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* feat: make content_blocks the source of truth for Message content Migrate Message.text from a Pydantic field to a @computed_field over content_blocks, and unify ContentBlock into the discriminated ContentType union so a Message's payload is one uniform shape. Schema changes: - Add 7 new content types (Image, Audio, Video, File, Reasoning, Usage, Citation) with validators for media sources, non-negative tokens, and ordered citation indices - Promote 'contents: list[ContentType]' to BaseContent so any node can nest (multimodal tool outputs, multi-step reasoning, grouped errors) - Fold ContentBlock into BaseContent and into the ContentType union with tag 'group'; content_blocks is now 'list[ContentType]' everywhere - Fix Data.__setattr__ to route through property descriptors via MRO walk Setter / serialization: - text setter appends a single TextContent at the end of content_blocks, preserving non-text blocks in chronological order (tool calls first, final text last) - model_post_init preserves explicit None in data['text'] when no TextContent exists in content_blocks, so callers can still distinguish 'text was never set' from 'text was set to empty string' from_lc_message: - Handle AIMessage tool_calls and usage_metadata regardless of whether content is a string or a list (tool-calling agents commonly emit content='' alongside tool_calls) - Tolerate explicit source=None in multimodal image payloads MessageResponse.from_message / MessageTable.from_message: - Accept any of (data['text'] set, text_stream pending, content_blocks non-empty) as 'content present', so tool-call-only and media-only messages persist rather than getting rejected as missing required fields Tests cover all new content types, the unified ContentType union, the text/content_blocks contract, the setter's chronological append, and the required-fields gate. (cherry picked from commit3b92500349) * feat: stable id on content blocks + plumb LangChain tool_call_id Adds an optional 'id: str | None' field to BaseContent for stable identity across re-emissions of the same logical block. Producers that have a natural id (LangChain tool_call_id, external API id, a UUID stamped before the first emission) set it; consumers use it for dedup and cross-frame correlation. Without an id, consumers fall back to position-derived dedup, which assumes content_blocks is append-only within a message lifetime. Plumbs LangChain's 'tool_call_id' through 'Message.from_lc_message' into 'ToolContent.id'. The same logical tool call across start, args streaming, and result lifecycle now carries the same id, so a re-fired add_message dedups to one ToolContent instead of producing duplicates. Tests cover id default/round-trip/inheritance across every concrete content type, plus tool_call_id stability across repeated conversion, multiple tool calls each keeping their own id, and tool_calls alongside string content. (cherry picked from commita3e8b40811) * fix(schema): MessageResponse parses microsecond timestamps and ContentBlock partial updates preserve unset fields Two schema regressions surfaced in QA across the content-blocks chain: 1. MessageResponse.timestamp was typed as a bare datetime, but Message.timestamp default is a string with microsecond precision and a UTC timezone label ('%Y-%m-%d %H:%M:%S.%f %Z') that Pydantic's default datetime parser rejects. Any freshly built Message routed through MessageResponse.from_message raised ValidationError. Reuse the shared str_to_timestamp_validator so MessageResponse accepts every format Message itself recognises. 2. ContentBlock.__init__ marked every field as model_fields_set, not just the discriminator. The override defeated exclude_unset for the group content type: a patch like ContentBlock(title='new') dumped every defaulted field and, when merged onto an existing block by aupdate_messages, overwrote fields the caller never touched. Mark only 'type' (the discriminator) so partial updates carry the variant tag without clobbering the rest. Adds regression tests in test_message_content_blocks.py: from_message round-trips Message.timestamp without crashing, and ContentBlock exclude_unset stays narrow to the explicit fields plus the discriminator. (cherry picked from commit6f6639374f) * fix(schema): address content_blocks review feedback - sync langflow-base ContentBlock.__init__ with the lfx copy (model_fields_set parity) so exclude_unset no longer clobbers type; add cross-module regression test - route MessageResponse content_blocks discriminator-first so stored flat blocks with contents=[] validate instead of raising - move Message SecretStr coercion into model_post_init and drop the dead validate_text before-validator - drop the no-op _fold_text_into_content_blocks validator - log a shape-only debug line when from_lc_message drops an undecodable image - type MessageResponse.content_blocks as list[ContentType] | None (cherry picked from commit 6c7cec8a529c38d2f9eb7a35f94277611c8696cc) * fix(agents): stop duplicating the final answer in content_blocks With content_blocks as the source of truth, Message.text is a computed field whose setter appends a trailing top-level TextContent. handle_on_chain_end also appended the same answer into the Agent Steps group, so the final answer rendered twice (assert 2 == 1 in test_multiple_events). The streaming path already relies on the setter alone; make the non-streaming path match. * chore: auto-bake note keys and regenerate backend locales/en.json [skip ci] * feat(schema): project new content_blocks back to the v1 wire shape The in-memory Message and the v2 (AG-UI) path use the new content_blocks union (groups tagged "group", every node carries id/contents, the agent answer is a trailing top-level TextContent). The v1 API keeps emitting the pre-1.11.0 shape via a pure legacy_render projection that runs only at the v1 boundaries: the v1 read/response models, the memories endpoint, the v1 build SSE stream, the webhook events SSE stream, and the /run response and stream. The build, webhook, and /run projections recurse so the Data mirror (data.data.content_blocks) is projected alongside the top-level copy. v2 serializes the live Message and keeps the new shape. * [autofix.ci] apply automated fixes * [autofix.ci] apply automated fixes (attempt 2/3) * fix(api): keep simple_run_flow returning RunResponse, project v1 at the HTTP boundary simple_run_flow is a shared helper, so wrapping its return in a JSONResponse to apply the v1 content_blocks projection broke internal callers that call .model_dump() on the result (the streaming run_flow_generator and get_build_results). Return the RunResponse object from the helper and apply the projection at the non-stream HTTP boundary in _run_flow_internal instead. The streaming path already projects the end event via _project_run_event. --------- Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com> Co-authored-by: autofix-ci[bot] <114827586+autofix-ci[bot]@users.noreply.github.com>
Langflow is a powerful platform for building and deploying AI-powered agents and workflows. It provides developers with both a visual authoring experience and built-in API and MCP servers that turn every workflow into a tool that can be integrated into applications built on any framework or stack. Langflow comes with batteries included and supports all major LLMs, vector databases and a growing library of AI tools.
✨ Highlight features
- Visual builder interface to quickly get started and iterate.
- Source code access lets you customize any component using Python.
- Interactive playground to immediately test and refine your flows with step-by-step control.
- Multi-agent orchestration with conversation management and retrieval.
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Install locally (recommended)
Requires Python 3.10–3.14 and uv (recommended package manager).
Install
From a fresh directory, run:
uv pip install langflow -U
The latest Langflow package is installed. For more information, see Install and run the Langflow OSS Python package.
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To start Langflow, run:
uv run langflow run
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