt_fallback=_run_agent_for_fallback,\n prefer_native=not has_tools,\n )\n except (\n ExceptionWithMessageError,\n ValueError,\n TypeError,\n NotImplementedError,\n AttributeError,\n ) as exc:\n await logger.aerror(f\"json_response.orchestration_failed: {exc}\")\n return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n async def get_memory_data(self):\n # Scope by flow_id so default playground session names (e.g. \"New Session 0\")\n # cannot leak chat history across unrelated flows. See issue #13059.\n # The helper also returns [] when n_messages == 0, preserving the\n # explicit \"memory disabled\" contract from MemoryComponent.retrieve_messages.\n messages = await aget_agent_chat_history(\n session_id=self.graph.session_id,\n flow_id=getattr(self.graph, \"flow_id\", None),\n context_id=self.context_id,\n n_messages=self.n_messages,\n )\n return [\n message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n ]\n\n def update_input_types(self, build_config: dotdict) -> dotdict:\n \"\"\"Update input types for all fields in build_config.\"\"\"\n for key, value in build_config.items():\n if isinstance(value, dict):\n if value.get(\"input_types\") is None:\n build_config[key][\"input_types\"] = []\n elif hasattr(value, \"input_types\") and value.input_types is None:\n value.input_types = []\n return build_config\n\n async def update_build_config(\n self,\n build_config: dotdict,\n field_value: list[dict],\n field_name: str | None = None,\n ) -> dotdict:\n # Update model options with caching (for all field changes).\n # The tool-calling constraint lives on the ModelInput's ``filters``\n # field (declared above); ``handle_model_input_update`` reads it\n # and applies the filter to both the dropdown options and the\n # sticky-default re-injection path.\n build_config = handle_model_input_update(\n component=self,\n build_config=dict(build_config),\n field_value=field_value,\n field_name=field_name,\n )\n build_config = dotdict(build_config)\n\n if field_name == \"model\":\n build_config = self.update_input_types(build_config)\n\n # Validate required keys\n default_keys = [\n \"code\",\n \"_type\",\n \"model\",\n \"tools\",\n \"input_value\",\n \"add_current_date_tool\",\n \"add_calculator_tool\",\n \"system_prompt\",\n \"agent_description\",\n \"max_iterations\",\n \"handle_parsing_errors\",\n \"verbose\",\n ]\n missing_keys = [key for key in default_keys if key not in build_config]\n if missing_keys:\n msg = f\"Missing required keys in build_config: {missing_keys}\"\n raise ValueError(msg)\n return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n async def _get_tools(self) -> list[Tool]:\n component_toolkit = get_component_toolkit()\n tools_names = self._build_tools_names()\n agent_description = self.get_tool_description()\n # TODO: Agent Description Depreciated Feature to be removed\n description = f\"{agent_description}{tools_names}\"\n\n tools = component_toolkit(component=self).get_tools(\n tool_name=\"Call_Agent\",\n tool_description=description,\n # here we do not use the shared callbacks as we are exposing the agent as a tool\n callbacks=self.get_langchain_callbacks(),\n )\n if hasattr(self, \"tools_metadata\"):\n tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n\n return tools\n" }, "context_id": { "_input_type": "MessageTextInput", diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Youtube Analysis.json b/src/backend/base/langflow/initial_setup/starter_projects/Youtube Analysis.json index 7a1648c018..2e29216629 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Youtube Analysis.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Youtube Analysis.json @@ -502,7 +502,7 @@ "last_updated": "2025-12-22T21:08:01.050Z", "legacy": false, "metadata": { - "code_hash": "ebac74bfcae6", + "code_hash": "443159257161", "dependencies": { "dependencies": [ { @@ -691,7 +691,7 @@ "show": true, "title_case": false, "type": "code", - "value": "from __future__ import annotations\n\nfrom contextlib import contextmanager\nfrom datetime import datetime, timezone\nfrom typing import TYPE_CHECKING, Any\n\nfrom lfx.components.models_and_agents.memory import MemoryComponent\n\nif TYPE_CHECKING:\n from langchain_core.tools import Tool\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.agents.default_system_prompt import DEFAULT_SYSTEM_PROMPT_TEMPLATE\nfrom lfx.base.agents.events import ExceptionWithMessageError\nfrom lfx.base.constants import STREAM_INFO_TEXT\nfrom lfx.base.models.unified_models import (\n get_language_model_options,\n get_llm,\n handle_model_input_update,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.components.agentics.helpers.model_config import validate_model_selection\nfrom lfx.components.helpers import CalculatorComponent, CurrentDateComponent\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.custom.custom_component.component import get_component_toolkit\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, ModelInput, StrInput\nfrom lfx.io import IntInput, MessageTextInput, MultilineInput, Output, SecretStrInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.schema.data import Data\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.schema.table import EditMode\n\n\ndef set_advanced_true(component_input):\n component_input.advanced = True\n return component_input\n\n\ndef _extract_text_content(value) -> str:\n \"\"\"Pull a string payload from a Message-like, AIMessage-like, or string value.\"\"\"\n if isinstance(value, str):\n return value\n text = getattr(value, \"text\", None)\n if isinstance(text, str):\n return text\n content = getattr(value, \"content\", None)\n if isinstance(content, str):\n return content\n return str(value) if value is not None else \"\"\n\n\n@contextmanager\ndef _suppress_send_message(component: Any):\n \"\"\"Temporarily replace component.send_message with a no-op for the duration of the block.\n\n Used during the structured-output prompt fallback: run_agent streams the agent's\n final answer through self.send_message (correct for message_response), but in\n json_response the orchestrator parses that text into structured Data which the\n downstream Chat Output emits — leaving the original emission in place produces a\n duplicate message in the playground. The original method is always restored on exit,\n even when the wrapped call raises.\n \"\"\"\n original = component.send_message\n\n async def _noop(message, *_args, **_kwargs):\n return message\n\n component.send_message = _noop\n try:\n yield\n finally:\n component.send_message = original\n\n\nclass AgentComponent(ToolCallingAgentComponent):\n display_name: str = \"Agent\"\n description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n documentation: str = \"https://docs.langflow.org/agents\"\n icon = \"bot\"\n beta = False\n name = \"Agent\"\n\n memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n inputs = [\n ModelInput(\n name=\"model\",\n display_name=\"Language Model\",\n info=\"Select your model provider\",\n real_time_refresh=True,\n required=True,\n # Agents require tool calling — the filter is honored by\n # ``handle_model_input_update`` so models that can't run with\n # tools never reach the picker (and any saved selection that\n # no longer satisfies the constraint is auto-replaced).\n filters={\"tool_calling\": True},\n ),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"API Key\",\n info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n real_time_refresh=True,\n advanced=True,\n ),\n DropdownInput(\n name=\"base_url_ibm_watsonx\",\n display_name=\"watsonx API Endpoint\",\n info=\"The base URL of the API (IBM watsonx.ai only)\",\n options=IBM_WATSONX_URLS,\n value=IBM_WATSONX_URLS[0],\n combobox=True,\n show=False,\n real_time_refresh=True,\n ),\n StrInput(\n name=\"project_id\",\n display_name=\"watsonx Project ID\",\n info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n show=False,\n required=False,\n ),\n MultilineInput(\n name=\"system_prompt\",\n display_name=\"Agent Instructions\",\n info=(\n \"System Prompt: Initial instructions and context provided to guide the agent's behavior. \"\n \"Supports dynamic placeholders: {current_date}, {model_name}, {optional_user_context}.\"\n ),\n value=DEFAULT_SYSTEM_PROMPT_TEMPLATE,\n advanced=False,\n ),\n MessageTextInput(\n name=\"context_id\",\n display_name=\"Context ID\",\n info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n value=\"\",\n advanced=True,\n ),\n IntInput(\n name=\"n_messages\",\n display_name=\"Number of Chat History Messages\",\n value=100,\n info=\"Number of chat history messages to retrieve.\",\n advanced=True,\n show=True,\n ),\n IntInput(\n name=\"max_tokens\",\n display_name=\"Max Tokens\",\n info=\"Maximum number of tokens to generate. Field name varies by provider.\",\n advanced=True,\n range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n ),\n MultilineInput(\n name=\"format_instructions\",\n display_name=\"Output Format Instructions\",\n info=\"Generic Template for structured output formatting. Valid only with Structured response.\",\n value=(\n \"You are an AI that extracts structured JSON objects from unstructured text. \"\n \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n \"Fill missing or ambiguous values with defaults: null for missing values. \"\n \"Remove exact duplicates but keep variations that have different field values. \"\n \"Always return valid JSON in the expected format, never throw errors. \"\n \"If multiple objects can be extracted, return them all in the structured format.\"\n ),\n advanced=True,\n ),\n TableInput(\n name=\"output_schema\",\n display_name=\"Output Schema\",\n info=(\n \"Schema Validation: Define the structure and data types for structured output. \"\n \"No validation if no output schema.\"\n ),\n advanced=True,\n required=False,\n value=[],\n table_schema=[\n {\n \"name\": \"name\",\n \"display_name\": \"Name\",\n \"type\": \"str\",\n \"description\": \"Specify the name of the output field.\",\n \"default\": \"field\",\n \"edit_mode\": EditMode.INLINE,\n },\n {\n \"name\": \"description\",\n \"display_name\": \"Description\",\n \"type\": \"str\",\n \"description\": \"Describe the purpose of the output field.\",\n \"default\": \"description of field\",\n \"edit_mode\": EditMode.POPOVER,\n },\n {\n \"name\": \"type\",\n \"display_name\": \"Type\",\n \"type\": \"str\",\n \"edit_mode\": EditMode.INLINE,\n \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n \"default\": \"str\",\n },\n {\n \"name\": \"multiple\",\n \"display_name\": \"As List\",\n \"type\": \"boolean\",\n \"description\": \"Set to True if this output field should be a list of the specified type.\",\n \"default\": \"False\",\n \"edit_mode\": EditMode.INLINE,\n },\n ],\n ),\n *LCToolsAgentComponent.get_base_inputs(),\n # removed memory inputs from agent component\n # *memory_inputs,\n BoolInput(\n name=\"stream\",\n display_name=\"Stream\",\n info=STREAM_INFO_TEXT,\n value=False,\n advanced=True,\n ),\n BoolInput(\n name=\"add_current_date_tool\",\n display_name=\"Current Date\",\n advanced=True,\n info=\"If true, will add a tool to the agent that returns the current date.\",\n value=True,\n ),\n BoolInput(\n name=\"add_calculator_tool\",\n display_name=\"Calculator\",\n advanced=True,\n info=(\n \"If true, adds a zero-config arithmetic calculator tool to the agent \"\n \"(safe: only +, -, *, /, ** operators via AST).\"\n ),\n value=True,\n ),\n ]\n outputs = [\n Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n Output(\n name=\"structured_response\",\n display_name=\"Structured Response\",\n method=\"json_response\",\n types=[\"Data\"],\n ),\n ]\n\n def _resolve_selected_model(self):\n \"\"\"Resolve the selected model, including legacy agent_llm/model_name inputs.\"\"\"\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n if isinstance(self.model, BaseLanguageModel):\n return self.model\n except ImportError:\n pass\n\n if isinstance(self.model, list) and self.model:\n return self.model\n\n legacy_provider = getattr(self, \"agent_llm\", None)\n legacy_model_name = getattr(self, \"model_name\", None)\n if not legacy_provider or not legacy_model_name:\n return self.model\n\n options = get_language_model_options(user_id=self.user_id)\n for option in options:\n if option.get(\"provider\") == legacy_provider and option.get(\"name\") == legacy_model_name:\n return [option]\n\n return [\n {\n \"name\": legacy_model_name,\n \"provider\": legacy_provider,\n \"metadata\": {},\n }\n ]\n\n def _get_max_tokens_value(self):\n \"\"\"Return the user-supplied max_tokens or None when unset/zero.\"\"\"\n val = getattr(self, \"max_tokens\", None)\n if val in {\"\", 0}:\n return None\n return val\n\n def _get_llm(self):\n \"\"\"Override parent to include max_tokens from the Agent's input field.\"\"\"\n return get_llm(\n model=self.model,\n user_id=self.user_id,\n api_key=getattr(self, \"api_key\", None),\n stream=bool(getattr(self, \"stream\", False)),\n max_tokens=self._get_max_tokens_value(),\n watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n watsonx_project_id=getattr(self, \"project_id\", None),\n )\n\n async def get_agent_requirements(self):\n \"\"\"Get the agent requirements for the agent.\"\"\"\n from langchain_core.tools import StructuredTool\n\n selected_model = self._resolve_selected_model()\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n is_connected_model = isinstance(selected_model, BaseLanguageModel)\n except ImportError:\n is_connected_model = False\n\n if not is_connected_model:\n validate_model_selection(selected_model)\n\n # Ensure _get_llm() uses the resolved model (e.g. from legacy agent_llm/model_name)\n self.model = selected_model\n llm_model = self._get_llm()\n if llm_model is None:\n msg = \"No language model selected. Please choose a model to proceed.\"\n raise ValueError(msg)\n\n # Get memory data\n self.chat_history = await self.get_memory_data()\n await logger.adebug(f\"Retrieved {len(self.chat_history)} chat history messages\")\n if isinstance(self.chat_history, Message):\n self.chat_history = [self.chat_history]\n\n # Add current date tool if enabled\n if self.add_current_date_tool:\n if not isinstance(self.tools, list): # type: ignore[has-type]\n self.tools = []\n current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n if not isinstance(current_date_tool, StructuredTool):\n msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n raise TypeError(msg)\n # Skip if an externally-connected tool already provides the same name.\n # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n if not any(getattr(t, \"name\", None) == current_date_tool.name for t in self.tools):\n self.tools.append(current_date_tool)\n\n # Add calculator tool if enabled (zero-config arithmetic)\n if getattr(self, \"add_calculator_tool\", False):\n if not isinstance(self.tools, list): # type: ignore[has-type]\n self.tools = []\n calculator_tool = (await CalculatorComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n if not isinstance(calculator_tool, StructuredTool):\n msg = \"CalculatorComponent must be converted to a StructuredTool\"\n raise TypeError(msg)\n # Skip if an externally-connected tool already provides the same name.\n # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n if not any(getattr(t, \"name\", None) == calculator_tool.name for t in self.tools):\n self.tools.append(calculator_tool)\n\n # Set shared callbacks for tracing the tools used by the agent\n self.set_tools_callbacks(self.tools, self._get_shared_callbacks())\n\n return llm_model, self.chat_history, self.tools\n\n def _get_resolved_model_name(self) -> str:\n \"\"\"Best-effort human-readable model name for {model_name} injection.\"\"\"\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n if isinstance(self.model, BaseLanguageModel):\n return type(self.model).__name__\n except ImportError:\n pass\n\n if isinstance(self.model, list) and self.model:\n first = self.model[0]\n if isinstance(first, dict):\n name = first.get(\"name\")\n if isinstance(name, str) and name:\n return name\n\n legacy_model_name = getattr(self, \"model_name\", None)\n if isinstance(legacy_model_name, str) and legacy_model_name:\n return legacy_model_name\n return \"\"\n\n def _inject_dynamic_prompt_values(self, prompt: str | None) -> str | None:\n \"\"\"Replace known env placeholders in the system prompt.\n\n Handles {current_date}, {model_name}, and {optional_user_context} (the\n last one ships with the structured DEFAULT_SYSTEM_PROMPT_TEMPLATE and\n is currently unused at the AgentComponent layer, so it resolves to \"\").\n Uses str.replace (not str.format) so user prompts containing literal\n braces such as JSON examples ({\"key\": 1}) never break the agent.\n \"\"\"\n if not prompt:\n return prompt\n replacements = {\n \"{current_date}\": datetime.now(tz=timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S UTC\"),\n \"{model_name}\": self._get_resolved_model_name(),\n \"{optional_user_context}\": \"\",\n }\n for placeholder, value in replacements.items():\n prompt = prompt.replace(placeholder, value)\n return prompt\n\n async def message_response(self) -> Message:\n try:\n llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n # Set up and run agent\n self.set(\n llm=llm_model,\n tools=self.tools or [],\n chat_history=self.chat_history,\n input_value=self.input_value,\n system_prompt=self._inject_dynamic_prompt_values(self.system_prompt),\n )\n agent = self.create_agent_runnable()\n result = await self.run_agent(agent)\n\n # Store result for potential JSON output\n self._agent_result = result\n\n except (ValueError, TypeError, KeyError) as e:\n await logger.aerror(f\"{type(e).__name__}: {e!s}\")\n raise\n except ExceptionWithMessageError as e:\n await logger.aerror(f\"ExceptionWithMessageError occurred: {e}\")\n raise\n # Avoid catching blind Exception; let truly unexpected exceptions propagate\n except Exception as e:\n await logger.aerror(f\"Unexpected error: {e!s}\")\n raise\n else:\n return result\n\n async def json_response(self) -> Data:\n \"\"\"Produce structured Data via native LLM structured output, with prompt-based fallback.\n\n Native path (no tools, llm has with_structured_output) bypasses the agent loop and\n returns provider-validated JSON. When tools are attached, falls back to running the\n agent with a schema-augmented system prompt and parsing the final message content.\n \"\"\"\n from lfx.components.models_and_agents.structured_output.structured_output_orchestrator import (\n orchestrate_structured_output,\n )\n\n try:\n llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n except (ValueError, TypeError) as exc:\n await logger.aerror(f\"json_response.requirements_failed: {exc}\")\n return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n injected_system_prompt = self._inject_dynamic_prompt_values(getattr(self, \"system_prompt\", \"\") or \"\") or \"\"\n format_instructions = getattr(self, \"format_instructions\", \"\") or \"\"\n output_schema = getattr(self, \"output_schema\", None) or []\n has_tools = bool(self.tools)\n\n async def _run_agent_for_fallback(augmented_prompt: str) -> str:\n self.set(\n llm=llm_model,\n tools=self.tools or [],\n chat_history=self.chat_history,\n input_value=self.input_value,\n system_prompt=augmented_prompt,\n )\n agent_runnable = self.create_agent_runnable()\n with _suppress_send_message(self):\n result = await self.run_agent(agent_runnable)\n return _extract_text_content(result)\n\n try:\n return await orchestrate_structured_output(\n llm=llm_model,\n output_schema=output_schema,\n system_prompt=injected_system_prompt,\n format_instructions=format_instructions,\n input_value=_extract_text_content(self.input_value),\n run_prompt_fallback=_run_agent_for_fallback,\n prefer_native=not has_tools,\n )\n except (\n ExceptionWithMessageError,\n ValueError,\n TypeError,\n NotImplementedError,\n AttributeError,\n ) as exc:\n await logger.aerror(f\"json_response.orchestration_failed: {exc}\")\n return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n async def get_memory_data(self):\n # TODO: This is a temporary fix to avoid message duplication. We should develop a function for this.\n messages = (\n await MemoryComponent(**self.get_base_args())\n .set(\n session_id=self.graph.session_id,\n context_id=self.context_id,\n order=\"Ascending\",\n n_messages=self.n_messages,\n )\n .retrieve_messages()\n )\n return [\n message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n ]\n\n def update_input_types(self, build_config: dotdict) -> dotdict:\n \"\"\"Update input types for all fields in build_config.\"\"\"\n for key, value in build_config.items():\n if isinstance(value, dict):\n if value.get(\"input_types\") is None:\n build_config[key][\"input_types\"] = []\n elif hasattr(value, \"input_types\") and value.input_types is None:\n value.input_types = []\n return build_config\n\n async def update_build_config(\n self,\n build_config: dotdict,\n field_value: list[dict],\n field_name: str | None = None,\n ) -> dotdict:\n # Update model options with caching (for all field changes).\n # The tool-calling constraint lives on the ModelInput's ``filters``\n # field (declared above); ``handle_model_input_update`` reads it\n # and applies the filter to both the dropdown options and the\n # sticky-default re-injection path.\n build_config = handle_model_input_update(\n component=self,\n build_config=dict(build_config),\n field_value=field_value,\n field_name=field_name,\n )\n build_config = dotdict(build_config)\n\n if field_name == \"model\":\n build_config = self.update_input_types(build_config)\n\n # Validate required keys\n default_keys = [\n \"code\",\n \"_type\",\n \"model\",\n \"tools\",\n \"input_value\",\n \"add_current_date_tool\",\n \"add_calculator_tool\",\n \"system_prompt\",\n \"agent_description\",\n \"max_iterations\",\n \"handle_parsing_errors\",\n \"verbose\",\n ]\n missing_keys = [key for key in default_keys if key not in build_config]\n if missing_keys:\n msg = f\"Missing required keys in build_config: {missing_keys}\"\n raise ValueError(msg)\n return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n async def _get_tools(self) -> list[Tool]:\n component_toolkit = get_component_toolkit()\n tools_names = self._build_tools_names()\n agent_description = self.get_tool_description()\n # TODO: Agent Description Depreciated Feature to be removed\n description = f\"{agent_description}{tools_names}\"\n\n tools = component_toolkit(component=self).get_tools(\n tool_name=\"Call_Agent\",\n tool_description=description,\n # here we do not use the shared callbacks as we are exposing the agent as a tool\n callbacks=self.get_langchain_callbacks(),\n )\n if hasattr(self, \"tools_metadata\"):\n tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n\n return tools\n" + "value": "from __future__ import annotations\n\nfrom contextlib import contextmanager\nfrom datetime import datetime, timezone\nfrom typing import TYPE_CHECKING, Any\n\nfrom lfx.components.models_and_agents.memory import MemoryComponent, aget_agent_chat_history\n\nif TYPE_CHECKING:\n from langchain_core.tools import Tool\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.agents.default_system_prompt import DEFAULT_SYSTEM_PROMPT_TEMPLATE\nfrom lfx.base.agents.events import ExceptionWithMessageError\nfrom lfx.base.constants import STREAM_INFO_TEXT\nfrom lfx.base.models.unified_models import (\n get_language_model_options,\n get_llm,\n handle_model_input_update,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.components.agentics.helpers.model_config import validate_model_selection\nfrom lfx.components.helpers import CalculatorComponent, CurrentDateComponent\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.custom.custom_component.component import get_component_toolkit\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, ModelInput, StrInput\nfrom lfx.io import IntInput, MessageTextInput, MultilineInput, Output, SecretStrInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.schema.data import Data\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.schema.table import EditMode\n\n\ndef set_advanced_true(component_input):\n component_input.advanced = True\n return component_input\n\n\ndef _extract_text_content(value) -> str:\n \"\"\"Pull a string payload from a Message-like, AIMessage-like, or string value.\"\"\"\n if isinstance(value, str):\n return value\n text = getattr(value, \"text\", None)\n if isinstance(text, str):\n return text\n content = getattr(value, \"content\", None)\n if isinstance(content, str):\n return content\n return str(value) if value is not None else \"\"\n\n\n@contextmanager\ndef _suppress_send_message(component: Any):\n \"\"\"Temporarily replace component.send_message with a no-op for the duration of the block.\n\n Used during the structured-output prompt fallback: run_agent streams the agent's\n final answer through self.send_message (correct for message_response), but in\n json_response the orchestrator parses that text into structured Data which the\n downstream Chat Output emits — leaving the original emission in place produces a\n duplicate message in the playground. The original method is always restored on exit,\n even when the wrapped call raises.\n \"\"\"\n original = component.send_message\n\n async def _noop(message, *_args, **_kwargs):\n return message\n\n component.send_message = _noop\n try:\n yield\n finally:\n component.send_message = original\n\n\nclass AgentComponent(ToolCallingAgentComponent):\n display_name: str = \"Agent\"\n description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n documentation: str = \"https://docs.langflow.org/agents\"\n icon = \"bot\"\n beta = False\n name = \"Agent\"\n\n memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n inputs = [\n ModelInput(\n name=\"model\",\n display_name=\"Language Model\",\n info=\"Select your model provider\",\n real_time_refresh=True,\n required=True,\n # Agents require tool calling — the filter is honored by\n # ``handle_model_input_update`` so models that can't run with\n # tools never reach the picker (and any saved selection that\n # no longer satisfies the constraint is auto-replaced).\n filters={\"tool_calling\": True},\n ),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"API Key\",\n info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n real_time_refresh=True,\n advanced=True,\n ),\n DropdownInput(\n name=\"base_url_ibm_watsonx\",\n display_name=\"watsonx API Endpoint\",\n info=\"The base URL of the API (IBM watsonx.ai only)\",\n options=IBM_WATSONX_URLS,\n value=IBM_WATSONX_URLS[0],\n combobox=True,\n show=False,\n real_time_refresh=True,\n ),\n StrInput(\n name=\"project_id\",\n display_name=\"watsonx Project ID\",\n info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n show=False,\n required=False,\n ),\n MultilineInput(\n name=\"system_prompt\",\n display_name=\"Agent Instructions\",\n info=(\n \"System Prompt: Initial instructions and context provided to guide the agent's behavior. \"\n \"Supports dynamic placeholders: {current_date}, {model_name}, {optional_user_context}.\"\n ),\n value=DEFAULT_SYSTEM_PROMPT_TEMPLATE,\n advanced=False,\n ),\n MessageTextInput(\n name=\"context_id\",\n display_name=\"Context ID\",\n info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n value=\"\",\n advanced=True,\n ),\n IntInput(\n name=\"n_messages\",\n display_name=\"Number of Chat History Messages\",\n value=100,\n info=\"Number of chat history messages to retrieve.\",\n advanced=True,\n show=True,\n ),\n IntInput(\n name=\"max_tokens\",\n display_name=\"Max Tokens\",\n info=\"Maximum number of tokens to generate. Field name varies by provider.\",\n advanced=True,\n range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n ),\n MultilineInput(\n name=\"format_instructions\",\n display_name=\"Output Format Instructions\",\n info=\"Generic Template for structured output formatting. Valid only with Structured response.\",\n value=(\n \"You are an AI that extracts structured JSON objects from unstructured text. \"\n \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n \"Fill missing or ambiguous values with defaults: null for missing values. \"\n \"Remove exact duplicates but keep variations that have different field values. \"\n \"Always return valid JSON in the expected format, never throw errors. \"\n \"If multiple objects can be extracted, return them all in the structured format.\"\n ),\n advanced=True,\n ),\n TableInput(\n name=\"output_schema\",\n display_name=\"Output Schema\",\n info=(\n \"Schema Validation: Define the structure and data types for structured output. \"\n \"No validation if no output schema.\"\n ),\n advanced=True,\n required=False,\n value=[],\n table_schema=[\n {\n \"name\": \"name\",\n \"display_name\": \"Name\",\n \"type\": \"str\",\n \"description\": \"Specify the name of the output field.\",\n \"default\": \"field\",\n \"edit_mode\": EditMode.INLINE,\n },\n {\n \"name\": \"description\",\n \"display_name\": \"Description\",\n \"type\": \"str\",\n \"description\": \"Describe the purpose of the output field.\",\n \"default\": \"description of field\",\n \"edit_mode\": EditMode.POPOVER,\n },\n {\n \"name\": \"type\",\n \"display_name\": \"Type\",\n \"type\": \"str\",\n \"edit_mode\": EditMode.INLINE,\n \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n \"default\": \"str\",\n },\n {\n \"name\": \"multiple\",\n \"display_name\": \"As List\",\n \"type\": \"boolean\",\n \"description\": \"Set to True if this output field should be a list of the specified type.\",\n \"default\": \"False\",\n \"edit_mode\": EditMode.INLINE,\n },\n ],\n ),\n *LCToolsAgentComponent.get_base_inputs(),\n # removed memory inputs from agent component\n # *memory_inputs,\n BoolInput(\n name=\"stream\",\n display_name=\"Stream\",\n info=STREAM_INFO_TEXT,\n value=False,\n advanced=True,\n ),\n BoolInput(\n name=\"add_current_date_tool\",\n display_name=\"Current Date\",\n advanced=True,\n info=\"If true, will add a tool to the agent that returns the current date.\",\n value=True,\n ),\n BoolInput(\n name=\"add_calculator_tool\",\n display_name=\"Calculator\",\n advanced=True,\n info=(\n \"If true, adds a zero-config arithmetic calculator tool to the agent \"\n \"(safe: only +, -, *, /, ** operators via AST).\"\n ),\n value=True,\n ),\n ]\n outputs = [\n Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n Output(\n name=\"structured_response\",\n display_name=\"Structured Response\",\n method=\"json_response\",\n types=[\"Data\"],\n ),\n ]\n\n def _resolve_selected_model(self):\n \"\"\"Resolve the selected model, including legacy agent_llm/model_name inputs.\"\"\"\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n if isinstance(self.model, BaseLanguageModel):\n return self.model\n except ImportError:\n pass\n\n if isinstance(self.model, list) and self.model:\n return self.model\n\n legacy_provider = getattr(self, \"agent_llm\", None)\n legacy_model_name = getattr(self, \"model_name\", None)\n if not legacy_provider or not legacy_model_name:\n return self.model\n\n options = get_language_model_options(user_id=self.user_id)\n for option in options:\n if option.get(\"provider\") == legacy_provider and option.get(\"name\") == legacy_model_name:\n return [option]\n\n return [\n {\n \"name\": legacy_model_name,\n \"provider\": legacy_provider,\n \"metadata\": {},\n }\n ]\n\n def _get_max_tokens_value(self):\n \"\"\"Return the user-supplied max_tokens or None when unset/zero.\"\"\"\n val = getattr(self, \"max_tokens\", None)\n if val in {\"\", 0}:\n return None\n return val\n\n def _get_llm(self):\n \"\"\"Override parent to include max_tokens from the Agent's input field.\"\"\"\n return get_llm(\n model=self.model,\n user_id=self.user_id,\n api_key=getattr(self, \"api_key\", None),\n stream=bool(getattr(self, \"stream\", False)),\n max_tokens=self._get_max_tokens_value(),\n watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n watsonx_project_id=getattr(self, \"project_id\", None),\n )\n\n async def get_agent_requirements(self):\n \"\"\"Get the agent requirements for the agent.\"\"\"\n from langchain_core.tools import StructuredTool\n\n selected_model = self._resolve_selected_model()\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n is_connected_model = isinstance(selected_model, BaseLanguageModel)\n except ImportError:\n is_connected_model = False\n\n if not is_connected_model:\n validate_model_selection(selected_model)\n\n # Ensure _get_llm() uses the resolved model (e.g. from legacy agent_llm/model_name)\n self.model = selected_model\n llm_model = self._get_llm()\n if llm_model is None:\n msg = \"No language model selected. Please choose a model to proceed.\"\n raise ValueError(msg)\n\n # Get memory data\n self.chat_history = await self.get_memory_data()\n await logger.adebug(f\"Retrieved {len(self.chat_history)} chat history messages\")\n if isinstance(self.chat_history, Message):\n self.chat_history = [self.chat_history]\n\n # Add current date tool if enabled\n if self.add_current_date_tool:\n if not isinstance(self.tools, list): # type: ignore[has-type]\n self.tools = []\n current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n if not isinstance(current_date_tool, StructuredTool):\n msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n raise TypeError(msg)\n # Skip if an externally-connected tool already provides the same name.\n # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n if not any(getattr(t, \"name\", None) == current_date_tool.name for t in self.tools):\n self.tools.append(current_date_tool)\n\n # Add calculator tool if enabled (zero-config arithmetic)\n if getattr(self, \"add_calculator_tool\", False):\n if not isinstance(self.tools, list): # type: ignore[has-type]\n self.tools = []\n calculator_tool = (await CalculatorComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n if not isinstance(calculator_tool, StructuredTool):\n msg = \"CalculatorComponent must be converted to a StructuredTool\"\n raise TypeError(msg)\n # Skip if an externally-connected tool already provides the same name.\n # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n if not any(getattr(t, \"name\", None) == calculator_tool.name for t in self.tools):\n self.tools.append(calculator_tool)\n\n # Set shared callbacks for tracing the tools used by the agent\n self.set_tools_callbacks(self.tools, self._get_shared_callbacks())\n\n return llm_model, self.chat_history, self.tools\n\n def _get_resolved_model_name(self) -> str:\n \"\"\"Best-effort human-readable model name for {model_name} injection.\"\"\"\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n if isinstance(self.model, BaseLanguageModel):\n return type(self.model).__name__\n except ImportError:\n pass\n\n if isinstance(self.model, list) and self.model:\n first = self.model[0]\n if isinstance(first, dict):\n name = first.get(\"name\")\n if isinstance(name, str) and name:\n return name\n\n legacy_model_name = getattr(self, \"model_name\", None)\n if isinstance(legacy_model_name, str) and legacy_model_name:\n return legacy_model_name\n return \"\"\n\n def _inject_dynamic_prompt_values(self, prompt: str | None) -> str | None:\n \"\"\"Replace known env placeholders in the system prompt.\n\n Handles {current_date}, {model_name}, and {optional_user_context} (the\n last one ships with the structured DEFAULT_SYSTEM_PROMPT_TEMPLATE and\n is currently unused at the AgentComponent layer, so it resolves to \"\").\n Uses str.replace (not str.format) so user prompts containing literal\n braces such as JSON examples ({\"key\": 1}) never break the agent.\n \"\"\"\n if not prompt:\n return prompt\n replacements = {\n \"{current_date}\": datetime.now(tz=timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S UTC\"),\n \"{model_name}\": self._get_resolved_model_name(),\n \"{optional_user_context}\": \"\",\n }\n for placeholder, value in replacements.items():\n prompt = prompt.replace(placeholder, value)\n return prompt\n\n async def message_response(self) -> Message:\n try:\n llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n # Set up and run agent\n self.set(\n llm=llm_model,\n tools=self.tools or [],\n chat_history=self.chat_history,\n input_value=self.input_value,\n system_prompt=self._inject_dynamic_prompt_values(self.system_prompt),\n )\n agent = self.create_agent_runnable()\n result = await self.run_agent(agent)\n\n # Store result for potential JSON output\n self._agent_result = result\n\n except (ValueError, TypeError, KeyError) as e:\n await logger.aerror(f\"{type(e).__name__}: {e!s}\")\n raise\n except ExceptionWithMessageError as e:\n await logger.aerror(f\"ExceptionWithMessageError occurred: {e}\")\n raise\n # Avoid catching blind Exception; let truly unexpected exceptions propagate\n except Exception as e:\n await logger.aerror(f\"Unexpected error: {e!s}\")\n raise\n else:\n return result\n\n async def json_response(self) -> Data:\n \"\"\"Produce structured Data via native LLM structured output, with prompt-based fallback.\n\n Native path (no tools, llm has with_structured_output) bypasses the agent loop and\n returns provider-validated JSON. When tools are attached, falls back to running the\n agent with a schema-augmented system prompt and parsing the final message content.\n \"\"\"\n from lfx.components.models_and_agents.structured_output.structured_output_orchestrator import (\n orchestrate_structured_output,\n )\n\n try:\n llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n except (ValueError, TypeError) as exc:\n await logger.aerror(f\"json_response.requirements_failed: {exc}\")\n return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n injected_system_prompt = self._inject_dynamic_prompt_values(getattr(self, \"system_prompt\", \"\") or \"\") or \"\"\n format_instructions = getattr(self, \"format_instructions\", \"\") or \"\"\n output_schema = getattr(self, \"output_schema\", None) or []\n has_tools = bool(self.tools)\n\n async def _run_agent_for_fallback(augmented_prompt: str) -> str:\n self.set(\n llm=llm_model,\n tools=self.tools or [],\n chat_history=self.chat_history,\n input_value=self.input_value,\n system_prompt=augmented_prompt,\n )\n agent_runnable = self.create_agent_runnable()\n with _suppress_send_message(self):\n result = await self.run_agent(agent_runnable)\n return _extract_text_content(result)\n\n try:\n return await orchestrate_structured_output(\n llm=llm_model,\n output_schema=output_schema,\n system_prompt=injected_system_prompt,\n format_instructions=format_instructions,\n input_value=_extract_text_content(self.input_value),\n run_prompt_fallback=_run_agent_for_fallback,\n prefer_native=not has_tools,\n )\n except (\n ExceptionWithMessageError,\n ValueError,\n TypeError,\n NotImplementedError,\n AttributeError,\n ) as exc:\n await logger.aerror(f\"json_response.orchestration_failed: {exc}\")\n return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n async def get_memory_data(self):\n # Scope by flow_id so default playground session names (e.g. \"New Session 0\")\n # cannot leak chat history across unrelated flows. See issue #13059.\n # The helper also returns [] when n_messages == 0, preserving the\n # explicit \"memory disabled\" contract from MemoryComponent.retrieve_messages.\n messages = await aget_agent_chat_history(\n session_id=self.graph.session_id,\n flow_id=getattr(self.graph, \"flow_id\", None),\n context_id=self.context_id,\n n_messages=self.n_messages,\n )\n return [\n message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n ]\n\n def update_input_types(self, build_config: dotdict) -> dotdict:\n \"\"\"Update input types for all fields in build_config.\"\"\"\n for key, value in build_config.items():\n if isinstance(value, dict):\n if value.get(\"input_types\") is None:\n build_config[key][\"input_types\"] = []\n elif hasattr(value, \"input_types\") and value.input_types is None:\n value.input_types = []\n return build_config\n\n async def update_build_config(\n self,\n build_config: dotdict,\n field_value: list[dict],\n field_name: str | None = None,\n ) -> dotdict:\n # Update model options with caching (for all field changes).\n # The tool-calling constraint lives on the ModelInput's ``filters``\n # field (declared above); ``handle_model_input_update`` reads it\n # and applies the filter to both the dropdown options and the\n # sticky-default re-injection path.\n build_config = handle_model_input_update(\n component=self,\n build_config=dict(build_config),\n field_value=field_value,\n field_name=field_name,\n )\n build_config = dotdict(build_config)\n\n if field_name == \"model\":\n build_config = self.update_input_types(build_config)\n\n # Validate required keys\n default_keys = [\n \"code\",\n \"_type\",\n \"model\",\n \"tools\",\n \"input_value\",\n \"add_current_date_tool\",\n \"add_calculator_tool\",\n \"system_prompt\",\n \"agent_description\",\n \"max_iterations\",\n \"handle_parsing_errors\",\n \"verbose\",\n ]\n missing_keys = [key for key in default_keys if key not in build_config]\n if missing_keys:\n msg = f\"Missing required keys in build_config: {missing_keys}\"\n raise ValueError(msg)\n return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n async def _get_tools(self) -> list[Tool]:\n component_toolkit = get_component_toolkit()\n tools_names = self._build_tools_names()\n agent_description = self.get_tool_description()\n # TODO: Agent Description Depreciated Feature to be removed\n description = f\"{agent_description}{tools_names}\"\n\n tools = component_toolkit(component=self).get_tools(\n tool_name=\"Call_Agent\",\n tool_description=description,\n # here we do not use the shared callbacks as we are exposing the agent as a tool\n callbacks=self.get_langchain_callbacks(),\n )\n if hasattr(self, \"tools_metadata\"):\n tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n\n return tools\n" }, "context_id": { "_input_type": "MessageTextInput", diff --git a/src/backend/tests/unit/components/models_and_agents/test_memory_flow_id_scoping.py b/src/backend/tests/unit/components/models_and_agents/test_memory_flow_id_scoping.py new file mode 100644 index 0000000000..b1e5a28cff --- /dev/null +++ b/src/backend/tests/unit/components/models_and_agents/test_memory_flow_id_scoping.py @@ -0,0 +1,540 @@ +"""Tests that MemoryComponent scopes chat history retrieval by flow_id. + +Regression test for https://github.com/langflow-ai/langflow/issues/13059 + +Before the fix, ``MemoryComponent.retrieve_messages`` called +``aget_messages`` without ``flow_id``. Because the Langflow playground assigns +default session names (e.g. "New Session 0") that are not unique across flows, +this caused chat history from Flow A to leak into Flow B whenever both used +the same session name. The Agent component is affected because it delegates +its chat-history fetch to this method. + +These tests assert that the call into ``aget_messages`` is now scoped by the +graph's ``flow_id`` (coerced to ``UUID``) so flows can no longer cross-read +each other's history. +""" + +from __future__ import annotations + +from types import SimpleNamespace +from unittest.mock import AsyncMock, patch +from uuid import UUID, uuid4 + +import pytest +from lfx.components.models_and_agents.memory import ( + MAX_CHAT_HISTORY_FETCH_LIMIT, + MemoryComponent, + _coerce_flow_id_to_uuid, + aget_agent_chat_history, +) + + +def _build_component( + flow_id: str | UUID | None, + session_id: str = "session-shared", +) -> MemoryComponent: + component = MemoryComponent() + component.set(session_id=session_id, n_messages=10, order="Ascending") + # ``Component.graph`` is a read-only property -> ``self._vertex.graph``, + # so we wire the underlying ``_vertex`` instead of assigning ``graph``. + component._vertex = SimpleNamespace(graph=SimpleNamespace(flow_id=flow_id, session_id=session_id)) + return component + + +class TestCoerceFlowIdToUuid: + """Validate the small UUID-coercion helper used by retrieve_messages.""" + + def test_returns_none_for_missing_values(self): + assert _coerce_flow_id_to_uuid(None) is None + assert _coerce_flow_id_to_uuid("") is None + + def test_passes_uuid_through_unchanged(self): + value = uuid4() + assert _coerce_flow_id_to_uuid(value) is value + + def test_parses_uuid_string(self): + raw = "11111111-1111-1111-1111-111111111111" + assert _coerce_flow_id_to_uuid(raw) == UUID(raw) + + def test_returns_none_for_invalid_string_and_does_not_raise(self): + # Synthetic/test flow IDs may not be UUIDs; we must degrade gracefully. + assert _coerce_flow_id_to_uuid("not-a-uuid") is None + + def test_invalid_flow_id_emits_structured_error_log(self): + """Fallback to unscoped retrieval is a privacy regression — alert on it. + + See PR #13087 review I3: this path re-enables the cross-flow leak + that issue #13059 closed. The log call uses ``error`` level + a + structured ``event`` tag so observability pipelines can alert. + """ + with patch("lfx.components.models_and_agents.memory.logger") as mock_logger: + _coerce_flow_id_to_uuid("not-a-uuid") + + mock_logger.error.assert_called_once() + call = mock_logger.error.call_args + assert "memory_flow_id_unscoped" in call.args[0] + assert call.kwargs.get("extra", {}).get("event") == "memory_flow_id_unscoped" + + +class TestRetrieveMessagesPassesFlowId: + """The leak fix: retrieve_messages must pass flow_id to aget_messages.""" + + @pytest.mark.asyncio + async def test_passes_graph_flow_id_as_uuid(self): + flow_id_str = "22222222-2222-2222-2222-222222222222" + component = _build_component(flow_id=flow_id_str) + + with patch( + "lfx.components.models_and_agents.memory.aget_messages", + new=AsyncMock(return_value=[]), + ) as mock_get: + await component.retrieve_messages() + + mock_get.assert_awaited_once() + kwargs = mock_get.await_args.kwargs + assert kwargs["flow_id"] == UUID(flow_id_str), ( + "retrieve_messages must scope by flow_id so default session names " + "do not leak chat history across flows (issue #13059)." + ) + assert kwargs["session_id"] == "session-shared" + + @pytest.mark.asyncio + async def test_isolates_two_flows_sharing_a_session_name(self): + """Reproduction of the leak: same session name, different flow_ids. + + Flow A and Flow B both use session ``New Session 0``. With the fix in + place, the call into ``aget_messages`` is scoped by each flow's UUID, + so the database query can no longer return cross-flow rows. + """ + flow_a = "aaaaaaaa-aaaa-aaaa-aaaa-aaaaaaaaaaaa" + flow_b = "bbbbbbbb-bbbb-bbbb-bbbb-bbbbbbbbbbbb" + shared_session = "New Session 0" + + with patch( + "lfx.components.models_and_agents.memory.aget_messages", + new=AsyncMock(return_value=[]), + ) as mock_get: + await _build_component(flow_id=flow_a, session_id=shared_session).retrieve_messages() + await _build_component(flow_id=flow_b, session_id=shared_session).retrieve_messages() + + assert mock_get.await_count == 2 + first_call_kwargs = mock_get.await_args_list[0].kwargs + second_call_kwargs = mock_get.await_args_list[1].kwargs + assert first_call_kwargs["flow_id"] == UUID(flow_a) + assert second_call_kwargs["flow_id"] == UUID(flow_b) + # Same session name reaches the DB layer, but flow_id now disambiguates. + assert first_call_kwargs["session_id"] == shared_session + assert second_call_kwargs["session_id"] == shared_session + + @pytest.mark.asyncio + async def test_falls_back_to_none_when_flow_id_missing(self): + component = _build_component(flow_id=None) + + with patch( + "lfx.components.models_and_agents.memory.aget_messages", + new=AsyncMock(return_value=[]), + ) as mock_get: + await component.retrieve_messages() + + assert mock_get.await_args.kwargs["flow_id"] is None + + @pytest.mark.asyncio + async def test_falls_back_to_none_when_flow_id_is_not_a_uuid(self): + """Tests with synthetic graph IDs must not crash retrieval.""" + component = _build_component(flow_id="not-a-uuid") + + with patch( + "lfx.components.models_and_agents.memory.aget_messages", + new=AsyncMock(return_value=[]), + ) as mock_get: + await component.retrieve_messages() + + assert mock_get.await_args.kwargs["flow_id"] is None + + @pytest.mark.asyncio + async def test_external_memory_path_is_untouched(self): + """External Memory providers have no concept of flow_id; do not pass it.""" + + class _FakeExternalMemory: + session_id: str | None = None + context_id: str | None = None + + async def aget_messages(self): + return [] + + component = _build_component(flow_id="33333333-3333-3333-3333-333333333333") + component.set(memory=_FakeExternalMemory()) + + with patch( + "lfx.components.models_and_agents.memory.aget_messages", + new=AsyncMock(return_value=[]), + ) as mock_get: + await component.retrieve_messages() + + mock_get.assert_not_awaited() + + @pytest.mark.asyncio + async def test_fetches_desc_and_reverses_for_ascending_order(self): + """PR #13087 review I2: avoid the >10k row ASC slice trap. + + Querying ``order=ASC, limit=MAX`` then slicing ``[-n:]`` would return + the chronological FIRST window on huge sessions. We query DESC with + the actual limit and reverse to the user-selected order, so the slice + is always the genuine most-recent messages. + """ + component = _build_component(flow_id="22222222-2222-2222-2222-222222222222") + # MemoryComponent ``order`` defaults to Ascending in _build_component. + # The mock returns DESC-ordered data (most recent first), as the DB would. + desc_rows = [SimpleNamespace(id=f"msg-{i}") for i in (4, 3, 2, 1, 0)] + with patch( + "lfx.components.models_and_agents.memory.aget_messages", + new=AsyncMock(return_value=desc_rows), + ) as mock_get: + result = await component.retrieve_messages() + + assert mock_get.await_args.kwargs["order"] == "DESC" + assert mock_get.await_args.kwargs["limit"] == 10 # _build_component sets n_messages=10 + assert [m.id for m in result] == ["msg-0", "msg-1", "msg-2", "msg-3", "msg-4"] + + @pytest.mark.asyncio + async def test_descending_order_passthrough_keeps_desc_view(self): + component = _build_component(flow_id="22222222-2222-2222-2222-222222222222") + component.set(order="Descending") + desc_rows = [SimpleNamespace(id=f"msg-{i}") for i in (4, 3, 2, 1, 0)] + with patch( + "lfx.components.models_and_agents.memory.aget_messages", + new=AsyncMock(return_value=desc_rows), + ): + result = await component.retrieve_messages() + + assert [m.id for m in result] == ["msg-4", "msg-3", "msg-2", "msg-1", "msg-0"] + + +class TestStoreMessagePassesFlowId: + """PR #13087 review I1: write/read flow_id handling must be symmetric. + + The original PR protected ``retrieve_messages`` against ``self.graph`` + raising on ad-hoc instantiation but left ``store_message`` calling + ``self.graph.flow_id`` directly — so a custom subclass or integration + test would crash on the write path before reaching the safe read path. + Both paths now go through ``_coerce_flow_id_to_uuid(_safe_graph_flow_id(self))``. + """ + + @pytest.mark.asyncio + async def test_store_passes_coerced_flow_id_to_astore_and_aget(self): + flow_id_str = "22222222-2222-2222-2222-222222222222" + component = _build_component(flow_id=flow_id_str) + component.set(mode="Store", message="hi", sender="User", sender_name="Tester") + + stored = SimpleNamespace(id="stored-1") + with ( + patch( + "lfx.components.models_and_agents.memory.astore_message", + new=AsyncMock(return_value=None), + ) as mock_store, + patch( + "lfx.components.models_and_agents.memory.aget_messages", + new=AsyncMock(return_value=[stored]), + ) as mock_get, + ): + await component.store_message() + + assert mock_store.await_args.kwargs["flow_id"] == UUID(flow_id_str) + assert mock_get.await_args.kwargs["flow_id"] == UUID(flow_id_str) + # Symmetric scope: the same coerced UUID is used for both calls. + assert mock_store.await_args.kwargs["flow_id"] == mock_get.await_args.kwargs["flow_id"] + + @pytest.mark.asyncio + async def test_store_does_not_crash_without_vertex(self): + """When _vertex is None, ``self.graph`` raises AttributeError. + + Pre-fix, ``store_message`` accessed ``self.graph.flow_id`` directly + and crashed before reaching the safe read path. Now both paths + share the same defensive lookup. + """ + component = MemoryComponent() + component.set( + session_id="s", + n_messages=10, + order="Ascending", + mode="Store", + message="hi", + sender="User", + sender_name="Tester", + ) + # No _vertex assigned — ``self.graph`` will raise AttributeError. + stored = SimpleNamespace(id="stored-1") + with ( + patch( + "lfx.components.models_and_agents.memory.astore_message", + new=AsyncMock(return_value=None), + ) as mock_store, + patch( + "lfx.components.models_and_agents.memory.aget_messages", + new=AsyncMock(return_value=[stored]), + ), + ): + await component.store_message() + + assert mock_store.await_args.kwargs["flow_id"] is None + + +class TestAgetAgentChatHistoryHelper: + """The shared helper centralizes the agent-side memory contract. + + Both ``AgentComponent`` and ``CugaComponent`` route through this helper, + so testing the helper directly covers their common behavior. + """ + + @pytest.mark.asyncio + async def test_passes_flow_id_as_uuid(self): + flow_id_str = "44444444-4444-4444-4444-444444444444" + + with patch( + "lfx.components.models_and_agents.memory.aget_messages", + new=AsyncMock(return_value=[]), + ) as mock_get: + await aget_agent_chat_history( + session_id="New Session 0", + flow_id=flow_id_str, + context_id="", + n_messages=10, + ) + + mock_get.assert_awaited_once() + kwargs = mock_get.await_args.kwargs + assert kwargs["flow_id"] == UUID(flow_id_str), "aget_agent_chat_history must scope by flow_id (issue #13059)." + assert kwargs["session_id"] == "New Session 0" + # PR #13087 review I2: query DESC + limit=n_messages so sessions + # exceeding MAX_CHAT_HISTORY_FETCH_LIMIT still get the real tail. + assert kwargs["order"] == "DESC" + assert kwargs["limit"] == 10 + + @pytest.mark.asyncio + async def test_n_messages_zero_short_circuits_without_querying(self): + """Regression: ``n_messages == 0`` means "memory disabled". + + Before this short-circuit, ``messages[-0:]`` returned the full + bounded result, so users who set the field to 0 to disable chat + memory unexpectedly got full history back. + """ + with patch( + "lfx.components.models_and_agents.memory.aget_messages", + new=AsyncMock(return_value=[SimpleNamespace(id=f"msg-{i}") for i in range(5)]), + ) as mock_get: + result = await aget_agent_chat_history( + session_id="s", + flow_id="66666666-6666-6666-6666-666666666666", + n_messages=0, + ) + + assert result == [] + mock_get.assert_not_awaited() + + @pytest.mark.asyncio + async def test_slices_to_n_messages_most_recent(self): + # DB returns DESC (most recent first); helper reverses to ASC. + desc_rows = [SimpleNamespace(id=f"msg-{i}") for i in (4, 3)] + + with patch( + "lfx.components.models_and_agents.memory.aget_messages", + new=AsyncMock(return_value=desc_rows), + ) as mock_get: + result = await aget_agent_chat_history( + session_id="s", + flow_id="77777777-7777-7777-7777-777777777777", + n_messages=2, + ) + + assert mock_get.await_args.kwargs["limit"] == 2 + assert mock_get.await_args.kwargs["order"] == "DESC" + assert [m.id for m in result] == ["msg-3", "msg-4"] + + @pytest.mark.asyncio + async def test_missing_n_messages_returns_all_fetched(self): + # DB returns DESC; helper reverses for the agent prompt. + desc_rows = [SimpleNamespace(id=f"msg-{i}") for i in (2, 1, 0)] + + with patch( + "lfx.components.models_and_agents.memory.aget_messages", + new=AsyncMock(return_value=desc_rows), + ) as mock_get: + result = await aget_agent_chat_history( + session_id="s", + flow_id=None, + n_messages=None, + ) + + # No explicit limit ⇒ fall back to MAX_CHAT_HISTORY_FETCH_LIMIT. + assert mock_get.await_args.kwargs["limit"] == MAX_CHAT_HISTORY_FETCH_LIMIT + assert [m.id for m in result] == ["msg-0", "msg-1", "msg-2"] + + @pytest.mark.asyncio + async def test_invalid_flow_id_falls_back_to_unscoped_query(self): + with patch( + "lfx.components.models_and_agents.memory.aget_messages", + new=AsyncMock(return_value=[]), + ) as mock_get: + await aget_agent_chat_history( + session_id="s", + flow_id="not-a-uuid", + n_messages=10, + ) + + assert mock_get.await_args.kwargs["flow_id"] is None + + @pytest.mark.asyncio + async def test_logs_warning_when_unbounded_fetch_hits_ceiling(self): + """PR #13087 review I2: surface the silent-truncation case. + + When the caller did not pass ``n_messages`` and the DB returned + exactly ``MAX_CHAT_HISTORY_FETCH_LIMIT`` rows, older history may + have been silently dropped. Emit a structured warning so on-call + has something to grep when a user reports forgotten context. + """ + # Simulate the DB returning exactly the ceiling — older rows truncated. + rows = [SimpleNamespace(id=f"msg-{i}") for i in range(MAX_CHAT_HISTORY_FETCH_LIMIT)] + with ( + patch("lfx.components.models_and_agents.memory.logger") as mock_logger, + patch( + "lfx.components.models_and_agents.memory.aget_messages", + new=AsyncMock(return_value=rows), + ), + ): + await aget_agent_chat_history( + session_id="s", + flow_id=None, + n_messages=None, + ) + + mock_logger.warning.assert_called_once() + call = mock_logger.warning.call_args + assert "memory_chat_history_limit_reached" in call.args[0] + assert call.kwargs.get("extra", {}).get("event") == "memory_chat_history_limit_reached" + + @pytest.mark.asyncio + async def test_explicit_n_messages_at_ceiling_does_not_warn(self): + """The ceiling warning is for ``n_messages=None`` only — explicit limits are honored.""" + rows = [SimpleNamespace(id=f"msg-{i}") for i in range(MAX_CHAT_HISTORY_FETCH_LIMIT)] + with ( + patch("lfx.components.models_and_agents.memory.logger") as mock_logger, + patch( + "lfx.components.models_and_agents.memory.aget_messages", + new=AsyncMock(return_value=rows), + ), + ): + await aget_agent_chat_history( + session_id="s", + flow_id=None, + n_messages=MAX_CHAT_HISTORY_FETCH_LIMIT, + ) + + mock_logger.warning.assert_not_called() + + +class TestAgentGetMemoryDataIntegration: + """End-to-end checks at the AgentComponent boundary.""" + + @staticmethod + def _make_agent(flow_id: str | UUID | None, session_id: str = "New Session 0", n_messages: int = 10): + from lfx.components.models_and_agents.agent import AgentComponent + + agent = AgentComponent.__new__(AgentComponent) + agent._vertex = SimpleNamespace(graph=SimpleNamespace(flow_id=flow_id, session_id=session_id)) + agent.context_id = "" + agent.n_messages = n_messages + agent.input_value = SimpleNamespace(id="current-input-id") + return agent + + @pytest.mark.asyncio + async def test_agent_routes_through_helper_with_flow_id(self): + flow_id_str = "88888888-8888-8888-8888-888888888888" + agent = self._make_agent(flow_id=flow_id_str) + + with patch( + "lfx.components.models_and_agents.agent.aget_agent_chat_history", + new=AsyncMock(return_value=[]), + ) as mock_helper: + await agent.get_memory_data() + + mock_helper.assert_awaited_once() + kwargs = mock_helper.await_args.kwargs + assert kwargs["flow_id"] == flow_id_str + assert kwargs["session_id"] == "New Session 0" + assert kwargs["n_messages"] == 10 + + @pytest.mark.asyncio + async def test_agent_filters_out_current_input_message(self): + """The agent must not echo the current input back as chat history.""" + agent = self._make_agent(flow_id="55555555-5555-5555-5555-555555555555") + + current = SimpleNamespace(id="current-input-id", text="ping") + old = SimpleNamespace(id="old-msg-id", text="earlier") + + with patch( + "lfx.components.models_and_agents.agent.aget_agent_chat_history", + new=AsyncMock(return_value=[old, current]), + ): + result = await agent.get_memory_data() + + assert [m.id for m in result] == ["old-msg-id"] + + @pytest.mark.asyncio + async def test_agent_n_messages_zero_disables_memory(self): + """Regression: setting "Number of Chat History Messages" to 0 must disable memory.""" + agent = self._make_agent(flow_id="99999999-9999-9999-9999-999999999999", n_messages=0) + + # Patch the underlying DB query so a regression resurfaces as a real call. + with patch( + "lfx.components.models_and_agents.memory.aget_messages", + new=AsyncMock(return_value=[SimpleNamespace(id=f"msg-{i}") for i in range(5)]), + ) as mock_get: + result = await agent.get_memory_data() + + assert result == [] + mock_get.assert_not_awaited() + + +class TestCugaGetMemoryDataIntegration: + """The Cuga agent had the same leak pattern; verify the fix reaches it too.""" + + @staticmethod + def _make_cuga(flow_id: str | UUID | None, session_id: str = "shared", n_messages: int = 10): + try: + from lfx.components.cuga import cuga_agent + except Exception as exc: # pragma: no cover - optional deps + pytest.skip(f"cuga_agent not importable in this env: {exc}") + + agent = cuga_agent.CugaComponent.__new__(cuga_agent.CugaComponent) + agent._vertex = SimpleNamespace(graph=SimpleNamespace(flow_id=flow_id, session_id=session_id)) + agent.n_messages = n_messages + agent.input_value = SimpleNamespace(id="current-input-id") + return agent + + @pytest.mark.asyncio + async def test_cuga_scopes_by_flow_id(self): + flow_id_str = "cccccccc-cccc-cccc-cccc-cccccccccccc" + agent = self._make_cuga(flow_id=flow_id_str) + + with patch( + "lfx.components.cuga.cuga_agent.aget_agent_chat_history", + new=AsyncMock(return_value=[]), + ) as mock_helper: + await agent.get_memory_data() + + kwargs = mock_helper.await_args.kwargs + assert kwargs["flow_id"] == flow_id_str + assert kwargs["session_id"] == "shared" + + @pytest.mark.asyncio + async def test_cuga_n_messages_zero_disables_memory(self): + agent = self._make_cuga(flow_id="dddddddd-dddd-dddd-dddd-dddddddddddd", n_messages=0) + + with patch( + "lfx.components.models_and_agents.memory.aget_messages", + new=AsyncMock(return_value=[SimpleNamespace(id=f"msg-{i}") for i in range(3)]), + ) as mock_get: + result = await agent.get_memory_data() + + assert result == [] + mock_get.assert_not_awaited() diff --git a/src/lfx/src/lfx/_assets/component_index.json b/src/lfx/src/lfx/_assets/component_index.json index e4f370f43c..fd2803923d 100644 --- a/src/lfx/src/lfx/_assets/component_index.json +++ b/src/lfx/src/lfx/_assets/component_index.json @@ -56733,7 +56733,7 @@ "icon": "bot", "legacy": false, "metadata": { - "code_hash": "8a418f0708c2", + "code_hash": "e20d0c155ed9", "dependencies": { "dependencies": [ { @@ -56915,7 +56915,7 @@ "show": true, "title_case": false, "type": "code", - "value": "import asyncio\nimport json\nimport traceback\nimport uuid\nfrom collections.abc import AsyncIterator\nfrom typing import TYPE_CHECKING, Any, cast\n\nfrom langchain_core.agents import AgentFinish\nfrom langchain_core.messages import AIMessage, HumanMessage\nfrom langchain_core.tools import StructuredTool\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.models.model_input_constants import (\n ALL_PROVIDER_FIELDS,\n MODEL_DYNAMIC_UPDATE_FIELDS,\n MODEL_PROVIDERS,\n MODEL_PROVIDERS_DICT,\n MODELS_METADATA,\n)\nfrom lfx.base.models.model_utils import get_model_name\nfrom lfx.components.helpers import CurrentDateComponent\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.components.models_and_agents.memory import MemoryComponent\nfrom lfx.custom.custom_component.component import _get_component_toolkit\nfrom lfx.custom.utils import update_component_build_config\nfrom lfx.field_typing import Tool\nfrom lfx.io import BoolInput, DropdownInput, IntInput, MultilineInput, Output\nfrom lfx.log.logger import logger\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\n\nif TYPE_CHECKING:\n from lfx.schema.log import SendMessageFunctionType\n\n\ndef set_advanced_true(component_input):\n \"\"\"Set the advanced flag to True for a component input.\n\n Args:\n component_input: The component input to modify\n\n Returns:\n The modified component input with advanced=True\n \"\"\"\n component_input.advanced = True\n return component_input\n\n\nMODEL_PROVIDERS_LIST = [\"OpenAI\"]\n\n\nclass CugaComponent(ToolCallingAgentComponent):\n \"\"\"Cuga Agent Component for advanced AI task execution.\n\n The Cuga component is an advanced AI agent that can execute complex tasks using\n various tools and browser automation. It supports custom instructions, web applications,\n and API interactions.\n\n Attributes:\n display_name: Human-readable name for the component\n description: Brief description of the component's purpose\n documentation: URL to component documentation\n icon: Icon identifier for the UI\n name: Internal component name\n \"\"\"\n\n display_name: str = \"Cuga\"\n description: str = \"Define the Cuga agent's instructions, then assign it a task.\"\n documentation: str = \"https://docs.langflow.org/bundles-cuga\"\n icon = \"bot\"\n name = \"Cuga\"\n\n memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n inputs = [\n DropdownInput(\n name=\"agent_llm\",\n display_name=\"Model Provider\",\n info=\"The provider of the language model that the agent will use to generate responses.\",\n options=[*MODEL_PROVIDERS_LIST, \"Custom\"],\n value=\"OpenAI\",\n real_time_refresh=True,\n input_types=[],\n options_metadata=[MODELS_METADATA[key] for key in MODEL_PROVIDERS_LIST] + [{\"icon\": \"brain\"}],\n ),\n *MODEL_PROVIDERS_DICT[\"OpenAI\"][\"inputs\"],\n MultilineInput(\n name=\"instructions\",\n display_name=\"Instructions\",\n info=(\n \"Custom instructions for the agent to adhere to during its operation.\\n\"\n \"Example:\\n\"\n \"## Plan\\n\"\n \"< planning instructions e.g. which tools and when to use>\\n\"\n \"## Answer\\n\"\n \"< final answer instructions how to answer>\"\n ),\n value=\"\",\n advanced=False,\n ),\n IntInput(\n name=\"n_messages\",\n display_name=\"Number of Chat History Messages\",\n value=100,\n info=\"Number of chat history messages to retrieve.\",\n advanced=True,\n show=True,\n ),\n *LCToolsAgentComponent.get_base_inputs(),\n BoolInput(\n name=\"add_current_date_tool\",\n display_name=\"Current Date\",\n advanced=True,\n info=\"If true, will add a tool to the agent that returns the current date.\",\n value=True,\n ),\n BoolInput(\n name=\"lite_mode\",\n display_name=\"Enable CugaLite\",\n info=\"Faster reasoning for simple tasks. Enable CugaLite for simple API tasks.\",\n value=True,\n advanced=True,\n ),\n IntInput(\n name=\"lite_mode_tool_threshold\",\n display_name=\"CugaLite Tool Threshold\",\n info=\"Route to CugaLite if app has fewer than this many tools.\",\n value=25,\n advanced=True,\n ),\n DropdownInput(\n name=\"decomposition_strategy\",\n display_name=\"Decomposition Strategy\",\n info=\"Strategy for task decomposition: 'flexible' allows multiple subtasks per app,\\n\"\n \" 'exact' enforces one subtask per app.\",\n options=[\"flexible\", \"exact\"],\n value=\"flexible\",\n advanced=True,\n ),\n BoolInput(\n name=\"browser_enabled\",\n display_name=\"Enable Browser\",\n info=\"Toggle to enable a built-in browser tool for web scraping and searching.\",\n value=False,\n advanced=True,\n ),\n MultilineInput(\n name=\"web_apps\",\n display_name=\"Web applications\",\n info=(\n \"Cuga will automatically start this web application when Enable Browser is true. \"\n \"Currently only supports one web application. Example: https://example.com\"\n ),\n value=\"\",\n advanced=True,\n ),\n ]\n outputs = [\n Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n ]\n\n async def call_agent(\n self, current_input: str, tools: list[Tool], history_messages: list[Message], llm\n ) -> AsyncIterator[dict[str, Any]]:\n \"\"\"Execute the Cuga agent with the given input and tools.\n\n This method initializes and runs the Cuga agent, processing the input through\n the agent's workflow and yielding events for real-time monitoring.\n\n Args:\n current_input: The user input to process\n tools: List of available tools for the agent\n history_messages: Previous conversation history\n llm: The language model instance to use\n\n Yields:\n dict: Agent events including tool usage, thinking, and final results\n\n Raises:\n ValueError: If there's an error in agent initialization\n TypeError: If there's a type error in processing\n RuntimeError: If there's a runtime error during execution\n ConnectionError: If there's a connection issue\n \"\"\"\n yield {\n \"event\": \"on_chain_start\",\n \"run_id\": str(uuid.uuid4()),\n \"name\": \"CUGA_initializing\",\n \"data\": {\"input\": {\"input\": current_input, \"chat_history\": []}},\n }\n logger.debug(f\"[CUGA] LLM MODEL TYPE: {type(llm)}\")\n if current_input:\n # Import settings first\n from cuga.config import settings\n\n # Use Dynaconf's set() method to update settings dynamically\n # This properly updates the settings object without corruption\n logger.debug(\"[CUGA] Updating CUGA settings via Dynaconf set() method\")\n\n settings.advanced_features.registry = False\n settings.advanced_features.lite_mode = self.lite_mode\n settings.advanced_features.lite_mode_tool_threshold = self.lite_mode_tool_threshold\n settings.advanced_features.decomposition_strategy = self.decomposition_strategy\n\n if self.browser_enabled:\n logger.debug(\"[CUGA] browser_enabled is true, setting mode to hybrid\")\n settings.advanced_features.mode = \"hybrid\"\n settings.advanced_features.use_vision = False\n else:\n logger.debug(\"[CUGA] browser_enabled is false, setting mode to api\")\n settings.advanced_features.mode = \"api\"\n\n from cuga.backend.activity_tracker.tracker import ActivityTracker\n from cuga.backend.cuga_graph.utils.agent_loop import StreamEvent\n from cuga.backend.cuga_graph.utils.controller import (\n AgentRunner as CugaAgent,\n )\n from cuga.backend.cuga_graph.utils.controller import (\n ExperimentResult as AgentResult,\n )\n from cuga.backend.llm.models import LLMManager\n from cuga.configurations.instructions_manager import InstructionsManager\n\n # Reset var_manager if this is the first message in history\n logger.debug(f\"[CUGA] Checking history_messages: count={len(history_messages) if history_messages else 0}\")\n if not history_messages or len(history_messages) == 0:\n logger.debug(\"[CUGA] First message in history detected, resetting var_manager\")\n else:\n logger.debug(f\"[CUGA] Continuing conversation with {len(history_messages)} previous messages\")\n\n llm_manager = LLMManager()\n llm_manager.set_llm(llm)\n instructions_manager = InstructionsManager()\n\n instructions_to_use = self.instructions or \"\"\n logger.debug(f\"[CUGA] instructions are: {instructions_to_use}\")\n instructions_manager.set_instructions_from_one_file(instructions_to_use)\n tracker = ActivityTracker()\n tracker.set_tools(tools)\n thread_id = self.graph.session_id\n logger.debug(f\"[CUGA] Using thread_id (session_id): {thread_id}\")\n cuga_agent = CugaAgent(browser_enabled=self.browser_enabled, thread_id=thread_id)\n if self.browser_enabled:\n await cuga_agent.initialize_freemode_env(start_url=self.web_apps.strip(), interface_mode=\"browser_only\")\n else:\n await cuga_agent.initialize_appworld_env()\n\n yield {\n \"event\": \"on_chain_start\",\n \"run_id\": str(uuid.uuid4()),\n \"name\": \"CUGA_thinking...\",\n \"data\": {\"input\": {\"input\": current_input, \"chat_history\": []}},\n }\n logger.debug(f\"[CUGA] current web apps are {self.web_apps}\")\n logger.debug(f\"[CUGA] Processing input: {current_input}\")\n try:\n # Convert history to LangChain format for the event\n logger.debug(f\"[CUGA] Converting {len(history_messages)} history messages to LangChain format\")\n lc_messages = []\n for i, msg in enumerate(history_messages):\n msg_text = getattr(msg, \"text\", \"N/A\")[:50] if hasattr(msg, \"text\") else \"N/A\"\n logger.debug(\n f\"[CUGA] Message {i}: type={type(msg)}, sender={getattr(msg, 'sender', 'N/A')}, \"\n f\"text={msg_text}...\"\n )\n if hasattr(msg, \"sender\") and msg.sender == \"Human\":\n lc_messages.append(HumanMessage(content=msg.text))\n else:\n lc_messages.append(AIMessage(content=msg.text))\n\n logger.debug(f\"[CUGA] Converted to {len(lc_messages)} LangChain messages\")\n await asyncio.sleep(0.5)\n\n # 2. Build final response\n response_parts = []\n\n response_parts.append(f\"Processed input: '{current_input}'\")\n response_parts.append(f\"Available tools: {len(tools)}\")\n last_event: StreamEvent | None = None\n tool_run_id: str | None = None\n # 3. Chain end event with AgentFinish\n async for event in cuga_agent.run_task_generic_yield(\n eval_mode=False, goal=current_input, chat_messages=lc_messages\n ):\n logger.debug(f\"[CUGA] recieved event {event}\")\n if last_event is not None and tool_run_id is not None:\n logger.debug(f\"[CUGA] last event {last_event}\")\n try:\n # TODO: Extract data\n data_dict = json.loads(last_event.data)\n except json.JSONDecodeError:\n data_dict = last_event.data\n if last_event.name == \"CodeAgent\" and \"code\" in data_dict:\n data_dict = data_dict[\"code\"]\n yield {\n \"event\": \"on_tool_end\",\n \"run_id\": tool_run_id,\n \"name\": last_event.name,\n \"data\": {\"output\": data_dict},\n }\n if isinstance(event, StreamEvent):\n tool_run_id = str(uuid.uuid4())\n last_event = StreamEvent(name=event.name, data=event.data)\n tool_event = {\n \"event\": \"on_tool_start\",\n \"run_id\": tool_run_id,\n \"name\": event.name,\n \"data\": {\"input\": {}},\n }\n logger.debug(f\"[CUGA] Yielding tool_start event: {event.name}\")\n yield tool_event\n\n if isinstance(event, AgentResult):\n task_result = event\n end_event = {\n \"event\": \"on_chain_end\",\n \"run_id\": str(uuid.uuid4()),\n \"name\": \"CugaAgent\",\n \"data\": {\"output\": AgentFinish(return_values={\"output\": task_result.answer}, log=\"\")},\n }\n answer_preview = task_result.answer[:100] if task_result.answer else \"None\"\n logger.info(f\"[CUGA] Yielding chain_end event with answer: {answer_preview}...\")\n yield end_event\n\n except (ValueError, TypeError, RuntimeError, ConnectionError) as e:\n logger.error(f\"[CUGA] An error occurred: {e!s}\")\n logger.error(f\"[CUGA] Traceback: {traceback.format_exc()}\")\n error_msg = f\"CUGA Agent error: {e!s}\"\n logger.error(f\"[CUGA] Error occurred: {error_msg}\")\n\n # Emit error event\n yield {\n \"event\": \"on_chain_error\",\n \"run_id\": str(uuid.uuid4()),\n \"name\": \"CugaAgent\",\n \"data\": {\"error\": error_msg},\n }\n\n async def message_response(self) -> Message:\n \"\"\"Generate a message response using the Cuga agent.\n\n This method processes the input through the Cuga agent and returns a structured\n message response. It handles agent initialization, tool setup, and event processing.\n\n Returns:\n Message: The agent's response message\n\n Raises:\n Exception: If there's an error during agent execution\n \"\"\"\n logger.debug(\"[CUGA] Starting Cuga agent run for message_response.\")\n logger.debug(f\"[CUGA] Agent input value: {self.input_value}\")\n\n # Validate input is not empty\n if not self.input_value or not str(self.input_value).strip():\n msg = \"Message cannot be empty. Please provide a valid message.\"\n raise ValueError(msg)\n\n try:\n from lfx.schema.content_block import ContentBlock\n from lfx.schema.message import MESSAGE_SENDER_AI\n\n llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n\n # Create agent message for event processing\n agent_message = Message(\n sender=MESSAGE_SENDER_AI,\n sender_name=\"Cuga\",\n properties={\"icon\": \"Bot\", \"state\": \"partial\"},\n content_blocks=[ContentBlock(title=\"Agent Steps\", contents=[])],\n session_id=self.graph.session_id or str(uuid.uuid4()),\n )\n\n # Pre-assign an ID for event processing, following the base agent pattern\n # This ensures streaming works even when not connected to ChatOutput\n if not self.is_connected_to_chat_output():\n # When not connected to ChatOutput, assign ID upfront for streaming support\n agent_message.data[\"id\"] = uuid.uuid4()\n\n # Get input text\n input_text = self.input_value.text if hasattr(self.input_value, \"text\") else str(self.input_value)\n\n # Create event iterator from call_agent\n event_iterator = self.call_agent(\n current_input=input_text, tools=self.tools or [], history_messages=self.chat_history, llm=llm_model\n )\n\n # Process events using the existing event processing system\n from lfx.base.agents.events import process_agent_events\n\n # Create a wrapper that forces DB updates for event handlers\n # This ensures the UI can see loading steps in real-time via polling\n async def force_db_update_send_message(message, id_=None, *, skip_db_update=False): # noqa: ARG001\n # Always persist to DB so polling-based UI shows loading steps in real-time\n content_blocks_len = len(message.content_blocks[0].contents) if message.content_blocks else 0\n logger.debug(\n f\"[CUGA] Sending message update - state: {message.properties.state}, \"\n f\"content_blocks: {content_blocks_len}\"\n )\n\n result = await self.send_message(message, id_=id_, skip_db_update=False)\n\n logger.debug(f\"[CUGA] Message processed with ID: {result.id}\")\n return result\n\n result = await process_agent_events(\n event_iterator, agent_message, cast(\"SendMessageFunctionType\", force_db_update_send_message)\n )\n\n logger.debug(\"[CUGA] Agent run finished successfully.\")\n logger.debug(f\"[CUGA] Agent output: {result}\")\n\n except Exception as e:\n logger.error(f\"[CUGA] Error in message_response: {e}\")\n logger.error(f\"[CUGA] An error occurred: {e!s}\")\n logger.error(f\"[CUGA] Traceback: {traceback.format_exc()}\")\n\n # Check if error is related to Playwright installation\n error_str = str(e).lower()\n if \"playwright install\" in error_str:\n msg = (\n \"Playwright is not installed. Please install Playwright Chromium using: \"\n \"uv run -m playwright install chromium\"\n )\n raise ValueError(msg) from e\n\n raise\n else:\n return result\n\n async def get_agent_requirements(self):\n \"\"\"Get the agent requirements for the Cuga agent.\n\n This method retrieves and configures all necessary components for the agent\n including the language model, chat history, and tools.\n\n Returns:\n tuple: A tuple containing (llm_model, chat_history, tools)\n\n Raises:\n ValueError: If no language model is selected or if there's an error\n in model initialization\n \"\"\"\n llm_model, display_name = await self.get_llm()\n if llm_model is None:\n msg = \"No language model selected. Please choose a model to proceed.\"\n raise ValueError(msg)\n self.model_name = get_model_name(llm_model, display_name=display_name)\n\n # Get memory data\n self.chat_history = await self.get_memory_data()\n if isinstance(self.chat_history, Message):\n self.chat_history = [self.chat_history]\n\n # Add current date tool if enabled\n if self.add_current_date_tool:\n if not isinstance(self.tools, list):\n self.tools = []\n current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n if not isinstance(current_date_tool, StructuredTool):\n msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n raise TypeError(msg)\n self.tools.append(current_date_tool)\n\n # --- ADDED LOGGING START ---\n logger.debug(\"[CUGA] Retrieved agent requirements: LLM, chat history, and tools.\")\n logger.debug(f\"[CUGA] LLM model: {self.model_name}\")\n logger.debug(f\"[CUGA] Number of chat history messages: {len(self.chat_history)}\")\n logger.debug(f\"[CUGA] Tools available: {[tool.name for tool in self.tools]}\")\n logger.debug(f\"[CUGA] metadata: {[tool.metadata for tool in self.tools]}\")\n # --- ADDED LOGGING END ---\n\n return llm_model, self.chat_history, self.tools\n\n async def get_memory_data(self):\n \"\"\"Retrieve chat history messages.\n\n This method fetches the conversation history from memory, excluding the current\n input message to avoid duplication.\n\n Returns:\n list: List of Message objects representing the chat history\n \"\"\"\n logger.debug(\"[CUGA] Retrieving chat history messages.\")\n logger.debug(f\"[CUGA] Session ID: {self.graph.session_id}\")\n logger.debug(f\"[CUGA] n_messages: {self.n_messages}\")\n logger.debug(f\"[CUGA] input_value: {self.input_value}\")\n logger.debug(f\"[CUGA] input_value type: {type(self.input_value)}\")\n logger.debug(f\"[CUGA] input_value id: {getattr(self.input_value, 'id', None)}\")\n\n messages = (\n await MemoryComponent(**self.get_base_args())\n .set(session_id=str(self.graph.session_id), order=\"Ascending\", n_messages=self.n_messages)\n .retrieve_messages()\n )\n logger.debug(f\"[CUGA] Retrieved {len(messages)} messages from memory\")\n return [\n message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n ]\n\n async def get_llm(self):\n \"\"\"Get language model for the Cuga agent.\n\n This method initializes and configures the language model based on the\n selected provider and parameters.\n\n Returns:\n tuple: A tuple containing (llm_model, display_name)\n\n Raises:\n ValueError: If the model provider is invalid or model initialization fails\n \"\"\"\n logger.debug(\"[CUGA] Getting language model for the agent.\")\n logger.debug(f\"[CUGA] Requested LLM provider: {self.agent_llm}\")\n\n if not isinstance(self.agent_llm, str):\n logger.debug(\"[CUGA] Agent LLM is already a model instance.\")\n return self.agent_llm, None\n\n try:\n provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n if not provider_info:\n msg = f\"Invalid model provider: {self.agent_llm}\"\n raise ValueError(msg)\n\n component_class = provider_info.get(\"component_class\")\n display_name = component_class.display_name\n inputs = provider_info.get(\"inputs\")\n prefix = provider_info.get(\"prefix\", \"\")\n logger.debug(f\"[CUGA] Successfully built LLM model from provider: {self.agent_llm}\")\n return self._build_llm_model(component_class, inputs, prefix), display_name\n\n except (AttributeError, ValueError, TypeError, RuntimeError) as e:\n await logger.aerror(f\"[CUGA] Error building {self.agent_llm} language model: {e!s}\")\n msg = f\"Failed to initialize language model: {e!s}\"\n raise ValueError(msg) from e\n\n def _build_llm_model(self, component, inputs, prefix=\"\"):\n \"\"\"Build LLM model with parameters.\n\n This method constructs a language model instance using the provided component\n class and input parameters.\n\n Args:\n component: The LLM component class to instantiate\n inputs: List of input field definitions\n prefix: Optional prefix for parameter names\n\n Returns:\n The configured LLM model instance\n \"\"\"\n model_kwargs = {}\n for input_ in inputs:\n if hasattr(self, f\"{prefix}{input_.name}\"):\n model_kwargs[input_.name] = getattr(self, f\"{prefix}{input_.name}\")\n return component.set(**model_kwargs).build_model()\n\n def set_component_params(self, component):\n \"\"\"Set component parameters based on provider.\n\n This method configures component parameters according to the selected\n model provider's requirements.\n\n Args:\n component: The component to configure\n\n Returns:\n The configured component\n \"\"\"\n provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n if provider_info:\n inputs = provider_info.get(\"inputs\")\n prefix = provider_info.get(\"prefix\")\n model_kwargs = {}\n for input_ in inputs:\n if hasattr(self, f\"{prefix}{input_.name}\"):\n model_kwargs[input_.name] = getattr(self, f\"{prefix}{input_.name}\")\n return component.set(**model_kwargs)\n return component\n\n def delete_fields(self, build_config: dotdict, fields: dict | list[str]) -> None:\n \"\"\"Delete specified fields from build_config.\n\n This method removes unwanted fields from the build configuration.\n\n Args:\n build_config: The build configuration dictionary\n fields: Fields to remove (can be dict or list of strings)\n \"\"\"\n for field in fields:\n build_config.pop(field, None)\n\n def update_input_types(self, build_config: dotdict) -> dotdict:\n \"\"\"Update input types for all fields in build_config.\n\n This method ensures all fields in the build configuration have proper\n input types defined.\n\n Args:\n build_config: The build configuration to update\n\n Returns:\n dotdict: Updated build configuration with input types\n \"\"\"\n for key, value in build_config.items():\n if isinstance(value, dict):\n if value.get(\"input_types\") is None:\n build_config[key][\"input_types\"] = []\n elif hasattr(value, \"input_types\") and value.input_types is None:\n value.input_types = []\n return build_config\n\n async def update_build_config(\n self, build_config: dotdict, field_value: str, field_name: str | None = None\n ) -> dotdict:\n \"\"\"Update build configuration based on field changes.\n\n This method dynamically updates the component's build configuration when\n certain fields change, particularly the model provider selection.\n\n Args:\n build_config: The current build configuration\n field_value: The new value for the field\n field_name: The name of the field being changed\n\n Returns:\n dotdict: Updated build configuration\n\n Raises:\n ValueError: If required keys are missing from the configuration\n \"\"\"\n if field_name in (\"agent_llm\",):\n build_config[\"agent_llm\"][\"value\"] = field_value\n provider_info = MODEL_PROVIDERS_DICT.get(field_value)\n if provider_info:\n component_class = provider_info.get(\"component_class\")\n if component_class and hasattr(component_class, \"update_build_config\"):\n build_config = await update_component_build_config(\n component_class, build_config, field_value, \"model_name\"\n )\n\n provider_configs: dict[str, tuple[dict, list[dict]]] = {\n provider: (\n MODEL_PROVIDERS_DICT[provider][\"fields\"],\n [\n MODEL_PROVIDERS_DICT[other_provider][\"fields\"]\n for other_provider in MODEL_PROVIDERS_DICT\n if other_provider != provider\n ],\n )\n for provider in MODEL_PROVIDERS_DICT\n }\n if field_value in provider_configs:\n fields_to_add, fields_to_delete = provider_configs[field_value]\n\n # Delete fields from other providers\n for fields in fields_to_delete:\n self.delete_fields(build_config, fields)\n\n # Add provider-specific fields\n if field_value == \"OpenAI\" and not any(field in build_config for field in fields_to_add):\n build_config.update(fields_to_add)\n else:\n build_config.update(fields_to_add)\n build_config[\"agent_llm\"][\"input_types\"] = []\n elif field_value == \"Custom\":\n # Delete all provider fields\n self.delete_fields(build_config, ALL_PROVIDER_FIELDS)\n # Update with custom component\n custom_component = DropdownInput(\n name=\"agent_llm\",\n display_name=\"Language Model\",\n options=[*sorted(MODEL_PROVIDERS), \"Custom\"],\n value=\"Custom\",\n real_time_refresh=True,\n input_types=[\"LanguageModel\"],\n options_metadata=[MODELS_METADATA[key] for key in sorted(MODELS_METADATA.keys())]\n + [{\"icon\": \"brain\"}],\n )\n build_config.update({\"agent_llm\": custom_component.to_dict()})\n\n # Update input types for all fields\n build_config = self.update_input_types(build_config)\n\n # Validate required keys\n default_keys = [\n \"code\",\n \"_type\",\n \"agent_llm\",\n \"tools\",\n \"input_value\",\n \"add_current_date_tool\",\n \"instructions\",\n \"agent_description\",\n \"max_iterations\",\n \"handle_parsing_errors\",\n \"verbose\",\n ]\n missing_keys = [key for key in default_keys if key not in build_config]\n if missing_keys:\n msg = f\"Missing required keys in build_config: {missing_keys}\"\n raise ValueError(msg)\n\n if (\n isinstance(self.agent_llm, str)\n and self.agent_llm in MODEL_PROVIDERS_DICT\n and field_name in MODEL_DYNAMIC_UPDATE_FIELDS\n ):\n provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n if provider_info:\n component_class = provider_info.get(\"component_class\")\n component_class = self.set_component_params(component_class)\n prefix = provider_info.get(\"prefix\")\n if component_class and hasattr(component_class, \"update_build_config\"):\n if isinstance(field_name, str) and isinstance(prefix, str):\n field_name = field_name.replace(prefix, \"\")\n build_config = await update_component_build_config(\n component_class, build_config, field_value, \"model_name\"\n )\n return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n async def _get_tools(self) -> list[Tool]:\n \"\"\"Build agent tools.\n\n This method constructs the list of tools available to the Cuga agent,\n including component tools and any additional configured tools.\n\n Returns:\n list[Tool]: List of available tools for the agent\n \"\"\"\n logger.debug(\"[CUGA] Building agent tools.\")\n component_toolkit = _get_component_toolkit()\n tools_names = self._build_tools_names()\n agent_description = self.get_tool_description()\n description = f\"{agent_description}{tools_names}\"\n tools = component_toolkit(component=self).get_tools(\n tool_name=\"Call_CugaAgent\", tool_description=description, callbacks=self.get_langchain_callbacks()\n )\n if hasattr(self, \"tools_metadata\"):\n tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n logger.debug(f\"[CUGA] Tools built: {[tool.name for tool in tools]}\")\n return tools\n" + "value": "import asyncio\nimport json\nimport traceback\nimport uuid\nfrom collections.abc import AsyncIterator\nfrom typing import TYPE_CHECKING, Any, cast\n\nfrom langchain_core.agents import AgentFinish\nfrom langchain_core.messages import AIMessage, HumanMessage\nfrom langchain_core.tools import StructuredTool\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.models.model_input_constants import (\n ALL_PROVIDER_FIELDS,\n MODEL_DYNAMIC_UPDATE_FIELDS,\n MODEL_PROVIDERS,\n MODEL_PROVIDERS_DICT,\n MODELS_METADATA,\n)\nfrom lfx.base.models.model_utils import get_model_name\nfrom lfx.components.helpers import CurrentDateComponent\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.components.models_and_agents.memory import MemoryComponent, aget_agent_chat_history\nfrom lfx.custom.custom_component.component import _get_component_toolkit\nfrom lfx.custom.utils import update_component_build_config\nfrom lfx.field_typing import Tool\nfrom lfx.io import BoolInput, DropdownInput, IntInput, MultilineInput, Output\nfrom lfx.log.logger import logger\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\n\nif TYPE_CHECKING:\n from lfx.schema.log import SendMessageFunctionType\n\n\ndef set_advanced_true(component_input):\n \"\"\"Set the advanced flag to True for a component input.\n\n Args:\n component_input: The component input to modify\n\n Returns:\n The modified component input with advanced=True\n \"\"\"\n component_input.advanced = True\n return component_input\n\n\nMODEL_PROVIDERS_LIST = [\"OpenAI\"]\n\n\nclass CugaComponent(ToolCallingAgentComponent):\n \"\"\"Cuga Agent Component for advanced AI task execution.\n\n The Cuga component is an advanced AI agent that can execute complex tasks using\n various tools and browser automation. It supports custom instructions, web applications,\n and API interactions.\n\n Attributes:\n display_name: Human-readable name for the component\n description: Brief description of the component's purpose\n documentation: URL to component documentation\n icon: Icon identifier for the UI\n name: Internal component name\n \"\"\"\n\n display_name: str = \"Cuga\"\n description: str = \"Define the Cuga agent's instructions, then assign it a task.\"\n documentation: str = \"https://docs.langflow.org/bundles-cuga\"\n icon = \"bot\"\n name = \"Cuga\"\n\n memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n inputs = [\n DropdownInput(\n name=\"agent_llm\",\n display_name=\"Model Provider\",\n info=\"The provider of the language model that the agent will use to generate responses.\",\n options=[*MODEL_PROVIDERS_LIST, \"Custom\"],\n value=\"OpenAI\",\n real_time_refresh=True,\n input_types=[],\n options_metadata=[MODELS_METADATA[key] for key in MODEL_PROVIDERS_LIST] + [{\"icon\": \"brain\"}],\n ),\n *MODEL_PROVIDERS_DICT[\"OpenAI\"][\"inputs\"],\n MultilineInput(\n name=\"instructions\",\n display_name=\"Instructions\",\n info=(\n \"Custom instructions for the agent to adhere to during its operation.\\n\"\n \"Example:\\n\"\n \"## Plan\\n\"\n \"< planning instructions e.g. which tools and when to use>\\n\"\n \"## Answer\\n\"\n \"< final answer instructions how to answer>\"\n ),\n value=\"\",\n advanced=False,\n ),\n IntInput(\n name=\"n_messages\",\n display_name=\"Number of Chat History Messages\",\n value=100,\n info=\"Number of chat history messages to retrieve.\",\n advanced=True,\n show=True,\n ),\n *LCToolsAgentComponent.get_base_inputs(),\n BoolInput(\n name=\"add_current_date_tool\",\n display_name=\"Current Date\",\n advanced=True,\n info=\"If true, will add a tool to the agent that returns the current date.\",\n value=True,\n ),\n BoolInput(\n name=\"lite_mode\",\n display_name=\"Enable CugaLite\",\n info=\"Faster reasoning for simple tasks. Enable CugaLite for simple API tasks.\",\n value=True,\n advanced=True,\n ),\n IntInput(\n name=\"lite_mode_tool_threshold\",\n display_name=\"CugaLite Tool Threshold\",\n info=\"Route to CugaLite if app has fewer than this many tools.\",\n value=25,\n advanced=True,\n ),\n DropdownInput(\n name=\"decomposition_strategy\",\n display_name=\"Decomposition Strategy\",\n info=\"Strategy for task decomposition: 'flexible' allows multiple subtasks per app,\\n\"\n \" 'exact' enforces one subtask per app.\",\n options=[\"flexible\", \"exact\"],\n value=\"flexible\",\n advanced=True,\n ),\n BoolInput(\n name=\"browser_enabled\",\n display_name=\"Enable Browser\",\n info=\"Toggle to enable a built-in browser tool for web scraping and searching.\",\n value=False,\n advanced=True,\n ),\n MultilineInput(\n name=\"web_apps\",\n display_name=\"Web applications\",\n info=(\n \"Cuga will automatically start this web application when Enable Browser is true. \"\n \"Currently only supports one web application. Example: https://example.com\"\n ),\n value=\"\",\n advanced=True,\n ),\n ]\n outputs = [\n Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n ]\n\n async def call_agent(\n self, current_input: str, tools: list[Tool], history_messages: list[Message], llm\n ) -> AsyncIterator[dict[str, Any]]:\n \"\"\"Execute the Cuga agent with the given input and tools.\n\n This method initializes and runs the Cuga agent, processing the input through\n the agent's workflow and yielding events for real-time monitoring.\n\n Args:\n current_input: The user input to process\n tools: List of available tools for the agent\n history_messages: Previous conversation history\n llm: The language model instance to use\n\n Yields:\n dict: Agent events including tool usage, thinking, and final results\n\n Raises:\n ValueError: If there's an error in agent initialization\n TypeError: If there's a type error in processing\n RuntimeError: If there's a runtime error during execution\n ConnectionError: If there's a connection issue\n \"\"\"\n yield {\n \"event\": \"on_chain_start\",\n \"run_id\": str(uuid.uuid4()),\n \"name\": \"CUGA_initializing\",\n \"data\": {\"input\": {\"input\": current_input, \"chat_history\": []}},\n }\n logger.debug(f\"[CUGA] LLM MODEL TYPE: {type(llm)}\")\n if current_input:\n # Import settings first\n from cuga.config import settings\n\n # Use Dynaconf's set() method to update settings dynamically\n # This properly updates the settings object without corruption\n logger.debug(\"[CUGA] Updating CUGA settings via Dynaconf set() method\")\n\n settings.advanced_features.registry = False\n settings.advanced_features.lite_mode = self.lite_mode\n settings.advanced_features.lite_mode_tool_threshold = self.lite_mode_tool_threshold\n settings.advanced_features.decomposition_strategy = self.decomposition_strategy\n\n if self.browser_enabled:\n logger.debug(\"[CUGA] browser_enabled is true, setting mode to hybrid\")\n settings.advanced_features.mode = \"hybrid\"\n settings.advanced_features.use_vision = False\n else:\n logger.debug(\"[CUGA] browser_enabled is false, setting mode to api\")\n settings.advanced_features.mode = \"api\"\n\n from cuga.backend.activity_tracker.tracker import ActivityTracker\n from cuga.backend.cuga_graph.utils.agent_loop import StreamEvent\n from cuga.backend.cuga_graph.utils.controller import (\n AgentRunner as CugaAgent,\n )\n from cuga.backend.cuga_graph.utils.controller import (\n ExperimentResult as AgentResult,\n )\n from cuga.backend.llm.models import LLMManager\n from cuga.configurations.instructions_manager import InstructionsManager\n\n # Reset var_manager if this is the first message in history\n logger.debug(f\"[CUGA] Checking history_messages: count={len(history_messages) if history_messages else 0}\")\n if not history_messages or len(history_messages) == 0:\n logger.debug(\"[CUGA] First message in history detected, resetting var_manager\")\n else:\n logger.debug(f\"[CUGA] Continuing conversation with {len(history_messages)} previous messages\")\n\n llm_manager = LLMManager()\n llm_manager.set_llm(llm)\n instructions_manager = InstructionsManager()\n\n instructions_to_use = self.instructions or \"\"\n logger.debug(f\"[CUGA] instructions are: {instructions_to_use}\")\n instructions_manager.set_instructions_from_one_file(instructions_to_use)\n tracker = ActivityTracker()\n tracker.set_tools(tools)\n thread_id = self.graph.session_id\n logger.debug(f\"[CUGA] Using thread_id (session_id): {thread_id}\")\n cuga_agent = CugaAgent(browser_enabled=self.browser_enabled, thread_id=thread_id)\n if self.browser_enabled:\n await cuga_agent.initialize_freemode_env(start_url=self.web_apps.strip(), interface_mode=\"browser_only\")\n else:\n await cuga_agent.initialize_appworld_env()\n\n yield {\n \"event\": \"on_chain_start\",\n \"run_id\": str(uuid.uuid4()),\n \"name\": \"CUGA_thinking...\",\n \"data\": {\"input\": {\"input\": current_input, \"chat_history\": []}},\n }\n logger.debug(f\"[CUGA] current web apps are {self.web_apps}\")\n logger.debug(f\"[CUGA] Processing input: {current_input}\")\n try:\n # Convert history to LangChain format for the event\n logger.debug(f\"[CUGA] Converting {len(history_messages)} history messages to LangChain format\")\n lc_messages = []\n for i, msg in enumerate(history_messages):\n msg_text = getattr(msg, \"text\", \"N/A\")[:50] if hasattr(msg, \"text\") else \"N/A\"\n logger.debug(\n f\"[CUGA] Message {i}: type={type(msg)}, sender={getattr(msg, 'sender', 'N/A')}, \"\n f\"text={msg_text}...\"\n )\n if hasattr(msg, \"sender\") and msg.sender == \"Human\":\n lc_messages.append(HumanMessage(content=msg.text))\n else:\n lc_messages.append(AIMessage(content=msg.text))\n\n logger.debug(f\"[CUGA] Converted to {len(lc_messages)} LangChain messages\")\n await asyncio.sleep(0.5)\n\n # 2. Build final response\n response_parts = []\n\n response_parts.append(f\"Processed input: '{current_input}'\")\n response_parts.append(f\"Available tools: {len(tools)}\")\n last_event: StreamEvent | None = None\n tool_run_id: str | None = None\n # 3. Chain end event with AgentFinish\n async for event in cuga_agent.run_task_generic_yield(\n eval_mode=False, goal=current_input, chat_messages=lc_messages\n ):\n logger.debug(f\"[CUGA] recieved event {event}\")\n if last_event is not None and tool_run_id is not None:\n logger.debug(f\"[CUGA] last event {last_event}\")\n try:\n # TODO: Extract data\n data_dict = json.loads(last_event.data)\n except json.JSONDecodeError:\n data_dict = last_event.data\n if last_event.name == \"CodeAgent\" and \"code\" in data_dict:\n data_dict = data_dict[\"code\"]\n yield {\n \"event\": \"on_tool_end\",\n \"run_id\": tool_run_id,\n \"name\": last_event.name,\n \"data\": {\"output\": data_dict},\n }\n if isinstance(event, StreamEvent):\n tool_run_id = str(uuid.uuid4())\n last_event = StreamEvent(name=event.name, data=event.data)\n tool_event = {\n \"event\": \"on_tool_start\",\n \"run_id\": tool_run_id,\n \"name\": event.name,\n \"data\": {\"input\": {}},\n }\n logger.debug(f\"[CUGA] Yielding tool_start event: {event.name}\")\n yield tool_event\n\n if isinstance(event, AgentResult):\n task_result = event\n end_event = {\n \"event\": \"on_chain_end\",\n \"run_id\": str(uuid.uuid4()),\n \"name\": \"CugaAgent\",\n \"data\": {\"output\": AgentFinish(return_values={\"output\": task_result.answer}, log=\"\")},\n }\n answer_preview = task_result.answer[:100] if task_result.answer else \"None\"\n logger.info(f\"[CUGA] Yielding chain_end event with answer: {answer_preview}...\")\n yield end_event\n\n except (ValueError, TypeError, RuntimeError, ConnectionError) as e:\n logger.error(f\"[CUGA] An error occurred: {e!s}\")\n logger.error(f\"[CUGA] Traceback: {traceback.format_exc()}\")\n error_msg = f\"CUGA Agent error: {e!s}\"\n logger.error(f\"[CUGA] Error occurred: {error_msg}\")\n\n # Emit error event\n yield {\n \"event\": \"on_chain_error\",\n \"run_id\": str(uuid.uuid4()),\n \"name\": \"CugaAgent\",\n \"data\": {\"error\": error_msg},\n }\n\n async def message_response(self) -> Message:\n \"\"\"Generate a message response using the Cuga agent.\n\n This method processes the input through the Cuga agent and returns a structured\n message response. It handles agent initialization, tool setup, and event processing.\n\n Returns:\n Message: The agent's response message\n\n Raises:\n Exception: If there's an error during agent execution\n \"\"\"\n logger.debug(\"[CUGA] Starting Cuga agent run for message_response.\")\n logger.debug(f\"[CUGA] Agent input value: {self.input_value}\")\n\n # Validate input is not empty\n if not self.input_value or not str(self.input_value).strip():\n msg = \"Message cannot be empty. Please provide a valid message.\"\n raise ValueError(msg)\n\n try:\n from lfx.schema.content_block import ContentBlock\n from lfx.schema.message import MESSAGE_SENDER_AI\n\n llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n\n # Create agent message for event processing\n agent_message = Message(\n sender=MESSAGE_SENDER_AI,\n sender_name=\"Cuga\",\n properties={\"icon\": \"Bot\", \"state\": \"partial\"},\n content_blocks=[ContentBlock(title=\"Agent Steps\", contents=[])],\n session_id=self.graph.session_id or str(uuid.uuid4()),\n )\n\n # Pre-assign an ID for event processing, following the base agent pattern\n # This ensures streaming works even when not connected to ChatOutput\n if not self.is_connected_to_chat_output():\n # When not connected to ChatOutput, assign ID upfront for streaming support\n agent_message.data[\"id\"] = uuid.uuid4()\n\n # Get input text\n input_text = self.input_value.text if hasattr(self.input_value, \"text\") else str(self.input_value)\n\n # Create event iterator from call_agent\n event_iterator = self.call_agent(\n current_input=input_text, tools=self.tools or [], history_messages=self.chat_history, llm=llm_model\n )\n\n # Process events using the existing event processing system\n from lfx.base.agents.events import process_agent_events\n\n # Create a wrapper that forces DB updates for event handlers\n # This ensures the UI can see loading steps in real-time via polling\n async def force_db_update_send_message(message, id_=None, *, skip_db_update=False): # noqa: ARG001\n # Always persist to DB so polling-based UI shows loading steps in real-time\n content_blocks_len = len(message.content_blocks[0].contents) if message.content_blocks else 0\n logger.debug(\n f\"[CUGA] Sending message update - state: {message.properties.state}, \"\n f\"content_blocks: {content_blocks_len}\"\n )\n\n result = await self.send_message(message, id_=id_, skip_db_update=False)\n\n logger.debug(f\"[CUGA] Message processed with ID: {result.id}\")\n return result\n\n result = await process_agent_events(\n event_iterator, agent_message, cast(\"SendMessageFunctionType\", force_db_update_send_message)\n )\n\n logger.debug(\"[CUGA] Agent run finished successfully.\")\n logger.debug(f\"[CUGA] Agent output: {result}\")\n\n except Exception as e:\n logger.error(f\"[CUGA] Error in message_response: {e}\")\n logger.error(f\"[CUGA] An error occurred: {e!s}\")\n logger.error(f\"[CUGA] Traceback: {traceback.format_exc()}\")\n\n # Check if error is related to Playwright installation\n error_str = str(e).lower()\n if \"playwright install\" in error_str:\n msg = (\n \"Playwright is not installed. Please install Playwright Chromium using: \"\n \"uv run -m playwright install chromium\"\n )\n raise ValueError(msg) from e\n\n raise\n else:\n return result\n\n async def get_agent_requirements(self):\n \"\"\"Get the agent requirements for the Cuga agent.\n\n This method retrieves and configures all necessary components for the agent\n including the language model, chat history, and tools.\n\n Returns:\n tuple: A tuple containing (llm_model, chat_history, tools)\n\n Raises:\n ValueError: If no language model is selected or if there's an error\n in model initialization\n \"\"\"\n llm_model, display_name = await self.get_llm()\n if llm_model is None:\n msg = \"No language model selected. Please choose a model to proceed.\"\n raise ValueError(msg)\n self.model_name = get_model_name(llm_model, display_name=display_name)\n\n # Get memory data\n self.chat_history = await self.get_memory_data()\n if isinstance(self.chat_history, Message):\n self.chat_history = [self.chat_history]\n\n # Add current date tool if enabled\n if self.add_current_date_tool:\n if not isinstance(self.tools, list):\n self.tools = []\n current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n if not isinstance(current_date_tool, StructuredTool):\n msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n raise TypeError(msg)\n self.tools.append(current_date_tool)\n\n # --- ADDED LOGGING START ---\n logger.debug(\"[CUGA] Retrieved agent requirements: LLM, chat history, and tools.\")\n logger.debug(f\"[CUGA] LLM model: {self.model_name}\")\n logger.debug(f\"[CUGA] Number of chat history messages: {len(self.chat_history)}\")\n logger.debug(f\"[CUGA] Tools available: {[tool.name for tool in self.tools]}\")\n logger.debug(f\"[CUGA] metadata: {[tool.metadata for tool in self.tools]}\")\n # --- ADDED LOGGING END ---\n\n return llm_model, self.chat_history, self.tools\n\n async def get_memory_data(self):\n \"\"\"Retrieve chat history messages.\n\n This method fetches the conversation history from memory, excluding the current\n input message to avoid duplication.\n\n Returns:\n list: List of Message objects representing the chat history\n \"\"\"\n logger.debug(\"[CUGA] Retrieving chat history messages.\")\n logger.debug(f\"[CUGA] Session ID: {self.graph.session_id}\")\n logger.debug(f\"[CUGA] n_messages: {self.n_messages}\")\n logger.debug(f\"[CUGA] input_value: {self.input_value}\")\n logger.debug(f\"[CUGA] input_value type: {type(self.input_value)}\")\n logger.debug(f\"[CUGA] input_value id: {getattr(self.input_value, 'id', None)}\")\n\n # Scope by flow_id (issue #13059): the ad-hoc MemoryComponent used previously\n # had no _vertex, so it could not see the running flow's flow_id and emitted\n # an unscoped query. The helper also honors n_messages == 0 as \"disabled\".\n messages = await aget_agent_chat_history(\n session_id=str(self.graph.session_id),\n flow_id=getattr(self.graph, \"flow_id\", None),\n n_messages=self.n_messages,\n )\n logger.debug(f\"[CUGA] Retrieved {len(messages)} messages from memory\")\n return [\n message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n ]\n\n async def get_llm(self):\n \"\"\"Get language model for the Cuga agent.\n\n This method initializes and configures the language model based on the\n selected provider and parameters.\n\n Returns:\n tuple: A tuple containing (llm_model, display_name)\n\n Raises:\n ValueError: If the model provider is invalid or model initialization fails\n \"\"\"\n logger.debug(\"[CUGA] Getting language model for the agent.\")\n logger.debug(f\"[CUGA] Requested LLM provider: {self.agent_llm}\")\n\n if not isinstance(self.agent_llm, str):\n logger.debug(\"[CUGA] Agent LLM is already a model instance.\")\n return self.agent_llm, None\n\n try:\n provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n if not provider_info:\n msg = f\"Invalid model provider: {self.agent_llm}\"\n raise ValueError(msg)\n\n component_class = provider_info.get(\"component_class\")\n display_name = component_class.display_name\n inputs = provider_info.get(\"inputs\")\n prefix = provider_info.get(\"prefix\", \"\")\n logger.debug(f\"[CUGA] Successfully built LLM model from provider: {self.agent_llm}\")\n return self._build_llm_model(component_class, inputs, prefix), display_name\n\n except (AttributeError, ValueError, TypeError, RuntimeError) as e:\n await logger.aerror(f\"[CUGA] Error building {self.agent_llm} language model: {e!s}\")\n msg = f\"Failed to initialize language model: {e!s}\"\n raise ValueError(msg) from e\n\n def _build_llm_model(self, component, inputs, prefix=\"\"):\n \"\"\"Build LLM model with parameters.\n\n This method constructs a language model instance using the provided component\n class and input parameters.\n\n Args:\n component: The LLM component class to instantiate\n inputs: List of input field definitions\n prefix: Optional prefix for parameter names\n\n Returns:\n The configured LLM model instance\n \"\"\"\n model_kwargs = {}\n for input_ in inputs:\n if hasattr(self, f\"{prefix}{input_.name}\"):\n model_kwargs[input_.name] = getattr(self, f\"{prefix}{input_.name}\")\n return component.set(**model_kwargs).build_model()\n\n def set_component_params(self, component):\n \"\"\"Set component parameters based on provider.\n\n This method configures component parameters according to the selected\n model provider's requirements.\n\n Args:\n component: The component to configure\n\n Returns:\n The configured component\n \"\"\"\n provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n if provider_info:\n inputs = provider_info.get(\"inputs\")\n prefix = provider_info.get(\"prefix\")\n model_kwargs = {}\n for input_ in inputs:\n if hasattr(self, f\"{prefix}{input_.name}\"):\n model_kwargs[input_.name] = getattr(self, f\"{prefix}{input_.name}\")\n return component.set(**model_kwargs)\n return component\n\n def delete_fields(self, build_config: dotdict, fields: dict | list[str]) -> None:\n \"\"\"Delete specified fields from build_config.\n\n This method removes unwanted fields from the build configuration.\n\n Args:\n build_config: The build configuration dictionary\n fields: Fields to remove (can be dict or list of strings)\n \"\"\"\n for field in fields:\n build_config.pop(field, None)\n\n def update_input_types(self, build_config: dotdict) -> dotdict:\n \"\"\"Update input types for all fields in build_config.\n\n This method ensures all fields in the build configuration have proper\n input types defined.\n\n Args:\n build_config: The build configuration to update\n\n Returns:\n dotdict: Updated build configuration with input types\n \"\"\"\n for key, value in build_config.items():\n if isinstance(value, dict):\n if value.get(\"input_types\") is None:\n build_config[key][\"input_types\"] = []\n elif hasattr(value, \"input_types\") and value.input_types is None:\n value.input_types = []\n return build_config\n\n async def update_build_config(\n self, build_config: dotdict, field_value: str, field_name: str | None = None\n ) -> dotdict:\n \"\"\"Update build configuration based on field changes.\n\n This method dynamically updates the component's build configuration when\n certain fields change, particularly the model provider selection.\n\n Args:\n build_config: The current build configuration\n field_value: The new value for the field\n field_name: The name of the field being changed\n\n Returns:\n dotdict: Updated build configuration\n\n Raises:\n ValueError: If required keys are missing from the configuration\n \"\"\"\n if field_name in (\"agent_llm\",):\n build_config[\"agent_llm\"][\"value\"] = field_value\n provider_info = MODEL_PROVIDERS_DICT.get(field_value)\n if provider_info:\n component_class = provider_info.get(\"component_class\")\n if component_class and hasattr(component_class, \"update_build_config\"):\n build_config = await update_component_build_config(\n component_class, build_config, field_value, \"model_name\"\n )\n\n provider_configs: dict[str, tuple[dict, list[dict]]] = {\n provider: (\n MODEL_PROVIDERS_DICT[provider][\"fields\"],\n [\n MODEL_PROVIDERS_DICT[other_provider][\"fields\"]\n for other_provider in MODEL_PROVIDERS_DICT\n if other_provider != provider\n ],\n )\n for provider in MODEL_PROVIDERS_DICT\n }\n if field_value in provider_configs:\n fields_to_add, fields_to_delete = provider_configs[field_value]\n\n # Delete fields from other providers\n for fields in fields_to_delete:\n self.delete_fields(build_config, fields)\n\n # Add provider-specific fields\n if field_value == \"OpenAI\" and not any(field in build_config for field in fields_to_add):\n build_config.update(fields_to_add)\n else:\n build_config.update(fields_to_add)\n build_config[\"agent_llm\"][\"input_types\"] = []\n elif field_value == \"Custom\":\n # Delete all provider fields\n self.delete_fields(build_config, ALL_PROVIDER_FIELDS)\n # Update with custom component\n custom_component = DropdownInput(\n name=\"agent_llm\",\n display_name=\"Language Model\",\n options=[*sorted(MODEL_PROVIDERS), \"Custom\"],\n value=\"Custom\",\n real_time_refresh=True,\n input_types=[\"LanguageModel\"],\n options_metadata=[MODELS_METADATA[key] for key in sorted(MODELS_METADATA.keys())]\n + [{\"icon\": \"brain\"}],\n )\n build_config.update({\"agent_llm\": custom_component.to_dict()})\n\n # Update input types for all fields\n build_config = self.update_input_types(build_config)\n\n # Validate required keys\n default_keys = [\n \"code\",\n \"_type\",\n \"agent_llm\",\n \"tools\",\n \"input_value\",\n \"add_current_date_tool\",\n \"instructions\",\n \"agent_description\",\n \"max_iterations\",\n \"handle_parsing_errors\",\n \"verbose\",\n ]\n missing_keys = [key for key in default_keys if key not in build_config]\n if missing_keys:\n msg = f\"Missing required keys in build_config: {missing_keys}\"\n raise ValueError(msg)\n\n if (\n isinstance(self.agent_llm, str)\n and self.agent_llm in MODEL_PROVIDERS_DICT\n and field_name in MODEL_DYNAMIC_UPDATE_FIELDS\n ):\n provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n if provider_info:\n component_class = provider_info.get(\"component_class\")\n component_class = self.set_component_params(component_class)\n prefix = provider_info.get(\"prefix\")\n if component_class and hasattr(component_class, \"update_build_config\"):\n if isinstance(field_name, str) and isinstance(prefix, str):\n field_name = field_name.replace(prefix, \"\")\n build_config = await update_component_build_config(\n component_class, build_config, field_value, \"model_name\"\n )\n return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n async def _get_tools(self) -> list[Tool]:\n \"\"\"Build agent tools.\n\n This method constructs the list of tools available to the Cuga agent,\n including component tools and any additional configured tools.\n\n Returns:\n list[Tool]: List of available tools for the agent\n \"\"\"\n logger.debug(\"[CUGA] Building agent tools.\")\n component_toolkit = _get_component_toolkit()\n tools_names = self._build_tools_names()\n agent_description = self.get_tool_description()\n description = f\"{agent_description}{tools_names}\"\n tools = component_toolkit(component=self).get_tools(\n tool_name=\"Call_CugaAgent\", tool_description=description, callbacks=self.get_langchain_callbacks()\n )\n if hasattr(self, \"tools_metadata\"):\n tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n logger.debug(f\"[CUGA] Tools built: {[tool.name for tool in tools]}\")\n return tools\n" }, "decomposition_strategy": { "_input_type": "DropdownInput", @@ -93655,7 +93655,7 @@ "icon": "bot", "legacy": false, "metadata": { - "code_hash": "ebac74bfcae6", + "code_hash": "443159257161", "dependencies": { "dependencies": [ { @@ -93844,7 +93844,7 @@ "show": true, "title_case": false, "type": "code", - "value": "from __future__ import annotations\n\nfrom contextlib import contextmanager\nfrom datetime import datetime, timezone\nfrom typing import TYPE_CHECKING, Any\n\nfrom lfx.components.models_and_agents.memory import MemoryComponent\n\nif TYPE_CHECKING:\n from langchain_core.tools import Tool\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.agents.default_system_prompt import DEFAULT_SYSTEM_PROMPT_TEMPLATE\nfrom lfx.base.agents.events import ExceptionWithMessageError\nfrom lfx.base.constants import STREAM_INFO_TEXT\nfrom lfx.base.models.unified_models import (\n get_language_model_options,\n get_llm,\n handle_model_input_update,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.components.agentics.helpers.model_config import validate_model_selection\nfrom lfx.components.helpers import CalculatorComponent, CurrentDateComponent\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.custom.custom_component.component import get_component_toolkit\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, ModelInput, StrInput\nfrom lfx.io import IntInput, MessageTextInput, MultilineInput, Output, SecretStrInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.schema.data import Data\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.schema.table import EditMode\n\n\ndef set_advanced_true(component_input):\n component_input.advanced = True\n return component_input\n\n\ndef _extract_text_content(value) -> str:\n \"\"\"Pull a string payload from a Message-like, AIMessage-like, or string value.\"\"\"\n if isinstance(value, str):\n return value\n text = getattr(value, \"text\", None)\n if isinstance(text, str):\n return text\n content = getattr(value, \"content\", None)\n if isinstance(content, str):\n return content\n return str(value) if value is not None else \"\"\n\n\n@contextmanager\ndef _suppress_send_message(component: Any):\n \"\"\"Temporarily replace component.send_message with a no-op for the duration of the block.\n\n Used during the structured-output prompt fallback: run_agent streams the agent's\n final answer through self.send_message (correct for message_response), but in\n json_response the orchestrator parses that text into structured Data which the\n downstream Chat Output emits — leaving the original emission in place produces a\n duplicate message in the playground. The original method is always restored on exit,\n even when the wrapped call raises.\n \"\"\"\n original = component.send_message\n\n async def _noop(message, *_args, **_kwargs):\n return message\n\n component.send_message = _noop\n try:\n yield\n finally:\n component.send_message = original\n\n\nclass AgentComponent(ToolCallingAgentComponent):\n display_name: str = \"Agent\"\n description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n documentation: str = \"https://docs.langflow.org/agents\"\n icon = \"bot\"\n beta = False\n name = \"Agent\"\n\n memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n inputs = [\n ModelInput(\n name=\"model\",\n display_name=\"Language Model\",\n info=\"Select your model provider\",\n real_time_refresh=True,\n required=True,\n # Agents require tool calling — the filter is honored by\n # ``handle_model_input_update`` so models that can't run with\n # tools never reach the picker (and any saved selection that\n # no longer satisfies the constraint is auto-replaced).\n filters={\"tool_calling\": True},\n ),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"API Key\",\n info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n real_time_refresh=True,\n advanced=True,\n ),\n DropdownInput(\n name=\"base_url_ibm_watsonx\",\n display_name=\"watsonx API Endpoint\",\n info=\"The base URL of the API (IBM watsonx.ai only)\",\n options=IBM_WATSONX_URLS,\n value=IBM_WATSONX_URLS[0],\n combobox=True,\n show=False,\n real_time_refresh=True,\n ),\n StrInput(\n name=\"project_id\",\n display_name=\"watsonx Project ID\",\n info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n show=False,\n required=False,\n ),\n MultilineInput(\n name=\"system_prompt\",\n display_name=\"Agent Instructions\",\n info=(\n \"System Prompt: Initial instructions and context provided to guide the agent's behavior. \"\n \"Supports dynamic placeholders: {current_date}, {model_name}, {optional_user_context}.\"\n ),\n value=DEFAULT_SYSTEM_PROMPT_TEMPLATE,\n advanced=False,\n ),\n MessageTextInput(\n name=\"context_id\",\n display_name=\"Context ID\",\n info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n value=\"\",\n advanced=True,\n ),\n IntInput(\n name=\"n_messages\",\n display_name=\"Number of Chat History Messages\",\n value=100,\n info=\"Number of chat history messages to retrieve.\",\n advanced=True,\n show=True,\n ),\n IntInput(\n name=\"max_tokens\",\n display_name=\"Max Tokens\",\n info=\"Maximum number of tokens to generate. Field name varies by provider.\",\n advanced=True,\n range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n ),\n MultilineInput(\n name=\"format_instructions\",\n display_name=\"Output Format Instructions\",\n info=\"Generic Template for structured output formatting. Valid only with Structured response.\",\n value=(\n \"You are an AI that extracts structured JSON objects from unstructured text. \"\n \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n \"Fill missing or ambiguous values with defaults: null for missing values. \"\n \"Remove exact duplicates but keep variations that have different field values. \"\n \"Always return valid JSON in the expected format, never throw errors. \"\n \"If multiple objects can be extracted, return them all in the structured format.\"\n ),\n advanced=True,\n ),\n TableInput(\n name=\"output_schema\",\n display_name=\"Output Schema\",\n info=(\n \"Schema Validation: Define the structure and data types for structured output. \"\n \"No validation if no output schema.\"\n ),\n advanced=True,\n required=False,\n value=[],\n table_schema=[\n {\n \"name\": \"name\",\n \"display_name\": \"Name\",\n \"type\": \"str\",\n \"description\": \"Specify the name of the output field.\",\n \"default\": \"field\",\n \"edit_mode\": EditMode.INLINE,\n },\n {\n \"name\": \"description\",\n \"display_name\": \"Description\",\n \"type\": \"str\",\n \"description\": \"Describe the purpose of the output field.\",\n \"default\": \"description of field\",\n \"edit_mode\": EditMode.POPOVER,\n },\n {\n \"name\": \"type\",\n \"display_name\": \"Type\",\n \"type\": \"str\",\n \"edit_mode\": EditMode.INLINE,\n \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n \"default\": \"str\",\n },\n {\n \"name\": \"multiple\",\n \"display_name\": \"As List\",\n \"type\": \"boolean\",\n \"description\": \"Set to True if this output field should be a list of the specified type.\",\n \"default\": \"False\",\n \"edit_mode\": EditMode.INLINE,\n },\n ],\n ),\n *LCToolsAgentComponent.get_base_inputs(),\n # removed memory inputs from agent component\n # *memory_inputs,\n BoolInput(\n name=\"stream\",\n display_name=\"Stream\",\n info=STREAM_INFO_TEXT,\n value=False,\n advanced=True,\n ),\n BoolInput(\n name=\"add_current_date_tool\",\n display_name=\"Current Date\",\n advanced=True,\n info=\"If true, will add a tool to the agent that returns the current date.\",\n value=True,\n ),\n BoolInput(\n name=\"add_calculator_tool\",\n display_name=\"Calculator\",\n advanced=True,\n info=(\n \"If true, adds a zero-config arithmetic calculator tool to the agent \"\n \"(safe: only +, -, *, /, ** operators via AST).\"\n ),\n value=True,\n ),\n ]\n outputs = [\n Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n Output(\n name=\"structured_response\",\n display_name=\"Structured Response\",\n method=\"json_response\",\n types=[\"Data\"],\n ),\n ]\n\n def _resolve_selected_model(self):\n \"\"\"Resolve the selected model, including legacy agent_llm/model_name inputs.\"\"\"\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n if isinstance(self.model, BaseLanguageModel):\n return self.model\n except ImportError:\n pass\n\n if isinstance(self.model, list) and self.model:\n return self.model\n\n legacy_provider = getattr(self, \"agent_llm\", None)\n legacy_model_name = getattr(self, \"model_name\", None)\n if not legacy_provider or not legacy_model_name:\n return self.model\n\n options = get_language_model_options(user_id=self.user_id)\n for option in options:\n if option.get(\"provider\") == legacy_provider and option.get(\"name\") == legacy_model_name:\n return [option]\n\n return [\n {\n \"name\": legacy_model_name,\n \"provider\": legacy_provider,\n \"metadata\": {},\n }\n ]\n\n def _get_max_tokens_value(self):\n \"\"\"Return the user-supplied max_tokens or None when unset/zero.\"\"\"\n val = getattr(self, \"max_tokens\", None)\n if val in {\"\", 0}:\n return None\n return val\n\n def _get_llm(self):\n \"\"\"Override parent to include max_tokens from the Agent's input field.\"\"\"\n return get_llm(\n model=self.model,\n user_id=self.user_id,\n api_key=getattr(self, \"api_key\", None),\n stream=bool(getattr(self, \"stream\", False)),\n max_tokens=self._get_max_tokens_value(),\n watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n watsonx_project_id=getattr(self, \"project_id\", None),\n )\n\n async def get_agent_requirements(self):\n \"\"\"Get the agent requirements for the agent.\"\"\"\n from langchain_core.tools import StructuredTool\n\n selected_model = self._resolve_selected_model()\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n is_connected_model = isinstance(selected_model, BaseLanguageModel)\n except ImportError:\n is_connected_model = False\n\n if not is_connected_model:\n validate_model_selection(selected_model)\n\n # Ensure _get_llm() uses the resolved model (e.g. from legacy agent_llm/model_name)\n self.model = selected_model\n llm_model = self._get_llm()\n if llm_model is None:\n msg = \"No language model selected. Please choose a model to proceed.\"\n raise ValueError(msg)\n\n # Get memory data\n self.chat_history = await self.get_memory_data()\n await logger.adebug(f\"Retrieved {len(self.chat_history)} chat history messages\")\n if isinstance(self.chat_history, Message):\n self.chat_history = [self.chat_history]\n\n # Add current date tool if enabled\n if self.add_current_date_tool:\n if not isinstance(self.tools, list): # type: ignore[has-type]\n self.tools = []\n current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n if not isinstance(current_date_tool, StructuredTool):\n msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n raise TypeError(msg)\n # Skip if an externally-connected tool already provides the same name.\n # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n if not any(getattr(t, \"name\", None) == current_date_tool.name for t in self.tools):\n self.tools.append(current_date_tool)\n\n # Add calculator tool if enabled (zero-config arithmetic)\n if getattr(self, \"add_calculator_tool\", False):\n if not isinstance(self.tools, list): # type: ignore[has-type]\n self.tools = []\n calculator_tool = (await CalculatorComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n if not isinstance(calculator_tool, StructuredTool):\n msg = \"CalculatorComponent must be converted to a StructuredTool\"\n raise TypeError(msg)\n # Skip if an externally-connected tool already provides the same name.\n # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n if not any(getattr(t, \"name\", None) == calculator_tool.name for t in self.tools):\n self.tools.append(calculator_tool)\n\n # Set shared callbacks for tracing the tools used by the agent\n self.set_tools_callbacks(self.tools, self._get_shared_callbacks())\n\n return llm_model, self.chat_history, self.tools\n\n def _get_resolved_model_name(self) -> str:\n \"\"\"Best-effort human-readable model name for {model_name} injection.\"\"\"\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n if isinstance(self.model, BaseLanguageModel):\n return type(self.model).__name__\n except ImportError:\n pass\n\n if isinstance(self.model, list) and self.model:\n first = self.model[0]\n if isinstance(first, dict):\n name = first.get(\"name\")\n if isinstance(name, str) and name:\n return name\n\n legacy_model_name = getattr(self, \"model_name\", None)\n if isinstance(legacy_model_name, str) and legacy_model_name:\n return legacy_model_name\n return \"\"\n\n def _inject_dynamic_prompt_values(self, prompt: str | None) -> str | None:\n \"\"\"Replace known env placeholders in the system prompt.\n\n Handles {current_date}, {model_name}, and {optional_user_context} (the\n last one ships with the structured DEFAULT_SYSTEM_PROMPT_TEMPLATE and\n is currently unused at the AgentComponent layer, so it resolves to \"\").\n Uses str.replace (not str.format) so user prompts containing literal\n braces such as JSON examples ({\"key\": 1}) never break the agent.\n \"\"\"\n if not prompt:\n return prompt\n replacements = {\n \"{current_date}\": datetime.now(tz=timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S UTC\"),\n \"{model_name}\": self._get_resolved_model_name(),\n \"{optional_user_context}\": \"\",\n }\n for placeholder, value in replacements.items():\n prompt = prompt.replace(placeholder, value)\n return prompt\n\n async def message_response(self) -> Message:\n try:\n llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n # Set up and run agent\n self.set(\n llm=llm_model,\n tools=self.tools or [],\n chat_history=self.chat_history,\n input_value=self.input_value,\n system_prompt=self._inject_dynamic_prompt_values(self.system_prompt),\n )\n agent = self.create_agent_runnable()\n result = await self.run_agent(agent)\n\n # Store result for potential JSON output\n self._agent_result = result\n\n except (ValueError, TypeError, KeyError) as e:\n await logger.aerror(f\"{type(e).__name__}: {e!s}\")\n raise\n except ExceptionWithMessageError as e:\n await logger.aerror(f\"ExceptionWithMessageError occurred: {e}\")\n raise\n # Avoid catching blind Exception; let truly unexpected exceptions propagate\n except Exception as e:\n await logger.aerror(f\"Unexpected error: {e!s}\")\n raise\n else:\n return result\n\n async def json_response(self) -> Data:\n \"\"\"Produce structured Data via native LLM structured output, with prompt-based fallback.\n\n Native path (no tools, llm has with_structured_output) bypasses the agent loop and\n returns provider-validated JSON. When tools are attached, falls back to running the\n agent with a schema-augmented system prompt and parsing the final message content.\n \"\"\"\n from lfx.components.models_and_agents.structured_output.structured_output_orchestrator import (\n orchestrate_structured_output,\n )\n\n try:\n llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n except (ValueError, TypeError) as exc:\n await logger.aerror(f\"json_response.requirements_failed: {exc}\")\n return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n injected_system_prompt = self._inject_dynamic_prompt_values(getattr(self, \"system_prompt\", \"\") or \"\") or \"\"\n format_instructions = getattr(self, \"format_instructions\", \"\") or \"\"\n output_schema = getattr(self, \"output_schema\", None) or []\n has_tools = bool(self.tools)\n\n async def _run_agent_for_fallback(augmented_prompt: str) -> str:\n self.set(\n llm=llm_model,\n tools=self.tools or [],\n chat_history=self.chat_history,\n input_value=self.input_value,\n system_prompt=augmented_prompt,\n )\n agent_runnable = self.create_agent_runnable()\n with _suppress_send_message(self):\n result = await self.run_agent(agent_runnable)\n return _extract_text_content(result)\n\n try:\n return await orchestrate_structured_output(\n llm=llm_model,\n output_schema=output_schema,\n system_prompt=injected_system_prompt,\n format_instructions=format_instructions,\n input_value=_extract_text_content(self.input_value),\n run_prompt_fallback=_run_agent_for_fallback,\n prefer_native=not has_tools,\n )\n except (\n ExceptionWithMessageError,\n ValueError,\n TypeError,\n NotImplementedError,\n AttributeError,\n ) as exc:\n await logger.aerror(f\"json_response.orchestration_failed: {exc}\")\n return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n async def get_memory_data(self):\n # TODO: This is a temporary fix to avoid message duplication. We should develop a function for this.\n messages = (\n await MemoryComponent(**self.get_base_args())\n .set(\n session_id=self.graph.session_id,\n context_id=self.context_id,\n order=\"Ascending\",\n n_messages=self.n_messages,\n )\n .retrieve_messages()\n )\n return [\n message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n ]\n\n def update_input_types(self, build_config: dotdict) -> dotdict:\n \"\"\"Update input types for all fields in build_config.\"\"\"\n for key, value in build_config.items():\n if isinstance(value, dict):\n if value.get(\"input_types\") is None:\n build_config[key][\"input_types\"] = []\n elif hasattr(value, \"input_types\") and value.input_types is None:\n value.input_types = []\n return build_config\n\n async def update_build_config(\n self,\n build_config: dotdict,\n field_value: list[dict],\n field_name: str | None = None,\n ) -> dotdict:\n # Update model options with caching (for all field changes).\n # The tool-calling constraint lives on the ModelInput's ``filters``\n # field (declared above); ``handle_model_input_update`` reads it\n # and applies the filter to both the dropdown options and the\n # sticky-default re-injection path.\n build_config = handle_model_input_update(\n component=self,\n build_config=dict(build_config),\n field_value=field_value,\n field_name=field_name,\n )\n build_config = dotdict(build_config)\n\n if field_name == \"model\":\n build_config = self.update_input_types(build_config)\n\n # Validate required keys\n default_keys = [\n \"code\",\n \"_type\",\n \"model\",\n \"tools\",\n \"input_value\",\n \"add_current_date_tool\",\n \"add_calculator_tool\",\n \"system_prompt\",\n \"agent_description\",\n \"max_iterations\",\n \"handle_parsing_errors\",\n \"verbose\",\n ]\n missing_keys = [key for key in default_keys if key not in build_config]\n if missing_keys:\n msg = f\"Missing required keys in build_config: {missing_keys}\"\n raise ValueError(msg)\n return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n async def _get_tools(self) -> list[Tool]:\n component_toolkit = get_component_toolkit()\n tools_names = self._build_tools_names()\n agent_description = self.get_tool_description()\n # TODO: Agent Description Depreciated Feature to be removed\n description = f\"{agent_description}{tools_names}\"\n\n tools = component_toolkit(component=self).get_tools(\n tool_name=\"Call_Agent\",\n tool_description=description,\n # here we do not use the shared callbacks as we are exposing the agent as a tool\n callbacks=self.get_langchain_callbacks(),\n )\n if hasattr(self, \"tools_metadata\"):\n tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n\n return tools\n" + "value": "from __future__ import annotations\n\nfrom contextlib import contextmanager\nfrom datetime import datetime, timezone\nfrom typing import TYPE_CHECKING, Any\n\nfrom lfx.components.models_and_agents.memory import MemoryComponent, aget_agent_chat_history\n\nif TYPE_CHECKING:\n from langchain_core.tools import Tool\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.agents.default_system_prompt import DEFAULT_SYSTEM_PROMPT_TEMPLATE\nfrom lfx.base.agents.events import ExceptionWithMessageError\nfrom lfx.base.constants import STREAM_INFO_TEXT\nfrom lfx.base.models.unified_models import (\n get_language_model_options,\n get_llm,\n handle_model_input_update,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.components.agentics.helpers.model_config import validate_model_selection\nfrom lfx.components.helpers import CalculatorComponent, CurrentDateComponent\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.custom.custom_component.component import get_component_toolkit\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, ModelInput, StrInput\nfrom lfx.io import IntInput, MessageTextInput, MultilineInput, Output, SecretStrInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.schema.data import Data\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.schema.table import EditMode\n\n\ndef set_advanced_true(component_input):\n component_input.advanced = True\n return component_input\n\n\ndef _extract_text_content(value) -> str:\n \"\"\"Pull a string payload from a Message-like, AIMessage-like, or string value.\"\"\"\n if isinstance(value, str):\n return value\n text = getattr(value, \"text\", None)\n if isinstance(text, str):\n return text\n content = getattr(value, \"content\", None)\n if isinstance(content, str):\n return content\n return str(value) if value is not None else \"\"\n\n\n@contextmanager\ndef _suppress_send_message(component: Any):\n \"\"\"Temporarily replace component.send_message with a no-op for the duration of the block.\n\n Used during the structured-output prompt fallback: run_agent streams the agent's\n final answer through self.send_message (correct for message_response), but in\n json_response the orchestrator parses that text into structured Data which the\n downstream Chat Output emits — leaving the original emission in place produces a\n duplicate message in the playground. The original method is always restored on exit,\n even when the wrapped call raises.\n \"\"\"\n original = component.send_message\n\n async def _noop(message, *_args, **_kwargs):\n return message\n\n component.send_message = _noop\n try:\n yield\n finally:\n component.send_message = original\n\n\nclass AgentComponent(ToolCallingAgentComponent):\n display_name: str = \"Agent\"\n description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n documentation: str = \"https://docs.langflow.org/agents\"\n icon = \"bot\"\n beta = False\n name = \"Agent\"\n\n memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n inputs = [\n ModelInput(\n name=\"model\",\n display_name=\"Language Model\",\n info=\"Select your model provider\",\n real_time_refresh=True,\n required=True,\n # Agents require tool calling — the filter is honored by\n # ``handle_model_input_update`` so models that can't run with\n # tools never reach the picker (and any saved selection that\n # no longer satisfies the constraint is auto-replaced).\n filters={\"tool_calling\": True},\n ),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"API Key\",\n info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n real_time_refresh=True,\n advanced=True,\n ),\n DropdownInput(\n name=\"base_url_ibm_watsonx\",\n display_name=\"watsonx API Endpoint\",\n info=\"The base URL of the API (IBM watsonx.ai only)\",\n options=IBM_WATSONX_URLS,\n value=IBM_WATSONX_URLS[0],\n combobox=True,\n show=False,\n real_time_refresh=True,\n ),\n StrInput(\n name=\"project_id\",\n display_name=\"watsonx Project ID\",\n info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n show=False,\n required=False,\n ),\n MultilineInput(\n name=\"system_prompt\",\n display_name=\"Agent Instructions\",\n info=(\n \"System Prompt: Initial instructions and context provided to guide the agent's behavior. \"\n \"Supports dynamic placeholders: {current_date}, {model_name}, {optional_user_context}.\"\n ),\n value=DEFAULT_SYSTEM_PROMPT_TEMPLATE,\n advanced=False,\n ),\n MessageTextInput(\n name=\"context_id\",\n display_name=\"Context ID\",\n info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n value=\"\",\n advanced=True,\n ),\n IntInput(\n name=\"n_messages\",\n display_name=\"Number of Chat History Messages\",\n value=100,\n info=\"Number of chat history messages to retrieve.\",\n advanced=True,\n show=True,\n ),\n IntInput(\n name=\"max_tokens\",\n display_name=\"Max Tokens\",\n info=\"Maximum number of tokens to generate. Field name varies by provider.\",\n advanced=True,\n range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n ),\n MultilineInput(\n name=\"format_instructions\",\n display_name=\"Output Format Instructions\",\n info=\"Generic Template for structured output formatting. Valid only with Structured response.\",\n value=(\n \"You are an AI that extracts structured JSON objects from unstructured text. \"\n \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n \"Fill missing or ambiguous values with defaults: null for missing values. \"\n \"Remove exact duplicates but keep variations that have different field values. \"\n \"Always return valid JSON in the expected format, never throw errors. \"\n \"If multiple objects can be extracted, return them all in the structured format.\"\n ),\n advanced=True,\n ),\n TableInput(\n name=\"output_schema\",\n display_name=\"Output Schema\",\n info=(\n \"Schema Validation: Define the structure and data types for structured output. \"\n \"No validation if no output schema.\"\n ),\n advanced=True,\n required=False,\n value=[],\n table_schema=[\n {\n \"name\": \"name\",\n \"display_name\": \"Name\",\n \"type\": \"str\",\n \"description\": \"Specify the name of the output field.\",\n \"default\": \"field\",\n \"edit_mode\": EditMode.INLINE,\n },\n {\n \"name\": \"description\",\n \"display_name\": \"Description\",\n \"type\": \"str\",\n \"description\": \"Describe the purpose of the output field.\",\n \"default\": \"description of field\",\n \"edit_mode\": EditMode.POPOVER,\n },\n {\n \"name\": \"type\",\n \"display_name\": \"Type\",\n \"type\": \"str\",\n \"edit_mode\": EditMode.INLINE,\n \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n \"default\": \"str\",\n },\n {\n \"name\": \"multiple\",\n \"display_name\": \"As List\",\n \"type\": \"boolean\",\n \"description\": \"Set to True if this output field should be a list of the specified type.\",\n \"default\": \"False\",\n \"edit_mode\": EditMode.INLINE,\n },\n ],\n ),\n *LCToolsAgentComponent.get_base_inputs(),\n # removed memory inputs from agent component\n # *memory_inputs,\n BoolInput(\n name=\"stream\",\n display_name=\"Stream\",\n info=STREAM_INFO_TEXT,\n value=False,\n advanced=True,\n ),\n BoolInput(\n name=\"add_current_date_tool\",\n display_name=\"Current Date\",\n advanced=True,\n info=\"If true, will add a tool to the agent that returns the current date.\",\n value=True,\n ),\n BoolInput(\n name=\"add_calculator_tool\",\n display_name=\"Calculator\",\n advanced=True,\n info=(\n \"If true, adds a zero-config arithmetic calculator tool to the agent \"\n \"(safe: only +, -, *, /, ** operators via AST).\"\n ),\n value=True,\n ),\n ]\n outputs = [\n Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n Output(\n name=\"structured_response\",\n display_name=\"Structured Response\",\n method=\"json_response\",\n types=[\"Data\"],\n ),\n ]\n\n def _resolve_selected_model(self):\n \"\"\"Resolve the selected model, including legacy agent_llm/model_name inputs.\"\"\"\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n if isinstance(self.model, BaseLanguageModel):\n return self.model\n except ImportError:\n pass\n\n if isinstance(self.model, list) and self.model:\n return self.model\n\n legacy_provider = getattr(self, \"agent_llm\", None)\n legacy_model_name = getattr(self, \"model_name\", None)\n if not legacy_provider or not legacy_model_name:\n return self.model\n\n options = get_language_model_options(user_id=self.user_id)\n for option in options:\n if option.get(\"provider\") == legacy_provider and option.get(\"name\") == legacy_model_name:\n return [option]\n\n return [\n {\n \"name\": legacy_model_name,\n \"provider\": legacy_provider,\n \"metadata\": {},\n }\n ]\n\n def _get_max_tokens_value(self):\n \"\"\"Return the user-supplied max_tokens or None when unset/zero.\"\"\"\n val = getattr(self, \"max_tokens\", None)\n if val in {\"\", 0}:\n return None\n return val\n\n def _get_llm(self):\n \"\"\"Override parent to include max_tokens from the Agent's input field.\"\"\"\n return get_llm(\n model=self.model,\n user_id=self.user_id,\n api_key=getattr(self, \"api_key\", None),\n stream=bool(getattr(self, \"stream\", False)),\n max_tokens=self._get_max_tokens_value(),\n watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n watsonx_project_id=getattr(self, \"project_id\", None),\n )\n\n async def get_agent_requirements(self):\n \"\"\"Get the agent requirements for the agent.\"\"\"\n from langchain_core.tools import StructuredTool\n\n selected_model = self._resolve_selected_model()\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n is_connected_model = isinstance(selected_model, BaseLanguageModel)\n except ImportError:\n is_connected_model = False\n\n if not is_connected_model:\n validate_model_selection(selected_model)\n\n # Ensure _get_llm() uses the resolved model (e.g. from legacy agent_llm/model_name)\n self.model = selected_model\n llm_model = self._get_llm()\n if llm_model is None:\n msg = \"No language model selected. Please choose a model to proceed.\"\n raise ValueError(msg)\n\n # Get memory data\n self.chat_history = await self.get_memory_data()\n await logger.adebug(f\"Retrieved {len(self.chat_history)} chat history messages\")\n if isinstance(self.chat_history, Message):\n self.chat_history = [self.chat_history]\n\n # Add current date tool if enabled\n if self.add_current_date_tool:\n if not isinstance(self.tools, list): # type: ignore[has-type]\n self.tools = []\n current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n if not isinstance(current_date_tool, StructuredTool):\n msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n raise TypeError(msg)\n # Skip if an externally-connected tool already provides the same name.\n # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n if not any(getattr(t, \"name\", None) == current_date_tool.name for t in self.tools):\n self.tools.append(current_date_tool)\n\n # Add calculator tool if enabled (zero-config arithmetic)\n if getattr(self, \"add_calculator_tool\", False):\n if not isinstance(self.tools, list): # type: ignore[has-type]\n self.tools = []\n calculator_tool = (await CalculatorComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n if not isinstance(calculator_tool, StructuredTool):\n msg = \"CalculatorComponent must be converted to a StructuredTool\"\n raise TypeError(msg)\n # Skip if an externally-connected tool already provides the same name.\n # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n if not any(getattr(t, \"name\", None) == calculator_tool.name for t in self.tools):\n self.tools.append(calculator_tool)\n\n # Set shared callbacks for tracing the tools used by the agent\n self.set_tools_callbacks(self.tools, self._get_shared_callbacks())\n\n return llm_model, self.chat_history, self.tools\n\n def _get_resolved_model_name(self) -> str:\n \"\"\"Best-effort human-readable model name for {model_name} injection.\"\"\"\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n if isinstance(self.model, BaseLanguageModel):\n return type(self.model).__name__\n except ImportError:\n pass\n\n if isinstance(self.model, list) and self.model:\n first = self.model[0]\n if isinstance(first, dict):\n name = first.get(\"name\")\n if isinstance(name, str) and name:\n return name\n\n legacy_model_name = getattr(self, \"model_name\", None)\n if isinstance(legacy_model_name, str) and legacy_model_name:\n return legacy_model_name\n return \"\"\n\n def _inject_dynamic_prompt_values(self, prompt: str | None) -> str | None:\n \"\"\"Replace known env placeholders in the system prompt.\n\n Handles {current_date}, {model_name}, and {optional_user_context} (the\n last one ships with the structured DEFAULT_SYSTEM_PROMPT_TEMPLATE and\n is currently unused at the AgentComponent layer, so it resolves to \"\").\n Uses str.replace (not str.format) so user prompts containing literal\n braces such as JSON examples ({\"key\": 1}) never break the agent.\n \"\"\"\n if not prompt:\n return prompt\n replacements = {\n \"{current_date}\": datetime.now(tz=timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S UTC\"),\n \"{model_name}\": self._get_resolved_model_name(),\n \"{optional_user_context}\": \"\",\n }\n for placeholder, value in replacements.items():\n prompt = prompt.replace(placeholder, value)\n return prompt\n\n async def message_response(self) -> Message:\n try:\n llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n # Set up and run agent\n self.set(\n llm=llm_model,\n tools=self.tools or [],\n chat_history=self.chat_history,\n input_value=self.input_value,\n system_prompt=self._inject_dynamic_prompt_values(self.system_prompt),\n )\n agent = self.create_agent_runnable()\n result = await self.run_agent(agent)\n\n # Store result for potential JSON output\n self._agent_result = result\n\n except (ValueError, TypeError, KeyError) as e:\n await logger.aerror(f\"{type(e).__name__}: {e!s}\")\n raise\n except ExceptionWithMessageError as e:\n await logger.aerror(f\"ExceptionWithMessageError occurred: {e}\")\n raise\n # Avoid catching blind Exception; let truly unexpected exceptions propagate\n except Exception as e:\n await logger.aerror(f\"Unexpected error: {e!s}\")\n raise\n else:\n return result\n\n async def json_response(self) -> Data:\n \"\"\"Produce structured Data via native LLM structured output, with prompt-based fallback.\n\n Native path (no tools, llm has with_structured_output) bypasses the agent loop and\n returns provider-validated JSON. When tools are attached, falls back to running the\n agent with a schema-augmented system prompt and parsing the final message content.\n \"\"\"\n from lfx.components.models_and_agents.structured_output.structured_output_orchestrator import (\n orchestrate_structured_output,\n )\n\n try:\n llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n except (ValueError, TypeError) as exc:\n await logger.aerror(f\"json_response.requirements_failed: {exc}\")\n return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n injected_system_prompt = self._inject_dynamic_prompt_values(getattr(self, \"system_prompt\", \"\") or \"\") or \"\"\n format_instructions = getattr(self, \"format_instructions\", \"\") or \"\"\n output_schema = getattr(self, \"output_schema\", None) or []\n has_tools = bool(self.tools)\n\n async def _run_agent_for_fallback(augmented_prompt: str) -> str:\n self.set(\n llm=llm_model,\n tools=self.tools or [],\n chat_history=self.chat_history,\n input_value=self.input_value,\n system_prompt=augmented_prompt,\n )\n agent_runnable = self.create_agent_runnable()\n with _suppress_send_message(self):\n result = await self.run_agent(agent_runnable)\n return _extract_text_content(result)\n\n try:\n return await orchestrate_structured_output(\n llm=llm_model,\n output_schema=output_schema,\n system_prompt=injected_system_prompt,\n format_instructions=format_instructions,\n input_value=_extract_text_content(self.input_value),\n run_prompt_fallback=_run_agent_for_fallback,\n prefer_native=not has_tools,\n )\n except (\n ExceptionWithMessageError,\n ValueError,\n TypeError,\n NotImplementedError,\n AttributeError,\n ) as exc:\n await logger.aerror(f\"json_response.orchestration_failed: {exc}\")\n return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n async def get_memory_data(self):\n # Scope by flow_id so default playground session names (e.g. \"New Session 0\")\n # cannot leak chat history across unrelated flows. See issue #13059.\n # The helper also returns [] when n_messages == 0, preserving the\n # explicit \"memory disabled\" contract from MemoryComponent.retrieve_messages.\n messages = await aget_agent_chat_history(\n session_id=self.graph.session_id,\n flow_id=getattr(self.graph, \"flow_id\", None),\n context_id=self.context_id,\n n_messages=self.n_messages,\n )\n return [\n message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n ]\n\n def update_input_types(self, build_config: dotdict) -> dotdict:\n \"\"\"Update input types for all fields in build_config.\"\"\"\n for key, value in build_config.items():\n if isinstance(value, dict):\n if value.get(\"input_types\") is None:\n build_config[key][\"input_types\"] = []\n elif hasattr(value, \"input_types\") and value.input_types is None:\n value.input_types = []\n return build_config\n\n async def update_build_config(\n self,\n build_config: dotdict,\n field_value: list[dict],\n field_name: str | None = None,\n ) -> dotdict:\n # Update model options with caching (for all field changes).\n # The tool-calling constraint lives on the ModelInput's ``filters``\n # field (declared above); ``handle_model_input_update`` reads it\n # and applies the filter to both the dropdown options and the\n # sticky-default re-injection path.\n build_config = handle_model_input_update(\n component=self,\n build_config=dict(build_config),\n field_value=field_value,\n field_name=field_name,\n )\n build_config = dotdict(build_config)\n\n if field_name == \"model\":\n build_config = self.update_input_types(build_config)\n\n # Validate required keys\n default_keys = [\n \"code\",\n \"_type\",\n \"model\",\n \"tools\",\n \"input_value\",\n \"add_current_date_tool\",\n \"add_calculator_tool\",\n \"system_prompt\",\n \"agent_description\",\n \"max_iterations\",\n \"handle_parsing_errors\",\n \"verbose\",\n ]\n missing_keys = [key for key in default_keys if key not in build_config]\n if missing_keys:\n msg = f\"Missing required keys in build_config: {missing_keys}\"\n raise ValueError(msg)\n return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n async def _get_tools(self) -> list[Tool]:\n component_toolkit = get_component_toolkit()\n tools_names = self._build_tools_names()\n agent_description = self.get_tool_description()\n # TODO: Agent Description Depreciated Feature to be removed\n description = f\"{agent_description}{tools_names}\"\n\n tools = component_toolkit(component=self).get_tools(\n tool_name=\"Call_Agent\",\n tool_description=description,\n # here we do not use the shared callbacks as we are exposing the agent as a tool\n callbacks=self.get_langchain_callbacks(),\n )\n if hasattr(self, \"tools_metadata\"):\n tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n\n return tools\n" }, "context_id": { "_input_type": "MessageTextInput", @@ -95251,7 +95251,7 @@ "icon": "message-square-more", "legacy": false, "metadata": { - "code_hash": "7608453efb4e", + "code_hash": "5619cf9008d5", "dependencies": { "dependencies": [ { @@ -95314,7 +95314,7 @@ "show": true, "title_case": false, "type": "code", - "value": "from typing import Any, cast\n\nfrom lfx.custom.custom_component.component import Component\nfrom lfx.helpers.data import data_to_text\nfrom lfx.inputs.inputs import DropdownInput, HandleInput, IntInput, MessageTextInput, MultilineInput, TabInput\nfrom lfx.memory import aget_messages, astore_message\nfrom lfx.schema.data import Data\nfrom lfx.schema.dataframe import DataFrame\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.template.field.base import Output\nfrom lfx.utils.component_utils import set_current_fields, set_field_display\nfrom lfx.utils.constants import MESSAGE_SENDER_AI, MESSAGE_SENDER_NAME_AI, MESSAGE_SENDER_USER\n\n\nclass MemoryComponent(Component):\n display_name = \"Message History\"\n description = \"Stores or retrieves stored chat messages from Langflow tables or an external memory.\"\n documentation: str = \"https://docs.langflow.org/message-history\"\n icon = \"message-square-more\"\n name = \"Memory\"\n default_keys = [\"mode\", \"memory\", \"session_id\", \"context_id\"]\n mode_config = {\n \"Store\": [\"message\", \"memory\", \"sender\", \"sender_name\", \"session_id\", \"context_id\"],\n \"Retrieve\": [\"n_messages\", \"order\", \"template\", \"memory\", \"session_id\", \"context_id\"],\n }\n\n inputs = [\n TabInput(\n name=\"mode\",\n display_name=\"Mode\",\n options=[\"Retrieve\", \"Store\"],\n value=\"Retrieve\",\n info=\"Operation mode: Store messages or Retrieve messages.\",\n real_time_refresh=True,\n ),\n MessageTextInput(\n name=\"message\",\n display_name=\"Message\",\n info=\"The chat message to be stored.\",\n tool_mode=True,\n dynamic=True,\n show=False,\n ),\n HandleInput(\n name=\"memory\",\n display_name=\"External Memory\",\n input_types=[\"Memory\"],\n info=\"Retrieve messages from an external memory. If empty, it will use the Langflow tables.\",\n advanced=True,\n ),\n DropdownInput(\n name=\"sender_type\",\n display_name=\"Sender Type\",\n options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER, \"Machine and User\"],\n value=\"Machine and User\",\n info=\"Filter by sender type.\",\n advanced=True,\n ),\n MessageTextInput(\n name=\"sender\",\n display_name=\"Sender\",\n info=\"The sender of the message. Might be Machine or User. \"\n \"If empty, the current sender parameter will be used.\",\n advanced=True,\n ),\n MessageTextInput(\n name=\"sender_name\",\n display_name=\"Sender Name\",\n info=\"Filter by sender name.\",\n advanced=True,\n show=False,\n ),\n IntInput(\n name=\"n_messages\",\n display_name=\"Number of Messages\",\n value=100,\n info=\"Number of messages to retrieve.\",\n advanced=True,\n show=True,\n ),\n MessageTextInput(\n name=\"session_id\",\n display_name=\"Session ID\",\n info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n value=\"\",\n advanced=True,\n ),\n MessageTextInput(\n name=\"context_id\",\n display_name=\"Context ID\",\n info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n value=\"\",\n advanced=True,\n ),\n DropdownInput(\n name=\"order\",\n display_name=\"Order\",\n options=[\"Ascending\", \"Descending\"],\n value=\"Ascending\",\n info=\"Order of the messages.\",\n advanced=True,\n tool_mode=True,\n required=True,\n ),\n MultilineInput(\n name=\"template\",\n display_name=\"Template\",\n info=\"The template to use for formatting the data. \"\n \"It can contain the keys {text}, {sender} or any other key in the message data.\",\n value=\"{sender_name}: {text}\",\n advanced=True,\n show=False,\n ),\n ]\n\n outputs = [\n Output(\n display_name=\"Messages\",\n name=\"messages_text\",\n method=\"retrieve_messages_as_text\",\n types=[\"Message\"],\n selected=\"Message\",\n dynamic=True,\n ),\n Output(\n display_name=\"Table\",\n name=\"dataframe\",\n method=\"retrieve_messages_dataframe\",\n types=[\"Table\"],\n selected=\"Table\",\n dynamic=True,\n ),\n ]\n\n def update_outputs(self, frontend_node: dict, field_name: str, field_value: Any) -> dict:\n \"\"\"Dynamically show only the relevant output based on the selected output type.\"\"\"\n if field_name == \"mode\":\n # Start with empty outputs\n frontend_node[\"outputs\"] = []\n if field_value == \"Store\":\n frontend_node[\"outputs\"] = [\n Output(\n display_name=\"Stored Messages\",\n name=\"stored_messages\",\n method=\"store_message\",\n types=[\"Message\"],\n selected=\"Message\",\n hidden=True,\n dynamic=True,\n )\n ]\n if field_value == \"Retrieve\":\n frontend_node[\"outputs\"] = [\n Output(\n display_name=\"Messages\",\n name=\"messages_text\",\n method=\"retrieve_messages_as_text\",\n types=[\"Message\"],\n selected=\"Message\",\n dynamic=True,\n ),\n Output(\n display_name=\"Table\",\n name=\"dataframe\",\n method=\"retrieve_messages_dataframe\",\n types=[\"Table\"],\n selected=\"Table\",\n dynamic=True,\n ),\n ]\n return frontend_node\n\n async def store_message(self) -> Message:\n message = Message(text=self.message) if isinstance(self.message, str) else self.message\n\n message.context_id = self.context_id or message.context_id\n message.session_id = self.session_id or message.session_id\n message.sender = self.sender or message.sender or MESSAGE_SENDER_AI\n message.sender_name = self.sender_name or message.sender_name or MESSAGE_SENDER_NAME_AI\n\n stored_messages: list[Message] = []\n\n if self.memory:\n self.memory.context_id = message.context_id\n self.memory.session_id = message.session_id\n lc_message = message.to_lc_message()\n await self.memory.aadd_messages([lc_message])\n\n stored_messages = await self.memory.aget_messages() or []\n\n stored_messages = [Message.from_lc_message(m) for m in stored_messages] if stored_messages else []\n\n if message.sender:\n stored_messages = [m for m in stored_messages if m.sender == message.sender]\n else:\n await astore_message(message, flow_id=self.graph.flow_id)\n stored_messages = (\n await aget_messages(\n session_id=message.session_id,\n context_id=message.context_id,\n sender_name=message.sender_name,\n sender=message.sender,\n )\n or []\n )\n\n if not stored_messages:\n msg = \"No messages were stored. Please ensure that the session ID and sender are properly set.\"\n raise ValueError(msg)\n\n stored_message = stored_messages[0]\n self.status = stored_message\n return stored_message\n\n async def retrieve_messages(self) -> Data:\n sender_type = self.sender_type\n sender_name = self.sender_name\n session_id = self.session_id\n context_id = self.context_id\n n_messages = self.n_messages\n order = \"DESC\" if self.order == \"Descending\" else \"ASC\"\n\n if sender_type == \"Machine and User\":\n sender_type = None\n\n if self.memory and not hasattr(self.memory, \"aget_messages\"):\n memory_name = type(self.memory).__name__\n err_msg = f\"External Memory object ({memory_name}) must have 'aget_messages' method.\"\n raise AttributeError(err_msg)\n # Check if n_messages is None or 0\n if n_messages == 0:\n stored = []\n elif self.memory:\n # override session_id\n self.memory.session_id = session_id\n self.memory.context_id = context_id\n\n stored = await self.memory.aget_messages()\n # langchain memories are supposed to return messages in ascending order\n\n if n_messages:\n stored = stored[-n_messages:] # Get last N messages first\n\n if order == \"DESC\":\n stored = stored[::-1] # Then reverse if needed\n\n stored = [Message.from_lc_message(m) for m in stored]\n if sender_type:\n expected_type = MESSAGE_SENDER_AI if sender_type == MESSAGE_SENDER_AI else MESSAGE_SENDER_USER\n stored = [m for m in stored if m.type == expected_type]\n else:\n # For internal memory, we always fetch the last N messages by ordering by DESC\n stored = await aget_messages(\n sender=sender_type,\n sender_name=sender_name,\n session_id=session_id,\n context_id=context_id,\n limit=10000,\n order=order,\n )\n if n_messages:\n stored = stored[-n_messages:] # Get last N messages\n\n # self.status = stored\n return cast(\"Data\", stored)\n\n async def retrieve_messages_as_text(self) -> Message:\n stored_text = data_to_text(self.template, await self.retrieve_messages())\n # self.status = stored_text\n return Message(text=stored_text)\n\n async def retrieve_messages_dataframe(self) -> DataFrame:\n \"\"\"Convert the retrieved messages into a DataFrame.\n\n Returns:\n DataFrame: A DataFrame containing the message data.\n \"\"\"\n messages = await self.retrieve_messages()\n return DataFrame(messages)\n\n def update_build_config(\n self,\n build_config: dotdict,\n field_value: Any, # noqa: ARG002\n field_name: str | None = None, # noqa: ARG002\n ) -> dotdict:\n return set_current_fields(\n build_config=build_config,\n action_fields=self.mode_config,\n selected_action=build_config[\"mode\"][\"value\"],\n default_fields=self.default_keys,\n func=set_field_display,\n )\n" + "value": "from typing import Any, cast\nfrom uuid import UUID\n\nfrom lfx.custom.custom_component.component import Component\nfrom lfx.helpers.data import data_to_text\nfrom lfx.inputs.inputs import DropdownInput, HandleInput, IntInput, MessageTextInput, MultilineInput, TabInput\nfrom lfx.log.logger import logger\nfrom lfx.memory import aget_messages, astore_message\nfrom lfx.schema.data import Data\nfrom lfx.schema.dataframe import DataFrame\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.template.field.base import Output\nfrom lfx.utils.component_utils import set_current_fields, set_field_display\nfrom lfx.utils.constants import MESSAGE_SENDER_AI, MESSAGE_SENDER_NAME_AI, MESSAGE_SENDER_USER\n\n# Cap on rows we will pull from the DB when the caller has not supplied an\n# explicit ``n_messages`` limit. This is a guardrail against unbounded fetches\n# (DB pressure, memory) on sessions that accumulate huge histories. The\n# trade-off is that sessions with more rows than this cap will not surface\n# their oldest messages when no ``n_messages`` is set. See ``aget_messages``\n# usage sites below for how this interacts with ordering.\nMAX_CHAT_HISTORY_FETCH_LIMIT = 10_000\n\n\ndef _coerce_flow_id_to_uuid(flow_id: str | UUID | None) -> UUID | None:\n \"\"\"Coerce a graph flow_id (typically str) to UUID for DB filtering.\n\n Returns ``None`` when ``flow_id`` is missing or cannot be parsed. The\n caller then falls back to the previous **unscoped** retrieval, which\n re-introduces the cross-flow leak that motivated PR #13087. We emit a\n structured ``error`` log on that path (rather than ``warning``) so\n observability can alert on it — see issue #13059 / PR #13087.\n \"\"\"\n if flow_id is None or flow_id == \"\":\n return None\n if isinstance(flow_id, UUID):\n return flow_id\n try:\n return UUID(str(flow_id))\n except (ValueError, TypeError, AttributeError):\n # Loud, structured signal — this path means chat history is being\n # served without flow-scoping, which is the privacy bug PR #13087\n # was created to close. Anything matching this event is a candidate\n # for an observability alert.\n logger.error(\n \"memory_flow_id_unscoped: flow_id %r is not a valid UUID; \"\n \"chat history will NOT be scoped by flow_id. This re-enables \"\n \"cross-flow leakage (issue #13059) for this request.\",\n flow_id,\n extra={\"event\": \"memory_flow_id_unscoped\", \"flow_id_repr\": repr(flow_id)},\n )\n return None\n\n\ndef _safe_graph_flow_id(component: Component) -> str | UUID | None:\n \"\"\"Best-effort lookup of the component's graph flow_id.\n\n ``Component.graph`` is a property that reaches into ``self._vertex.graph``;\n when a MemoryComponent is constructed ad-hoc (e.g. by the Agent component\n via ``MemoryComponent(**self.get_base_args())``), ``_vertex`` is ``None`` and\n accessing the property raises ``AttributeError``. Swallow that here so\n retrieval falls back to the previous unscoped behavior rather than crashing.\n \"\"\"\n try:\n graph = component.graph\n except AttributeError:\n return None\n return getattr(graph, \"flow_id\", None)\n\n\nasync def aget_agent_chat_history(\n *,\n session_id: str | UUID | None,\n flow_id: str | UUID | None,\n context_id: str | None = None,\n n_messages: int | None = None,\n) -> list[Message]:\n \"\"\"Fetch chat history for an agent, scoped to a single flow.\n\n Centralizes the contract previously implemented by\n ``MemoryComponent.retrieve_messages`` for agent callers:\n\n * Returns ``[]`` when ``n_messages == 0`` (memory explicitly disabled).\n Without this short-circuit, the bounded query would still execute and\n the caller's ``messages[-0:]`` would return everything.\n * Scopes by ``flow_id`` (coerced to ``UUID``) so default playground\n session names cannot leak history across flows (issue #13059).\n * Returns up to ``n_messages`` most recent messages in ascending order.\n Queries in ``DESC`` order with ``limit=n_messages`` so sessions with\n more than ``MAX_CHAT_HISTORY_FETCH_LIMIT`` rows still see the genuine\n most-recent slice, not the chronological first window.\n \"\"\"\n if n_messages == 0:\n return []\n fetch_limit = n_messages if n_messages else MAX_CHAT_HISTORY_FETCH_LIMIT\n messages = await aget_messages(\n session_id=session_id,\n context_id=context_id,\n flow_id=_coerce_flow_id_to_uuid(flow_id),\n limit=fetch_limit,\n order=\"DESC\",\n )\n if not n_messages and len(messages) == MAX_CHAT_HISTORY_FETCH_LIMIT:\n # We hit the unbounded-fetch ceiling. The caller will likely see\n # stale \"most-recent\" history. Flag this so on-call has something\n # to grep when a user reports forgotten context.\n logger.warning(\n \"memory_chat_history_limit_reached: hit MAX_CHAT_HISTORY_FETCH_LIMIT=%d \"\n \"without an explicit n_messages; older messages were not returned.\",\n MAX_CHAT_HISTORY_FETCH_LIMIT,\n extra={\"event\": \"memory_chat_history_limit_reached\"},\n )\n # ``aget_messages`` returned DESC; reverse to ASC for the agent's prompt.\n return list(reversed(messages))\n\n\nclass MemoryComponent(Component):\n display_name = \"Message History\"\n description = \"Stores or retrieves stored chat messages from Langflow tables or an external memory.\"\n documentation: str = \"https://docs.langflow.org/message-history\"\n icon = \"message-square-more\"\n name = \"Memory\"\n default_keys = [\"mode\", \"memory\", \"session_id\", \"context_id\"]\n mode_config = {\n \"Store\": [\"message\", \"memory\", \"sender\", \"sender_name\", \"session_id\", \"context_id\"],\n \"Retrieve\": [\"n_messages\", \"order\", \"template\", \"memory\", \"session_id\", \"context_id\"],\n }\n\n inputs = [\n TabInput(\n name=\"mode\",\n display_name=\"Mode\",\n options=[\"Retrieve\", \"Store\"],\n value=\"Retrieve\",\n info=\"Operation mode: Store messages or Retrieve messages.\",\n real_time_refresh=True,\n ),\n MessageTextInput(\n name=\"message\",\n display_name=\"Message\",\n info=\"The chat message to be stored.\",\n tool_mode=True,\n dynamic=True,\n show=False,\n ),\n HandleInput(\n name=\"memory\",\n display_name=\"External Memory\",\n input_types=[\"Memory\"],\n info=\"Retrieve messages from an external memory. If empty, it will use the Langflow tables.\",\n advanced=True,\n ),\n DropdownInput(\n name=\"sender_type\",\n display_name=\"Sender Type\",\n options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER, \"Machine and User\"],\n value=\"Machine and User\",\n info=\"Filter by sender type.\",\n advanced=True,\n ),\n MessageTextInput(\n name=\"sender\",\n display_name=\"Sender\",\n info=\"The sender of the message. Might be Machine or User. \"\n \"If empty, the current sender parameter will be used.\",\n advanced=True,\n ),\n MessageTextInput(\n name=\"sender_name\",\n display_name=\"Sender Name\",\n info=\"Filter by sender name.\",\n advanced=True,\n show=False,\n ),\n IntInput(\n name=\"n_messages\",\n display_name=\"Number of Messages\",\n value=100,\n info=\"Number of messages to retrieve.\",\n advanced=True,\n show=True,\n ),\n MessageTextInput(\n name=\"session_id\",\n display_name=\"Session ID\",\n info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n value=\"\",\n advanced=True,\n ),\n MessageTextInput(\n name=\"context_id\",\n display_name=\"Context ID\",\n info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n value=\"\",\n advanced=True,\n ),\n DropdownInput(\n name=\"order\",\n display_name=\"Order\",\n options=[\"Ascending\", \"Descending\"],\n value=\"Ascending\",\n info=\"Order of the messages.\",\n advanced=True,\n tool_mode=True,\n required=True,\n ),\n MultilineInput(\n name=\"template\",\n display_name=\"Template\",\n info=\"The template to use for formatting the data. \"\n \"It can contain the keys {text}, {sender} or any other key in the message data.\",\n value=\"{sender_name}: {text}\",\n advanced=True,\n show=False,\n ),\n ]\n\n outputs = [\n Output(\n display_name=\"Messages\",\n name=\"messages_text\",\n method=\"retrieve_messages_as_text\",\n types=[\"Message\"],\n selected=\"Message\",\n dynamic=True,\n ),\n Output(\n display_name=\"Table\",\n name=\"dataframe\",\n method=\"retrieve_messages_dataframe\",\n types=[\"Table\"],\n selected=\"Table\",\n dynamic=True,\n ),\n ]\n\n def update_outputs(self, frontend_node: dict, field_name: str, field_value: Any) -> dict:\n \"\"\"Dynamically show only the relevant output based on the selected output type.\"\"\"\n if field_name == \"mode\":\n # Start with empty outputs\n frontend_node[\"outputs\"] = []\n if field_value == \"Store\":\n frontend_node[\"outputs\"] = [\n Output(\n display_name=\"Stored Messages\",\n name=\"stored_messages\",\n method=\"store_message\",\n types=[\"Message\"],\n selected=\"Message\",\n hidden=True,\n dynamic=True,\n )\n ]\n if field_value == \"Retrieve\":\n frontend_node[\"outputs\"] = [\n Output(\n display_name=\"Messages\",\n name=\"messages_text\",\n method=\"retrieve_messages_as_text\",\n types=[\"Message\"],\n selected=\"Message\",\n dynamic=True,\n ),\n Output(\n display_name=\"Table\",\n name=\"dataframe\",\n method=\"retrieve_messages_dataframe\",\n types=[\"Table\"],\n selected=\"Table\",\n dynamic=True,\n ),\n ]\n return frontend_node\n\n async def store_message(self) -> Message:\n message = Message(text=self.message) if isinstance(self.message, str) else self.message\n\n message.context_id = self.context_id or message.context_id\n message.session_id = self.session_id or message.session_id\n message.sender = self.sender or message.sender or MESSAGE_SENDER_AI\n message.sender_name = self.sender_name or message.sender_name or MESSAGE_SENDER_NAME_AI\n\n stored_messages: list[Message] = []\n\n if self.memory:\n self.memory.context_id = message.context_id\n self.memory.session_id = message.session_id\n lc_message = message.to_lc_message()\n await self.memory.aadd_messages([lc_message])\n\n stored_messages = await self.memory.aget_messages() or []\n\n stored_messages = [Message.from_lc_message(m) for m in stored_messages] if stored_messages else []\n\n if message.sender:\n stored_messages = [m for m in stored_messages if m.sender == message.sender]\n else:\n # Single coerced scope used for both the write and the read-back,\n # so a missing/ad-hoc ``_vertex`` cannot crash the write half while\n # the read half degrades gracefully. See PR #13087 review I1.\n flow_id_scope = _coerce_flow_id_to_uuid(_safe_graph_flow_id(self))\n await astore_message(message, flow_id=flow_id_scope)\n stored_messages = (\n await aget_messages(\n session_id=message.session_id,\n context_id=message.context_id,\n sender_name=message.sender_name,\n sender=message.sender,\n flow_id=flow_id_scope,\n )\n or []\n )\n\n if not stored_messages:\n msg = \"No messages were stored. Please ensure that the session ID and sender are properly set.\"\n raise ValueError(msg)\n\n stored_message = stored_messages[0]\n self.status = stored_message\n return stored_message\n\n async def retrieve_messages(self) -> Data:\n sender_type = self.sender_type\n sender_name = self.sender_name\n session_id = self.session_id\n context_id = self.context_id\n n_messages = self.n_messages\n order = \"DESC\" if self.order == \"Descending\" else \"ASC\"\n\n if sender_type == \"Machine and User\":\n sender_type = None\n\n if self.memory and not hasattr(self.memory, \"aget_messages\"):\n memory_name = type(self.memory).__name__\n err_msg = f\"External Memory object ({memory_name}) must have 'aget_messages' method.\"\n raise AttributeError(err_msg)\n # Check if n_messages is None or 0\n if n_messages == 0:\n stored = []\n elif self.memory:\n # override session_id\n self.memory.session_id = session_id\n self.memory.context_id = context_id\n\n stored = await self.memory.aget_messages()\n # langchain memories are supposed to return messages in ascending order\n\n if n_messages:\n stored = stored[-n_messages:] # Get last N messages first\n\n if order == \"DESC\":\n stored = stored[::-1] # Then reverse if needed\n\n stored = [Message.from_lc_message(m) for m in stored]\n if sender_type:\n expected_type = MESSAGE_SENDER_AI if sender_type == MESSAGE_SENDER_AI else MESSAGE_SENDER_USER\n stored = [m for m in stored if m.type == expected_type]\n else:\n # For internal memory, fetch the last N messages by ordering DESC at the\n # DB layer so sessions with more than ``MAX_CHAT_HISTORY_FETCH_LIMIT``\n # rows still return the genuine most-recent slice rather than the\n # chronological first window. See PR #13087 review I2.\n #\n # Scope by flow_id so default session names (e.g. \"New Session 0\") do not\n # leak chat history across unrelated flows. See issue #13059.\n flow_id_scope = _coerce_flow_id_to_uuid(_safe_graph_flow_id(self))\n fetch_limit = n_messages if n_messages else MAX_CHAT_HISTORY_FETCH_LIMIT\n stored = await aget_messages(\n sender=sender_type,\n sender_name=sender_name,\n session_id=session_id,\n context_id=context_id,\n flow_id=flow_id_scope,\n limit=fetch_limit,\n order=\"DESC\",\n )\n if not n_messages and len(stored) == MAX_CHAT_HISTORY_FETCH_LIMIT:\n logger.warning(\n \"memory_chat_history_limit_reached: hit MAX_CHAT_HISTORY_FETCH_LIMIT=%d \"\n \"without an explicit n_messages; older messages were not returned.\",\n MAX_CHAT_HISTORY_FETCH_LIMIT,\n extra={\"event\": \"memory_chat_history_limit_reached\"},\n )\n # Honor the user-selected order: we fetched DESC, reverse if ASC requested.\n if order == \"ASC\":\n stored = list(reversed(stored))\n\n # self.status = stored\n return cast(\"Data\", stored)\n\n async def retrieve_messages_as_text(self) -> Message:\n stored_text = data_to_text(self.template, await self.retrieve_messages())\n # self.status = stored_text\n return Message(text=stored_text)\n\n async def retrieve_messages_dataframe(self) -> DataFrame:\n \"\"\"Convert the retrieved messages into a DataFrame.\n\n Returns:\n DataFrame: A DataFrame containing the message data.\n \"\"\"\n messages = await self.retrieve_messages()\n return DataFrame(messages)\n\n def update_build_config(\n self,\n build_config: dotdict,\n field_value: Any, # noqa: ARG002\n field_name: str | None = None, # noqa: ARG002\n ) -> dotdict:\n return set_current_fields(\n build_config=build_config,\n action_fields=self.mode_config,\n selected_action=build_config[\"mode\"][\"value\"],\n default_fields=self.default_keys,\n func=set_field_display,\n )\n" }, "context_id": { "_input_type": "MessageTextInput", @@ -121244,6 +121244,6 @@ "num_components": 362, "num_modules": 97 }, - "sha256": "ce5711eddb435f48fe454fa8a6a8fa596a3e16ad510e7d3f40d11f08d86f625e", + "sha256": "e1954247754f49271d30599efc239751dab4402e6d60af58b3e1c8289fc34863", "version": "0.5.0" } diff --git a/src/lfx/src/lfx/components/cuga/cuga_agent.py b/src/lfx/src/lfx/components/cuga/cuga_agent.py index 41a11bf53b..33347af212 100644 --- a/src/lfx/src/lfx/components/cuga/cuga_agent.py +++ b/src/lfx/src/lfx/components/cuga/cuga_agent.py @@ -20,7 +20,7 @@ from lfx.base.models.model_input_constants import ( from lfx.base.models.model_utils import get_model_name from lfx.components.helpers import CurrentDateComponent from lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent -from lfx.components.models_and_agents.memory import MemoryComponent +from lfx.components.models_and_agents.memory import MemoryComponent, aget_agent_chat_history from lfx.custom.custom_component.component import _get_component_toolkit from lfx.custom.utils import update_component_build_config from lfx.field_typing import Tool @@ -488,10 +488,13 @@ class CugaComponent(ToolCallingAgentComponent): logger.debug(f"[CUGA] input_value type: {type(self.input_value)}") logger.debug(f"[CUGA] input_value id: {getattr(self.input_value, 'id', None)}") - messages = ( - await MemoryComponent(**self.get_base_args()) - .set(session_id=str(self.graph.session_id), order="Ascending", n_messages=self.n_messages) - .retrieve_messages() + # Scope by flow_id (issue #13059): the ad-hoc MemoryComponent used previously + # had no _vertex, so it could not see the running flow's flow_id and emitted + # an unscoped query. The helper also honors n_messages == 0 as "disabled". + messages = await aget_agent_chat_history( + session_id=str(self.graph.session_id), + flow_id=getattr(self.graph, "flow_id", None), + n_messages=self.n_messages, ) logger.debug(f"[CUGA] Retrieved {len(messages)} messages from memory") return [ diff --git a/src/lfx/src/lfx/components/models_and_agents/agent.py b/src/lfx/src/lfx/components/models_and_agents/agent.py index 3b6800f750..8b2091ceae 100644 --- a/src/lfx/src/lfx/components/models_and_agents/agent.py +++ b/src/lfx/src/lfx/components/models_and_agents/agent.py @@ -4,7 +4,7 @@ from contextlib import contextmanager from datetime import datetime, timezone from typing import TYPE_CHECKING, Any -from lfx.components.models_and_agents.memory import MemoryComponent +from lfx.components.models_and_agents.memory import MemoryComponent, aget_agent_chat_history if TYPE_CHECKING: from langchain_core.tools import Tool @@ -491,16 +491,15 @@ class AgentComponent(ToolCallingAgentComponent): return Data(data={"content": "", "error": str(exc)}) async def get_memory_data(self): - # TODO: This is a temporary fix to avoid message duplication. We should develop a function for this. - messages = ( - await MemoryComponent(**self.get_base_args()) - .set( - session_id=self.graph.session_id, - context_id=self.context_id, - order="Ascending", - n_messages=self.n_messages, - ) - .retrieve_messages() + # Scope by flow_id so default playground session names (e.g. "New Session 0") + # cannot leak chat history across unrelated flows. See issue #13059. + # The helper also returns [] when n_messages == 0, preserving the + # explicit "memory disabled" contract from MemoryComponent.retrieve_messages. + messages = await aget_agent_chat_history( + session_id=self.graph.session_id, + flow_id=getattr(self.graph, "flow_id", None), + context_id=self.context_id, + n_messages=self.n_messages, ) return [ message for message in messages if getattr(message, "id", None) != getattr(self.input_value, "id", None) diff --git a/src/lfx/src/lfx/components/models_and_agents/memory.py b/src/lfx/src/lfx/components/models_and_agents/memory.py index 6df0b5d5a9..d3b6fcd7dd 100644 --- a/src/lfx/src/lfx/components/models_and_agents/memory.py +++ b/src/lfx/src/lfx/components/models_and_agents/memory.py @@ -1,8 +1,10 @@ from typing import Any, cast +from uuid import UUID from lfx.custom.custom_component.component import Component from lfx.helpers.data import data_to_text from lfx.inputs.inputs import DropdownInput, HandleInput, IntInput, MessageTextInput, MultilineInput, TabInput +from lfx.log.logger import logger from lfx.memory import aget_messages, astore_message from lfx.schema.data import Data from lfx.schema.dataframe import DataFrame @@ -12,6 +14,106 @@ from lfx.template.field.base import Output from lfx.utils.component_utils import set_current_fields, set_field_display from lfx.utils.constants import MESSAGE_SENDER_AI, MESSAGE_SENDER_NAME_AI, MESSAGE_SENDER_USER +# Cap on rows we will pull from the DB when the caller has not supplied an +# explicit ``n_messages`` limit. This is a guardrail against unbounded fetches +# (DB pressure, memory) on sessions that accumulate huge histories. The +# trade-off is that sessions with more rows than this cap will not surface +# their oldest messages when no ``n_messages`` is set. See ``aget_messages`` +# usage sites below for how this interacts with ordering. +MAX_CHAT_HISTORY_FETCH_LIMIT = 10_000 + + +def _coerce_flow_id_to_uuid(flow_id: str | UUID | None) -> UUID | None: + """Coerce a graph flow_id (typically str) to UUID for DB filtering. + + Returns ``None`` when ``flow_id`` is missing or cannot be parsed. The + caller then falls back to the previous **unscoped** retrieval, which + re-introduces the cross-flow leak that motivated PR #13087. We emit a + structured ``error`` log on that path (rather than ``warning``) so + observability can alert on it — see issue #13059 / PR #13087. + """ + if flow_id is None or flow_id == "": + return None + if isinstance(flow_id, UUID): + return flow_id + try: + return UUID(str(flow_id)) + except (ValueError, TypeError, AttributeError): + # Loud, structured signal — this path means chat history is being + # served without flow-scoping, which is the privacy bug PR #13087 + # was created to close. Anything matching this event is a candidate + # for an observability alert. + logger.error( + "memory_flow_id_unscoped: flow_id %r is not a valid UUID; " + "chat history will NOT be scoped by flow_id. This re-enables " + "cross-flow leakage (issue #13059) for this request.", + flow_id, + extra={"event": "memory_flow_id_unscoped", "flow_id_repr": repr(flow_id)}, + ) + return None + + +def _safe_graph_flow_id(component: Component) -> str | UUID | None: + """Best-effort lookup of the component's graph flow_id. + + ``Component.graph`` is a property that reaches into ``self._vertex.graph``; + when a MemoryComponent is constructed ad-hoc (e.g. by the Agent component + via ``MemoryComponent(**self.get_base_args())``), ``_vertex`` is ``None`` and + accessing the property raises ``AttributeError``. Swallow that here so + retrieval falls back to the previous unscoped behavior rather than crashing. + """ + try: + graph = component.graph + except AttributeError: + return None + return getattr(graph, "flow_id", None) + + +async def aget_agent_chat_history( + *, + session_id: str | UUID | None, + flow_id: str | UUID | None, + context_id: str | None = None, + n_messages: int | None = None, +) -> list[Message]: + """Fetch chat history for an agent, scoped to a single flow. + + Centralizes the contract previously implemented by + ``MemoryComponent.retrieve_messages`` for agent callers: + + * Returns ``[]`` when ``n_messages == 0`` (memory explicitly disabled). + Without this short-circuit, the bounded query would still execute and + the caller's ``messages[-0:]`` would return everything. + * Scopes by ``flow_id`` (coerced to ``UUID``) so default playground + session names cannot leak history across flows (issue #13059). + * Returns up to ``n_messages`` most recent messages in ascending order. + Queries in ``DESC`` order with ``limit=n_messages`` so sessions with + more than ``MAX_CHAT_HISTORY_FETCH_LIMIT`` rows still see the genuine + most-recent slice, not the chronological first window. + """ + if n_messages == 0: + return [] + fetch_limit = n_messages if n_messages else MAX_CHAT_HISTORY_FETCH_LIMIT + messages = await aget_messages( + session_id=session_id, + context_id=context_id, + flow_id=_coerce_flow_id_to_uuid(flow_id), + limit=fetch_limit, + order="DESC", + ) + if not n_messages and len(messages) == MAX_CHAT_HISTORY_FETCH_LIMIT: + # We hit the unbounded-fetch ceiling. The caller will likely see + # stale "most-recent" history. Flag this so on-call has something + # to grep when a user reports forgotten context. + logger.warning( + "memory_chat_history_limit_reached: hit MAX_CHAT_HISTORY_FETCH_LIMIT=%d " + "without an explicit n_messages; older messages were not returned.", + MAX_CHAT_HISTORY_FETCH_LIMIT, + extra={"event": "memory_chat_history_limit_reached"}, + ) + # ``aget_messages`` returned DESC; reverse to ASC for the agent's prompt. + return list(reversed(messages)) + class MemoryComponent(Component): display_name = "Message History" @@ -194,13 +296,18 @@ class MemoryComponent(Component): if message.sender: stored_messages = [m for m in stored_messages if m.sender == message.sender] else: - await astore_message(message, flow_id=self.graph.flow_id) + # Single coerced scope used for both the write and the read-back, + # so a missing/ad-hoc ``_vertex`` cannot crash the write half while + # the read half degrades gracefully. See PR #13087 review I1. + flow_id_scope = _coerce_flow_id_to_uuid(_safe_graph_flow_id(self)) + await astore_message(message, flow_id=flow_id_scope) stored_messages = ( await aget_messages( session_id=message.session_id, context_id=message.context_id, sender_name=message.sender_name, sender=message.sender, + flow_id=flow_id_scope, ) or [] ) @@ -250,17 +357,34 @@ class MemoryComponent(Component): expected_type = MESSAGE_SENDER_AI if sender_type == MESSAGE_SENDER_AI else MESSAGE_SENDER_USER stored = [m for m in stored if m.type == expected_type] else: - # For internal memory, we always fetch the last N messages by ordering by DESC + # For internal memory, fetch the last N messages by ordering DESC at the + # DB layer so sessions with more than ``MAX_CHAT_HISTORY_FETCH_LIMIT`` + # rows still return the genuine most-recent slice rather than the + # chronological first window. See PR #13087 review I2. + # + # Scope by flow_id so default session names (e.g. "New Session 0") do not + # leak chat history across unrelated flows. See issue #13059. + flow_id_scope = _coerce_flow_id_to_uuid(_safe_graph_flow_id(self)) + fetch_limit = n_messages if n_messages else MAX_CHAT_HISTORY_FETCH_LIMIT stored = await aget_messages( sender=sender_type, sender_name=sender_name, session_id=session_id, context_id=context_id, - limit=10000, - order=order, + flow_id=flow_id_scope, + limit=fetch_limit, + order="DESC", ) - if n_messages: - stored = stored[-n_messages:] # Get last N messages + if not n_messages and len(stored) == MAX_CHAT_HISTORY_FETCH_LIMIT: + logger.warning( + "memory_chat_history_limit_reached: hit MAX_CHAT_HISTORY_FETCH_LIMIT=%d " + "without an explicit n_messages; older messages were not returned.", + MAX_CHAT_HISTORY_FETCH_LIMIT, + extra={"event": "memory_chat_history_limit_reached"}, + ) + # Honor the user-selected order: we fetched DESC, reverse if ASC requested. + if order == "ASC": + stored = list(reversed(stored)) # self.status = stored return cast("Data", stored)