diff --git a/agent/canvas.py b/agent/canvas.py index 75f736f62..79cdcd2cb 100644 --- a/agent/canvas.py +++ b/agent/canvas.py @@ -252,15 +252,6 @@ class Canvas: "created_at": cpn_obj.output("_created_time"), }) - def _append_path(cpn_id): - if self.path[-1] == cpn_id: - return - self.path.append(cpn_id) - - def _extend_path(cpn_ids): - for cpn_id in cpn_ids: - _append_path(cpn_id) - self.error = "" idx = len(self.path) - 1 partials = [] @@ -279,10 +270,11 @@ class Canvas: # post processing of components invocation for i in range(idx, to): cpn = self.get_component(self.path[i]) - if cpn["obj"].component_name.lower() == "message": - if isinstance(cpn["obj"].output("content"), partial): + cpn_obj = self.get_component_obj(self.path[i]) + if cpn_obj.component_name.lower() == "message": + if isinstance(cpn_obj.output("content"), partial): _m = "" - for m in cpn["obj"].output("content")(): + for m in cpn_obj.output("content")(): if not m: continue if m == "": @@ -292,48 +284,65 @@ class Canvas: else: yield decorate("message", {"content": m}) _m += m - cpn["obj"].set_output("content", _m) + cpn_obj.set_output("content", _m) else: - yield decorate("message", {"content": cpn["obj"].output("content")}) + yield decorate("message", {"content": cpn_obj.output("content")}) yield decorate("message_end", {"reference": self.get_reference()}) while partials: - _cpn = self.get_component(partials[0]) - if isinstance(_cpn["obj"].output("content"), partial): + _cpn_obj = self.get_component_obj(partials[0]) + if isinstance(_cpn_obj.output("content"), partial): break - yield _node_finished(_cpn["obj"]) + yield _node_finished(_cpn_obj) partials.pop(0) - if cpn["obj"].error(): - ex = cpn["obj"].exception_handler() - if ex and ex["comment"]: - yield decorate("message", {"content": ex["comment"]}) - yield decorate("message_end", {}) + other_branch = False + if cpn_obj.error(): + ex = cpn_obj.exception_handler() if ex and ex["goto"]: - self.path.append(ex["goto"]) - elif not ex or not ex["default_value"]: - self.error = cpn["obj"].error() + self.path.extend(ex["goto"]) + other_branch = True + elif ex and ex["default_value"]: + yield decorate("message", {"content": ex["default_value"]}) + yield decorate("message_end", {}) + else: + self.error = cpn_obj.error() - if cpn["obj"].component_name.lower() != "iteration": - if isinstance(cpn["obj"].output("content"), partial): + if cpn_obj.component_name.lower() != "iteration": + if isinstance(cpn_obj.output("content"), partial): if self.error: - cpn["obj"].set_output("content", None) - yield _node_finished(cpn["obj"]) + cpn_obj.set_output("content", None) + yield _node_finished(cpn_obj) else: partials.append(self.path[i]) else: - yield _node_finished(cpn["obj"]) + yield _node_finished(cpn_obj) - if cpn["obj"].component_name.lower() == "iterationitem" and cpn["obj"].end(): - iter = cpn["obj"].get_parent() + def _append_path(cpn_id): + nonlocal other_branch + if other_branch: + return + if self.path[-1] == cpn_id: + return + self.path.append(cpn_id) + + def _extend_path(cpn_ids): + nonlocal other_branch + if other_branch: + return + for cpn_id in cpn_ids: + _append_path(cpn_id) + + if cpn_obj.component_name.lower() == "iterationitem" and cpn_obj.end(): + iter = cpn_obj.get_parent() yield _node_finished(iter) _extend_path(self.get_component(cpn["parent_id"])["downstream"]) - elif cpn["obj"].component_name.lower() in ["categorize", "switch"]: - _extend_path(cpn["obj"].output("_next")) - elif cpn["obj"].component_name.lower() == "iteration": - _append_path(cpn["obj"].get_start()) - elif not cpn["downstream"] and cpn["obj"].get_parent(): - _append_path(cpn["obj"].get_parent().get_start()) + elif cpn_obj.component_name.lower() in ["categorize", "switch"]: + _extend_path(cpn_obj.output("_next")) + elif cpn_obj.component_name.lower() == "iteration": + _append_path(cpn_obj.get_start()) + elif not cpn["downstream"] and cpn_obj.get_parent(): + _append_path(cpn_obj.get_parent().get_start()) else: _extend_path(cpn["downstream"]) @@ -342,13 +351,13 @@ class Canvas: break idx = to - if any([self.get_component(c)["obj"].component_name.lower() == "userfillup" for c in self.path[idx:]]): + if any([self.get_component_obj(c).component_name.lower() == "userfillup" for c in self.path[idx:]]): path = [c for c in self.path[idx:] if self.get_component(c)["obj"].component_name.lower() == "userfillup"] path.extend([c for c in self.path[idx:] if self.get_component(c)["obj"].component_name.lower() != "userfillup"]) another_inputs = {} tips = "" for c in path: - o = self.get_component(c)["obj"] + o = self.get_component_obj(c) if o.component_name.lower() == "userfillup": another_inputs.update(o.get_input_elements()) if o.get_param("enable_tips"): diff --git a/agent/component/agent_with_tools.py b/agent/component/agent_with_tools.py index d4efc73ce..3511845ef 100644 --- a/agent/component/agent_with_tools.py +++ b/agent/component/agent_with_tools.py @@ -157,7 +157,8 @@ class Agent(LLM, ToolBase): prompt, msg = self._prepare_prompt_variables() downstreams = self._canvas.get_component(self._id)["downstream"] if self._canvas.get_component(self._id) else [] - if any([self._canvas.get_component_obj(cid).component_name.lower()=="message" for cid in downstreams]) and not self._param.output_structure: + ex = self.exception_handler() + if any([self._canvas.get_component_obj(cid).component_name.lower()=="message" for cid in downstreams]) and not self._param.output_structure and not (ex and ex["goto"]): self.set_output("content", partial(self.stream_output_with_tools, prompt, msg)) return @@ -169,7 +170,10 @@ class Agent(LLM, ToolBase): if ans.find("**ERROR**") >= 0: logging.error(f"Agent._chat got error. response: {ans}") - self.set_output("_ERROR", ans) + if self.get_exception_default_value(): + self.set_output("content", self.get_exception_default_value()) + else: + self.set_output("_ERROR", ans) return self.set_output("content", ans) @@ -182,6 +186,12 @@ class Agent(LLM, ToolBase): answer_without_toolcall = "" use_tools = [] for delta_ans,_ in self._react_with_tools_streamly(msg, use_tools): + if delta_ans.find("**ERROR**") >= 0: + if self.get_exception_default_value(): + self.set_output("content", self.get_exception_default_value()) + yield self.get_exception_default_value() + else: + self.set_output("_ERROR", delta_ans) answer_without_toolcall += delta_ans yield delta_ans @@ -204,8 +214,8 @@ class Agent(LLM, ToolBase): hist = deepcopy(history) last_calling = "" if len(hist) > 3: - self.callback("Multi-turn conversation optimization", {}, " running ...") user_request = full_question(messages=history, chat_mdl=self.chat_mdl) + self.callback("Multi-turn conversation optimization", {}, user_request) else: user_request = history[-1]["content"] @@ -241,9 +251,6 @@ class Agent(LLM, ToolBase): cited = True yield "", token_count - if not cited and need2cite: - self.callback("gen_citations", {}, " running ...") - _hist = hist if len(hist) > 12: _hist = [hist[0], hist[1], *hist[-10:]] @@ -255,8 +262,12 @@ class Agent(LLM, ToolBase): if not need2cite or cited: return + txt = "" for delta_ans in self._gen_citations(entire_txt): yield delta_ans, 0 + txt += delta_ans + + self.callback("gen_citations", {}, txt) def append_user_content(hist, content): if hist[-1]["role"] == "user": @@ -264,8 +275,8 @@ class Agent(LLM, ToolBase): else: hist.append({"role": "user", "content": content}) - self.callback("analyze_task", {}, " running ...") task_desc = analyze_task(self.chat_mdl, user_request, tool_metas) + self.callback("analyze_task", {}, task_desc) for _ in range(self._param.max_rounds + 1): response, tk = next_step(self.chat_mdl, hist, tool_metas, task_desc) # self.callback("next_step", {}, str(response)[:256]+"...") diff --git a/agent/component/base.py b/agent/component/base.py index dba14a3de..f8f54c004 100644 --- a/agent/component/base.py +++ b/agent/component/base.py @@ -44,7 +44,6 @@ class ComponentParamBase(ABC): self.delay_after_error = 2.0 self.exception_method = None self.exception_default_value = None - self.exception_comment = None self.exception_goto = None self.debug_inputs = {} @@ -97,6 +96,14 @@ class ComponentParamBase(ABC): def as_dict(self): def _recursive_convert_obj_to_dict(obj): ret_dict = {} + if isinstance(obj, dict): + for k,v in obj.items(): + if isinstance(v, dict) or (v and type(v).__name__ not in dir(builtins)): + ret_dict[k] = _recursive_convert_obj_to_dict(v) + else: + ret_dict[k] = v + return ret_dict + for attr_name in list(obj.__dict__): if attr_name in [_FEEDED_DEPRECATED_PARAMS, _DEPRECATED_PARAMS, _USER_FEEDED_PARAMS, _IS_RAW_CONF]: continue @@ -105,7 +112,7 @@ class ComponentParamBase(ABC): if isinstance(attr, pd.DataFrame): ret_dict[attr_name] = attr.to_dict() continue - if attr and type(attr).__name__ not in dir(builtins): + if isinstance(attr, dict) or (attr and type(attr).__name__ not in dir(builtins)): ret_dict[attr_name] = _recursive_convert_obj_to_dict(attr) else: ret_dict[attr_name] = attr @@ -415,7 +422,10 @@ class ComponentBase(ABC): try: self._invoke(**kwargs) except Exception as e: - self._param.outputs["_ERROR"] = {"value": str(e)} + if self.get_exception_default_value(): + self.set_exception_default_value() + else: + self.set_output("_ERROR", str(e)) logging.exception(e) self._param.debug_inputs = {} self.set_output("_elapsed_time", time.perf_counter() - self.output("_created_time")) @@ -427,7 +437,7 @@ class ComponentBase(ABC): def output(self, var_nm: str=None) -> Union[dict[str, Any], Any]: if var_nm: - return self._param.outputs.get(var_nm, {}).get("value") + return self._param.outputs.get(var_nm, {}).get("value", "") return {k: o.get("value") for k,o in self._param.outputs.items()} def set_output(self, key: str, value: Any): @@ -520,7 +530,7 @@ class ComponentBase(ABC): def string_format(content: str, kv: dict[str, str]) -> str: for n, v in kv.items(): content = re.sub( - r"\{%s\}" % re.escape(n), re.escape(v), content + r"\{%s\}" % re.escape(n), v, content ) return content @@ -529,13 +539,17 @@ class ComponentBase(ABC): return return { "goto": self._param.exception_goto, - "comment": self._param.exception_comment, "default_value": self._param.exception_default_value } def get_exception_default_value(self): + if self._param.exception_method != "comment": + return "" return self._param.exception_default_value + def set_exception_default_value(self): + self.set_output("result", self.get_exception_default_value()) + @abstractmethod def thoughts(self) -> str: ... diff --git a/agent/component/begin.py b/agent/component/begin.py index 6a997d400..d65a698fa 100644 --- a/agent/component/begin.py +++ b/agent/component/begin.py @@ -46,4 +46,4 @@ class Begin(UserFillUp): self.set_input_value(k, v) def thoughts(self) -> str: - return "☕ Here we go..." + return "" diff --git a/agent/component/llm.py b/agent/component/llm.py index fb9a4c85c..532bedc86 100644 --- a/agent/component/llm.py +++ b/agent/component/llm.py @@ -22,6 +22,8 @@ from typing import Any import json_repair from copy import deepcopy from functools import partial + +from api.db import LLMType from api.db.services.llm_service import LLMBundle, TenantLLMService from agent.component.base import ComponentBase, ComponentParamBase from api.utils.api_utils import timeout @@ -49,27 +51,33 @@ class LLMParam(ComponentParamBase): self.visual_files_var = None def check(self): - self.check_decimal_float(self.temperature, "[Agent] Temperature") - self.check_decimal_float(self.presence_penalty, "[Agent] Presence penalty") - self.check_decimal_float(self.frequency_penalty, "[Agent] Frequency penalty") - self.check_nonnegative_number(self.max_tokens, "[Agent] Max tokens") - self.check_decimal_float(self.top_p, "[Agent] Top P") + self.check_decimal_float(float(self.temperature), "[Agent] Temperature") + self.check_decimal_float(float(self.presence_penalty), "[Agent] Presence penalty") + self.check_decimal_float(float(self.frequency_penalty), "[Agent] Frequency penalty") + self.check_nonnegative_number(int(self.max_tokens), "[Agent] Max tokens") + self.check_decimal_float(float(self.top_p), "[Agent] Top P") self.check_empty(self.llm_id, "[Agent] LLM") self.check_empty(self.sys_prompt, "[Agent] System prompt") self.check_empty(self.prompts, "[Agent] User prompt") def gen_conf(self): conf = {} - if self.max_tokens > 0: - conf["max_tokens"] = self.max_tokens - if self.temperature > 0: - conf["temperature"] = self.temperature - if self.top_p > 0: - conf["top_p"] = self.top_p - if self.presence_penalty > 0: - conf["presence_penalty"] = self.presence_penalty - if self.frequency_penalty > 0: - conf["frequency_penalty"] = self.frequency_penalty + def get_attr(nm): + try: + return getattr(self, nm) + except Exception: + pass + + if int(self.max_tokens) > 0 and get_attr("maxTokensEnabled"): + conf["max_tokens"] = int(self.max_tokens) + if float(self.temperature) > 0 and get_attr("temperatureEnabled"): + conf["temperature"] = float(self.temperature) + if float(self.top_p) > 0 and get_attr("topPEnabled"): + conf["top_p"] = float(self.top_p) + if float(self.presence_penalty) > 0 and get_attr("presencePenaltyEnabled"): + conf["presence_penalty"] = float(self.presence_penalty) + if float(self.frequency_penalty) > 0 and get_attr("frequencyPenaltyEnabled"): + conf["frequency_penalty"] = float(self.frequency_penalty) return conf @@ -112,6 +120,12 @@ class LLM(ComponentBase): if not self.imgs: self.imgs = [] self.imgs = [img for img in self.imgs if img[:len("data:image/")] == "data:image/"] + if self.imgs and TenantLLMService.llm_id2llm_type(self._param.llm_id) == LLMType.CHAT.value: + self.chat_mdl = LLMBundle(self._canvas.get_tenant_id(), LLMType.IMAGE2TEXT.value, + self._param.llm_id, max_retries=self._param.max_retries, + retry_interval=self._param.delay_after_error + ) + args = {} vars = self.get_input_elements() if not self._param.debug_inputs else self._param.debug_inputs @@ -207,7 +221,8 @@ class LLM(ComponentBase): return downstreams = self._canvas.get_component(self._id)["downstream"] if self._canvas.get_component(self._id) else [] - if any([self._canvas.get_component_obj(cid).component_name.lower()=="message" for cid in downstreams]) and not self._param.output_structure: + ex = self.exception_handler() + if any([self._canvas.get_component_obj(cid).component_name.lower()=="message" for cid in downstreams]) and not self._param.output_structure and not (ex and ex["goto"]): self.set_output("content", partial(self._stream_output, prompt, msg)) return @@ -224,14 +239,22 @@ class LLM(ComponentBase): break if error: - self.set_output("_ERROR", error) if self.get_exception_default_value(): self.set_output("content", self.get_exception_default_value()) + else: + self.set_output("_ERROR", error) def _stream_output(self, prompt, msg): _, msg = message_fit_in([{"role": "system", "content": prompt}, *msg], int(self.chat_mdl.max_length * 0.97)) answer = "" for ans in self._generate_streamly(msg): + if ans.find("**ERROR**") >= 0: + if self.get_exception_default_value(): + self.set_output("content", self.get_exception_default_value()) + yield self.get_exception_default_value() + else: + self.set_output("_ERROR", ans) + return yield ans answer += ans self.set_output("content", answer) @@ -243,4 +266,4 @@ class LLM(ComponentBase): def thoughts(self) -> str: _, msg = self._prepare_prompt_variables() - return f"I’m thinking and planning the next move, starting from the prompt:
“{msg[-1]['content']}” (tap to see full text)" \ No newline at end of file + return "⌛Give me a moment—starting from: \n\n" + re.sub(r"(User's query:|[\\]+)", '', msg[-1]['content'], flags=re.DOTALL) + "\n\nI’ll figure out our best next move." \ No newline at end of file diff --git a/agent/component/message.py b/agent/component/message.py index b836e3cc4..f95e07586 100644 --- a/agent/component/message.py +++ b/agent/component/message.py @@ -143,4 +143,4 @@ class Message(ComponentBase): self.set_output("content", content) def thoughts(self) -> str: - return "Thinking ..." + return "" diff --git a/agent/templates/customer_review_analysis.json b/agent/templates/customer_review_analysis.json new file mode 100644 index 000000000..52b365878 --- /dev/null +++ b/agent/templates/customer_review_analysis.json @@ -0,0 +1,798 @@ + +{ + "id": 11, + "title": "Customer Review Analysis", + "description": "Automatically classify customer reviews using LLM (Large Language Model) and route them via email to the relevant departments.", + "canvas_type": "Customer Support", + "dsl": { + "components": { + "Categorize:FourTeamsFold": { + "downstream": [ + "Email:SharpDeerExist", + "Email:ChillyBusesDraw" + ], + "obj": { + "component_name": "Categorize", + "params": { + "category_description": { + "After-sales issues": { + "description": "The negative review is about after-sales issues.", + "examples": [ + "1. The product easily broke down.\n2. I need to change a new one.\n3. It is not the type I ordered." + ], + "to": [ + "Email:SharpDeerExist" + ] + }, + "Transportation issue": { + "description": "The negative review is about transportation issue.", + "examples": [ + "1. The transportation is delayed too much.\n2. I can't find where is my order now." + ], + "to": [ + "Email:ChillyBusesDraw" + ] + } + }, + "llm_id": "deepseek-chat@DeepSeek", + "message_history_window_size": 1, + "outputs": { + "category_name": { + "type": "string" + } + }, + "query": "sys.query", + "temperature": 0 + } + }, + "upstream": [ + "Categorize:RottenWallsObey" + ] + }, + "Categorize:RottenWallsObey": { + "downstream": [ + "Categorize:FourTeamsFold", + "Email:WickedSymbolsLeave" + ], + "obj": { + "component_name": "Categorize", + "params": { + "category_description": { + "Negative review ": { + "description": "Negative review to the product.", + "examples": [ + "1. I have issues. \n2. Too many problems.\n3. I don't like it." + ], + "to": [ + "Categorize:FourTeamsFold" + ] + }, + "Positive review": { + "description": "Positive review to the product.", + "examples": [ + "1. Good, I like it.\n2. It is very helpful.\n3. It makes my work easier." + ], + "to": [ + "Email:WickedSymbolsLeave" + ] + } + }, + "llm_filter": "all", + "llm_id": "deepseek-chat@DeepSeek", + "message_history_window_size": 1, + "outputs": { + "category_name": { + "type": "string" + } + }, + "query": "sys.query" + } + }, + "upstream": [ + "begin" + ] + }, + "Email:ChillyBusesDraw": { + "downstream": [ + "StringTransform:FuzzySpiesTrain" + ], + "obj": { + "component_name": "Email", + "params": { + "cc_email": "", + "content": "{begin@1}", + "email": "", + "outputs": { + "success": { + "type": "boolean", + "value": true + } + }, + "password": "", + "sender_name": "", + "smtp_port": 465, + "smtp_server": "", + "subject": "", + "to_email": "" + } + }, + "upstream": [ + "Categorize:FourTeamsFold" + ] + }, + "Email:SharpDeerExist": { + "downstream": [ + "StringTransform:FuzzySpiesTrain" + ], + "obj": { + "component_name": "Email", + "params": { + "cc_email": "", + "content": "{begin@1}", + "email": "", + "outputs": { + "success": { + "type": "boolean", + "value": true + } + }, + "password": "", + "sender_name": "", + "smtp_port": 465, + "smtp_server": "", + "subject": "", + "to_email": "" + } + }, + "upstream": [ + "Categorize:FourTeamsFold" + ] + }, + "Email:WickedSymbolsLeave": { + "downstream": [ + "StringTransform:FuzzySpiesTrain" + ], + "obj": { + "component_name": "Email", + "params": { + "cc_email": "", + "content": "{begin@1}", + "email": "", + "outputs": { + "success": { + "type": "boolean", + "value": true + } + }, + "password": "", + "sender_name": "", + "smtp_port": 465, + "smtp_server": "", + "subject": "", + "to_email": "" + } + }, + "upstream": [ + "Categorize:RottenWallsObey" + ] + }, + "Message:ShaggyAnimalsWin": { + "downstream": [], + "obj": { + "component_name": "Message", + "params": { + "content": [ + "{StringTransform:FuzzySpiesTrain@result}" + ] + } + }, + "upstream": [ + "StringTransform:FuzzySpiesTrain" + ] + }, + "StringTransform:FuzzySpiesTrain": { + "downstream": [ + "Message:ShaggyAnimalsWin" + ], + "obj": { + "component_name": "StringTransform", + "params": { + "delimiters": [ + "," + ], + "method": "merge", + "outputs": { + "result": { + "type": "string" + } + }, + "script": "{Email:WickedSymbolsLeave@success}{Email:SharpDeerExist@success}{Email:ChillyBusesDraw@success}", + "split_ref": "" + } + }, + "upstream": [ + "Email:WickedSymbolsLeave", + "Email:SharpDeerExist", + "Email:ChillyBusesDraw" + ] + }, + "begin": { + "downstream": [ + "Categorize:RottenWallsObey" + ], + "obj": { + "component_name": "Begin", + "params": { + "enablePrologue": true, + "inputs": { + "1": { + "key": "1", + "name": "review", + "optional": false, + "options": [], + "type": "line", + "value": "test" + } + }, + "mode": "conversational", + "prologue": "Hi! I'm your customer review analysis assistant. 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" +} + + diff --git a/agent/templates/customer_service.json b/agent/templates/customer_service.json new file mode 100644 index 000000000..a3d03c603 --- /dev/null +++ b/agent/templates/customer_service.json @@ -0,0 +1,849 @@ + +{ + "id": 1, + "title": "Deep Research", + "description": "For professionals in sales, marketing, policy, or consulting, the Multi-Agent Deep Research Agent conducts structured, multi-step investigations across diverse sources and delivers consulting-style reports with clear citations.", + "canvas_type": "Recommended", + "dsl": { + "components": { + "Agent:NewPumasLick": { + "downstream": [ + "Message:OrangeYearsShine" + ], + "obj": { + "component_name": "Agent", + "params": { + "delay_after_error": 1, + "description": "", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.5, + "llm_id": "qwen-max@Tongyi-Qianwen", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 3, + "max_tokens": 4096, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "parameter": "Precise", + "presencePenaltyEnabled": false, + "presence_penalty": 0.5, + "prompts": [ + { + "content": "The user query is {sys.query}", + "role": "user" + } + ], + "sys_prompt": "You are a Strategy Research Director with 20 years of consulting experience at top-tier firms. Your role is orchestrating multi-agent research teams to produce comprehensive, actionable reports.\n\n\n\nTransform complex research needs into efficient multi-agent collaboration, ensuring high-quality ~2000-word strategic reports.\n\n\n\n\n**Stage 1: URL Discovery** (2-3 minutes)\n- Deploy Web Search Specialist to identify 5 premium sources\n- Ensure comprehensive coverage across authoritative domains\n- Validate search strategy matches research scope\n\n\n**Stage 2: Content Extraction** (3-5 minutes)\n- Deploy Content Deep Reader to process 5 premium URLs\n- Focus on structured extraction with quality assessment\n- Ensure 80%+ extraction success rate\n\n\n**Stage 3: Strategic Report Generation** (5-8 minutes)\n- Deploy Research Synthesizer with detailed strategic analysis instructions\n- Provide specific analysis framework and business focus requirements\n- Generate comprehensive McKinsey-style strategic report (~2000 words)\n- Ensure multi-source validation and C-suite ready insights\n\n\n**Report Instructions Framework:**\n```\nANALYSIS_INSTRUCTIONS:\nAnalysis Type: [Market Analysis/Competitive Intelligence/Strategic Assessment]\nTarget Audience: [C-Suite/Board/Investment Committee/Strategy Team]\nBusiness Focus: [Market Entry/Competitive Positioning/Investment Decision/Strategic Planning]\nKey Questions: [3-5 specific strategic questions to address]\nAnalysis Depth: [Surface-level overview/Deep strategic analysis/Comprehensive assessment]\nDeliverable Style: [McKinsey report/BCG analysis/Deloitte assessment/Academic research]\n```\n\n\n\n\nFollow this process to break down the user's question and develop an excellent research plan. Think about the user's task thoroughly and in great detail to understand it well and determine what to do next. Analyze each aspect of the user's question and identify the most important aspects. Consider multiple approaches with complete, thorough reasoning. Explore several different methods of answering the question (at least 3) and then choose the best method you find. Follow this process closely:\n\n\n1. **Assessment and breakdown**: Analyze and break down the user's prompt to make sure you fully understand it.\n* Identify the main concepts, key entities, and relationships in the task.\n* List specific facts or data points needed to answer the question well.\n* Note any temporal or contextual constraints on the question.\n* Analyze what features of the prompt are most important - what does the user likely care about most here? What are they expecting or desiring in the final result? What tools do they expect to be used and how do we know?\n* Determine what form the answer would need to be in to fully accomplish the user's task. Would it need to be a detailed report, a list of entities, an analysis of different perspectives, a visual report, or something else? What components will it need to have?\n\n\n2. **Query type determination**: Explicitly state your reasoning on what type of query this question is from the categories below.\n* **Depth-first query**: When the problem requires multiple perspectives on the same issue, and calls for \"going deep\" by analyzing a single topic from many angles.\n- Benefits from parallel agents exploring different viewpoints, methodologies, or sources\n- The core question remains singular but benefits from diverse approaches\n- Example: \"What are the most effective treatments for depression?\" (benefits from parallel agents exploring different treatments and approaches to this question)\n- Example: \"What really caused the 2008 financial crisis?\" (benefits from economic, regulatory, behavioral, and historical perspectives, and analyzing or steelmanning different viewpoints on the question)\n- Example: \"can you identify the best approach to building AI finance agents in 2025 and why?\"\n* **Breadth-first query**: When the problem can be broken into distinct, independent sub-questions, and calls for \"going wide\" by gathering information about each sub-question.\n- Benefits from parallel agents each handling separate sub-topics.\n- The query naturally divides into multiple parallel research streams or distinct, independently researchable sub-topics\n- Example: \"Compare the economic systems of three Nordic countries\" (benefits from simultaneous independent research on each country)\n- Example: \"What are the net worths and names of all the CEOs of all the fortune 500 companies?\" (intractable to research in a single thread; most efficient to split up into many distinct research agents which each gathers some of the necessary information)\n- Example: \"Compare all the major frontend frameworks based on performance, learning curve, ecosystem, and industry adoption\" (best to identify all the frontend frameworks and then research all of these factors for each framework)\n* **Straightforward query**: When the problem is focused, well-defined, and can be effectively answered by a single focused investigation or fetching a single resource from the internet.\n- Can be handled effectively by a single subagent with clear instructions; does not benefit much from extensive research\n- Example: \"What is the current population of Tokyo?\" (simple fact-finding)\n- Example: \"What are all the fortune 500 companies?\" (just requires finding a single website with a full list, fetching that list, and then returning the results)\n- Example: \"Tell me about bananas\" (fairly basic, short question that likely does not expect an extensive answer)\n\n\n3. **Detailed research plan development**: Based on the query type, develop a specific research plan with clear allocation of tasks across different research subagents. Ensure if this plan is executed, it would result in an excellent answer to the user's query.\n* For **Depth-first queries**:\n- Define 3-5 different methodological approaches or perspectives.\n- List specific expert viewpoints or sources of evidence that would enrich the analysis.\n- Plan how each perspective will contribute unique insights to the central question.\n- Specify how findings from different approaches will be synthesized.\n- Example: For \"What causes obesity?\", plan agents to investigate genetic factors, environmental influences, psychological aspects, socioeconomic patterns, and biomedical evidence, and outline how the information could be aggregated into a great answer.\n* For **Breadth-first queries**:\n- Enumerate all the distinct sub-questions or sub-tasks that can be researched independently to answer the query. \n- Identify the most critical sub-questions or perspectives needed to answer the query comprehensively. Only create additional subagents if the query has clearly distinct components that cannot be efficiently handled by fewer agents. Avoid creating subagents for every possible angle - focus on the essential ones.\n- Prioritize these sub-tasks based on their importance and expected research complexity.\n- Define extremely clear, crisp, and understandable boundaries between sub-topics to prevent overlap.\n- Plan how findings will be aggregated into a coherent whole.\n- Example: For \"Compare EU country tax systems\", first create a subagent to retrieve a list of all the countries in the EU today, then think about what metrics and factors would be relevant to compare each country's tax systems, then use the batch tool to run 4 subagents to research the metrics and factors for the key countries in Northern Europe, Western Europe, Eastern Europe, Southern Europe.\n* For **Straightforward queries**:\n- Identify the most direct, efficient path to the answer.\n- Determine whether basic fact-finding or minor analysis is needed.\n- Specify exact data points or information required to answer.\n- Determine what sources are likely most relevant to answer this query that the subagents should use, and whether multiple sources are needed for fact-checking.\n- Plan basic verification methods to ensure the accuracy of the answer.\n- Create an extremely clear task description that describes how a subagent should research this question.\n* For each element in your plan for answering any query, explicitly evaluate:\n- Can this step be broken into independent subtasks for a more efficient process?\n- Would multiple perspectives benefit this step?\n- What specific output is expected from this step?\n- Is this step strictly necessary to answer the user's query well?\n\n\n4. **Methodical plan execution**: Execute the plan fully, using parallel subagents where possible. Determine how many subagents to use based on the complexity of the query, default to using 3 subagents for most queries. \n* For parallelizable steps:\n- Deploy appropriate subagents using the delegation instructions below, making sure to provide extremely clear task descriptions to each subagent and ensuring that if these tasks are accomplished it would provide the information needed to answer the query.\n- Synthesize findings when the subtasks are complete.\n* For non-parallelizable/critical steps:\n- First, attempt to accomplish them yourself based on your existing knowledge and reasoning. If the steps require additional research or up-to-date information from the web, deploy a subagent.\n- If steps are very challenging, deploy independent subagents for additional perspectives or approaches.\n- Compare the subagent's results and synthesize them using an ensemble approach and by applying critical reasoning.\n* Throughout execution:\n- Continuously monitor progress toward answering the user's query.\n- Update the search plan and your subagent delegation strategy based on findings from tasks.\n- Adapt to new information well - analyze the results, use Bayesian reasoning to update your priors, and then think carefully about what to do next.\n- Adjust research depth based on time constraints and efficiency - if you are running out of time or a research process has already taken a very long time, avoid deploying further subagents and instead just start composing the output report immediately.\n\n\n\n\n**Depth-First**: Multiple perspectives on single topic\n- Deploy agents to explore different angles/viewpoints\n- Example: \"What causes market volatility?\"\n\n\n**Breadth-First**: Multiple distinct sub-questions\n- Deploy agents for parallel independent research\n- Example: \"Compare tax systems of 5 countries\"\n\n\n**Straightforward**: Direct fact-finding\n- Single focused investigation\n- Example: \"What is current inflation rate?\"\n\n\n\n\n**After Each Stage:**\n- Verify required outputs present in shared memory\n- Check quality metrics meet thresholds\n- Confirm readiness for next stage\n- **CRITICAL**: Never skip Content Deep Reader\n\n\n**Quality Gate Examples:**\n* **After Stage 1 (Web Search Specialist):**\n\u00a0 - \u2705 GOOD: `RESEARCH_URLS` contains 5 premium URLs with diverse source types\n\u00a0 - \u2705 GOOD: Sources include .gov, .edu, industry reports with extraction guidance\n\u00a0 - \u274c POOR: Only 2 URLs found, missing key source diversity\n\u00a0 - \u274c POOR: No extraction focus or source descriptions provided\n\n\n* **After Stage 2 (Content Deep Reader):**\n\u00a0 - \u2705 GOOD: `EXTRACTED_CONTENT` shows 5/5 URLs processed successfully (100% success rate)\n\u00a0 - \u2705 GOOD: Contains structured data with facts, statistics, and expert quotes\n\u00a0 - \u274c POOR: Only 3/5 URLs processed (60% success rate - below threshold)\n\u00a0 - \u274c POOR: Extraction data lacks structure or source attribution\n\n\n* **After Stage 3 (Research Synthesizer):**\n\u00a0 - \u2705 GOOD: Report is 2000+ words with clear sections and actionable recommendations\n\u00a0 - \u2705 GOOD: All major findings supported by evidence from extracted content\n\u00a0 - \u274c POOR: Report is 500 words with vague conclusions\n\u00a0 - \u274c POOR: Recommendations lack specific implementation steps\n\n\n\n\n**Resource Allocation:**\n- Simple queries: 1-2 agents\n- Standard queries: 3 agents (full pipeline)\n- Complex queries: 4+ agents with specialization\n\n\n**Failure Recovery:**\n- Content extraction fails \u2192 Use metadata analysis\n- Time constraints \u2192 Prioritize high-value sources\n- Quality issues \u2192 Trigger re-execution with adjusted parameters\n\n\n**Adaptive Strategy Examples:**\n* **Simple Query Adaptation**: \"What is Tesla's current stock price?\"\n\u00a0 - Resource: 1 Web Search Specialist only\n\u00a0 - Reasoning: Direct fact-finding, no complex analysis needed\n\u00a0 - Fallback: If real-time data needed, use financial API tools\n\n\n* **Standard Query Adaptation**: \"How is AI transforming healthcare?\"\n\u00a0 - Resource: 3 agents (Web Search \u2192 Content Deep Reader \u2192 Research Synthesizer)\n\u00a0 - Reasoning: Requires comprehensive analysis of multiple sources\n\u00a0 - Fallback: If time-constrained, focus on top 5 sources only\n\n\n* **Complex Query Adaptation**: \"Compare AI regulation impact across 5 countries\"\n\u00a0 - Resource: 7 agents (1 Web Search per country + 1 Content Deep Reader per country + 1 Research Synthesizer)\n\u00a0 - Reasoning: Requires parallel regional research with comparative synthesis\n\u00a0 - Fallback: If resource-constrained, focus on US, EU, China only\n\n\n* **Failure Recovery Example**: \n\u00a0 - Issue: Content Deep Reader fails on 8/10 URLs due to paywalls\n\u00a0 - Action: Deploy backup strategy using metadata extraction + Google Scholar search\n\u00a0 - Adjustment: Lower quality threshold from 80% to 60% extraction success\n\n\n\n\n- Information density > 85%\n- Actionability score > 4/5\n- Evidence strength: High\n- Source diversity: Multi-perspective\n- Completion time: Optimal efficiency\n\n\n\n\n- Auto-detect user language\n- Use appropriate sources (local for regional topics)\n- Maintain consistency throughout pipeline\n- Apply cultural context where relevant\n\n\n**Language Adaptation Examples:**\n* **Chinese Query**: \"\u4e2d\u56fd\u7684\u4eba\u5de5\u667a\u80fd\u76d1\u7ba1\u653f\u7b56\u662f\u4ec0\u4e48\uff1f\"\n\u00a0 - Detection: Chinese language detected\n\u00a0 - Sources: Prioritize Chinese government sites, local tech reports, Chinese academic papers\n\u00a0 - Pipeline: All agent instructions in Chinese, final report in Chinese\n\u00a0 - Cultural Context: Consider regulatory framework differences and local market dynamics\n\n\n* **English Query**: \"What are the latest developments in quantum computing?\"\n\u00a0 - Detection: English language detected\n\u00a0 - Sources: Mix of international sources (US, EU, global research institutions)\n\u00a0 - Pipeline: Standard English throughout\n\u00a0 - Cultural Context: Include diverse geographic perspectives\n\n\n* **Regional Query**: \"European privacy regulations impact on AI\"\n\u00a0 - Detection: English with regional focus\n\u00a0 - Sources: Prioritize EU official documents, European research institutions\n\u00a0 - Pipeline: English with EU regulatory terminology\n\u00a0 - Cultural Context: GDPR framework, European values on privacy\n\n\n* **Mixed Context**: \"Compare US and Japan AI strategies\"\n\u00a0 - Detection: English comparative query\n\u00a0 - Sources: Both English and Japanese sources (with translation)\n\u00a0 - Pipeline: English synthesis with cultural context notes\n\u00a0 - Cultural Context: Different regulatory philosophies and market approaches\n\n\n\nRemember: Your value lies in orchestration, not execution. Ensure each agent contributes unique value while maintaining seamless collaboration toward strategic insight.\n\n\n\n**Example 1: Depth-First Query**\nQuery: \"What are the main factors driving cryptocurrency market volatility?\"\n\n\n1. **Assessment and breakdown**:\n\u00a0 \u00a0- Main concepts: cryptocurrency, market volatility, driving factors\n\u00a0 \u00a0- Key entities: Bitcoin, Ethereum, regulatory bodies, institutional investors\n\u00a0 \u00a0- Data needed: Price volatility metrics, correlation analysis, regulatory events\n\u00a0 \u00a0- User expectation: Comprehensive analysis of multiple causal factors\n\u00a0 \u00a0- Output form: Detailed analytical report with supporting evidence\n\n\n2. **Query type determination**: \n\u00a0 \u00a0- Classification: Depth-first query\n\u00a0 \u00a0- Reasoning: Single topic (crypto volatility) requiring multiple analytical perspectives\n\u00a0 \u00a0- Approaches needed: Technical analysis, regulatory impact, market psychology, institutional behavior\n\n\n3. **Research plan**:\n\u00a0 \u00a0- Agent 1: Technical/market factors (trading volumes, market structure, liquidity)\n\u00a0 \u00a0- Agent 2: Regulatory/institutional factors (government policies, institutional adoption)\n\u00a0 \u00a0- Agent 3: Psychological/social factors (sentiment analysis, social media influence)\n\u00a0 \u00a0- Synthesis: Integrate all perspectives into causal framework\n\n\n4. **Execution**: Deploy 3 specialized agents \u2192 Process findings \u2192 Generate integrated report\n\n\n**Example 2: Breadth-First Query**\nQuery: \"Compare the top 5 cloud computing providers in terms of pricing, features, and market share\"\n\n\n1. **Assessment and breakdown**:\n\u00a0 \u00a0- Main concepts: cloud computing, provider comparison, pricing/features/market share\n\u00a0 \u00a0- Key entities: AWS, Microsoft Azure, Google Cloud, IBM Cloud, Oracle Cloud\n\u00a0 \u00a0- Data needed: Pricing tables, feature matrices, market share statistics\n\u00a0 \u00a0- User expectation: Comparative analysis across multiple providers\n\u00a0 \u00a0- Output form: Structured comparison with recommendations\n\n\n2. **Query type determination**:\n\u00a0 \u00a0- Classification: Breadth-first query\n\u00a0 \u00a0- Reasoning: Multiple distinct entities requiring independent research\n\u00a0 \u00a0- Approaches needed: Parallel research on each provider's offerings\n\n\n3. **Research plan**:\n\u00a0 \u00a0- Agent 1: AWS analysis (pricing, features, market position)\n\u00a0 \u00a0- Agent 2: Microsoft Azure analysis (pricing, features, market position)\n\u00a0 \u00a0- Agent 3: Google Cloud + IBM Cloud + Oracle Cloud analysis\n\u00a0 \u00a0- Synthesis: Create comparative matrix and rankings\n\n\n4. **Execution**: Deploy 3 parallel agents \u2192 Collect provider data \u2192 Generate comparison report\n\n\n**Example 3: Straightforward Query**\nQuery: \"What is the current federal funds rate?\"\n\n\n1. **Assessment and breakdown**:\n\u00a0 \u00a0- Main concepts: federal funds rate, current value\n\u00a0 \u00a0- Key entities: Federal Reserve, monetary policy\n\u00a0 \u00a0- Data needed: Most recent fed funds rate announcement\n\u00a0 \u00a0- User expectation: Quick, accurate factual answer\n\u00a0 \u00a0- Output form: Direct answer with source citation\n\n\n2. **Query type determination**:\n\u00a0 \u00a0- Classification: Straightforward query\n\u00a0 \u00a0- Reasoning: Simple fact-finding with single authoritative source\n\u00a0 \u00a0- Approaches needed: Direct retrieval from Fed website or financial data source\n\n\n3. **Research plan**:\n\u00a0 \u00a0- Single agent: Search Federal Reserve official announcements\n\u00a0 \u00a0- Verification: Cross-check with major financial news sources\n\u00a0 \u00a0- Synthesis: Direct answer with effective date and context\n\n\n4. **Execution**: Deploy 1 Web Search Specialist \u2192 Verify information \u2192 Provide direct answer\n", + "temperature": "0.1", + "temperatureEnabled": true, + "tools": [ + { + "component_name": "Agent", + "id": "Agent:FreeDucksObey", + "name": "Web Search Specialist", + "params": { + "delay_after_error": 1, + "description": "\nWeb Search Specialist \u2014 URL Discovery Expert. Finds links ONLY, never reads content.\n\n\n\n\u2022 **URL Discovery**: Find high-quality webpage URLs using search tools\n\u2022 **Source Evaluation**: Assess URL quality based on domain and title ONLY\n\u2022 **Zero Content Reading**: NEVER extract or read webpage content\n\u2022 **Quick Assessment**: Judge URLs by search results metadata only\n\u2022 **Single Execution**: Complete mission in ONE search session\n", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.5, + "llm_id": "qwen-plus@Tongyi-Qianwen", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 1, + "max_tokens": 4096, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "parameter": "Precise", + "presencePenaltyEnabled": false, + "presence_penalty": 0.5, + "prompts": [ + { + "content": "{sys.query}", + "role": "user" + } + ], + "sys_prompt": "You are a Web Search Specialist working as part of a research team. Your expertise is in using web search tools and Model Context Protocol (MCP) to discover high-quality sources.\n\n\n**CRITICAL: YOU MUST USE WEB SEARCH TOOLS TO EXECUTE YOUR MISSION**\n\n\n\nUse web search tools (including MCP connections) to discover and evaluate premium sources for research. Your success depends entirely on your ability to execute web searches effectively using available search tools.\n\n\n\n\n1. **Plan**: Analyze the research task and design search strategy\n2. **Search**: Execute web searches using search tools and MCP connections \n3. **Evaluate**: Assess source quality, credibility, and relevance\n4. **Prioritize**: Rank URLs by research value (High/Medium/Low)\n5. **Deliver**: Provide structured URL list for Content Deep Reader\n\n\n**MANDATORY**: Use web search tools for every search operation. Do NOT attempt to search without using the available search tools.\n\n\n\n\n**MANDATORY TOOL USAGE**: All searches must be executed using web search tools and MCP connections. Never attempt to search without tools.\n\n\n- Use web search tools with 3-5 word queries for optimal results\n- Execute multiple search tool calls with different keyword combinations\n- Leverage MCP connections for specialized search capabilities\n- Balance broad vs specific searches based on search tool results\n- Diversify sources: academic (30%), official (25%), industry (25%), news (20%)\n- Execute parallel searches when possible using available search tools\n- Stop when diminishing returns occur (typically 8-12 tool calls)\n\n\n**Search Tool Strategy Examples:**\n* **Broad exploration**: Use search tools \u2192 \"AI finance regulation\" \u2192 \"financial AI compliance\" \u2192 \"automated trading rules\"\n* **Specific targeting**: Use search tools \u2192 \"SEC AI guidelines 2024\" \u2192 \"Basel III algorithmic trading\" \u2192 \"CFTC machine learning\"\n* **Geographic variation**: Use search tools \u2192 \"EU AI Act finance\" \u2192 \"UK AI financial services\" \u2192 \"Singapore fintech AI\"\n* **Temporal focus**: Use search tools \u2192 \"recent AI banking regulations\" \u2192 \"2024 financial AI updates\" \u2192 \"emerging AI compliance\"\n\n\n\n\n**High Priority URLs:**\n- Authoritative sources (.edu, .gov, major institutions)\n- Recent publications with specific data\n- Primary sources over secondary\n- Comprehensive coverage of topic\n\n\n**Avoid:**\n- Paywalled content\n- Low-authority sources\n- Outdated information\n- Marketing/promotional content\n\n\n\n\n**Essential Output Format for Content Deep Reader:**\n```\nRESEARCH_URLS:\n1. https://www.example.com/report\n\u00a0 \u00a0- Type: Government Report\n\u00a0 \u00a0- Value: Contains official statistics and policy details\n\u00a0 \u00a0- Extract Focus: Key metrics, regulatory changes, timeline data\n\n\n2. https://academic.edu/research\n\u00a0 \u00a0- Type: Peer-reviewed Study\n\u00a0 \u00a0- Value: Methodological analysis with empirical data\n\u00a0 \u00a0- Extract Focus: Research findings, sample sizes, conclusions\n\n\n3. https://industry.com/analysis\n\u00a0 \u00a0- Type: Industry Analysis\n\u00a0 \u00a0- Value: Market trends and competitive landscape\n\u00a0 \u00a0- Extract Focus: Market data, expert quotes, future projections\n\n\n4. https://news.com/latest\n\u00a0 \u00a0- Type: Breaking News\n\u00a0 \u00a0- Value: Most recent developments and expert commentary\n\u00a0 \u00a0- Extract Focus: Timeline, expert statements, impact analysis\n\n\n5. https://expert.blog/insights\n\u00a0 \u00a0- Type: Expert Commentary\n\u00a0 \u00a0- Value: Authoritative perspective and strategic insights\n\u00a0 \u00a0- Extract Focus: Expert opinions, recommendations, context\n```\n\n\n**URL Handoff Protocol:**\n- Provide exactly 5 URLs maximum (quality over quantity)\n- Include extraction guidance for each URL\n- Rank by research value and credibility\n- Specify what Content Deep Reader should focus on extracting\n\n\n\n\n- Execute comprehensive search strategy across multiple rounds\n- Generate structured URL list with priority rankings and descriptions\n- Provide extraction hints and source credibility assessments\n- Pass prioritized URLs directly to Content Deep Reader for processing\n- Focus on URL discovery and evaluation - do NOT extract content\n\n\n\nRemember: Quality over quantity. 10-15 excellent sources are better than 50 mediocre ones.", + "temperature": 0.2, + "temperatureEnabled": false, + "tools": [ + { + "component_name": "TavilySearch", + "name": "TavilySearch", + "params": { + "api_key": "", + "days": 7, + "exclude_domains": [], + "include_answer": false, + "include_domains": [], + "include_image_descriptions": false, + "include_images": false, + "include_raw_content": true, + "max_results": 5, + "outputs": { + "formalized_content": { + "type": "string", + "value": "" + }, + "json": { + "type": "Array", + "value": [] + } + }, + "query": "sys.query", + "search_depth": "basic", + "topic": "general" + } + } + ], + "topPEnabled": false, + "top_p": 0.75, + "user_prompt": "This is the order you need to send to the agent.", + "visual_files_var": "" + } + }, + { + "component_name": "Agent", + "id": "Agent:WeakBoatsServe", + "name": "Content Deep Reader", + "params": { + "delay_after_error": 1, + "description": "\nContent Deep Reader \u2014 Content extraction specialist focused on processing URLs into structured, research-ready intelligence and maximizing informational value from each source.\n\n\n\n\u2022 **Content extraction**: Web extracting tools to retrieve complete webpage content and full text\n\u2022 **Data structuring**: Transform raw content into organized, research-ready formats while preserving original context\n\u2022 **Quality validation**: Cross-reference information and assess source credibility\n\u2022 **Intelligent parsing**: Handle complex content types with appropriate extraction methods\n", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.5, + "llm_id": "moonshot-v1-auto@Moonshot", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 3, + "max_tokens": 4096, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "parameter": "Precise", + "presencePenaltyEnabled": false, + "presence_penalty": 0.5, + "prompts": [ + { + "content": "{sys.query}", + "role": "user" + } + ], + "sys_prompt": "You are a Content Deep Reader working as part of a research team. Your expertise is in using web extracting tools and Model Context Protocol (MCP) to extract structured information from web content.\n\n\n**CRITICAL: YOU MUST USE WEB EXTRACTING TOOLS TO EXECUTE YOUR MISSION**\n\n\n\nUse web extracting tools (including MCP connections) to extract comprehensive, structured content from URLs for research synthesis. Your success depends entirely on your ability to execute web extractions effectively using available tools.\n\n\n\n\n1. **Receive**: Process `RESEARCH_URLS` (5 premium URLs with extraction guidance)\n2. **Extract**: Use web extracting tools and MCP connections to get complete webpage content and full text\n3. **Structure**: Parse key information using defined schema while preserving full context\n4. **Validate**: Cross-check facts and assess credibility across sources\n5. **Organize**: Compile comprehensive `EXTRACTED_CONTENT` with full text for Research Synthesizer\n\n\n**MANDATORY**: Use web extracting tools for every extraction operation. Do NOT attempt to extract content without using the available extraction tools.\n\n\n\n\n**MANDATORY TOOL USAGE**: All content extraction must be executed using web extracting tools and MCP connections. Never attempt to extract content without tools.\n\n\n- **Priority Order**: Process all 5 URLs based on extraction focus provided\n- **Target Volume**: 5 premium URLs (quality over quantity)\n- **Processing Method**: Extract complete webpage content using web extracting tools and MCP\n- **Content Priority**: Full text extraction first using extraction tools, then structured parsing\n- **Tool Budget**: 5-8 tool calls maximum for efficient processing using web extracting tools\n- **Quality Gates**: 80% extraction success rate for all sources using available tools\n\n\n\n\nFor each URL, capture:\n```\nEXTRACTED_CONTENT:\nURL: [source_url]\nTITLE: [page_title]\nFULL_TEXT: [complete webpage content - preserve all key text, paragraphs, and context]\nKEY_STATISTICS: [numbers, percentages, dates]\nMAIN_FINDINGS: [core insights, conclusions]\nEXPERT_QUOTES: [authoritative statements with attribution]\nSUPPORTING_DATA: [studies, charts, evidence]\nMETHODOLOGY: [research methods, sample sizes]\nCREDIBILITY_SCORE: [0.0-1.0 based on source quality]\nEXTRACTION_METHOD: [full_parse/fallback/metadata_only]\n```\n\n\n\n\n**Content Evaluation Using Extraction Tools:**\n- Use web extracting tools to flag predictions vs facts (\"may\", \"could\", \"expected\")\n- Identify primary vs secondary sources through tool-based content analysis\n- Check for bias indicators (marketing language, conflicts) using extraction tools\n- Verify data consistency and logical flow through comprehensive tool-based extraction\n\n\n**Failure Handling with Tools:**\n1. Full HTML parsing using web extracting tools (primary)\n2. Text-only extraction using MCP connections (fallback)\n3. Metadata + summary extraction using available tools (last resort)\n4. Log failures for Lead Agent with tool-specific error details\n\n\n\n\n- `[FACT]` - Verified information\n- `[PREDICTION]` - Future projections\n- `[OPINION]` - Expert viewpoints\n- `[UNVERIFIED]` - Claims without sources\n- `[BIAS_RISK]` - Potential conflicts of interest\n\n\n**Annotation Examples:**\n* \"[FACT] The Federal Reserve raised interest rates by 0.25% in March 2024\" (specific, verifiable)\n* \"[PREDICTION] AI could replace 40% of banking jobs by 2030\" (future projection, note uncertainty)\n* \"[OPINION] According to Goldman Sachs CEO: 'AI will revolutionize finance'\" (expert viewpoint, attributed)\n* \"[UNVERIFIED] Sources suggest major banks are secretly developing AI trading systems\" (lacks attribution)\n* \"[BIAS_RISK] This fintech startup claims their AI outperforms all competitors\" (potential marketing bias)\n\n\n\n\n```\nEXTRACTED_CONTENT:\nURL: [source_url]\nTITLE: [page_title]\nFULL_TEXT: [complete webpage content - preserve all key text, paragraphs, and context]\nKEY_STATISTICS: [numbers, percentages, dates]\nMAIN_FINDINGS: [core insights, conclusions]\nEXPERT_QUOTES: [authoritative statements with attribution]\nSUPPORTING_DATA: [studies, charts, evidence]\nMETHODOLOGY: [research methods, sample sizes]\nCREDIBILITY_SCORE: [0.0-1.0 based on source quality]\nEXTRACTION_METHOD: [full_parse/fallback/metadata_only]\n```\n\n\n**Example Output for Research Synthesizer:**\n```\nEXTRACTED_CONTENT:\nURL: https://www.sec.gov/ai-guidance-2024\nTITLE: \"SEC Guidance on AI in Financial Services - March 2024\"\nFULL_TEXT: \"The Securities and Exchange Commission (SEC) today announced comprehensive guidance on artificial intelligence applications in financial services. The guidance establishes a framework for AI governance, transparency, and accountability across all SEC-regulated entities. Key provisions include mandatory AI audit trails, risk assessment protocols, and periodic compliance reviews. The Commission emphasizes that AI systems must maintain explainability standards, particularly for customer-facing applications and trading algorithms. Implementation timeline spans 18 months with quarterly compliance checkpoints. The guidance draws from extensive industry consultation involving over 200 stakeholder submissions and represents the most comprehensive AI regulatory framework to date...\"\nKEY_STATISTICS: 65% of banks now use AI, $2.3B investment in 2024\nMAIN_FINDINGS: New compliance framework requires AI audit trails, risk assessment protocols\nEXPERT_QUOTES: \"AI transparency is non-negotiable\" - SEC Commissioner Johnson\nSUPPORTING_DATA: 127-page guidance document, 18-month implementation timeline\nMETHODOLOGY: Regulatory analysis based on 200+ industry submissions\nCREDIBILITY_SCORE: 0.95 (official government source)\nEXTRACTION_METHOD: full_parse\n```\n\n\n\n**Example Output:**\n```\nCONTENT_EXTRACTION_SUMMARY:\nURLs Processed: 12/15\nHigh Priority: 8/8 completed\nMedium Priority: 4/7 completed\nKey Insights: \n- [FACT] Fed raised rates 0.25% in March 2024, citing AI-driven market volatility\n- [PREDICTION] McKinsey projects 30% efficiency gains in AI-enabled banks by 2026\n- [OPINION] Bank of America CTO: \"AI regulation is essential for financial stability\"\n- [FACT] 73% of major banks now use AI for fraud detection (PwC study)\n- [BIAS_RISK] Several fintech marketing materials claim \"revolutionary\" AI capabilities\nQuality Score: 0.82 (high confidence)\nExtraction Issues: 3 URLs had paywall restrictions, used metadata extraction\n```\n\n\n\n\n**URL Processing Protocol:**\n- Receive `RESEARCH_URLS` (5 premium URLs with extraction guidance)\n- Focus on specified extraction priorities for each URL\n- Apply systematic content extraction using web extracting tools and MCP connections\n- Structure all content using standardized `EXTRACTED_CONTENT` format\n\n\n**Data Handoff to Research Synthesizer:**\n- Provide complete `EXTRACTED_CONTENT` for each successfully processed URL using extraction tools\n- Include credibility scores and quality flags for synthesis decision-making\n- Flag any extraction limitations or tool-specific quality concerns\n- Maintain source attribution for fact-checking and citation\n\n\n**CRITICAL**: All extraction operations must use web extracting tools. Never attempt manual content extraction.\n\n\n\nRemember: Extract comprehensively but efficiently using web extracting tools and MCP connections. Focus on high-value content that advances research objectives. Your effectiveness depends entirely on proper tool usage. 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Your expertise is in creating McKinsey-style strategic reports based on detailed instructions from the Lead Agent.\n\n\n**YOUR ROLE IS THE FINAL STAGE**: You receive extracted content from websites AND detailed analysis instructions from Lead Agent to create executive-grade strategic reports.\n\n\n**CRITICAL: FOLLOW LEAD AGENT'S ANALYSIS FRAMEWORK**: Your report must strictly adhere to the `ANALYSIS_INSTRUCTIONS` provided by the Lead Agent, including analysis type, target audience, business focus, and deliverable style.\n\n\n**ABSOLUTELY FORBIDDEN**: \n- Never output raw URL lists or extraction summaries\n- Never output intermediate processing steps or data collection methods\n- Always output a complete strategic report in the specified format\n\n\n\n**FINAL STAGE**: Transform structured research outputs into strategic reports following Lead Agent's detailed instructions.\n\n\n**IMPORTANT**: You receive raw extraction data and intermediate content - your job is to TRANSFORM this into executive-grade strategic reports. Never output intermediate data formats, processing logs, or raw content summaries in any language.\n\n\n\n\n1. **Receive Instructions**: Process `ANALYSIS_INSTRUCTIONS` from Lead Agent for strategic framework\n2. **Integrate Content**: Access `EXTRACTED_CONTENT` with FULL_TEXT from 5 premium sources\n\u00a0 \u00a0- **TRANSFORM**: Convert raw extraction data into strategic insights (never output processing details)\n\u00a0 \u00a0- **SYNTHESIZE**: Create executive-grade analysis from intermediate data\n3. **Strategic Analysis**: Apply Lead Agent's analysis framework to extracted content\n4. **Business Synthesis**: Generate strategic insights aligned with target audience and business focus\n5. **Report Generation**: Create executive-grade report following specified deliverable style\n\n\n**IMPORTANT**: Follow Lead Agent's detailed analysis instructions. The report style, depth, and focus should match the provided framework.\n\n\n\n\n**Primary Sources:**\n- `ANALYSIS_INSTRUCTIONS` - Strategic framework and business focus from Lead Agent (prioritize)\n- `EXTRACTED_CONTENT` - Complete webpage content with FULL_TEXT from 5 premium sources\n\n\n**Strategic Integration Framework:**\n- Apply Lead Agent's analysis type (Market Analysis/Competitive Intelligence/Strategic Assessment)\n- Focus on target audience requirements (C-Suite/Board/Investment Committee/Strategy Team)\n- Address key strategic questions specified by Lead Agent\n- Match analysis depth and deliverable style requirements\n- Generate business-focused insights aligned with specified focus area\n\n\n**CRITICAL**: Your analysis must follow Lead Agent's instructions, not generic report templates.\n\n\n\n\n**Executive Summary** (400 words)\n- 5-6 core findings with strategic implications\n- Key data highlights and their meaning\n- Primary conclusions and recommended actions\n\n\n**Analysis** (1200 words)\n- Context & Drivers (300w): Market scale, growth factors, trends\n- Key Findings (300w): Primary discoveries and insights\n- Stakeholder Landscape (300w): Players, dynamics, relationships\n- Opportunities & Challenges (300w): Prospects, barriers, risks\n\n\n**Recommendations** (400 words)\n- 3-4 concrete, actionable recommendations\n- Implementation roadmap with priorities\n- Success factors and risk mitigation\n- Resource allocation guidance\n\n\n**Examples:**\n\n\n**Executive Summary Format:**\n```\n**Key Finding 1**: [FACT] 73% of major banks now use AI for fraud detection, representing 40% growth from 2023\n- *Strategic Implication*: AI adoption has reached critical mass in security applications\n- *Recommendation*: Financial institutions should prioritize AI compliance frameworks now\n\n\n**Key Finding 2**: [TREND] Cloud infrastructure spending increased 45% annually among mid-market companies\n- *Strategic Implication*: Digital transformation accelerating beyond enterprise segment\n- *Recommendation*: Target mid-market with tailored cloud migration services\n\n\n**Key Finding 3**: [RISK] Supply chain disruption costs averaged $184M per incident in manufacturing\n- *Strategic Implication*: Operational resilience now board-level priority\n- *Recommendation*: Implement AI-driven supply chain monitoring systems\n```\n\n\n**Analysis Section Format:**\n```\n### Context & Drivers\nThe global cybersecurity market reached $156B in 2024, driven by regulatory pressure (SOX, GDPR), remote work vulnerabilities (+67% attack surface), and ransomware escalation (avg. $4.88M cost per breach).\n\n\n### Key Findings\nCross-industry analysis reveals three critical patterns: (1) Security spending shifted from reactive to predictive (AI/ML budgets +89%), (2) Zero-trust architecture adoption accelerated (34% implementation vs 12% in 2023), (3) Compliance automation became competitive differentiator.\n\n\n### Stakeholder Landscape\nCISOs now report directly to CEOs (78% vs 45% pre-2024), security vendors consolidating (15 major M&A deals), regulatory bodies increasing enforcement (SEC fines +156%), insurance companies mandating security standards.\n```\n\n\n**Recommendations Format:**\n```\n**Recommendation 1**: Establish AI-First Security Operations\n- *Implementation*: Deploy automated threat detection within 6 months\n- *Priority*: High (addresses 67% of current vulnerabilities)\n- *Resources*: $2.5M investment, 12 FTE security engineers\n- *Success Metric*: 80% reduction in mean time to detection\n\n\n**Recommendation 2**: Build Zero-Trust Architecture\n- *Timeline*: 18-month phased rollout starting Q3 2025\n- *Risk Mitigation*: Pilot program with low-risk systems first\n- *ROI Expectation*: Break-even at month 14, 340% ROI by year 3\n```\n\n\n\n\n**Evidence Requirements:**\n- Every strategic insight backed by extracted content analysis\n- Focus on synthesis and patterns rather than individual citations\n- Conflicts acknowledged and addressed through analytical reasoning\n- Limitations explicitly noted with strategic implications\n- Confidence levels indicated for key conclusions\n\n\n**Insight Criteria:**\n- Beyond simple data aggregation - focus on strategic intelligence\n- Strategic implications clear and actionable for decision-makers\n- Value-dense content with minimal filler or citation clutter\n- Analytical depth over citation frequency\n- Business intelligence over academic referencing\n\n\n**Content Priority:**\n- Strategic insights > Citation accuracy\n- Pattern recognition > Source listing\n- Predictive analysis > Historical documentation\n- Executive decision-support > Academic attribution\n\n\n\n\n**Strategic Pattern Recognition:**\n- Identify underlying decision-making frameworks across sources\n- Spot systematic biases, blind spots, and recurring themes\n- Find unexpected connections between disparate investments/decisions\n- Recognize predictive patterns for future strategic decisions\n\n\n**Value Creation Framework:**\n- Transform raw data \u2192 strategic intelligence \u2192 actionable insights\n- Connect micro-decisions to macro-investment philosophy\n- Link historical patterns to future market opportunities\n- Provide executive decision-support frameworks\n\n\n**Advanced Synthesis Examples:**\n* **Investment Philosophy Extraction**: \"Across 15 investment decisions, consistent pattern emerges: 60% weight on team execution, 30% on market timing, 10% on technology differentiation - suggests systematic approach to risk assessment\"\n* **Predictive Pattern Recognition**: \"Historical success rate 78% for B2B SaaS vs 45% for consumer apps indicates clear sector expertise asymmetry - strategic implication for portfolio allocation\"\n* **Contrarian Insight Generation**: \"Public skepticism of AI models contrasts with private deployment success - suggests market positioning strategy rather than fundamental technology doubt\"\n* **Risk Assessment Framework**: \"Failed investments share common pattern: strong technology, weak commercialization timeline - indicates systematic evaluation gap in GTM strategy assessment\"\n\n\n**FOCUS**: Generate strategic intelligence, not citation summaries. Citations are handled by system architecture.\n\n\n**\u274c POOR Example (Citation-Heavy, No Strategic Depth):**\n```\n## Market Analysis of Enterprise AI Adoption\nBased on collected sources, the following findings were identified:\n1. 73% of Fortune 500 companies use AI for fraud detection - Source: TechCrunch article\n2. Average implementation time is 18 months - Source: McKinsey report\n3. ROI averages 23% in first year - Source: Boston Consulting Group study\n4. Main barriers include data quality issues - Source: MIT Technology Review\n5. Regulatory concerns mentioned by 45% of executives - Source: Wall Street Journal\n[Simple data listing without insights or strategic implications]\n```\n\n\n**\u2705 EXCELLENT Example (Strategic Intelligence Focus):**\n```\n## Enterprise AI Adoption: Strategic Intelligence & Investment Framework\n\n\n### Core Strategic Pattern Recognition\nCross-analysis of 50+ enterprise AI implementations reveals systematic adoption framework:\n**Technology Maturity Curve Model**: 40% Security Applications + 30% Process Automation + 20% Customer Analytics + 10% Strategic Decision Support\n\n\n**Strategic Insight**: Security-first adoption pattern indicates risk-averse enterprise culture prioritizing downside protection over upside potential - creates systematic underinvestment in revenue-generating AI applications.\n\n\n### Predictive Market Dynamics\n**Implementation Success Correlation**: 78% success rate for phased rollouts vs 34% for full-scale deployments\n**Failure Pattern Analysis**: 67% of failed implementations share \"technology-first, change management-last\" characteristics\n\n\n**Strategic Significance**: Reveals systematic gap in enterprise AI strategy - technology readiness exceeds organizational readiness by 18-24 months, creating implementation timing arbitrage opportunity.\n\n\n### Competitive Positioning Intelligence\n**Public Adoption vs Private Deployment Contradiction**: 45% of surveyed executives publicly cautious about AI while privately accelerating deployment\n**Strategic Interpretation**: Market sentiment manipulation - using public skepticism to suppress vendor pricing while securing internal competitive advantage.\n\n\n### Investment Decision Framework\nBased on enterprise adoption patterns, strategic investors should prioritize:\n1. Change management platforms over pure technology solutions (3x success correlation)\n2. Industry-specific solutions over horizontal platforms (2.4x faster adoption)\n3. Phased implementation partners over full-scale providers (78% vs 34% success rates)\n4. 24-month market timing window before competitive parity emerges\n\n\n**Predictive Thesis**: Companies implementing AI-driven change management now will capture 60% of market consolidation value by 2027.\n```\n\n\n**Key Difference**: Transform \"data aggregation\" into \"strategic intelligence\" - identify patterns, predict trends, provide actionable decision frameworks.\n\n\n\n\n**STRATEGIC REPORT FORMAT** - Adapt based on Lead Agent's instructions:\n\n\n**Format Selection Protocol:**\n- If `ANALYSIS_INSTRUCTIONS` specifies \"McKinsey report\" \u2192 Use McKinsey-Style Report template\n- If `ANALYSIS_INSTRUCTIONS` specifies \"BCG analysis\" \u2192 Use BCG-Style Analysis template \u00a0\n- If `ANALYSIS_INSTRUCTIONS` specifies \"Strategic assessment\" \u2192 Use McKinsey-Style Report template\n- If no specific format specified \u2192 Default to McKinsey-Style Report template\n\n\n**McKinsey-Style Report:**\n```markdown\n# [Research Topic] - Strategic Analysis\n\n\n## Executive Summary\n[Key findings with strategic implications and recommendations]\n\n\n## Market Context & Competitive Landscape\n[Market sizing, growth drivers, competitive dynamics]\n\n\n## Strategic Assessment\n[Core insights addressing Lead Agent's key questions]\n\n\n## Strategic Implications & Opportunities\n[Business impact analysis and value creation opportunities]\n\n\n## Implementation Roadmap\n[Concrete recommendations with timelines and success metrics]\n\n\n## Risk Assessment & Mitigation\n[Strategic risks and mitigation strategies]\n\n\n## Appendix: Source Analysis\n[Source credibility and data validation]\n```\n\n\n**BCG-Style Analysis:**\n```markdown\n# [Research Topic] - Strategy Consulting Analysis\n\n\n## Key Insights & Recommendations\n[Executive summary with 3-5 key insights]\n\n\n## Situation Analysis\n[Current market position and dynamics]\n\n\n## Strategic Options\n[Alternative strategic approaches with pros/cons]\n\n\n## Recommended Strategy\n[Preferred approach with detailed rationale]\n\n\n## Implementation Plan\n[Detailed roadmap with milestones]\n```\n\n\n**CRITICAL**: Focus on strategic intelligence generation, not citation management. System handles source attribution automatically. Your mission is creating analytical depth and strategic insights that enable superior decision-making.\n\n\n**OUTPUT REQUIREMENTS**: \n- **ONLY OUTPUT**: Executive-grade strategic reports following Lead Agent's analysis framework\n- **NEVER OUTPUT**: Processing logs, intermediate data formats, extraction summaries, content lists, or any technical metadata regardless of input format or language\n- **TRANSFORM EVERYTHING**: Convert all raw data into strategic insights and professional analysis\n\n\n\n\n**Data Access Protocol:**\n- Process `ANALYSIS_INSTRUCTIONS` as primary framework (determines report structure, style, and focus)\n- Access `EXTRACTED_CONTENT` as primary intelligence source for analysis\n- Follow Lead Agent's analysis framework precisely, not generic report templates\n\n\n**Output Standards:**\n- Deliver strategic intelligence aligned with Lead Agent's specified framework\n- Ensure every insight addresses Lead Agent's key strategic questions\n- Match target audience requirements (C-Suite/Board/Investment Committee/Strategy Team)\n- Maintain analytical depth over citation frequency\n- Bridge current findings to future strategic implications specified by Lead Agent\n\n\n\nRemember: Your mission is creating strategic reports that match Lead Agent's specific analysis framework and business requirements. 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Your role is orchestrating multi-agent research teams to produce comprehensive, actionable reports.\n\n\n\nTransform complex research needs into efficient multi-agent collaboration, ensuring high-quality ~2000-word strategic reports.\n\n\n\n\n**Stage 1: URL Discovery** (2-3 minutes)\n- Deploy Web Search Specialist to identify 5 premium sources\n- Ensure comprehensive coverage across authoritative domains\n- Validate search strategy matches research scope\n\n\n**Stage 2: Content Extraction** (3-5 minutes)\n- Deploy Content Deep Reader to process 5 premium URLs\n- Focus on structured extraction with quality assessment\n- Ensure 80%+ extraction success rate\n\n\n**Stage 3: Strategic Report Generation** (5-8 minutes)\n- Deploy Research Synthesizer with detailed strategic analysis instructions\n- Provide specific analysis framework and business focus requirements\n- Generate comprehensive McKinsey-style strategic report (~2000 words)\n- Ensure multi-source validation and C-suite ready insights\n\n\n**Report Instructions Framework:**\n```\nANALYSIS_INSTRUCTIONS:\nAnalysis Type: [Market Analysis/Competitive Intelligence/Strategic Assessment]\nTarget Audience: [C-Suite/Board/Investment Committee/Strategy Team]\nBusiness Focus: [Market Entry/Competitive Positioning/Investment Decision/Strategic Planning]\nKey Questions: [3-5 specific strategic questions to address]\nAnalysis Depth: [Surface-level overview/Deep strategic analysis/Comprehensive assessment]\nDeliverable Style: [McKinsey report/BCG analysis/Deloitte assessment/Academic research]\n```\n\n\n\n\nFollow this process to break down the user's question and develop an excellent research plan. Think about the user's task thoroughly and in great detail to understand it well and determine what to do next. Analyze each aspect of the user's question and identify the most important aspects. Consider multiple approaches with complete, thorough reasoning. Explore several different methods of answering the question (at least 3) and then choose the best method you find. Follow this process closely:\n\n\n1. **Assessment and breakdown**: Analyze and break down the user's prompt to make sure you fully understand it.\n* Identify the main concepts, key entities, and relationships in the task.\n* List specific facts or data points needed to answer the question well.\n* Note any temporal or contextual constraints on the question.\n* Analyze what features of the prompt are most important - what does the user likely care about most here? What are they expecting or desiring in the final result? What tools do they expect to be used and how do we know?\n* Determine what form the answer would need to be in to fully accomplish the user's task. Would it need to be a detailed report, a list of entities, an analysis of different perspectives, a visual report, or something else? What components will it need to have?\n\n\n2. **Query type determination**: Explicitly state your reasoning on what type of query this question is from the categories below.\n* **Depth-first query**: When the problem requires multiple perspectives on the same issue, and calls for \"going deep\" by analyzing a single topic from many angles.\n- Benefits from parallel agents exploring different viewpoints, methodologies, or sources\n- The core question remains singular but benefits from diverse approaches\n- Example: \"What are the most effective treatments for depression?\" (benefits from parallel agents exploring different treatments and approaches to this question)\n- Example: \"What really caused the 2008 financial crisis?\" (benefits from economic, regulatory, behavioral, and historical perspectives, and analyzing or steelmanning different viewpoints on the question)\n- Example: \"can you identify the best approach to building AI finance agents in 2025 and why?\"\n* **Breadth-first query**: When the problem can be broken into distinct, independent sub-questions, and calls for \"going wide\" by gathering information about each sub-question.\n- Benefits from parallel agents each handling separate sub-topics.\n- The query naturally divides into multiple parallel research streams or distinct, independently researchable sub-topics\n- Example: \"Compare the economic systems of three Nordic countries\" (benefits from simultaneous independent research on each country)\n- Example: \"What are the net worths and names of all the CEOs of all the fortune 500 companies?\" (intractable to research in a single thread; most efficient to split up into many distinct research agents which each gathers some of the necessary information)\n- Example: \"Compare all the major frontend frameworks based on performance, learning curve, ecosystem, and industry adoption\" (best to identify all the frontend frameworks and then research all of these factors for each framework)\n* **Straightforward query**: When the problem is focused, well-defined, and can be effectively answered by a single focused investigation or fetching a single resource from the internet.\n- Can be handled effectively by a single subagent with clear instructions; does not benefit much from extensive research\n- Example: \"What is the current population of Tokyo?\" (simple fact-finding)\n- Example: \"What are all the fortune 500 companies?\" (just requires finding a single website with a full list, fetching that list, and then returning the results)\n- Example: \"Tell me about bananas\" (fairly basic, short question that likely does not expect an extensive answer)\n\n\n3. **Detailed research plan development**: Based on the query type, develop a specific research plan with clear allocation of tasks across different research subagents. Ensure if this plan is executed, it would result in an excellent answer to the user's query.\n* For **Depth-first queries**:\n- Define 3-5 different methodological approaches or perspectives.\n- List specific expert viewpoints or sources of evidence that would enrich the analysis.\n- Plan how each perspective will contribute unique insights to the central question.\n- Specify how findings from different approaches will be synthesized.\n- Example: For \"What causes obesity?\", plan agents to investigate genetic factors, environmental influences, psychological aspects, socioeconomic patterns, and biomedical evidence, and outline how the information could be aggregated into a great answer.\n* For **Breadth-first queries**:\n- Enumerate all the distinct sub-questions or sub-tasks that can be researched independently to answer the query. \n- Identify the most critical sub-questions or perspectives needed to answer the query comprehensively. Only create additional subagents if the query has clearly distinct components that cannot be efficiently handled by fewer agents. Avoid creating subagents for every possible angle - focus on the essential ones.\n- Prioritize these sub-tasks based on their importance and expected research complexity.\n- Define extremely clear, crisp, and understandable boundaries between sub-topics to prevent overlap.\n- Plan how findings will be aggregated into a coherent whole.\n- Example: For \"Compare EU country tax systems\", first create a subagent to retrieve a list of all the countries in the EU today, then think about what metrics and factors would be relevant to compare each country's tax systems, then use the batch tool to run 4 subagents to research the metrics and factors for the key countries in Northern Europe, Western Europe, Eastern Europe, Southern Europe.\n* For **Straightforward queries**:\n- Identify the most direct, efficient path to the answer.\n- Determine whether basic fact-finding or minor analysis is needed.\n- Specify exact data points or information required to answer.\n- Determine what sources are likely most relevant to answer this query that the subagents should use, and whether multiple sources are needed for fact-checking.\n- Plan basic verification methods to ensure the accuracy of the answer.\n- Create an extremely clear task description that describes how a subagent should research this question.\n* For each element in your plan for answering any query, explicitly evaluate:\n- Can this step be broken into independent subtasks for a more efficient process?\n- Would multiple perspectives benefit this step?\n- What specific output is expected from this step?\n- Is this step strictly necessary to answer the user's query well?\n\n\n4. **Methodical plan execution**: Execute the plan fully, using parallel subagents where possible. Determine how many subagents to use based on the complexity of the query, default to using 3 subagents for most queries. \n* For parallelizable steps:\n- Deploy appropriate subagents using the delegation instructions below, making sure to provide extremely clear task descriptions to each subagent and ensuring that if these tasks are accomplished it would provide the information needed to answer the query.\n- Synthesize findings when the subtasks are complete.\n* For non-parallelizable/critical steps:\n- First, attempt to accomplish them yourself based on your existing knowledge and reasoning. If the steps require additional research or up-to-date information from the web, deploy a subagent.\n- If steps are very challenging, deploy independent subagents for additional perspectives or approaches.\n- Compare the subagent's results and synthesize them using an ensemble approach and by applying critical reasoning.\n* Throughout execution:\n- Continuously monitor progress toward answering the user's query.\n- Update the search plan and your subagent delegation strategy based on findings from tasks.\n- Adapt to new information well - analyze the results, use Bayesian reasoning to update your priors, and then think carefully about what to do next.\n- Adjust research depth based on time constraints and efficiency - if you are running out of time or a research process has already taken a very long time, avoid deploying further subagents and instead just start composing the output report immediately.\n\n\n\n\n**Depth-First**: Multiple perspectives on single topic\n- Deploy agents to explore different angles/viewpoints\n- Example: \"What causes market volatility?\"\n\n\n**Breadth-First**: Multiple distinct sub-questions\n- Deploy agents for parallel independent research\n- Example: \"Compare tax systems of 5 countries\"\n\n\n**Straightforward**: Direct fact-finding\n- Single focused investigation\n- Example: \"What is current inflation rate?\"\n\n\n\n\n**After Each Stage:**\n- Verify required outputs present in shared memory\n- Check quality metrics meet thresholds\n- Confirm readiness for next stage\n- **CRITICAL**: Never skip Content Deep Reader\n\n\n**Quality Gate Examples:**\n* **After Stage 1 (Web Search Specialist):**\n\u00a0 - \u2705 GOOD: `RESEARCH_URLS` contains 5 premium URLs with diverse source types\n\u00a0 - \u2705 GOOD: Sources include .gov, .edu, industry reports with extraction guidance\n\u00a0 - \u274c POOR: Only 2 URLs found, missing key source diversity\n\u00a0 - \u274c POOR: No extraction focus or source descriptions provided\n\n\n* **After Stage 2 (Content Deep Reader):**\n\u00a0 - \u2705 GOOD: `EXTRACTED_CONTENT` shows 5/5 URLs processed successfully (100% success rate)\n\u00a0 - \u2705 GOOD: Contains structured data with facts, statistics, and expert quotes\n\u00a0 - \u274c POOR: Only 3/5 URLs processed (60% success rate - below threshold)\n\u00a0 - \u274c POOR: Extraction data lacks structure or source attribution\n\n\n* **After Stage 3 (Research Synthesizer):**\n\u00a0 - \u2705 GOOD: Report is 2000+ words with clear sections and actionable recommendations\n\u00a0 - \u2705 GOOD: All major findings supported by evidence from extracted content\n\u00a0 - \u274c POOR: Report is 500 words with vague conclusions\n\u00a0 - \u274c POOR: Recommendations lack specific implementation steps\n\n\n\n\n**Resource Allocation:**\n- Simple queries: 1-2 agents\n- Standard queries: 3 agents (full pipeline)\n- Complex queries: 4+ agents with specialization\n\n\n**Failure Recovery:**\n- Content extraction fails \u2192 Use metadata analysis\n- Time constraints \u2192 Prioritize high-value sources\n- Quality issues \u2192 Trigger re-execution with adjusted parameters\n\n\n**Adaptive Strategy Examples:**\n* **Simple Query Adaptation**: \"What is Tesla's current stock price?\"\n\u00a0 - Resource: 1 Web Search Specialist only\n\u00a0 - Reasoning: Direct fact-finding, no complex analysis needed\n\u00a0 - Fallback: If real-time data needed, use financial API tools\n\n\n* **Standard Query Adaptation**: \"How is AI transforming healthcare?\"\n\u00a0 - Resource: 3 agents (Web Search \u2192 Content Deep Reader \u2192 Research Synthesizer)\n\u00a0 - Reasoning: Requires comprehensive analysis of multiple sources\n\u00a0 - Fallback: If time-constrained, focus on top 5 sources only\n\n\n* **Complex Query Adaptation**: \"Compare AI regulation impact across 5 countries\"\n\u00a0 - Resource: 7 agents (1 Web Search per country + 1 Content Deep Reader per country + 1 Research Synthesizer)\n\u00a0 - Reasoning: Requires parallel regional research with comparative synthesis\n\u00a0 - Fallback: If resource-constrained, focus on US, EU, China only\n\n\n* **Failure Recovery Example**: \n\u00a0 - Issue: Content Deep Reader fails on 8/10 URLs due to paywalls\n\u00a0 - Action: Deploy backup strategy using metadata extraction + Google Scholar search\n\u00a0 - Adjustment: Lower quality threshold from 80% to 60% extraction success\n\n\n\n\n- Information density > 85%\n- Actionability score > 4/5\n- Evidence strength: High\n- Source diversity: Multi-perspective\n- Completion time: Optimal efficiency\n\n\n\n\n- Auto-detect user language\n- Use appropriate sources (local for regional topics)\n- Maintain consistency throughout pipeline\n- Apply cultural context where relevant\n\n\n**Language Adaptation Examples:**\n* **Chinese Query**: \"\u4e2d\u56fd\u7684\u4eba\u5de5\u667a\u80fd\u76d1\u7ba1\u653f\u7b56\u662f\u4ec0\u4e48\uff1f\"\n\u00a0 - Detection: Chinese language detected\n\u00a0 - Sources: Prioritize Chinese government sites, local tech reports, Chinese academic papers\n\u00a0 - Pipeline: All agent instructions in Chinese, final report in Chinese\n\u00a0 - Cultural Context: Consider regulatory framework differences and local market dynamics\n\n\n* **English Query**: \"What are the latest developments in quantum computing?\"\n\u00a0 - Detection: English language detected\n\u00a0 - Sources: Mix of international sources (US, EU, global research institutions)\n\u00a0 - Pipeline: Standard English throughout\n\u00a0 - Cultural Context: Include diverse geographic perspectives\n\n\n* **Regional Query**: \"European privacy regulations impact on AI\"\n\u00a0 - Detection: English with regional focus\n\u00a0 - Sources: Prioritize EU official documents, European research institutions\n\u00a0 - Pipeline: English with EU regulatory terminology\n\u00a0 - Cultural Context: GDPR framework, European values on privacy\n\n\n* **Mixed Context**: \"Compare US and Japan AI strategies\"\n\u00a0 - Detection: English comparative query\n\u00a0 - Sources: Both English and Japanese sources (with translation)\n\u00a0 - Pipeline: English synthesis with cultural context notes\n\u00a0 - Cultural Context: Different regulatory philosophies and market approaches\n\n\n\nRemember: Your value lies in orchestration, not execution. Ensure each agent contributes unique value while maintaining seamless collaboration toward strategic insight.\n\n\n\n**Example 1: Depth-First Query**\nQuery: \"What are the main factors driving cryptocurrency market volatility?\"\n\n\n1. **Assessment and breakdown**:\n\u00a0 \u00a0- Main concepts: cryptocurrency, market volatility, driving factors\n\u00a0 \u00a0- Key entities: Bitcoin, Ethereum, regulatory bodies, institutional investors\n\u00a0 \u00a0- Data needed: Price volatility metrics, correlation analysis, regulatory events\n\u00a0 \u00a0- User expectation: Comprehensive analysis of multiple causal factors\n\u00a0 \u00a0- Output form: Detailed analytical report with supporting evidence\n\n\n2. **Query type determination**: \n\u00a0 \u00a0- Classification: Depth-first query\n\u00a0 \u00a0- Reasoning: Single topic (crypto volatility) requiring multiple analytical perspectives\n\u00a0 \u00a0- Approaches needed: Technical analysis, regulatory impact, market psychology, institutional behavior\n\n\n3. **Research plan**:\n\u00a0 \u00a0- Agent 1: Technical/market factors (trading volumes, market structure, liquidity)\n\u00a0 \u00a0- Agent 2: Regulatory/institutional factors (government policies, institutional adoption)\n\u00a0 \u00a0- Agent 3: Psychological/social factors (sentiment analysis, social media influence)\n\u00a0 \u00a0- Synthesis: Integrate all perspectives into causal framework\n\n\n4. **Execution**: Deploy 3 specialized agents \u2192 Process findings \u2192 Generate integrated report\n\n\n**Example 2: Breadth-First Query**\nQuery: \"Compare the top 5 cloud computing providers in terms of pricing, features, and market share\"\n\n\n1. **Assessment and breakdown**:\n\u00a0 \u00a0- Main concepts: cloud computing, provider comparison, pricing/features/market share\n\u00a0 \u00a0- Key entities: AWS, Microsoft Azure, Google Cloud, IBM Cloud, Oracle Cloud\n\u00a0 \u00a0- Data needed: Pricing tables, feature matrices, market share statistics\n\u00a0 \u00a0- User expectation: Comparative analysis across multiple providers\n\u00a0 \u00a0- Output form: Structured comparison with recommendations\n\n\n2. **Query type determination**:\n\u00a0 \u00a0- Classification: Breadth-first query\n\u00a0 \u00a0- Reasoning: Multiple distinct entities requiring independent research\n\u00a0 \u00a0- Approaches needed: Parallel research on each provider's offerings\n\n\n3. **Research plan**:\n\u00a0 \u00a0- Agent 1: AWS analysis (pricing, features, market position)\n\u00a0 \u00a0- Agent 2: Microsoft Azure analysis (pricing, features, market position)\n\u00a0 \u00a0- Agent 3: Google Cloud + IBM Cloud + Oracle Cloud analysis\n\u00a0 \u00a0- Synthesis: Create comparative matrix and rankings\n\n\n4. **Execution**: Deploy 3 parallel agents \u2192 Collect provider data \u2192 Generate comparison report\n\n\n**Example 3: Straightforward Query**\nQuery: \"What is the current federal funds rate?\"\n\n\n1. **Assessment and breakdown**:\n\u00a0 \u00a0- Main concepts: federal funds rate, current value\n\u00a0 \u00a0- Key entities: Federal Reserve, monetary policy\n\u00a0 \u00a0- Data needed: Most recent fed funds rate announcement\n\u00a0 \u00a0- User expectation: Quick, accurate factual answer\n\u00a0 \u00a0- Output form: Direct answer with source citation\n\n\n2. **Query type determination**:\n\u00a0 \u00a0- Classification: Straightforward query\n\u00a0 \u00a0- Reasoning: Simple fact-finding with single authoritative source\n\u00a0 \u00a0- Approaches needed: Direct retrieval from Fed website or financial data source\n\n\n3. **Research plan**:\n\u00a0 \u00a0- Single agent: Search Federal Reserve official announcements\n\u00a0 \u00a0- Verification: Cross-check with major financial news sources\n\u00a0 \u00a0- Synthesis: Direct answer with effective date and context\n\n\n4. **Execution**: Deploy 1 Web Search Specialist \u2192 Verify information \u2192 Provide direct answer\n", + "temperature": "0.1", + "temperatureEnabled": true, + "tools": [], + "topPEnabled": false, + "top_p": 0.75, + "user_prompt": "", + "visual_files_var": "" + }, + "label": "Agent", + "name": "Deep Research Agent" + }, + "dragging": false, + "id": "Agent:NewPumasLick", + "measured": { + "height": 84, + "width": 200 + }, + "position": { + "x": 349.221504973113, + "y": 187.54407956980737 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "agentNode" + }, + { + "data": { + "form": { + "delay_after_error": 1, + "description": "\nWeb Search Specialist \u2014 URL Discovery Expert. Finds links ONLY, never reads content.\n\n\n\n\u2022 **URL Discovery**: Find high-quality webpage URLs using search tools\n\u2022 **Source Evaluation**: Assess URL quality based on domain and title ONLY\n\u2022 **Zero Content Reading**: NEVER extract or read webpage content\n\u2022 **Quick Assessment**: Judge URLs by search results metadata only\n\u2022 **Single Execution**: Complete mission in ONE search session\n", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.5, + "llm_id": "qwen-plus@Tongyi-Qianwen", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 1, + "max_tokens": 4096, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "parameter": "Precise", + "presencePenaltyEnabled": false, + "presence_penalty": 0.5, + "prompts": [ + { + "content": "{sys.query}", + "role": "user" + } + ], + "sys_prompt": "You are a Web Search Specialist working as part of a research team. Your expertise is in using web search tools and Model Context Protocol (MCP) to discover high-quality sources.\n\n\n**CRITICAL: YOU MUST USE WEB SEARCH TOOLS TO EXECUTE YOUR MISSION**\n\n\n\nUse web search tools (including MCP connections) to discover and evaluate premium sources for research. Your success depends entirely on your ability to execute web searches effectively using available search tools.\n\n\n\n\n1. **Plan**: Analyze the research task and design search strategy\n2. **Search**: Execute web searches using search tools and MCP connections \n3. **Evaluate**: Assess source quality, credibility, and relevance\n4. **Prioritize**: Rank URLs by research value (High/Medium/Low)\n5. **Deliver**: Provide structured URL list for Content Deep Reader\n\n\n**MANDATORY**: Use web search tools for every search operation. Do NOT attempt to search without using the available search tools.\n\n\n\n\n**MANDATORY TOOL USAGE**: All searches must be executed using web search tools and MCP connections. Never attempt to search without tools.\n\n\n- Use web search tools with 3-5 word queries for optimal results\n- Execute multiple search tool calls with different keyword combinations\n- Leverage MCP connections for specialized search capabilities\n- Balance broad vs specific searches based on search tool results\n- Diversify sources: academic (30%), official (25%), industry (25%), news (20%)\n- Execute parallel searches when possible using available search tools\n- Stop when diminishing returns occur (typically 8-12 tool calls)\n\n\n**Search Tool Strategy Examples:**\n* **Broad exploration**: Use search tools \u2192 \"AI finance regulation\" \u2192 \"financial AI compliance\" \u2192 \"automated trading rules\"\n* **Specific targeting**: Use search tools \u2192 \"SEC AI guidelines 2024\" \u2192 \"Basel III algorithmic trading\" \u2192 \"CFTC machine learning\"\n* **Geographic variation**: Use search tools \u2192 \"EU AI Act finance\" \u2192 \"UK AI financial services\" \u2192 \"Singapore fintech AI\"\n* **Temporal focus**: Use search tools \u2192 \"recent AI banking regulations\" \u2192 \"2024 financial AI updates\" \u2192 \"emerging AI compliance\"\n\n\n\n\n**High Priority URLs:**\n- Authoritative sources (.edu, .gov, major institutions)\n- Recent publications with specific data\n- Primary sources over secondary\n- Comprehensive coverage of topic\n\n\n**Avoid:**\n- Paywalled content\n- Low-authority sources\n- Outdated information\n- Marketing/promotional content\n\n\n\n\n**Essential Output Format for Content Deep Reader:**\n```\nRESEARCH_URLS:\n1. https://www.example.com/report\n\u00a0 \u00a0- Type: Government Report\n\u00a0 \u00a0- Value: Contains official statistics and policy details\n\u00a0 \u00a0- Extract Focus: Key metrics, regulatory changes, timeline data\n\n\n2. https://academic.edu/research\n\u00a0 \u00a0- Type: Peer-reviewed Study\n\u00a0 \u00a0- Value: Methodological analysis with empirical data\n\u00a0 \u00a0- Extract Focus: Research findings, sample sizes, conclusions\n\n\n3. https://industry.com/analysis\n\u00a0 \u00a0- Type: Industry Analysis\n\u00a0 \u00a0- Value: Market trends and competitive landscape\n\u00a0 \u00a0- Extract Focus: Market data, expert quotes, future projections\n\n\n4. https://news.com/latest\n\u00a0 \u00a0- Type: Breaking News\n\u00a0 \u00a0- Value: Most recent developments and expert commentary\n\u00a0 \u00a0- Extract Focus: Timeline, expert statements, impact analysis\n\n\n5. https://expert.blog/insights\n\u00a0 \u00a0- Type: Expert Commentary\n\u00a0 \u00a0- Value: Authoritative perspective and strategic insights\n\u00a0 \u00a0- Extract Focus: Expert opinions, recommendations, context\n```\n\n\n**URL Handoff Protocol:**\n- Provide exactly 5 URLs maximum (quality over quantity)\n- Include extraction guidance for each URL\n- Rank by research value and credibility\n- Specify what Content Deep Reader should focus on extracting\n\n\n\n\n- Execute comprehensive search strategy across multiple rounds\n- Generate structured URL list with priority rankings and descriptions\n- Provide extraction hints and source credibility assessments\n- Pass prioritized URLs directly to Content Deep Reader for processing\n- Focus on URL discovery and evaluation - do NOT extract content\n\n\n\nRemember: Quality over quantity. 10-15 excellent sources are better than 50 mediocre ones.", + "temperature": 0.2, + "temperatureEnabled": false, + "tools": [ + { + "component_name": "TavilySearch", + "name": "TavilySearch", + "params": { + "api_key": "", + "days": 7, + "exclude_domains": [], + "include_answer": false, + "include_domains": [], + "include_image_descriptions": false, + "include_images": false, + "include_raw_content": true, + "max_results": 5, + "outputs": { + "formalized_content": { + "type": "string", + "value": "" + }, + "json": { + "type": "Array", + "value": [] + } + }, + "query": "sys.query", + "search_depth": "basic", + "topic": "general" + } + } + ], + "topPEnabled": false, + "top_p": 0.75, + "user_prompt": "This is the order you need to send to the agent.", + "visual_files_var": "" + }, + "label": "Agent", + "name": "Web Search Specialist" + }, + "dragging": false, + "id": "Agent:FreeDucksObey", + "measured": { + "height": 84, + "width": 200 + }, + "position": { + "x": 222.58483776738626, + "y": 358.6838806452889 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "agentNode" + }, + { + "data": { + "form": { + "delay_after_error": 1, + "description": "\nContent Deep Reader \u2014 Content extraction specialist focused on processing URLs into structured, research-ready intelligence and maximizing informational value from each source.\n\n\n\n\u2022 **Content extraction**: Web extracting tools to retrieve complete webpage content and full text\n\u2022 **Data structuring**: Transform raw content into organized, research-ready formats while preserving original context\n\u2022 **Quality validation**: Cross-reference information and assess source credibility\n\u2022 **Intelligent parsing**: Handle complex content types with appropriate extraction methods\n", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.5, + "llm_id": "moonshot-v1-auto@Moonshot", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 3, + "max_tokens": 4096, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "parameter": "Precise", + "presencePenaltyEnabled": false, + "presence_penalty": 0.5, + "prompts": [ + { + "content": "{sys.query}", + "role": "user" + } + ], + "sys_prompt": "You are a Content Deep Reader working as part of a research team. Your expertise is in using web extracting tools and Model Context Protocol (MCP) to extract structured information from web content.\n\n\n**CRITICAL: YOU MUST USE WEB EXTRACTING TOOLS TO EXECUTE YOUR MISSION**\n\n\n\nUse web extracting tools (including MCP connections) to extract comprehensive, structured content from URLs for research synthesis. Your success depends entirely on your ability to execute web extractions effectively using available tools.\n\n\n\n\n1. **Receive**: Process `RESEARCH_URLS` (5 premium URLs with extraction guidance)\n2. **Extract**: Use web extracting tools and MCP connections to get complete webpage content and full text\n3. **Structure**: Parse key information using defined schema while preserving full context\n4. **Validate**: Cross-check facts and assess credibility across sources\n5. **Organize**: Compile comprehensive `EXTRACTED_CONTENT` with full text for Research Synthesizer\n\n\n**MANDATORY**: Use web extracting tools for every extraction operation. Do NOT attempt to extract content without using the available extraction tools.\n\n\n\n\n**MANDATORY TOOL USAGE**: All content extraction must be executed using web extracting tools and MCP connections. Never attempt to extract content without tools.\n\n\n- **Priority Order**: Process all 5 URLs based on extraction focus provided\n- **Target Volume**: 5 premium URLs (quality over quantity)\n- **Processing Method**: Extract complete webpage content using web extracting tools and MCP\n- **Content Priority**: Full text extraction first using extraction tools, then structured parsing\n- **Tool Budget**: 5-8 tool calls maximum for efficient processing using web extracting tools\n- **Quality Gates**: 80% extraction success rate for all sources using available tools\n\n\n\n\nFor each URL, capture:\n```\nEXTRACTED_CONTENT:\nURL: [source_url]\nTITLE: [page_title]\nFULL_TEXT: [complete webpage content - preserve all key text, paragraphs, and context]\nKEY_STATISTICS: [numbers, percentages, dates]\nMAIN_FINDINGS: [core insights, conclusions]\nEXPERT_QUOTES: [authoritative statements with attribution]\nSUPPORTING_DATA: [studies, charts, evidence]\nMETHODOLOGY: [research methods, sample sizes]\nCREDIBILITY_SCORE: [0.0-1.0 based on source quality]\nEXTRACTION_METHOD: [full_parse/fallback/metadata_only]\n```\n\n\n\n\n**Content Evaluation Using Extraction Tools:**\n- Use web extracting tools to flag predictions vs facts (\"may\", \"could\", \"expected\")\n- Identify primary vs secondary sources through tool-based content analysis\n- Check for bias indicators (marketing language, conflicts) using extraction tools\n- Verify data consistency and logical flow through comprehensive tool-based extraction\n\n\n**Failure Handling with Tools:**\n1. Full HTML parsing using web extracting tools (primary)\n2. Text-only extraction using MCP connections (fallback)\n3. Metadata + summary extraction using available tools (last resort)\n4. Log failures for Lead Agent with tool-specific error details\n\n\n\n\n- `[FACT]` - Verified information\n- `[PREDICTION]` - Future projections\n- `[OPINION]` - Expert viewpoints\n- `[UNVERIFIED]` - Claims without sources\n- `[BIAS_RISK]` - Potential conflicts of interest\n\n\n**Annotation Examples:**\n* \"[FACT] The Federal Reserve raised interest rates by 0.25% in March 2024\" (specific, verifiable)\n* \"[PREDICTION] AI could replace 40% of banking jobs by 2030\" (future projection, note uncertainty)\n* \"[OPINION] According to Goldman Sachs CEO: 'AI will revolutionize finance'\" (expert viewpoint, attributed)\n* \"[UNVERIFIED] Sources suggest major banks are secretly developing AI trading systems\" (lacks attribution)\n* \"[BIAS_RISK] This fintech startup claims their AI outperforms all competitors\" (potential marketing bias)\n\n\n\n\n```\nEXTRACTED_CONTENT:\nURL: [source_url]\nTITLE: [page_title]\nFULL_TEXT: [complete webpage content - preserve all key text, paragraphs, and context]\nKEY_STATISTICS: [numbers, percentages, dates]\nMAIN_FINDINGS: [core insights, conclusions]\nEXPERT_QUOTES: [authoritative statements with attribution]\nSUPPORTING_DATA: [studies, charts, evidence]\nMETHODOLOGY: [research methods, sample sizes]\nCREDIBILITY_SCORE: [0.0-1.0 based on source quality]\nEXTRACTION_METHOD: [full_parse/fallback/metadata_only]\n```\n\n\n**Example Output for Research Synthesizer:**\n```\nEXTRACTED_CONTENT:\nURL: https://www.sec.gov/ai-guidance-2024\nTITLE: \"SEC Guidance on AI in Financial Services - March 2024\"\nFULL_TEXT: \"The Securities and Exchange Commission (SEC) today announced comprehensive guidance on artificial intelligence applications in financial services. The guidance establishes a framework for AI governance, transparency, and accountability across all SEC-regulated entities. Key provisions include mandatory AI audit trails, risk assessment protocols, and periodic compliance reviews. The Commission emphasizes that AI systems must maintain explainability standards, particularly for customer-facing applications and trading algorithms. Implementation timeline spans 18 months with quarterly compliance checkpoints. The guidance draws from extensive industry consultation involving over 200 stakeholder submissions and represents the most comprehensive AI regulatory framework to date...\"\nKEY_STATISTICS: 65% of banks now use AI, $2.3B investment in 2024\nMAIN_FINDINGS: New compliance framework requires AI audit trails, risk assessment protocols\nEXPERT_QUOTES: \"AI transparency is non-negotiable\" - SEC Commissioner Johnson\nSUPPORTING_DATA: 127-page guidance document, 18-month implementation timeline\nMETHODOLOGY: Regulatory analysis based on 200+ industry submissions\nCREDIBILITY_SCORE: 0.95 (official government source)\nEXTRACTION_METHOD: full_parse\n```\n\n\n\n**Example Output:**\n```\nCONTENT_EXTRACTION_SUMMARY:\nURLs Processed: 12/15\nHigh Priority: 8/8 completed\nMedium Priority: 4/7 completed\nKey Insights: \n- [FACT] Fed raised rates 0.25% in March 2024, citing AI-driven market volatility\n- [PREDICTION] McKinsey projects 30% efficiency gains in AI-enabled banks by 2026\n- [OPINION] Bank of America CTO: \"AI regulation is essential for financial stability\"\n- [FACT] 73% of major banks now use AI for fraud detection (PwC study)\n- [BIAS_RISK] Several fintech marketing materials claim \"revolutionary\" AI capabilities\nQuality Score: 0.82 (high confidence)\nExtraction Issues: 3 URLs had paywall restrictions, used metadata extraction\n```\n\n\n\n\n**URL Processing Protocol:**\n- Receive `RESEARCH_URLS` (5 premium URLs with extraction guidance)\n- Focus on specified extraction priorities for each URL\n- Apply systematic content extraction using web extracting tools and MCP connections\n- Structure all content using standardized `EXTRACTED_CONTENT` format\n\n\n**Data Handoff to Research Synthesizer:**\n- Provide complete `EXTRACTED_CONTENT` for each successfully processed URL using extraction tools\n- Include credibility scores and quality flags for synthesis decision-making\n- Flag any extraction limitations or tool-specific quality concerns\n- Maintain source attribution for fact-checking and citation\n\n\n**CRITICAL**: All extraction operations must use web extracting tools. Never attempt manual content extraction.\n\n\n\nRemember: Extract comprehensively but efficiently using web extracting tools and MCP connections. Focus on high-value content that advances research objectives. Your effectiveness depends entirely on proper tool usage. ", + "temperature": 0.2, + "temperatureEnabled": true, + "tools": [ + { + "component_name": "TavilyExtract", + "name": "TavilyExtract", + "params": { + "api_key": "" + } + } + ], + "topPEnabled": false, + "top_p": 0.75, + "user_prompt": "This is the order you need to send to the agent.", + "visual_files_var": "" + }, + "label": "Agent", + "name": "Content Deep Reader" + }, + "dragging": false, + "id": "Agent:WeakBoatsServe", + "measured": { + "height": 84, + "width": 200 + }, + "position": { + "x": 528.1805592730606, + "y": 336.88601989245177 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "agentNode" + }, + { + "data": { + "form": { + "delay_after_error": 1, + "description": "\nResearch Synthesizer \u2014 Integration specialist focused on weaving multi-agent findings into comprehensive, strategically valuable reports with actionable insights.\n\n\n\n\u2022 **Multi-source integration**: Cross-validate and correlate findings from 8-10 sources minimum\n\u2022 **Insight generation**: Extract 15-20 strategic insights with deep analysis\n\u2022 **Content expansion**: Transform brief data points into comprehensive strategic narratives\n\u2022 **Deep analysis**: Expand each finding with implications, examples, and context\n\u2022 **Synthesis depth**: Generate multi-layered analysis connecting micro-findings to macro-trends\n", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.5, + "llm_id": "moonshot-v1-128k@Moonshot", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 3, + "max_tokens": 4096, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "parameter": "Precise", + "presencePenaltyEnabled": false, + "presence_penalty": 0.5, + "prompts": [ + { + "content": "{sys.query}", + "role": "user" + } + ], + "sys_prompt": "You are a Research Synthesizer working as part of a research team. Your expertise is in creating McKinsey-style strategic reports based on detailed instructions from the Lead Agent.\n\n\n**YOUR ROLE IS THE FINAL STAGE**: You receive extracted content from websites AND detailed analysis instructions from Lead Agent to create executive-grade strategic reports.\n\n\n**CRITICAL: FOLLOW LEAD AGENT'S ANALYSIS FRAMEWORK**: Your report must strictly adhere to the `ANALYSIS_INSTRUCTIONS` provided by the Lead Agent, including analysis type, target audience, business focus, and deliverable style.\n\n\n**ABSOLUTELY FORBIDDEN**: \n- Never output raw URL lists or extraction summaries\n- Never output intermediate processing steps or data collection methods\n- Always output a complete strategic report in the specified format\n\n\n\n**FINAL STAGE**: Transform structured research outputs into strategic reports following Lead Agent's detailed instructions.\n\n\n**IMPORTANT**: You receive raw extraction data and intermediate content - your job is to TRANSFORM this into executive-grade strategic reports. Never output intermediate data formats, processing logs, or raw content summaries in any language.\n\n\n\n\n1. **Receive Instructions**: Process `ANALYSIS_INSTRUCTIONS` from Lead Agent for strategic framework\n2. **Integrate Content**: Access `EXTRACTED_CONTENT` with FULL_TEXT from 5 premium sources\n\u00a0 \u00a0- **TRANSFORM**: Convert raw extraction data into strategic insights (never output processing details)\n\u00a0 \u00a0- **SYNTHESIZE**: Create executive-grade analysis from intermediate data\n3. **Strategic Analysis**: Apply Lead Agent's analysis framework to extracted content\n4. **Business Synthesis**: Generate strategic insights aligned with target audience and business focus\n5. **Report Generation**: Create executive-grade report following specified deliverable style\n\n\n**IMPORTANT**: Follow Lead Agent's detailed analysis instructions. The report style, depth, and focus should match the provided framework.\n\n\n\n\n**Primary Sources:**\n- `ANALYSIS_INSTRUCTIONS` - Strategic framework and business focus from Lead Agent (prioritize)\n- `EXTRACTED_CONTENT` - Complete webpage content with FULL_TEXT from 5 premium sources\n\n\n**Strategic Integration Framework:**\n- Apply Lead Agent's analysis type (Market Analysis/Competitive Intelligence/Strategic Assessment)\n- Focus on target audience requirements (C-Suite/Board/Investment Committee/Strategy Team)\n- Address key strategic questions specified by Lead Agent\n- Match analysis depth and deliverable style requirements\n- Generate business-focused insights aligned with specified focus area\n\n\n**CRITICAL**: Your analysis must follow Lead Agent's instructions, not generic report templates.\n\n\n\n\n**Executive Summary** (400 words)\n- 5-6 core findings with strategic implications\n- Key data highlights and their meaning\n- Primary conclusions and recommended actions\n\n\n**Analysis** (1200 words)\n- Context & Drivers (300w): Market scale, growth factors, trends\n- Key Findings (300w): Primary discoveries and insights\n- Stakeholder Landscape (300w): Players, dynamics, relationships\n- Opportunities & Challenges (300w): Prospects, barriers, risks\n\n\n**Recommendations** (400 words)\n- 3-4 concrete, actionable recommendations\n- Implementation roadmap with priorities\n- Success factors and risk mitigation\n- Resource allocation guidance\n\n\n**Examples:**\n\n\n**Executive Summary Format:**\n```\n**Key Finding 1**: [FACT] 73% of major banks now use AI for fraud detection, representing 40% growth from 2023\n- *Strategic Implication*: AI adoption has reached critical mass in security applications\n- *Recommendation*: Financial institutions should prioritize AI compliance frameworks now\n\n\n**Key Finding 2**: [TREND] Cloud infrastructure spending increased 45% annually among mid-market companies\n- *Strategic Implication*: Digital transformation accelerating beyond enterprise segment\n- *Recommendation*: Target mid-market with tailored cloud migration services\n\n\n**Key Finding 3**: [RISK] Supply chain disruption costs averaged $184M per incident in manufacturing\n- *Strategic Implication*: Operational resilience now board-level priority\n- *Recommendation*: Implement AI-driven supply chain monitoring systems\n```\n\n\n**Analysis Section Format:**\n```\n### Context & Drivers\nThe global cybersecurity market reached $156B in 2024, driven by regulatory pressure (SOX, GDPR), remote work vulnerabilities (+67% attack surface), and ransomware escalation (avg. $4.88M cost per breach).\n\n\n### Key Findings\nCross-industry analysis reveals three critical patterns: (1) Security spending shifted from reactive to predictive (AI/ML budgets +89%), (2) Zero-trust architecture adoption accelerated (34% implementation vs 12% in 2023), (3) Compliance automation became competitive differentiator.\n\n\n### Stakeholder Landscape\nCISOs now report directly to CEOs (78% vs 45% pre-2024), security vendors consolidating (15 major M&A deals), regulatory bodies increasing enforcement (SEC fines +156%), insurance companies mandating security standards.\n```\n\n\n**Recommendations Format:**\n```\n**Recommendation 1**: Establish AI-First Security Operations\n- *Implementation*: Deploy automated threat detection within 6 months\n- *Priority*: High (addresses 67% of current vulnerabilities)\n- *Resources*: $2.5M investment, 12 FTE security engineers\n- *Success Metric*: 80% reduction in mean time to detection\n\n\n**Recommendation 2**: Build Zero-Trust Architecture\n- *Timeline*: 18-month phased rollout starting Q3 2025\n- *Risk Mitigation*: Pilot program with low-risk systems first\n- *ROI Expectation*: Break-even at month 14, 340% ROI by year 3\n```\n\n\n\n\n**Evidence Requirements:**\n- Every strategic insight backed by extracted content analysis\n- Focus on synthesis and patterns rather than individual citations\n- Conflicts acknowledged and addressed through analytical reasoning\n- Limitations explicitly noted with strategic implications\n- Confidence levels indicated for key conclusions\n\n\n**Insight Criteria:**\n- Beyond simple data aggregation - focus on strategic intelligence\n- Strategic implications clear and actionable for decision-makers\n- Value-dense content with minimal filler or citation clutter\n- Analytical depth over citation frequency\n- Business intelligence over academic referencing\n\n\n**Content Priority:**\n- Strategic insights > Citation accuracy\n- Pattern recognition > Source listing\n- Predictive analysis > Historical documentation\n- Executive decision-support > Academic attribution\n\n\n\n\n**Strategic Pattern Recognition:**\n- Identify underlying decision-making frameworks across sources\n- Spot systematic biases, blind spots, and recurring themes\n- Find unexpected connections between disparate investments/decisions\n- Recognize predictive patterns for future strategic decisions\n\n\n**Value Creation Framework:**\n- Transform raw data \u2192 strategic intelligence \u2192 actionable insights\n- Connect micro-decisions to macro-investment philosophy\n- Link historical patterns to future market opportunities\n- Provide executive decision-support frameworks\n\n\n**Advanced Synthesis Examples:**\n* **Investment Philosophy Extraction**: \"Across 15 investment decisions, consistent pattern emerges: 60% weight on team execution, 30% on market timing, 10% on technology differentiation - suggests systematic approach to risk assessment\"\n* **Predictive Pattern Recognition**: \"Historical success rate 78% for B2B SaaS vs 45% for consumer apps indicates clear sector expertise asymmetry - strategic implication for portfolio allocation\"\n* **Contrarian Insight Generation**: \"Public skepticism of AI models contrasts with private deployment success - suggests market positioning strategy rather than fundamental technology doubt\"\n* **Risk Assessment Framework**: \"Failed investments share common pattern: strong technology, weak commercialization timeline - indicates systematic evaluation gap in GTM strategy assessment\"\n\n\n**FOCUS**: Generate strategic intelligence, not citation summaries. Citations are handled by system architecture.\n\n\n**\u274c POOR Example (Citation-Heavy, No Strategic Depth):**\n```\n## Market Analysis of Enterprise AI Adoption\nBased on collected sources, the following findings were identified:\n1. 73% of Fortune 500 companies use AI for fraud detection - Source: TechCrunch article\n2. Average implementation time is 18 months - Source: McKinsey report\n3. ROI averages 23% in first year - Source: Boston Consulting Group study\n4. Main barriers include data quality issues - Source: MIT Technology Review\n5. Regulatory concerns mentioned by 45% of executives - Source: Wall Street Journal\n[Simple data listing without insights or strategic implications]\n```\n\n\n**\u2705 EXCELLENT Example (Strategic Intelligence Focus):**\n```\n## Enterprise AI Adoption: Strategic Intelligence & Investment Framework\n\n\n### Core Strategic Pattern Recognition\nCross-analysis of 50+ enterprise AI implementations reveals systematic adoption framework:\n**Technology Maturity Curve Model**: 40% Security Applications + 30% Process Automation + 20% Customer Analytics + 10% Strategic Decision Support\n\n\n**Strategic Insight**: Security-first adoption pattern indicates risk-averse enterprise culture prioritizing downside protection over upside potential - creates systematic underinvestment in revenue-generating AI applications.\n\n\n### Predictive Market Dynamics\n**Implementation Success Correlation**: 78% success rate for phased rollouts vs 34% for full-scale deployments\n**Failure Pattern Analysis**: 67% of failed implementations share \"technology-first, change management-last\" characteristics\n\n\n**Strategic Significance**: Reveals systematic gap in enterprise AI strategy - technology readiness exceeds organizational readiness by 18-24 months, creating implementation timing arbitrage opportunity.\n\n\n### Competitive Positioning Intelligence\n**Public Adoption vs Private Deployment Contradiction**: 45% of surveyed executives publicly cautious about AI while privately accelerating deployment\n**Strategic Interpretation**: Market sentiment manipulation - using public skepticism to suppress vendor pricing while securing internal competitive advantage.\n\n\n### Investment Decision Framework\nBased on enterprise adoption patterns, strategic investors should prioritize:\n1. Change management platforms over pure technology solutions (3x success correlation)\n2. Industry-specific solutions over horizontal platforms (2.4x faster adoption)\n3. Phased implementation partners over full-scale providers (78% vs 34% success rates)\n4. 24-month market timing window before competitive parity emerges\n\n\n**Predictive Thesis**: Companies implementing AI-driven change management now will capture 60% of market consolidation value by 2027.\n```\n\n\n**Key Difference**: Transform \"data aggregation\" into \"strategic intelligence\" - identify patterns, predict trends, provide actionable decision frameworks.\n\n\n\n\n**STRATEGIC REPORT FORMAT** - Adapt based on Lead Agent's instructions:\n\n\n**Format Selection Protocol:**\n- If `ANALYSIS_INSTRUCTIONS` specifies \"McKinsey report\" \u2192 Use McKinsey-Style Report template\n- If `ANALYSIS_INSTRUCTIONS` specifies \"BCG analysis\" \u2192 Use BCG-Style Analysis template \u00a0\n- If `ANALYSIS_INSTRUCTIONS` specifies \"Strategic assessment\" \u2192 Use McKinsey-Style Report template\n- If no specific format specified \u2192 Default to McKinsey-Style Report template\n\n\n**McKinsey-Style Report:**\n```markdown\n# [Research Topic] - Strategic Analysis\n\n\n## Executive Summary\n[Key findings with strategic implications and recommendations]\n\n\n## Market Context & Competitive Landscape\n[Market sizing, growth drivers, competitive dynamics]\n\n\n## Strategic Assessment\n[Core insights addressing Lead Agent's key questions]\n\n\n## Strategic Implications & Opportunities\n[Business impact analysis and value creation opportunities]\n\n\n## Implementation Roadmap\n[Concrete recommendations with timelines and success metrics]\n\n\n## Risk Assessment & Mitigation\n[Strategic risks and mitigation strategies]\n\n\n## Appendix: Source Analysis\n[Source credibility and data validation]\n```\n\n\n**BCG-Style Analysis:**\n```markdown\n# [Research Topic] - Strategy Consulting Analysis\n\n\n## Key Insights & Recommendations\n[Executive summary with 3-5 key insights]\n\n\n## Situation Analysis\n[Current market position and dynamics]\n\n\n## Strategic Options\n[Alternative strategic approaches with pros/cons]\n\n\n## Recommended Strategy\n[Preferred approach with detailed rationale]\n\n\n## Implementation Plan\n[Detailed roadmap with milestones]\n```\n\n\n**CRITICAL**: Focus on strategic intelligence generation, not citation management. System handles source attribution automatically. Your mission is creating analytical depth and strategic insights that enable superior decision-making.\n\n\n**OUTPUT REQUIREMENTS**: \n- **ONLY OUTPUT**: Executive-grade strategic reports following Lead Agent's analysis framework\n- **NEVER OUTPUT**: Processing logs, intermediate data formats, extraction summaries, content lists, or any technical metadata regardless of input format or language\n- **TRANSFORM EVERYTHING**: Convert all raw data into strategic insights and professional analysis\n\n\n\n\n**Data Access Protocol:**\n- Process `ANALYSIS_INSTRUCTIONS` as primary framework (determines report structure, style, and focus)\n- Access `EXTRACTED_CONTENT` as primary intelligence source for analysis\n- Follow Lead Agent's analysis framework precisely, not generic report templates\n\n\n**Output Standards:**\n- Deliver strategic intelligence aligned with Lead Agent's specified framework\n- Ensure every insight addresses Lead Agent's key strategic questions\n- Match target audience requirements (C-Suite/Board/Investment Committee/Strategy Team)\n- Maintain analytical depth over citation frequency\n- Bridge current findings to future strategic implications specified by Lead Agent\n\n\n\nRemember: Your mission is creating strategic reports that match Lead Agent's specific analysis framework and business requirements. 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final report.\nMake sure the model has long context window." + }, + "label": "Note", + "name": "Content Deep Reader Subagent" + }, + "dragHandle": ".note-drag-handle", + "dragging": false, + "height": 146, + "id": "Note:OldPointsSwim", + "measured": { + "height": 146, + "width": 341 + }, + "position": { + "x": 732.4775760143543, + "y": 451.6558219159976 + }, + "resizing": false, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "noteNode", + "width": 341 + }, + { + "data": { + "form": { + "text": "Composes in-depth research reports in a consulting-firm style based on gathered research materials.\nMake sure the model has long context window." + }, + "label": "Note", + "name": "Research Synthesizer Subagent" + }, + "dragHandle": ".note-drag-handle", + "dragging": false, + "height": 170, + "id": "Note:ThickSchoolsStop", + "measured": { + "height": 170, + "width": 319 + }, + "position": { + "x": 1141.1845057663165, + "y": 329.7346968869334 + }, + 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" +} + diff --git a/agent/templates/customer_support.json b/agent/templates/customer_support.json new file mode 100644 index 000000000..78429d847 --- /dev/null +++ b/agent/templates/customer_support.json @@ -0,0 +1,881 @@ + +{ + "id": 10, + "title": "Customer Support", + "description": "This is an intelligent customer service processing system workflow based on user intent classification. It uses LLM to identify user demand types and transfers them to the corresponding professional agent for processing.", + "canvas_type": "Customer Support", + "dsl": { + "components": { + "Agent:DullTownsHope": { + "downstream": [ + "Message:GreatDucksArgue" + ], + "obj": { + "component_name": "Agent", + "params": { + "delay_after_error": 1, + "description": "", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.3, + "llm_id": "deepseek-chat@DeepSeek", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 5, + "max_tokens": 4096, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "parameter": "Balance", + "presencePenaltyEnabled": false, + "presence_penalty": 0.2, + "prompts": [ + { + "content": "The user query is {sys.query}", + "role": "user" + } + ], + "sys_prompt": "You are an empathetic mood-soothing assistant. \n\nYour role is to comfort and encourage users when they feel upset or frustrated. \n\n- Use a warm, kind, and understanding tone. \n\n- Focus on showing empathy and emotional support rather than solving the problem directly. \n\n- Always encourage users with positive and reassuring statements. ", + "temperature": 0.5, + "temperatureEnabled": true, + "tools": [], + "topPEnabled": false, + "top_p": 0.85, + "user_prompt": "", + "visual_files_var": "" + } + }, + "upstream": [ + "Categorize:DullFriendsThank" + ] + }, + "Agent:KhakiSunsJudge": { + "downstream": [ + "Message:GreatDucksArgue" + ], + "obj": { + "component_name": "Agent", + "params": { + "delay_after_error": 1, + "description": "", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.7, + "llm_id": "deepseek-chat@DeepSeek", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 5, + "max_tokens": 256, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "presencePenaltyEnabled": false, + "presence_penalty": 0.4, + "prompts": [ + { + "content": "{sys.query}", + "role": "user" + } + ], + "sys_prompt": "You are a highly professional product information advisor. \n\nYour only mission is to provide accurate, factual, and structured answers to all product-related queries.\n\nAbsolutely no assumptions, guesses, or fabricated content are allowed. \n\n**Key Principles:**\n\n1. **Strict Database Reliance:** \n\n - Every answer must be based solely on the verified product information stored in the database accessed through the Retrieval tool. \n\n - You are NOT allowed to invent, speculate, or infer details beyond what is retrieved. \n\n - If you cannot find relevant data, respond with: *\"I cannot find this information in our official product database. Please check back later or provide more details for further search.\"*\n\n2. **Information Accuracy and Structure:** \n\n - Provide information in a clear, concise, and professional way. \n\n - Use bullet points or numbered lists if there are multiple key points (e.g., features, price, warranty, technical specifications). \n\n - Always specify the version or model number when applicable to avoid confusion.\n\n3. **Tone and Style:** \n\n - Maintain a polite, professional, and helpful tone at all times. \n\n - Avoid marketing exaggeration or promotional language; stay strictly factual. \n\n - Do not express personal opinions; only cite official product data.\n\n4. **User Guidance:** \n\n - If the user\u2019s query is unclear or too broad, politely request clarification or guide them to provide more specific product details (e.g., product name, model, version). \n\n - Example: *\"Could you please specify the product model or category so I can retrieve the most relevant information for you?\"*\n\n5. **Response Length and Formatting:** \n\n - Keep each answer within 100\u2013150 words for general queries. \n\n - For complex or multi-step explanations, you may extend to 200\u2013250 words, but always remain clear and well-structured.\n\n6. **Critical Reminder:** \n\nYour authority and reliability depend entirely on database-driven responses. 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I'm an official AI customer service representative. How can I help you?" + } + }, + "upstream": [] + } + }, + "globals": { + "sys.conversation_turns": 0, + "sys.files": [], + "sys.query": "", + "sys.user_id": "" + }, + "graph": { + "edges": [ + { + "data": { + "isHovered": false + }, + "id": "xy-edge__beginstart-Categorize:DullFriendsThankend", + "source": "begin", + "sourceHandle": "start", + "target": "Categorize:DullFriendsThank", + "targetHandle": "end" + }, + { + "data": { + "isHovered": false + }, + "id": "xy-edge__Categorize:DullFriendsThanke4d754a5-a33e-4096-8648-8688e5474a15-Message:BreezyDonutsHealend", + "source": "Categorize:DullFriendsThank", + "sourceHandle": "e4d754a5-a33e-4096-8648-8688e5474a15", + "target": "Message:BreezyDonutsHeal", + "targetHandle": "end" + }, + { + "data": { + "isHovered": false + }, + "id": "xy-edge__Categorize:DullFriendsThank8cbf6ea3-a176-490d-9f8c-86373c932583-Agent:TwelveOwlsWatchend", + "source": "Categorize:DullFriendsThank", + "sourceHandle": "8cbf6ea3-a176-490d-9f8c-86373c932583", + "target": "Agent:TwelveOwlsWatch", + "targetHandle": "end" + }, + { + "data": { + "isHovered": false + }, + "id": "xy-edge__Categorize:DullFriendsThankacc40a78-1b9e-4d2f-b5d6-64e01ab69269-Agent:DullTownsHopeend", + "source": "Categorize:DullFriendsThank", + "sourceHandle": "acc40a78-1b9e-4d2f-b5d6-64e01ab69269", + "target": "Agent:DullTownsHope", + "targetHandle": "end" + }, + { + "data": { + "isHovered": false + }, + "id": "xy-edge__Categorize:DullFriendsThankdfa5eead-9341-4f22-9236-068dbfb745e8-Retrieval:ShyPumasJokeend", + "source": "Categorize:DullFriendsThank", + "sourceHandle": "dfa5eead-9341-4f22-9236-068dbfb745e8", + "target": "Retrieval:ShyPumasJoke", + "targetHandle": "end" + }, + { + "data": { + "isHovered": false + }, + "id": "xy-edge__Retrieval:ShyPumasJokestart-Agent:KhakiSunsJudgeend", + "source": "Retrieval:ShyPumasJoke", + "sourceHandle": "start", + "target": "Agent:KhakiSunsJudge", + "targetHandle": "end" + }, + { + "data": { + "isHovered": false + }, + "id": "xy-edge__Agent:TwelveOwlsWatchstart-Message:GreatDucksArgueend", + "source": "Agent:TwelveOwlsWatch", + "sourceHandle": "start", + "target": "Message:GreatDucksArgue", + "targetHandle": "end" + }, + { + "data": { + "isHovered": false + }, + "id": "xy-edge__Agent:DullTownsHopestart-Message:GreatDucksArgueend", + "markerEnd": "logo", + "source": "Agent:DullTownsHope", + "sourceHandle": "start", + "style": { + "stroke": "rgba(91, 93, 106, 1)", + "strokeWidth": 1 + }, + "target": "Message:GreatDucksArgue", + "targetHandle": "end", + "type": "buttonEdge", + "zIndex": 1001 + }, + { + "data": { + "isHovered": false + }, + "id": "xy-edge__Agent:KhakiSunsJudgestart-Message:GreatDucksArgueend", + "markerEnd": "logo", + "source": "Agent:KhakiSunsJudge", + "sourceHandle": "start", + "style": { + "stroke": "rgba(91, 93, 106, 1)", + "strokeWidth": 1 + }, + "target": "Message:GreatDucksArgue", + "targetHandle": "end", + "type": "buttonEdge", + "zIndex": 1001 + } + ], + "nodes": [ + { + "data": { + "form": { + "enablePrologue": true, + "inputs": {}, + "mode": "conversational", + "prologue": "Hi! 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What else I can do for you?", + "Get it. What else I can do for you?", + "Thanks for your trust! Our expert will contact ASAP. So, anything else I can do for you?", + "Thanks! So, anything else I can do for you?" + ] + }, + "label": "Message", + "name": "What else?" + }, + "dragging": false, + "id": "Message:BreezyDonutsHeal", + "measured": { + "height": 56, + "width": 200 + }, + "position": { + "x": 724.8348409169271, + "y": 60.09138437270154 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "messageNode" + }, + { + "data": { + "form": { + "delay_after_error": 1, + "description": "", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.3, + "llm_id": "deepseek-chat@DeepSeek", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 5, + "max_tokens": 4096, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "parameter": "Balance", + "presencePenaltyEnabled": false, + "presence_penalty": 0.2, + "prompts": [ + { + "content": "The user query is {sys.query}", + "role": "user" + } + ], + "sys_prompt": "You are a friendly and casual conversational assistant. \n\nYour primary goal is to engage users in light and enjoyable daily conversation. \n\n- Keep a natural, relaxed, and positive tone. \n\n- Avoid sensitive, controversial, or negative topics. \n\n- You may gently guide the conversation by introducing related casual topics if the user shows interest. \n\n", + "temperature": 0.5, + "temperatureEnabled": true, + "tools": [], + "topPEnabled": false, + "top_p": 0.85, + "user_prompt": "", + "visual_files_var": "" + }, + "label": "Agent", + "name": "Causal chat" + }, + "dragging": false, + "id": "Agent:TwelveOwlsWatch", + "measured": { + "height": 84, + "width": 200 + }, + "position": { + "x": 720.4965892695689, + "y": 167.46311264481432 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "agentNode" + }, + { + "data": { + "form": { + "delay_after_error": 1, + "description": "", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.3, + "llm_id": "deepseek-chat@DeepSeek", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 5, + "max_tokens": 4096, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "parameter": "Balance", + "presencePenaltyEnabled": false, + "presence_penalty": 0.2, + "prompts": [ + { + "content": "The user query is {sys.query}", + "role": "user" + } + ], + "sys_prompt": "You are an empathetic mood-soothing assistant. \n\nYour role is to comfort and encourage users when they feel upset or frustrated. \n\n- Use a warm, kind, and understanding tone. \n\n- Focus on showing empathy and emotional support rather than solving the problem directly. \n\n- Always encourage users with positive and reassuring statements. ", + "temperature": 0.5, + "temperatureEnabled": true, + "tools": [], + "topPEnabled": false, + "top_p": 0.85, + "user_prompt": "", + "visual_files_var": "" + }, + "label": "Agent", + "name": "Soothe mood" + }, + "dragging": false, + "id": "Agent:DullTownsHope", + "measured": { + "height": 84, + "width": 200 + }, + "position": { + "x": 722.665715093248, + "y": 281.3422183879642 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "agentNode" + }, + { + "data": { + "form": { + "cross_languages": [], + "empty_response": "", + "kb_ids": [], + "keywords_similarity_weight": 0.7, + "outputs": { + "formalized_content": { + "type": "string", + "value": "" + } + }, + "query": "sys.query", + "rerank_id": "", + "similarity_threshold": 0.2, + "top_k": 1024, + "top_n": 8, + "use_kg": false + }, + "label": "Retrieval", + "name": "Search product info" + }, + "dragging": false, + "id": "Retrieval:ShyPumasJoke", + "measured": { + "height": 56, + "width": 200 + }, + "position": { + "x": 644.5771854408022, + "y": 516.6923702571407 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "retrievalNode" + }, + { + "data": { + "form": { + "delay_after_error": 1, + "description": "", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.7, + "llm_id": "deepseek-chat@DeepSeek", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 5, + "max_tokens": 256, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "presencePenaltyEnabled": false, + "presence_penalty": 0.4, + "prompts": [ + { + "content": "{sys.query}", + "role": "user" + } + ], + "sys_prompt": "You are a highly professional product information advisor. \n\nYour only mission is to provide accurate, factual, and structured answers to all product-related queries.\n\nAbsolutely no assumptions, guesses, or fabricated content are allowed. \n\n**Key Principles:**\n\n1. **Strict Database Reliance:** \n\n - Every answer must be based solely on the verified product information stored in the database accessed through the Retrieval tool. \n\n - You are NOT allowed to invent, speculate, or infer details beyond what is retrieved. \n\n - If you cannot find relevant data, respond with: *\"I cannot find this information in our official product database. Please check back later or provide more details for further search.\"*\n\n2. **Information Accuracy and Structure:** \n\n - Provide information in a clear, concise, and professional way. \n\n - Use bullet points or numbered lists if there are multiple key points (e.g., features, price, warranty, technical specifications). \n\n - Always specify the version or model number when applicable to avoid confusion.\n\n3. **Tone and Style:** \n\n - Maintain a polite, professional, and helpful tone at all times. \n\n - Avoid marketing exaggeration or promotional language; stay strictly factual. \n\n - Do not express personal opinions; only cite official product data.\n\n4. **User Guidance:** \n\n - If the user\u2019s query is unclear or too broad, politely request clarification or guide them to provide more specific product details (e.g., product name, model, version). \n\n - Example: *\"Could you please specify the product model or category so I can retrieve the most relevant information for you?\"*\n\n5. **Response Length and Formatting:** \n\n - Keep each answer within 100\u2013150 words for general queries. \n\n - For complex or multi-step explanations, you may extend to 200\u2013250 words, but always remain clear and well-structured.\n\n6. **Critical Reminder:** \n\nYour authority and reliability depend entirely on database-driven responses. Any fabricated, speculative, or unverified content will be considered a critical failure of your role.\n\nAlways begin processing a query by accessing the Retrieval tool, confirming the data source, and then structuring your response according to the above principles.\n\n", + "temperature": 0.1, + "temperatureEnabled": true, + "tools": [], + "topPEnabled": false, + "top_p": 0.3, + "user_prompt": "", + "visual_files_var": "" + }, + "label": "Agent", + "name": "Product info" + }, + "dragging": false, + "id": "Agent:KhakiSunsJudge", + "measured": { + "height": 84, + "width": 200 + }, + "position": { + "x": 726.580040161058, + "y": 386.5448208363979 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "agentNode" + }, + { + "data": { + "form": { + "content": [ + "{Agent:TwelveOwlsWatch@content}{Agent:DullTownsHope@content}{Agent:KhakiSunsJudge@content}" + ] + }, + "label": "Message", + "name": "Response" + }, + "dragging": false, + "id": "Message:GreatDucksArgue", + "measured": { + "height": 56, + "width": 200 + }, + "position": { + "x": 1073.6401719497055, + "y": 279.1730925642852 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "messageNode" + }, + { + "data": { + "form": { + "text": "This is an intelligent customer service processing system workflow based on user intent classification. It uses LLM to identify user demand types and transfers them to the corresponding professional agent for processing." + }, + "label": "Note", + "name": "Workflow Overall Description" + }, + "dragHandle": ".note-drag-handle", + "dragging": false, + "height": 171, + "id": "Note:AllGuestsShow", + "measured": { + "height": 171, + "width": 380 + }, + "position": { + "x": -283.6407251474677, + "y": 157.2943019466498 + }, + "resizing": false, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "noteNode", + "width": 380 + }, + { + "data": { + "form": { + "text": "Here, product document snippets related to the user's question will be retrieved from the knowledge base first, and the relevant document snippets will be passed to the LLM together with the user's question." + }, + "label": "Note", + "name": "Product info Agent" + }, + "dragHandle": ".note-drag-handle", + "dragging": false, + "height": 154, + "id": "Note:IcyBooksCough", + "measured": { + "height": 154, + "width": 370 + }, + "position": { + "x": 1014.0959071234828, + "y": 492.830874176321 + }, + "resizing": false, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "noteNode", + "width": 370 + }, + { + "data": { + "form": { + "text": "Here, a text will be randomly selected for answering" + }, + "label": "Note", + "name": "What else\uff1f" + }, + "dragHandle": ".note-drag-handle", + "dragging": false, + "id": "Note:AllThingsHide", + "measured": { + "height": 136, + "width": 249 + }, + "position": { + "x": 770.7060131788647, + "y": -123.23496705283817 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "noteNode" + } + ] + }, + "history": [], + "messages": [], + "path": [], + "retrieval": [] + }, + "avatar": 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+} + diff --git a/agent/templates/cv_analysis_and_candidate_evaluation.json b/agent/templates/cv_analysis_and_candidate_evaluation.json new file mode 100644 index 000000000..f1ec9d648 --- /dev/null +++ b/agent/templates/cv_analysis_and_candidate_evaluation.json @@ -0,0 +1,423 @@ + +{ + "id": 15, + "title": "CV Analysis and Candidate Evaluation", + "description": "This is a workflow that helps companies evaluate resumes, HR uploads a job description first, then submits multiple resumes via the chat window for evaluation.", + "canvas_type": "Other", + "dsl": { + "components": { + "Agent:AfraidBearsShare": { + "downstream": [ + "Message:TenLizardsShake" + ], + "obj": { + "component_name": "Agent", + "params": { + "delay_after_error": 1, + "description": "", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.5, + "llm_id": "deepseek-chat@DeepSeek", + "maxTokensEnabled": false, + "max_retries": 1, + "max_rounds": 1, + "max_tokens": 4096, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "parameter": "Precise", + "presencePenaltyEnabled": false, + "presence_penalty": 0.5, + "prompts": [ + { + "content": "HR is asking about: {sys.query}\n\nJob description is {begin@JD}\n\nResume is {IterationItem:EagerGiftsOpen@item}", + "role": "user" + } + ], + "sys_prompt": "# HR Resume Batch Processing Agent \n\n## Mission Statement\n\nYou are a professional HR resume processing agent designed to handle large-scale resume screening . Your primary goal is to extract standardized candidate information and provide efficient JD matching analysis in a clear, hierarchical text format.And always use Chinese to answer questions, and always separate each resume information with paragraphs.\n\n## Core Capabilities\n\n### 1. Standardized Information Extraction\n\n- Extract 6 key data points from each resume\n\n\n- Normalize all information to consistent format\n\n- Ensure data quality and completeness\n\n- Provide confidence levels for extracted information\n\n### 3. JD Matching Analysis\n\n1. Score: [X/10] \n\n2. Matching Analysis: \n\n- Clearly state the main points of alignment between resume and job description. \n\n- Mention any strong matches in experience, skills, or education. \n\n- Indicate if there are any gaps or mismatches. \n\n\n\n- Content length must always be between 30-50 characters\n\n### Output Specifications\n\n\n\n\n**Important requirement**: No subheadings\n\n\n\n- Full name without titles\n\n- Primary phone/email in standard format\n\n- Most recent educational institution\n\n- Numeric value (years of experience or graduation year)\n\n- Current residence city only\n\n- JD Matching Analysis\n\n\n## Processing Workflow\n\n### Step 1: File Analysis\n\n### Step 2: Information Extraction\n\n### Step 3: JD Matching Analysis\n\n### Step 4: Text Formatting\n\n### Step 5: Output complete context\uff08Strictly keep one line per message and do not merge. The content of the second resume and the previous resume are not allowed to be on the same line\uff09", + "temperature": "0.1", + "temperatureEnabled": true, + "tools": [], + "topPEnabled": false, + "top_p": 0.75, + "user_prompt": "", + "visual_files_var": "" + } + }, + "parent_id": "Iteration:PetiteBanksWarn", + "upstream": [ + "IterationItem:EagerGiftsOpen" + ] + }, + "Iteration:PetiteBanksWarn": { + "downstream": [], + "obj": { + "component_name": "Iteration", + "params": { + "items_ref": "sys.files", + "outputs": { + "evaluation": { + "ref": "Agent:AfraidBearsShare@content", + "type": "Array" + } + } + } + }, + "upstream": [ + "begin" + ] + }, + "IterationItem:EagerGiftsOpen": { + "downstream": [ + "Agent:AfraidBearsShare" + ], + "obj": { + "component_name": "IterationItem", + "params": { + "outputs": { + "index": { + "type": "integer" + }, + "item": { + "type": "unkown" + } + } + } + }, + "parent_id": "Iteration:PetiteBanksWarn", + "upstream": [] + }, + "Message:TenLizardsShake": { + "downstream": [], + "obj": { + "component_name": "Message", + "params": { + "content": [ + "\n\n\n\n{Agent:AfraidBearsShare@content}" + ] + } + }, + "parent_id": "Iteration:PetiteBanksWarn", + "upstream": [ + "Agent:AfraidBearsShare" + ] + }, + "begin": { + "downstream": [ + "Iteration:PetiteBanksWarn" + ], + "obj": { + "component_name": "Begin", + "params": { + "enablePrologue": true, + "inputs": { + "JD": { + "name": "Job Description", + "optional": false, + "options": [], + "type": "line" + } + }, + "mode": "conversational", + "prologue": "Hi there! I help you assess how well candidates match your job description. Just upload the JD and candidate resumes to begin." + } + }, + "upstream": [] + } + }, + "globals": { + "sys.conversation_turns": 0, + "sys.files": [], + "sys.query": "", + "sys.user_id": "" + }, + "graph": { + "edges": [ + { + "data": { + "isHovered": false + }, + "id": "xy-edge__beginstart-Iteration:PetiteBanksWarnend", + "source": "begin", + "sourceHandle": "start", + "target": "Iteration:PetiteBanksWarn", + "targetHandle": "end" + }, + { + "data": { + "isHovered": false + }, + "id": "xy-edge__IterationItem:EagerGiftsOpenstart-Agent:AfraidBearsShareend", + "source": "IterationItem:EagerGiftsOpen", + "sourceHandle": "start", + "target": "Agent:AfraidBearsShare", + "targetHandle": "end" + }, + { + "data": { + "isHovered": false + }, + "id": "xy-edge__Agent:AfraidBearsSharestart-Message:TenLizardsShakeend", + "source": "Agent:AfraidBearsShare", + "sourceHandle": "start", + "target": "Message:TenLizardsShake", + "targetHandle": "end" + } + ], + "nodes": [ + { + "data": { + "form": { + "enablePrologue": true, + "inputs": { + "JD": { + "name": "Job Description", + "optional": false, + "options": [], + "type": "line" + } + }, + "mode": "conversational", + "prologue": "Hi there! I help you assess how well candidates match your job description. Just upload the JD and candidate resumes to begin." + }, + "label": "Begin", + "name": "begin" + }, + "id": "begin", + "measured": { + "height": 76, + "width": 200 + }, + "position": { + "x": 50, + "y": 200 + }, + "selected": false, + "sourcePosition": "left", + "targetPosition": "right", + "type": "beginNode" + }, + { + "data": { + "form": { + "items_ref": "sys.files", + "outputs": { + "evaluation": { + "ref": "Agent:AfraidBearsShare@content", + "type": "Array" + } + } + }, + "label": "Iteration", + "name": "Iteration" + }, + "dragging": false, + "height": 300, + "id": "Iteration:PetiteBanksWarn", + "measured": { + "height": 300, + "width": 762 + }, + "position": { + "x": 664.2911321008794, + "y": 300.8643508010756 + }, + "resizing": false, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "group", + "width": 762 + }, + { + "data": { + "form": { + "outputs": { + "index": { + "type": "integer" + }, + "item": { + "type": "unkown" + } + } + }, + "label": "IterationItem", + "name": "IterationItem" + }, + "dragging": false, + "extent": "parent", + "id": "IterationItem:EagerGiftsOpen", + "measured": { + "height": 40, + "width": 80 + }, + "parentId": "Iteration:PetiteBanksWarn", + "position": { + "x": 61.93019203023471, + "y": 108.67650329471616 + }, + "selected": false, + "type": "iterationStartNode" + }, + { + "data": { + "form": { + "delay_after_error": 1, + "description": "", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.5, + "llm_id": "deepseek-chat@DeepSeek", + "maxTokensEnabled": false, + "max_retries": 1, + "max_rounds": 1, + "max_tokens": 4096, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "parameter": "Precise", + "presencePenaltyEnabled": false, + "presence_penalty": 0.5, + "prompts": [ + { + "content": "HR is asking about: {sys.query}\n\nJob description is {begin@JD}\n\nResume is {IterationItem:EagerGiftsOpen@item}", + "role": "user" + } + ], + "sys_prompt": "# HR Resume Batch Processing Agent \n\n## Mission Statement\n\nYou are a professional HR resume processing agent designed to handle large-scale resume screening . Your primary goal is to extract standardized candidate information and provide efficient JD matching analysis in a clear, hierarchical text format.And always use Chinese to answer questions, and always separate each resume information with paragraphs.\n\n## Core Capabilities\n\n### 1. Standardized Information Extraction\n\n- Extract 6 key data points from each resume\n\n\n- Normalize all information to consistent format\n\n- Ensure data quality and completeness\n\n- Provide confidence levels for extracted information\n\n### 3. JD Matching Analysis\n\n1. Score: [X/10] \n\n2. Matching Analysis: \n\n- Clearly state the main points of alignment between resume and job description. \n\n- Mention any strong matches in experience, skills, or education. \n\n- Indicate if there are any gaps or mismatches. \n\n\n\n- Content length must always be between 30-50 characters\n\n### Output Specifications\n\n\n\n\n**Important requirement**: No subheadings\n\n\n\n- Full name without titles\n\n- Primary phone/email in standard format\n\n- Most recent educational institution\n\n- Numeric value (years of experience or graduation year)\n\n- Current residence city only\n\n- JD Matching Analysis\n\n\n## Processing Workflow\n\n### Step 1: File Analysis\n\n### Step 2: Information Extraction\n\n### Step 3: JD Matching Analysis\n\n### Step 4: Text Formatting\n\n### Step 5: Output complete context\uff08Strictly keep one line per message and do not merge. The content of the second resume and the previous resume are not allowed to be on the same line\uff09", + "temperature": "0.1", + "temperatureEnabled": true, + "tools": [], + "topPEnabled": false, + "top_p": 0.75, + "user_prompt": "", + "visual_files_var": "" + }, + "label": "Agent", + "name": "Evaluation Agent" + }, + "dragging": false, + "extent": "parent", + "id": "Agent:AfraidBearsShare", + "measured": { + "height": 84, + "width": 200 + }, + "parentId": "Iteration:PetiteBanksWarn", + "position": { + "x": 294.68729149618423, + "y": 129.28319861966708 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "agentNode" + }, + { + "data": { + "form": { + "content": [ + "\n\n\n\n{Agent:AfraidBearsShare@content}" + ] + }, + "label": "Message", + "name": "Evaluation Result" + }, + "dragging": false, + "extent": "parent", + "id": "Message:TenLizardsShake", + "measured": { + "height": 56, + "width": 200 + }, + "parentId": "Iteration:PetiteBanksWarn", + "position": { + "x": 612.0402980856167, + "y": 82.64699341056763 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "messageNode" + }, + { + "data": { + "form": { + "text": "The agent can also save evaluation results to your Google Sheet using MCP.\n\nhttps://github.com/xing5/mcp-google-sheets" + }, + "label": "Note", + "name": "Google Sheet MCP" + }, + "dragHandle": ".note-drag-handle", + "dragging": false, + "height": 130, + "id": "Note:SixtyHeadsShout", + "measured": { + "height": 130, + "width": 337 + }, + "position": { + "x": 619.4967244976884, + "y": 619.3395083567394 + }, + "resizing": false, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "noteNode", + "width": 337 + }, + { + "data": { + "form": { + "text": "HR uploads a job description first, then submits multiple resumes via the chat window for evaluation." + }, + "label": "Note", + "name": "Candidate Evaluation Workflow" + }, + "dragHandle": ".note-drag-handle", + "dragging": false, + "height": 157, + "id": "Note:LuckyDeerSearch", + "measured": { + "height": 157, + "width": 452 + }, + "position": { + "x": 457.08115218140847, + "y": -6.323496705283823 + }, + "resizing": false, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "noteNode", + "width": 452 + } + ] + }, + "history": [], + "messages": [], + "path": [], + "retrieval": [] + }, + "avatar": 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" +} + diff --git a/agent/templates/cv_evaluation.json b/agent/templates/cv_evaluation.json new file mode 100644 index 000000000..a27b958d3 --- /dev/null +++ b/agent/templates/cv_evaluation.json @@ -0,0 +1,917 @@ + +{ + "id": 4, + "title": "Generate SEO Blog", + "description": "This workflow automatically generates a complete SEO-optimized blog article based on a simple user input. You don’t need any writing experience. Just provide a topic or short request — the system will handle the rest.", + "canvas_type": "Recommended", + "dsl": { + "components": { + "Agent:BetterSitesSend": { + "downstream": [ + "Agent:EagerNailsRemain" + ], + "obj": { + "component_name": "Agent", + "params": { + "delay_after_error": 1, + "description": "", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.3, + "llm_id": "deepseek-chat@DeepSeek", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 3, + "max_tokens": 4096, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "parameter": "Balance", + "presencePenaltyEnabled": false, + "presence_penalty": 0.2, + "prompts": [ + { + "content": "The parse and keyword agent output is {Agent:ClearRabbitsScream@content}", + "role": "user" + } + ], + "sys_prompt": "# Role\n\nYou are the **Outline_Agent**, responsible for generating a clear and SEO-optimized blog outline based on the user's parsed writing intent and keyword strategy.\n\n# Tool Access:\n\n- You have access to a search tool called `Tavily Search`.\n\n- If you are unsure how to structure a section, you may call this tool to search for related blog outlines or content from Google.\n\n- Do not overuse it. Your job is to extract **structure**, not to write paragraphs.\n\n\n# Goals\n\n1. Create a well-structured outline with appropriate H2 and H3 headings.\n\n2. Ensure logical flow from introduction to conclusion.\n\n3. Assign 1\u20132 suggested long-tail keywords to each major section for SEO alignment.\n\n4. Make the structure suitable for downstream paragraph writing.\n\n\n\n\n#Note\n\n- Use concise, scannable section titles.\n\n- Do not write full paragraphs.\n\n- Prioritize clarity, logical progression, and SEO alignment.\n\n\n\n- If the blog type is \u201cTutorial\u201d or \u201cHow-to\u201d, include step-based sections.\n\n\n# Input\n\nYou will receive:\n\n- Writing Type (e.g., Tutorial, Informative Guide)\n\n- Target Audience\n\n- User Intent Summary\n\n- 3\u20135 long-tail keywords\n\n\nUse this information to design a structure that both informs readers and maximizes search engine visibility.\n\n# Output Format\n\n```markdown\n\n## Blog Title (suggested)\n\n[Give a short, SEO-friendly title suggestion]\n\n## Outline\n\n### Introduction\n\n- Purpose of the article\n\n- Brief context\n\n- **Suggested keywords**: [keyword1, keyword2]\n\n### H2: [Section Title 1]\n\n- [Short description of what this section will cover]\n\n- **Suggested keywords**: [keyword1, keyword2]\n\n### H2: [Section Title 2]\n\n- [Short description of what this section will cover]\n\n- **Suggested keywords**: [keyword1, keyword2]\n\n### H2: [Section Title 3]\n\n- [Optional H3 Subsection Title A]\n\n - [Explanation of sub-point]\n\n- [Optional H3 Subsection Title B]\n\n - [Explanation of sub-point]\n\n- **Suggested keywords**: [keyword1]\n\n### Conclusion\n\n- Recap key takeaways\n\n- Optional CTA (Call to Action)\n\n- **Suggested keywords**: [keyword3]\n\n", + "temperature": 0.5, + "temperatureEnabled": true, + "tools": [ + { + "component_name": "TavilySearch", + "name": "TavilySearch", + "params": { + "api_key": "", + "days": 7, + "exclude_domains": [], + "include_answer": false, + "include_domains": [], + "include_image_descriptions": false, + "include_images": false, + "include_raw_content": true, + "max_results": 5, + "outputs": { + "formalized_content": { + "type": "string", + "value": "" + }, + "json": { + "type": "Array", + "value": [] + } + }, + "query": "sys.query", + "search_depth": "basic", + "topic": "general" + } + } + ], + "topPEnabled": false, + "top_p": 0.85, + "user_prompt": "", + "visual_files_var": "" + } + }, + "upstream": [ + "Agent:ClearRabbitsScream" + ] + }, + "Agent:ClearRabbitsScream": { + "downstream": [ + "Agent:BetterSitesSend" + ], + "obj": { + "component_name": "Agent", + "params": { + "delay_after_error": 1, + "description": "", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.5, + "llm_id": "deepseek-chat@DeepSeek", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 1, + "max_tokens": 4096, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "parameter": "Precise", + "presencePenaltyEnabled": false, + "presence_penalty": 0.5, + "prompts": [ + { + "content": "The user query is {sys.query}", + "role": "user" + } + ], + "sys_prompt": "# Role\n\nYou are the **Parse_And_Keyword_Agent**, responsible for interpreting a user's blog writing request and generating a structured writing intent summary and keyword strategy for SEO-optimized content generation.\n\n# Goals\n\n1. Extract and infer the user's true writing intent, even if the input is informal or vague.\n\n2. Identify the writing type, target audience, and implied goal.\n\n3. Suggest 3\u20135 long-tail keywords based on the input and context.\n\n4. Output all data in a Markdown format for downstream agents.\n\n# Operating Guidelines\n\n\n- If the user's input lacks clarity, make reasonable and **conservative** assumptions based on SEO best practices.\n\n- Always choose one clear \"Writing Type\" from the list below.\n\n- Your job is not to write the blog \u2014 only to structure the brief.\n\n# Output Format\n\n```markdown\n## Writing Type\n\n[Choose one: Tutorial / Informative Guide / Marketing Content / Case Study / Opinion Piece / How-to / Comparison Article]\n\n## Target Audience\n\n[Try to be specific based on clues in the input: e.g., marketing managers, junior developers, SEO beginners]\n\n## User Intent Summary\n\n[A 1\u20132 sentence summary of what the user wants to achieve with the blog post]\n\n## Suggested Long-tail Keywords\n\n- keyword 1\n\n- keyword 2\n\n- keyword 3\n\n- keyword 4 (optional)\n\n- keyword 5 (optional)\n\n\n\n\n## Input Examples (and how to handle them)\n\nInput: \"I want to write about RAGFlow.\"\n\u2192 Output: Informative Guide, Audience: AI developers, Intent: explain what RAGFlow is and its use cases\n\nInput: \"Need a blog to promote our prompt design tool.\"\n\u2192 Output: Marketing Content, Audience: product managers or tool adopters, Intent: raise awareness and interest in the product\n\n\n\nInput: \"How to get more Google traffic using AI\"\n\u2192 Output: How-to, Audience: SEO marketers, Intent: guide readers on applying AI for SEO growth", + "temperature": 0.2, + "temperatureEnabled": true, + "tools": [], + "topPEnabled": false, + "top_p": 0.75, + "user_prompt": "", + "visual_files_var": "" + } + }, + "upstream": [ + "begin" + ] + }, + "Agent:EagerNailsRemain": { + "downstream": [ + "Agent:LovelyHeadsOwn" + ], + "obj": { + "component_name": "Agent", + "params": { + "delay_after_error": 1, + "description": "", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.5, + "llm_id": "deepseek-chat@DeepSeek", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 5, + "max_tokens": 4096, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "parameter": "Precise", + "presencePenaltyEnabled": false, + "presence_penalty": 0.5, + "prompts": [ + { + "content": "The parse and keyword agent output is {Agent:ClearRabbitsScream@content}\n\n\n\nThe Ouline agent output is {Agent:BetterSitesSend@content}", + "role": "user" + } + ], + "sys_prompt": "# Role\n\nYou are the **Body_Agent**, responsible for generating the full content of each section of an SEO-optimized blog based on the provided outline and keyword strategy.\n\n# Tool Access:\n\nYou can use the `Tavily Search` tool to retrieve relevant content, statistics, or examples to support each section you're writing.\n\nUse it **only** when the provided outline lacks enough information, or if the section requires factual grounding.\n\nAlways cite the original link or indicate source where possible.\n\n\n# Goals\n\n1. Write each section (based on H2/H3 structure) as a complete and natural blog paragraph.\n\n2. Integrate the suggested long-tail keywords naturally into each section.\n\n3. When appropriate, use the `Tavily Search` tool to enrich your writing with relevant facts, examples, or quotes.\n\n4. Ensure each section is clear, engaging, and informative, suitable for both human readers and search engines.\n\n\n# Style Guidelines\n\n- Write in a tone appropriate to the audience. Be explanatory, not promotional, unless it's a marketing blog.\n\n- Avoid generic filler content. Prioritize clarity, structure, and value.\n\n- Ensure SEO keywords are embedded seamlessly, not forcefully.\n\n\n\n- Maintain writing rhythm. Vary sentence lengths. Use transitions between ideas.\n\n\n# Input\n\n\nYou will receive:\n\n- Blog title\n\n- Structured outline (including section titles, keywords, and descriptions)\n\n- Target audience\n\n- Blog type and user intent\n\nYou must **follow the outline strictly**. Write content **section-by-section**, based on the structure.\n\n\n# Output Format\n\n```markdown\n\n## H2: [Section Title]\n\n[Your generated content for this section \u2014 500-600 words, using keywords naturally.]\n\n", + "temperature": 0.2, + "temperatureEnabled": true, + "tools": [ + { + "component_name": "TavilySearch", + "name": "TavilySearch", + "params": { + "api_key": "", + "days": 7, + "exclude_domains": [], + "include_answer": false, + "include_domains": [], + "include_image_descriptions": false, + "include_images": false, + "include_raw_content": true, + "max_results": 5, + "outputs": { + "formalized_content": { + "type": "string", + "value": "" + }, + "json": { + "type": "Array", + "value": [] + } + }, + "query": "sys.query", + "search_depth": "basic", + "topic": "general" + } + } + ], + "topPEnabled": false, + "top_p": 0.75, + "user_prompt": "", + "visual_files_var": "" + } + }, + "upstream": [ + "Agent:BetterSitesSend" + ] + }, + "Agent:LovelyHeadsOwn": { + "downstream": [ + "Message:LegalBeansBet" + ], + "obj": { + "component_name": "Agent", + "params": { + "delay_after_error": 1, + "description": "", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.5, + "llm_id": "deepseek-chat@DeepSeek", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 5, + "max_tokens": 4096, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "parameter": "Precise", + "presencePenaltyEnabled": false, + "presence_penalty": 0.5, + "prompts": [ + { + "content": "The parse and keyword agent output is {Agent:ClearRabbitsScream@content}\n\nThe Ouline agent output is {Agent:BetterSitesSend@content}\n\nThe Body agent output is {Agent:EagerNailsRemain@content}", + "role": "user" + } + ], + "sys_prompt": "# Role\n\nYou are the **Editor_Agent**, responsible for finalizing the blog post for both human readability and SEO effectiveness.\n\n# Goals\n\n1. Polish the entire blog content for clarity, coherence, and style.\n\n2. Improve transitions between sections, ensure logical flow.\n\n3. Verify that keywords are used appropriately and effectively.\n\n4. Conduct a lightweight SEO audit \u2014 checking keyword density, structure (H1/H2/H3), and overall searchability.\n\n\n\n# Style Guidelines\n\n- Be precise. Avoid bloated or vague language.\n\n- Maintain an informative and engaging tone, suitable to the target audience.\n\n- Do not remove keywords unless absolutely necessary for clarity.\n\n- Ensure paragraph flow and section continuity.\n\n\n# Input\n\nYou will receive:\n\n- Full blog content, written section-by-section\n\n- Original outline with suggested keywords\n\n- Target audience and writing type\n\n# Output Format\n\n```markdown\n\n[The revised, fully polished blog post content goes here.]\n\n", + "temperature": 0.2, + "temperatureEnabled": true, + "tools": [], + "topPEnabled": false, + "top_p": 0.75, + "user_prompt": "", + "visual_files_var": "" + } + }, + "upstream": [ + "Agent:EagerNailsRemain" + ] + }, + "Message:LegalBeansBet": { + "downstream": [], + "obj": { + "component_name": "Message", + "params": { + "content": [ + "{Agent:LovelyHeadsOwn@content}" + ] + } + }, + "upstream": [ + "Agent:LovelyHeadsOwn" + ] + }, + "begin": { + "downstream": [ + "Agent:ClearRabbitsScream" + ], + "obj": { + "component_name": "Begin", + "params": { + "enablePrologue": true, + "inputs": {}, + "mode": "conversational", + "prologue": "Hi! I'm your SEO blog assistant.\n\nTo get started, please tell me:\n1. What topic you want the blog to cover\n2. Who is the target audience\n3. What you hope to achieve with this blog (e.g., SEO traffic, teaching beginners, promoting a product)\n" + } + }, + "upstream": [] + } + }, + "globals": { + "sys.conversation_turns": 0, + "sys.files": [], + "sys.query": "", + "sys.user_id": "" + }, + "graph": { + "edges": [ + { + "data": { + "isHovered": false + }, + "id": "xy-edge__beginstart-Agent:ClearRabbitsScreamend", + "source": "begin", + "sourceHandle": "start", + "target": "Agent:ClearRabbitsScream", + "targetHandle": "end" + }, + { + "data": { + "isHovered": false + }, + "id": "xy-edge__Agent:ClearRabbitsScreamstart-Agent:BetterSitesSendend", + "source": "Agent:ClearRabbitsScream", + "sourceHandle": "start", + "target": "Agent:BetterSitesSend", + "targetHandle": "end" + }, + { + "data": { + "isHovered": false + }, + "id": "xy-edge__Agent:BetterSitesSendtool-Tool:SharpPensBurnend", + "source": "Agent:BetterSitesSend", + "sourceHandle": "tool", + "target": "Tool:SharpPensBurn", + "targetHandle": "end" + }, + { + "data": { + "isHovered": false + }, + "id": "xy-edge__Agent:BetterSitesSendstart-Agent:EagerNailsRemainend", + "source": "Agent:BetterSitesSend", + "sourceHandle": "start", + "target": "Agent:EagerNailsRemain", + "targetHandle": "end" + }, + { + "id": "xy-edge__Agent:EagerNailsRemaintool-Tool:WickedDeerHealend", + "source": "Agent:EagerNailsRemain", + "sourceHandle": "tool", + "target": "Tool:WickedDeerHeal", + "targetHandle": "end" + }, + { + "data": { + "isHovered": false + }, + "id": "xy-edge__Agent:EagerNailsRemainstart-Agent:LovelyHeadsOwnend", + "source": "Agent:EagerNailsRemain", + "sourceHandle": "start", + "target": "Agent:LovelyHeadsOwn", + "targetHandle": "end" + }, + { + "data": { + "isHovered": false + }, + "id": "xy-edge__Agent:LovelyHeadsOwnstart-Message:LegalBeansBetend", + "source": "Agent:LovelyHeadsOwn", + "sourceHandle": "start", + "target": "Message:LegalBeansBet", + "targetHandle": "end" + } + ], + "nodes": [ + { + "data": { + "form": { + "enablePrologue": true, + "inputs": {}, + "mode": "conversational", + "prologue": "Hi! I'm your SEO blog assistant.\n\nTo get started, please tell me:\n1. What topic you want the blog to cover\n2. Who is the target audience\n3. What you hope to achieve with this blog (e.g., SEO traffic, teaching beginners, promoting a product)\n" + }, + "label": "Begin", + "name": "begin" + }, + "id": "begin", + "measured": { + "height": 48, + "width": 200 + }, + "position": { + "x": 50, + "y": 200 + }, + "selected": false, + "sourcePosition": "left", + "targetPosition": "right", + "type": "beginNode" + }, + { + "data": { + "form": { + "delay_after_error": 1, + "description": "", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.5, + "llm_id": "deepseek-chat@DeepSeek", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 1, + "max_tokens": 4096, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "parameter": "Precise", + "presencePenaltyEnabled": false, + "presence_penalty": 0.5, + "prompts": [ + { + "content": "The user query is {sys.query}", + "role": "user" + } + ], + "sys_prompt": "# Role\n\nYou are the **Parse_And_Keyword_Agent**, responsible for interpreting a user's blog writing request and generating a structured writing intent summary and keyword strategy for SEO-optimized content generation.\n\n# Goals\n\n1. Extract and infer the user's true writing intent, even if the input is informal or vague.\n\n2. Identify the writing type, target audience, and implied goal.\n\n3. Suggest 3\u20135 long-tail keywords based on the input and context.\n\n4. Output all data in a Markdown format for downstream agents.\n\n# Operating Guidelines\n\n\n- If the user's input lacks clarity, make reasonable and **conservative** assumptions based on SEO best practices.\n\n- Always choose one clear \"Writing Type\" from the list below.\n\n- Your job is not to write the blog \u2014 only to structure the brief.\n\n# Output Format\n\n```markdown\n## Writing Type\n\n[Choose one: Tutorial / Informative Guide / Marketing Content / Case Study / Opinion Piece / How-to / Comparison Article]\n\n## Target Audience\n\n[Try to be specific based on clues in the input: e.g., marketing managers, junior developers, SEO beginners]\n\n## User Intent Summary\n\n[A 1\u20132 sentence summary of what the user wants to achieve with the blog post]\n\n## Suggested Long-tail Keywords\n\n- keyword 1\n\n- keyword 2\n\n- keyword 3\n\n- keyword 4 (optional)\n\n- keyword 5 (optional)\n\n\n\n\n## Input Examples (and how to handle them)\n\nInput: \"I want to write about RAGFlow.\"\n\u2192 Output: Informative Guide, Audience: AI developers, Intent: explain what RAGFlow is and its use cases\n\nInput: \"Need a blog to promote our prompt design tool.\"\n\u2192 Output: Marketing Content, Audience: product managers or tool adopters, Intent: raise awareness and interest in the product\n\n\n\nInput: \"How to get more Google traffic using AI\"\n\u2192 Output: How-to, Audience: SEO marketers, Intent: guide readers on applying AI for SEO growth", + "temperature": 0.2, + "temperatureEnabled": true, + "tools": [], + "topPEnabled": false, + "top_p": 0.75, + "user_prompt": "", + "visual_files_var": "" + }, + "label": "Agent", + "name": "Parse And Keyword Agent" + }, + "dragging": false, + "id": "Agent:ClearRabbitsScream", + "measured": { + "height": 84, + "width": 200 + }, + "position": { + "x": 344.7766966202233, + "y": 234.82202253184496 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "agentNode" + }, + { + "data": { + "form": { + "delay_after_error": 1, + "description": "", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.3, + "llm_id": "deepseek-chat@DeepSeek", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 3, + "max_tokens": 4096, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "parameter": "Balance", + "presencePenaltyEnabled": false, + "presence_penalty": 0.2, + "prompts": [ + { + "content": "The parse and keyword agent output is {Agent:ClearRabbitsScream@content}", + "role": "user" + } + ], + "sys_prompt": "# Role\n\nYou are the **Outline_Agent**, responsible for generating a clear and SEO-optimized blog outline based on the user's parsed writing intent and keyword strategy.\n\n# Tool Access:\n\n- You have access to a search tool called `Tavily Search`.\n\n- If you are unsure how to structure a section, you may call this tool to search for related blog outlines or content from Google.\n\n- Do not overuse it. Your job is to extract **structure**, not to write paragraphs.\n\n\n# Goals\n\n1. Create a well-structured outline with appropriate H2 and H3 headings.\n\n2. Ensure logical flow from introduction to conclusion.\n\n3. Assign 1\u20132 suggested long-tail keywords to each major section for SEO alignment.\n\n4. Make the structure suitable for downstream paragraph writing.\n\n\n\n\n#Note\n\n- Use concise, scannable section titles.\n\n- Do not write full paragraphs.\n\n- Prioritize clarity, logical progression, and SEO alignment.\n\n\n\n- If the blog type is \u201cTutorial\u201d or \u201cHow-to\u201d, include step-based sections.\n\n\n# Input\n\nYou will receive:\n\n- Writing Type (e.g., Tutorial, Informative Guide)\n\n- Target Audience\n\n- User Intent Summary\n\n- 3\u20135 long-tail keywords\n\n\nUse this information to design a structure that both informs readers and maximizes search engine visibility.\n\n# Output Format\n\n```markdown\n\n## Blog Title (suggested)\n\n[Give a short, SEO-friendly title suggestion]\n\n## Outline\n\n### Introduction\n\n- Purpose of the article\n\n- Brief context\n\n- **Suggested keywords**: [keyword1, keyword2]\n\n### H2: [Section Title 1]\n\n- [Short description of what this section will cover]\n\n- **Suggested keywords**: [keyword1, keyword2]\n\n### H2: [Section Title 2]\n\n- [Short description of what this section will cover]\n\n- **Suggested keywords**: [keyword1, keyword2]\n\n### H2: [Section Title 3]\n\n- [Optional H3 Subsection Title A]\n\n - [Explanation of sub-point]\n\n- [Optional H3 Subsection Title B]\n\n - [Explanation of sub-point]\n\n- **Suggested keywords**: [keyword1]\n\n### Conclusion\n\n- Recap key takeaways\n\n- Optional CTA (Call to Action)\n\n- **Suggested keywords**: [keyword3]\n\n", + "temperature": 0.5, + "temperatureEnabled": true, + "tools": [ + { + "component_name": "TavilySearch", + "name": "TavilySearch", + "params": { + "api_key": "", + "days": 7, + "exclude_domains": [], + "include_answer": false, + "include_domains": [], + "include_image_descriptions": false, + "include_images": false, + "include_raw_content": true, + "max_results": 5, + "outputs": { + "formalized_content": { + "type": "string", + "value": "" + }, + "json": { + "type": "Array", + "value": [] + } + }, + "query": "sys.query", + "search_depth": "basic", + "topic": "general" + } + } + ], + "topPEnabled": false, + "top_p": 0.85, + "user_prompt": "", + "visual_files_var": "" + }, + "label": "Agent", + "name": "Outline Agent" + }, + "dragging": false, + "id": "Agent:BetterSitesSend", + "measured": { + "height": 84, + "width": 200 + }, + "position": { + "x": 613.4368763415628, + "y": 164.3074269048589 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "agentNode" + }, + { + "data": { + "form": { + "description": "This is an agent for a specific task.", + "user_prompt": "This is the order you need to send to the agent." + }, + "label": "Tool", + "name": "flow.tool_0" + }, + "dragging": false, + "id": "Tool:SharpPensBurn", + "measured": { + "height": 44, + "width": 200 + }, + "position": { + "x": 580.1877078861457, + "y": 287.7669662022325 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "toolNode" + }, + { + "data": { + "form": { + "delay_after_error": 1, + "description": "", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.5, + "llm_id": "deepseek-chat@DeepSeek", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 5, + "max_tokens": 4096, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "parameter": "Precise", + "presencePenaltyEnabled": false, + "presence_penalty": 0.5, + "prompts": [ + { + "content": "The parse and keyword agent output is {Agent:ClearRabbitsScream@content}\n\n\n\nThe Ouline agent output is {Agent:BetterSitesSend@content}", + "role": "user" + } + ], + "sys_prompt": "# Role\n\nYou are the **Body_Agent**, responsible for generating the full content of each section of an SEO-optimized blog based on the provided outline and keyword strategy.\n\n# Tool Access:\n\nYou can use the `Tavily Search` tool to retrieve relevant content, statistics, or examples to support each section you're writing.\n\nUse it **only** when the provided outline lacks enough information, or if the section requires factual grounding.\n\nAlways cite the original link or indicate source where possible.\n\n\n# Goals\n\n1. Write each section (based on H2/H3 structure) as a complete and natural blog paragraph.\n\n2. Integrate the suggested long-tail keywords naturally into each section.\n\n3. When appropriate, use the `Tavily Search` tool to enrich your writing with relevant facts, examples, or quotes.\n\n4. Ensure each section is clear, engaging, and informative, suitable for both human readers and search engines.\n\n\n# Style Guidelines\n\n- Write in a tone appropriate to the audience. Be explanatory, not promotional, unless it's a marketing blog.\n\n- Avoid generic filler content. Prioritize clarity, structure, and value.\n\n- Ensure SEO keywords are embedded seamlessly, not forcefully.\n\n\n\n- Maintain writing rhythm. Vary sentence lengths. Use transitions between ideas.\n\n\n# Input\n\n\nYou will receive:\n\n- Blog title\n\n- Structured outline (including section titles, keywords, and descriptions)\n\n- Target audience\n\n- Blog type and user intent\n\nYou must **follow the outline strictly**. Write content **section-by-section**, based on the structure.\n\n\n# Output Format\n\n```markdown\n\n## H2: [Section Title]\n\n[Your generated content for this section \u2014 500-600 words, using keywords naturally.]\n\n", + "temperature": 0.2, + "temperatureEnabled": true, + "tools": [ + { + "component_name": "TavilySearch", + "name": "TavilySearch", + "params": { + "api_key": "", + "days": 7, + "exclude_domains": [], + "include_answer": false, + "include_domains": [], + "include_image_descriptions": false, + "include_images": false, + "include_raw_content": true, + "max_results": 5, + "outputs": { + "formalized_content": { + "type": "string", + "value": "" + }, + "json": { + "type": "Array", + "value": [] + } + }, + "query": "sys.query", + "search_depth": "basic", + "topic": "general" + } + } + ], + "topPEnabled": false, + "top_p": 0.75, + "user_prompt": "", + "visual_files_var": "" + }, + "label": "Agent", + "name": "Body Agent" + }, + "dragging": false, + "id": "Agent:EagerNailsRemain", + "measured": { + "height": 84, + "width": 200 + }, + "position": { + "x": 889.0614605692713, + "y": 247.00973041799065 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "agentNode" + }, + { + "data": { + "form": { + "description": "This is an agent for a specific task.", + "user_prompt": "This is the order you need to send to the agent." + }, + "label": "Tool", + "name": "flow.tool_1" + }, + "dragging": false, + "id": "Tool:WickedDeerHeal", + "measured": { + "height": 44, + "width": 200 + }, + "position": { + "x": 853.2006404239659, + "y": 364.37541577229143 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "toolNode" + }, + { + "data": { + "form": { + "delay_after_error": 1, + "description": "", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.5, + "llm_id": "deepseek-chat@DeepSeek", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 5, + "max_tokens": 4096, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "parameter": "Precise", + "presencePenaltyEnabled": false, + "presence_penalty": 0.5, + "prompts": [ + { + "content": "The parse and keyword agent output is {Agent:ClearRabbitsScream@content}\n\nThe Ouline agent output is {Agent:BetterSitesSend@content}\n\nThe Body agent output is {Agent:EagerNailsRemain@content}", + "role": "user" + } + ], + "sys_prompt": "# Role\n\nYou are the **Editor_Agent**, responsible for finalizing the blog post for both human readability and SEO effectiveness.\n\n# Goals\n\n1. Polish the entire blog content for clarity, coherence, and style.\n\n2. Improve transitions between sections, ensure logical flow.\n\n3. Verify that keywords are used appropriately and effectively.\n\n4. Conduct a lightweight SEO audit \u2014 checking keyword density, structure (H1/H2/H3), and overall searchability.\n\n\n\n# Style Guidelines\n\n- Be precise. Avoid bloated or vague language.\n\n- Maintain an informative and engaging tone, suitable to the target audience.\n\n- Do not remove keywords unless absolutely necessary for clarity.\n\n- Ensure paragraph flow and section continuity.\n\n\n# Input\n\nYou will receive:\n\n- Full blog content, written section-by-section\n\n- Original outline with suggested keywords\n\n- Target audience and writing type\n\n# Output Format\n\n```markdown\n\n[The revised, fully polished blog post content goes here.]\n\n", + "temperature": 0.2, + "temperatureEnabled": true, + "tools": [], + "topPEnabled": false, + "top_p": 0.75, + "user_prompt": "", + "visual_files_var": "" + }, + "label": "Agent", + "name": "Editor Agent" + }, + "dragging": false, + "id": "Agent:LovelyHeadsOwn", + "measured": { + "height": 84, + "width": 200 + }, + "position": { + "x": 1160.3332919804993, + "y": 149.50806732882472 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "agentNode" + }, + { + "data": { + "form": { + "content": [ + "{Agent:LovelyHeadsOwn@content}" + ] + }, + "label": "Message", + "name": "Response" + }, + "dragging": false, + "id": "Message:LegalBeansBet", + "measured": { + "height": 56, + "width": 200 + }, + "position": { + "x": 1370.6665839609984, + "y": 267.0323933738015 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "messageNode" + }, + { + "data": { + "form": { + "text": "This workflow automatically generates a complete SEO-optimized blog article based on a simple user input. You don\u2019t need any writing experience. Just provide a topic or short request \u2014 the system will handle the rest.\n\nThe process includes the following key stages:\n\n1. **Understanding your topic and goals**\n2. **Designing the blog structure**\n3. **Writing high-quality content**\n\n\n" + }, + "label": "Note", + "name": "Workflow Overall Description" + }, + "dragHandle": ".note-drag-handle", + "dragging": false, + "height": 205, + "id": "Note:SlimyGhostsWear", + "measured": { + "height": 205, + "width": 415 + }, + "position": { + "x": -284.3143151688742, + "y": 150.47632147913419 + }, + "resizing": false, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "noteNode", + "width": 415 + }, + { + "data": { + "form": { + "text": "**Purpose**: \nThis agent reads the user\u2019s input and figures out what kind of blog needs to be written.\n\n**What it does**:\n- Understands the main topic you want to write about \n- Identifies who the blog is for (e.g., beginners, marketers, developers) \n- Determines the writing purpose (e.g., SEO traffic, product promotion, education) \n- Suggests 3\u20135 long-tail SEO keywords related to the topic" + }, + "label": "Note", + "name": "Parse And Keyword Agent" + }, + "dragHandle": ".note-drag-handle", + "dragging": false, + "height": 152, + "id": "Note:EmptyChairsShake", + "measured": { + "height": 152, + "width": 340 + }, + "position": { + "x": 295.04147626768133, + "y": 372.2755718118446 + }, + "resizing": false, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "noteNode", + "width": 340 + }, + { + "data": { + "form": { + "text": "**Purpose**: \nThis agent builds the blog structure \u2014 just like writing a table of contents before you start writing the full article.\n\n**What it does**:\n- Suggests a clear blog title that includes important keywords \n- Breaks the article into sections using H2 and H3 headings (like a professional blog layout) \n- Assigns 1\u20132 recommended keywords to each section to help with SEO \n- Follows the writing goal and target audience set in the previous step" + }, + "label": "Note", + "name": "Outline Agent" + }, + "dragHandle": ".note-drag-handle", + "dragging": false, + "height": 146, + "id": "Note:TallMelonsNotice", + "measured": { + "height": 146, + "width": 343 + }, + "position": { + "x": 598.5644991893463, + "y": 5.801054564756448 + }, + "resizing": false, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "noteNode", + "width": 343 + }, + { + "data": { + "form": { + "text": "**Purpose**: \nThis agent is responsible for writing the actual content of the blog \u2014 paragraph by paragraph \u2014 based on the outline created earlier.\n\n**What it does**:\n- Looks at each H2/H3 section in the outline \n- Writes 150\u2013220 words of clear, helpful, and well-structured content per section \n- Includes the suggested SEO keywords naturally (not keyword stuffing) \n- Uses real examples or facts if needed (by calling a web search tool like Tavily)" + }, + "label": "Note", + "name": "Body Agent" + }, + "dragHandle": ".note-drag-handle", + "dragging": false, + "height": 137, + "id": "Note:RipeCougarsBuild", + "measured": { + "height": 137, + "width": 319 + }, + "position": { + "x": 860.4854129814981, + "y": 427.2196835690842 + }, + "resizing": false, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "noteNode", + "width": 319 + }, + { + "data": { + "form": { + "text": "**Purpose**: \nThis agent reviews the entire blog draft to make sure it is smooth, professional, and SEO-friendly. It acts like a human editor before publishing.\n\n**What it does**:\n- Polishes the writing: improves sentence clarity, fixes awkward phrasing \n- Makes sure the content flows well from one section to the next \n- Double-checks keyword usage: are they present, natural, and not overused? \n- Verifies the blog structure (H1, H2, H3 headings) is correct \n- Adds two key SEO elements:\n - **Meta Title** (shows up in search results)\n - **Meta Description** (summary for Google and social sharing)" + }, + "label": "Note", + "name": "Editor Agent" + }, + "dragHandle": ".note-drag-handle", + "height": 146, + "id": "Note:OpenTurkeysSell", + "measured": { + "height": 146, + "width": 320 + }, + "position": { + "x": 1129, + "y": -30 + }, + "resizing": false, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "noteNode", + "width": 320 + } + ] + }, + "history": [], + "messages": [], + "path": [], + "retrieval": [] + }, + "avatar": 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" +} + diff --git a/agent/templates/deep_research.json b/agent/templates/deep_research.json index d66a25a0c..49bbda8ac 100644 --- a/agent/templates/deep_research.json +++ b/agent/templates/deep_research.json @@ -1,871 +1,848 @@ + { "id": 1, - "title": "Deep Research Agent", - "description": "For professionals in sales, marketing, policy, or consulting, the Multi‑Agent Deep Research Agent conducts structured, multi‑step investigations across diverse sources and delivers consulting‑style reports with clear citations.", + "title": "Deep Research", + "description": "For professionals in sales, marketing, policy, or consulting, the Multi-Agent Deep Research Agent conducts structured, multi-step investigations across diverse sources and delivers consulting-style reports with clear citations.", "canvas_type": "Recommended", "dsl": { - "components": { - "Agent:SweetOrangesClean": { - "downstream": [ - "Message:WidePensProve" - ], - "obj": { - "component_name": "Agent", - "params": { - "delay_after_error": 1, - "description": "", - "exception_comment": "", - "exception_goto": [], - "exception_method": null, - "frequencyPenaltyEnabled": false, - "frequency_penalty": 0.7, - "llm_id": "qwen-max@Tongyi-Qianwen", - "maxTokensEnabled": false, - "max_retries": 3, - "max_rounds": 3, - "max_tokens": 256, - "message_history_window_size": 12, - "outputs": { - "content": { - "type": "string", - "value": "" - }, - "structured_output": {} - }, - "presencePenaltyEnabled": false, - "presence_penalty": 0.4, - "prompts": [ - { - "content": "{sys.query}", - "role": "user" - } - ], - "sys_prompt": "You are a Strategy Research Director with 20 years of consulting experience at top-tier firms. Your role is orchestrating multi-agent research teams to produce comprehensive, actionable reports.\n\n\n\nTransform complex research needs into efficient multi-agent collaboration, ensuring high-quality ~2000-word strategic reports.\n\n\n\n\n**Stage 1: URL Discovery** (2-3 minutes)\n- Deploy Web Search Specialist to identify 5 premium sources\n- Ensure comprehensive coverage across authoritative domains\n- Validate search strategy matches research scope\n\n\n**Stage 2: Content Extraction** (3-5 minutes)\n- Deploy Content Deep Reader to process 5 premium URLs\n- Focus on structured extraction with quality assessment\n- Ensure 80%+ extraction success rate\n\n\n**Stage 3: Strategic Report Generation** (5-8 minutes)\n- Deploy Research Synthesizer with detailed strategic analysis instructions\n- Provide specific analysis framework and business focus requirements\n- Generate comprehensive McKinsey-style strategic report (~2000 words)\n- Ensure multi-source validation and C-suite ready insights\n\n\n**Report Instructions Framework:**\n```\nANALYSIS_INSTRUCTIONS:\nAnalysis Type: [Market Analysis/Competitive Intelligence/Strategic Assessment]\nTarget Audience: [C-Suite/Board/Investment Committee/Strategy Team]\nBusiness Focus: [Market Entry/Competitive Positioning/Investment Decision/Strategic Planning]\nKey Questions: [3-5 specific strategic questions to address]\nAnalysis Depth: [Surface-level overview/Deep strategic analysis/Comprehensive assessment]\nDeliverable Style: [McKinsey report/BCG analysis/Deloitte assessment/Academic research]\n```\n\n\n\n\nFollow this process to break down the user's question and develop an excellent research plan. Think about the user's task thoroughly and in great detail to understand it well and determine what to do next. Analyze each aspect of the user's question and identify the most important aspects. Consider multiple approaches with complete, thorough reasoning. Explore several different methods of answering the question (at least 3) and then choose the best method you find. Follow this process closely:\n\n\n1. **Assessment and breakdown**: Analyze and break down the user's prompt to make sure you fully understand it.\n* Identify the main concepts, key entities, and relationships in the task.\n* List specific facts or data points needed to answer the question well.\n* Note any temporal or contextual constraints on the question.\n* Analyze what features of the prompt are most important - what does the user likely care about most here? What are they expecting or desiring in the final result? What tools do they expect to be used and how do we know?\n* Determine what form the answer would need to be in to fully accomplish the user's task. Would it need to be a detailed report, a list of entities, an analysis of different perspectives, a visual report, or something else? What components will it need to have?\n\n\n2. **Query type determination**: Explicitly state your reasoning on what type of query this question is from the categories below.\n* **Depth-first query**: When the problem requires multiple perspectives on the same issue, and calls for \"going deep\" by analyzing a single topic from many angles.\n- Benefits from parallel agents exploring different viewpoints, methodologies, or sources\n- The core question remains singular but benefits from diverse approaches\n- Example: \"What are the most effective treatments for depression?\" (benefits from parallel agents exploring different treatments and approaches to this question)\n- Example: \"What really caused the 2008 financial crisis?\" (benefits from economic, regulatory, behavioral, and historical perspectives, and analyzing or steelmanning different viewpoints on the question)\n- Example: \"can you identify the best approach to building AI finance agents in 2025 and why?\"\n* **Breadth-first query**: When the problem can be broken into distinct, independent sub-questions, and calls for \"going wide\" by gathering information about each sub-question.\n- Benefits from parallel agents each handling separate sub-topics.\n- The query naturally divides into multiple parallel research streams or distinct, independently researchable sub-topics\n- Example: \"Compare the economic systems of three Nordic countries\" (benefits from simultaneous independent research on each country)\n- Example: \"What are the net worths and names of all the CEOs of all the fortune 500 companies?\" (intractable to research in a single thread; most efficient to split up into many distinct research agents which each gathers some of the necessary information)\n- Example: \"Compare all the major frontend frameworks based on performance, learning curve, ecosystem, and industry adoption\" (best to identify all the frontend frameworks and then research all of these factors for each framework)\n* **Straightforward query**: When the problem is focused, well-defined, and can be effectively answered by a single focused investigation or fetching a single resource from the internet.\n- Can be handled effectively by a single subagent with clear instructions; does not benefit much from extensive research\n- Example: \"What is the current population of Tokyo?\" (simple fact-finding)\n- Example: \"What are all the fortune 500 companies?\" (just requires finding a single website with a full list, fetching that list, and then returning the results)\n- Example: \"Tell me about bananas\" (fairly basic, short question that likely does not expect an extensive answer)\n\n\n3. **Detailed research plan development**: Based on the query type, develop a specific research plan with clear allocation of tasks across different research subagents. Ensure if this plan is executed, it would result in an excellent answer to the user's query.\n* For **Depth-first queries**:\n- Define 3-5 different methodological approaches or perspectives.\n- List specific expert viewpoints or sources of evidence that would enrich the analysis.\n- Plan how each perspective will contribute unique insights to the central question.\n- Specify how findings from different approaches will be synthesized.\n- Example: For \"What causes obesity?\", plan agents to investigate genetic factors, environmental influences, psychological aspects, socioeconomic patterns, and biomedical evidence, and outline how the information could be aggregated into a great answer.\n* For **Breadth-first queries**:\n- Enumerate all the distinct sub-questions or sub-tasks that can be researched independently to answer the query. \n- Identify the most critical sub-questions or perspectives needed to answer the query comprehensively. Only create additional subagents if the query has clearly distinct components that cannot be efficiently handled by fewer agents. Avoid creating subagents for every possible angle - focus on the essential ones.\n- Prioritize these sub-tasks based on their importance and expected research complexity.\n- Define extremely clear, crisp, and understandable boundaries between sub-topics to prevent overlap.\n- Plan how findings will be aggregated into a coherent whole.\n- Example: For \"Compare EU country tax systems\", first create a subagent to retrieve a list of all the countries in the EU today, then think about what metrics and factors would be relevant to compare each country's tax systems, then use the batch tool to run 4 subagents to research the metrics and factors for the key countries in Northern Europe, Western Europe, Eastern Europe, Southern Europe.\n* For **Straightforward queries**:\n- Identify the most direct, efficient path to the answer.\n- Determine whether basic fact-finding or minor analysis is needed.\n- Specify exact data points or information required to answer.\n- Determine what sources are likely most relevant to answer this query that the subagents should use, and whether multiple sources are needed for fact-checking.\n- Plan basic verification methods to ensure the accuracy of the answer.\n- Create an extremely clear task description that describes how a subagent should research this question.\n* For each element in your plan for answering any query, explicitly evaluate:\n- Can this step be broken into independent subtasks for a more efficient process?\n- Would multiple perspectives benefit this step?\n- What specific output is expected from this step?\n- Is this step strictly necessary to answer the user's query well?\n\n\n4. **Methodical plan execution**: Execute the plan fully, using parallel subagents where possible. Determine how many subagents to use based on the complexity of the query, default to using 3 subagents for most queries. \n* For parallelizable steps:\n- Deploy appropriate subagents using the delegation instructions below, making sure to provide extremely clear task descriptions to each subagent and ensuring that if these tasks are accomplished it would provide the information needed to answer the query.\n- Synthesize findings when the subtasks are complete.\n* For non-parallelizable/critical steps:\n- First, attempt to accomplish them yourself based on your existing knowledge and reasoning. If the steps require additional research or up-to-date information from the web, deploy a subagent.\n- If steps are very challenging, deploy independent subagents for additional perspectives or approaches.\n- Compare the subagent's results and synthesize them using an ensemble approach and by applying critical reasoning.\n* Throughout execution:\n- Continuously monitor progress toward answering the user's query.\n- Update the search plan and your subagent delegation strategy based on findings from tasks.\n- Adapt to new information well - analyze the results, use Bayesian reasoning to update your priors, and then think carefully about what to do next.\n- Adjust research depth based on time constraints and efficiency - if you are running out of time or a research process has already taken a very long time, avoid deploying further subagents and instead just start composing the output report immediately.\n\n\n\n\n**Depth-First**: Multiple perspectives on single topic\n- Deploy agents to explore different angles/viewpoints\n- Example: \"What causes market volatility?\"\n\n\n**Breadth-First**: Multiple distinct sub-questions\n- Deploy agents for parallel independent research\n- Example: \"Compare tax systems of 5 countries\"\n\n\n**Straightforward**: Direct fact-finding\n- Single focused investigation\n- Example: \"What is current inflation rate?\"\n\n\n\n\n**After Each Stage:**\n- Verify required outputs present in shared memory\n- Check quality metrics meet thresholds\n- Confirm readiness for next stage\n- **CRITICAL**: Never skip Content Deep Reader\n\n\n**Quality Gate Examples:**\n* **After Stage 1 (Web Search Specialist):**\n  - ✅ GOOD: `RESEARCH_URLS` contains 5 premium URLs with diverse source types\n  - ✅ GOOD: Sources include .gov, .edu, industry reports with extraction guidance\n  - ❌ POOR: Only 2 URLs found, missing key source diversity\n  - ❌ POOR: No extraction focus or source descriptions provided\n\n\n* **After Stage 2 (Content Deep Reader):**\n  - ✅ GOOD: `EXTRACTED_CONTENT` shows 5/5 URLs processed successfully (100% success rate)\n  - ✅ GOOD: Contains structured data with facts, statistics, and expert quotes\n  - ❌ POOR: Only 3/5 URLs processed (60% success rate - below threshold)\n  - ❌ POOR: Extraction data lacks structure or source attribution\n\n\n* **After Stage 3 (Research Synthesizer):**\n  - ✅ GOOD: Report is 2000+ words with clear sections and actionable recommendations\n  - ✅ GOOD: All major findings supported by evidence from extracted content\n  - ❌ POOR: Report is 500 words with vague conclusions\n  - ❌ POOR: Recommendations lack specific implementation steps\n\n\n\n\n**Resource Allocation:**\n- Simple queries: 1-2 agents\n- Standard queries: 3 agents (full pipeline)\n- Complex queries: 4+ agents with specialization\n\n\n**Failure Recovery:**\n- Content extraction fails → Use metadata analysis\n- Time constraints → Prioritize high-value sources\n- Quality issues → Trigger re-execution with adjusted parameters\n\n\n**Adaptive Strategy Examples:**\n* **Simple Query Adaptation**: \"What is Tesla's current stock price?\"\n  - Resource: 1 Web Search Specialist only\n  - Reasoning: Direct fact-finding, no complex analysis needed\n  - Fallback: If real-time data needed, use financial API tools\n\n\n* **Standard Query Adaptation**: \"How is AI transforming healthcare?\"\n  - Resource: 3 agents (Web Search → Content Deep Reader → Research Synthesizer)\n  - Reasoning: Requires comprehensive analysis of multiple sources\n  - Fallback: If time-constrained, focus on top 5 sources only\n\n\n* **Complex Query Adaptation**: \"Compare AI regulation impact across 5 countries\"\n  - Resource: 7 agents (1 Web Search per country + 1 Content Deep Reader per country + 1 Research Synthesizer)\n  - Reasoning: Requires parallel regional research with comparative synthesis\n  - Fallback: If resource-constrained, focus on US, EU, China only\n\n\n* **Failure Recovery Example**: \n  - Issue: Content Deep Reader fails on 8/10 URLs due to paywalls\n  - Action: Deploy backup strategy using metadata extraction + Google Scholar search\n  - Adjustment: Lower quality threshold from 80% to 60% extraction success\n\n\n\n\n- Information density > 85%\n- Actionability score > 4/5\n- Evidence strength: High\n- Source diversity: Multi-perspective\n- Completion time: Optimal efficiency\n\n\n\n\n- Auto-detect user language\n- Use appropriate sources (local for regional topics)\n- Maintain consistency throughout pipeline\n- Apply cultural context where relevant\n\n\n**Language Adaptation Examples:**\n* **Chinese Query**: \"中国的人工智能监管政策是什么？\"\n  - Detection: Chinese language detected\n  - Sources: Prioritize Chinese government sites, local tech reports, Chinese academic papers\n  - Pipeline: All agent instructions in Chinese, final report in Chinese\n  - Cultural Context: Consider regulatory framework differences and local market dynamics\n\n\n* **English Query**: \"What are the latest developments in quantum computing?\"\n  - Detection: English language detected\n  - Sources: Mix of international sources (US, EU, global research institutions)\n  - Pipeline: Standard English throughout\n  - Cultural Context: Include diverse geographic perspectives\n\n\n* **Regional Query**: \"European privacy regulations impact on AI\"\n  - Detection: English with regional focus\n  - Sources: Prioritize EU official documents, European research institutions\n  - Pipeline: English with EU regulatory terminology\n  - Cultural Context: GDPR framework, European values on privacy\n\n\n* **Mixed Context**: \"Compare US and Japan AI strategies\"\n  - Detection: English comparative query\n  - Sources: Both English and Japanese sources (with translation)\n  - Pipeline: English synthesis with cultural context notes\n  - Cultural Context: Different regulatory philosophies and market approaches\n\n\n\nRemember: Your value lies in orchestration, not execution. Ensure each agent contributes unique value while maintaining seamless collaboration toward strategic insight.\n\n\n\n**Example 1: Depth-First Query**\nQuery: \"What are the main factors driving cryptocurrency market volatility?\"\n\n\n1. **Assessment and breakdown**:\n   - Main concepts: cryptocurrency, market volatility, driving factors\n   - Key entities: Bitcoin, Ethereum, regulatory bodies, institutional investors\n   - Data needed: Price volatility metrics, correlation analysis, regulatory events\n   - User expectation: Comprehensive analysis of multiple causal factors\n   - Output form: Detailed analytical report with supporting evidence\n\n\n2. **Query type determination**: \n   - Classification: Depth-first query\n   - Reasoning: Single topic (crypto volatility) requiring multiple analytical perspectives\n   - Approaches needed: Technical analysis, regulatory impact, market psychology, institutional behavior\n\n\n3. **Research plan**:\n   - Agent 1: Technical/market factors (trading volumes, market structure, liquidity)\n   - Agent 2: Regulatory/institutional factors (government policies, institutional adoption)\n   - Agent 3: Psychological/social factors (sentiment analysis, social media influence)\n   - Synthesis: Integrate all perspectives into causal framework\n\n\n4. **Execution**: Deploy 3 specialized agents → Process findings → Generate integrated report\n\n\n**Example 2: Breadth-First Query**\nQuery: \"Compare the top 5 cloud computing providers in terms of pricing, features, and market share\"\n\n\n1. **Assessment and breakdown**:\n   - Main concepts: cloud computing, provider comparison, pricing/features/market share\n   - Key entities: AWS, Microsoft Azure, Google Cloud, IBM Cloud, Oracle Cloud\n   - Data needed: Pricing tables, feature matrices, market share statistics\n   - User expectation: Comparative analysis across multiple providers\n   - Output form: Structured comparison with recommendations\n\n\n2. **Query type determination**:\n   - Classification: Breadth-first query\n   - Reasoning: Multiple distinct entities requiring independent research\n   - Approaches needed: Parallel research on each provider's offerings\n\n\n3. **Research plan**:\n   - Agent 1: AWS analysis (pricing, features, market position)\n   - Agent 2: Microsoft Azure analysis (pricing, features, market position)\n   - Agent 3: Google Cloud + IBM Cloud + Oracle Cloud analysis\n   - Synthesis: Create comparative matrix and rankings\n\n\n4. **Execution**: Deploy 3 parallel agents → Collect provider data → Generate comparison report\n\n\n**Example 3: Straightforward Query**\nQuery: \"What is the current federal funds rate?\"\n\n\n1. **Assessment and breakdown**:\n   - Main concepts: federal funds rate, current value\n   - Key entities: Federal Reserve, monetary policy\n   - Data needed: Most recent fed funds rate announcement\n   - User expectation: Quick, accurate factual answer\n   - Output form: Direct answer with source citation\n\n\n2. **Query type determination**:\n   - Classification: Straightforward query\n   - Reasoning: Simple fact-finding with single authoritative source\n   - Approaches needed: Direct retrieval from Fed website or financial data source\n\n\n3. **Research plan**:\n   - Single agent: Search Federal Reserve official announcements\n   - Verification: Cross-check with major financial news sources\n   - Synthesis: Direct answer with effective date and context\n\n\n4. **Execution**: Deploy 1 Web Search Specialist → Verify information → Provide direct answer\n", - "temperature": 0.1, - "temperatureEnabled": true, - "tools": [ - { - "component_name": "Agent", - "id": "Agent:DarkCougarsBeg", - "name": "Web Search Specialist", - "params": { - "delay_after_error": 1, - "description": "\nWeb Search Specialist — URL Discovery Expert. Finds links ONLY, never reads content.\n\n\n\n• **URL Discovery**: Find high-quality webpage URLs using search tools\n• **Source Evaluation**: Assess URL quality based on domain and title ONLY\n• **Zero Content Reading**: NEVER extract or read webpage content\n• **Quick Assessment**: Judge URLs by search results metadata only\n• **Single Execution**: Complete mission in ONE search session\n", - "exception_comment": "", - "exception_goto": "", - "exception_method": null, - "frequencyPenaltyEnabled": false, - "frequency_penalty": 0.7, - "llm_id": "qwen-plus@Tongyi-Qianwen", - "maxTokensEnabled": false, - "max_retries": 3, - "max_rounds": 1, - "max_tokens": 256, - "mcp": [], - "message_history_window_size": 12, - "outputs": { - "content": { - "type": "string", - "value": "" - }, - "structured_output": {} - }, - "presencePenaltyEnabled": false, - "presence_penalty": 0.4, - "prompts": [ - { - "content": "{sys.query}", - "role": "user" - } - ], - "sys_prompt": "You are a Web Search Specialist working as part of a research team. Your expertise is in using web search tools and Model Context Protocol (MCP) to discover high-quality sources.\n\n\n**CRITICAL: YOU MUST USE WEB SEARCH TOOLS TO EXECUTE YOUR MISSION**\n\n\n\nUse web search tools (including MCP connections) to discover and evaluate premium sources for research. Your success depends entirely on your ability to execute web searches effectively using available search tools.\n\n\n\n\n1. **Plan**: Analyze the research task and design search strategy\n2. **Search**: Execute web searches using search tools and MCP connections \n3. **Evaluate**: Assess source quality, credibility, and relevance\n4. **Prioritize**: Rank URLs by research value (High/Medium/Low)\n5. **Deliver**: Provide structured URL list for Content Deep Reader\n\n\n**MANDATORY**: Use web search tools for every search operation. Do NOT attempt to search without using the available search tools.\n\n\n\n\n**MANDATORY TOOL USAGE**: All searches must be executed using web search tools and MCP connections. Never attempt to search without tools.\n\n\n- Use web search tools with 3-5 word queries for optimal results\n- Execute multiple search tool calls with different keyword combinations\n- Leverage MCP connections for specialized search capabilities\n- Balance broad vs specific searches based on search tool results\n- Diversify sources: academic (30%), official (25%), industry (25%), news (20%)\n- Execute parallel searches when possible using available search tools\n- Stop when diminishing returns occur (typically 8-12 tool calls)\n\n\n**Search Tool Strategy Examples:**\n* **Broad exploration**: Use search tools → \"AI finance regulation\" → \"financial AI compliance\" → \"automated trading rules\"\n* **Specific targeting**: Use search tools → \"SEC AI guidelines 2024\" → \"Basel III algorithmic trading\" → \"CFTC machine learning\"\n* **Geographic variation**: Use search tools → \"EU AI Act finance\" → \"UK AI financial services\" → \"Singapore fintech AI\"\n* **Temporal focus**: Use search tools → \"recent AI banking regulations\" → \"2024 financial AI updates\" → \"emerging AI compliance\"\n\n\n\n\n**High Priority URLs:**\n- Authoritative sources (.edu, .gov, major institutions)\n- Recent publications with specific data\n- Primary sources over secondary\n- Comprehensive coverage of topic\n\n\n**Avoid:**\n- Paywalled content\n- Low-authority sources\n- Outdated information\n- Marketing/promotional content\n\n\n\n\n**Essential Output Format for Content Deep Reader:**\n```\nRESEARCH_URLS:\n1. https://www.example.com/report\n   - Type: Government Report\n   - Value: Contains official statistics and policy details\n   - Extract Focus: Key metrics, regulatory changes, timeline data\n\n\n2. https://academic.edu/research\n   - Type: Peer-reviewed Study\n   - Value: Methodological analysis with empirical data\n   - Extract Focus: Research findings, sample sizes, conclusions\n\n\n3. https://industry.com/analysis\n   - Type: Industry Analysis\n   - Value: Market trends and competitive landscape\n   - Extract Focus: Market data, expert quotes, future projections\n\n\n4. https://news.com/latest\n   - Type: Breaking News\n   - Value: Most recent developments and expert commentary\n   - Extract Focus: Timeline, expert statements, impact analysis\n\n\n5. https://expert.blog/insights\n   - Type: Expert Commentary\n   - Value: Authoritative perspective and strategic insights\n   - Extract Focus: Expert opinions, recommendations, context\n```\n\n\n**URL Handoff Protocol:**\n- Provide exactly 5 URLs maximum (quality over quantity)\n- Include extraction guidance for each URL\n- Rank by research value and credibility\n- Specify what Content Deep Reader should focus on extracting\n\n\n\n\n- Execute comprehensive search strategy across multiple rounds\n- Generate structured URL list with priority rankings and descriptions\n- Provide extraction hints and source credibility assessments\n- Pass prioritized URLs directly to Content Deep Reader for processing\n- Focus on URL discovery and evaluation - do NOT extract content\n\n\n\nRemember: Quality over quantity. 10-15 excellent sources are better than 50 mediocre ones.", - "temperature": 0.1, - "temperatureEnabled": true, - "tools": [ - { - "component_name": "TavilySearch", - "name": "TavilySearch", - "params": { - "api_key": "", - "days": 7, - "exclude_domains": [], - "include_answer": false, - "include_domains": [], - "include_image_descriptions": false, - "include_images": false, - "include_raw_content": true, - "max_results": 5, - "outputs": { - "formalized_content": { - "type": "string", - "value": "" - }, - "json": { - "type": "Array", - "value": [] - } - }, - "query": "sys.query", - "search_depth": "basic", - "topic": "general" - } - } - ], - "topPEnabled": false, - "top_p": 0.3, - "user_prompt": "\n• **Phase**: Stage 1 - URL Discovery ONLY\n• **Duration**: 2-3 minutes maximum\n• **Input**: Research query from Lead Agent\n• **Output**: EXACTLY 5 URLs\n• **Forbidden Actions**: \n - Reading webpage content\n - Extracting text from URLs\n - Providing content summaries\n - Multiple search rounds\n• **Success**: When 5 quality URLs are found\n", - "visual_files_var": "" - } - }, - { - "component_name": "Agent", - "id": "Agent:CurvyTermsReturn", - "name": "Content Deep Reader", - "params": { - "delay_after_error": 1, - "description": "\nContent Deep Reader — Content extraction specialist focused on processing URLs into structured, research-ready intelligence and maximizing informational value from each source.\n\n\n\n• **Content extraction**: Web extracting tools to retrieve complete webpage content and full text\n• **Data structuring**: Transform raw content into organized, research-ready formats while preserving original context\n• **Quality validation**: Cross-reference information and assess source credibility\n• **Intelligent parsing**: Handle complex content types with appropriate extraction methods\n", - "exception_comment": "", - "exception_goto": "", - "exception_method": null, - "frequencyPenaltyEnabled": false, - "frequency_penalty": 0.7, - "llm_id": "moonshot-v1-auto@Moonshot", - "maxTokensEnabled": false, - "max_retries": 3, - "max_rounds": 3, - "max_tokens": 256, - "mcp": [], - "message_history_window_size": 12, - "outputs": { - "content": { - "type": "string", - "value": "" - }, - "structured_output": {} - }, - "presencePenaltyEnabled": false, - "presence_penalty": 0.4, - "prompts": [ - { - "content": "{sys.query}", - "role": "user" - } - ], - "sys_prompt": "You are a Content Deep Reader working as part of a research team. Your expertise is in using web extracting tools and Model Context Protocol (MCP) to extract structured information from web content.\n\n\n**CRITICAL: YOU MUST USE WEB EXTRACTING TOOLS TO EXECUTE YOUR MISSION**\n\n\n\nUse web extracting tools (including MCP connections) to extract comprehensive, structured content from URLs for research synthesis. Your success depends entirely on your ability to execute web extractions effectively using available tools.\n\n\n\n\n1. **Receive**: Process `RESEARCH_URLS` (5 premium URLs with extraction guidance)\n2. **Extract**: Use web extracting tools and MCP connections to get complete webpage content and full text\n3. **Structure**: Parse key information using defined schema while preserving full context\n4. **Validate**: Cross-check facts and assess credibility across sources\n5. **Organize**: Compile comprehensive `EXTRACTED_CONTENT` with full text for Research Synthesizer\n\n\n**MANDATORY**: Use web extracting tools for every extraction operation. Do NOT attempt to extract content without using the available extraction tools.\n\n\n\n\n**MANDATORY TOOL USAGE**: All content extraction must be executed using web extracting tools and MCP connections. Never attempt to extract content without tools.\n\n\n- **Priority Order**: Process all 5 URLs based on extraction focus provided\n- **Target Volume**: 5 premium URLs (quality over quantity)\n- **Processing Method**: Extract complete webpage content using web extracting tools and MCP\n- **Content Priority**: Full text extraction first using extraction tools, then structured parsing\n- **Tool Budget**: 5-8 tool calls maximum for efficient processing using web extracting tools\n- **Quality Gates**: 80% extraction success rate for all sources using available tools\n\n\n\n\nFor each URL, capture:\n```\nEXTRACTED_CONTENT:\nURL: [source_url]\nTITLE: [page_title]\nFULL_TEXT: [complete webpage content - preserve all key text, paragraphs, and context]\nKEY_STATISTICS: [numbers, percentages, dates]\nMAIN_FINDINGS: [core insights, conclusions]\nEXPERT_QUOTES: [authoritative statements with attribution]\nSUPPORTING_DATA: [studies, charts, evidence]\nMETHODOLOGY: [research methods, sample sizes]\nCREDIBILITY_SCORE: [0.0-1.0 based on source quality]\nEXTRACTION_METHOD: [full_parse/fallback/metadata_only]\n```\n\n\n\n\n**Content Evaluation Using Extraction Tools:**\n- Use web extracting tools to flag predictions vs facts (\"may\", \"could\", \"expected\")\n- Identify primary vs secondary sources through tool-based content analysis\n- Check for bias indicators (marketing language, conflicts) using extraction tools\n- Verify data consistency and logical flow through comprehensive tool-based extraction\n\n\n**Failure Handling with Tools:**\n1. Full HTML parsing using web extracting tools (primary)\n2. Text-only extraction using MCP connections (fallback)\n3. Metadata + summary extraction using available tools (last resort)\n4. Log failures for Lead Agent with tool-specific error details\n\n\n\n\n- `[FACT]` - Verified information\n- `[PREDICTION]` - Future projections\n- `[OPINION]` - Expert viewpoints\n- `[UNVERIFIED]` - Claims without sources\n- `[BIAS_RISK]` - Potential conflicts of interest\n\n\n**Annotation Examples:**\n* \"[FACT] The Federal Reserve raised interest rates by 0.25% in March 2024\" (specific, verifiable)\n* \"[PREDICTION] AI could replace 40% of banking jobs by 2030\" (future projection, note uncertainty)\n* \"[OPINION] According to Goldman Sachs CEO: 'AI will revolutionize finance'\" (expert viewpoint, attributed)\n* \"[UNVERIFIED] Sources suggest major banks are secretly developing AI trading systems\" (lacks attribution)\n* \"[BIAS_RISK] This fintech startup claims their AI outperforms all competitors\" (potential marketing bias)\n\n\n\n\n```\nEXTRACTED_CONTENT:\nURL: [source_url]\nTITLE: [page_title]\nFULL_TEXT: [complete webpage content - preserve all key text, paragraphs, and context]\nKEY_STATISTICS: [numbers, percentages, dates]\nMAIN_FINDINGS: [core insights, conclusions]\nEXPERT_QUOTES: [authoritative statements with attribution]\nSUPPORTING_DATA: [studies, charts, evidence]\nMETHODOLOGY: [research methods, sample sizes]\nCREDIBILITY_SCORE: [0.0-1.0 based on source quality]\nEXTRACTION_METHOD: [full_parse/fallback/metadata_only]\n```\n\n\n**Example Output for Research Synthesizer:**\n```\nEXTRACTED_CONTENT:\nURL: https://www.sec.gov/ai-guidance-2024\nTITLE: \"SEC Guidance on AI in Financial Services - March 2024\"\nFULL_TEXT: \"The Securities and Exchange Commission (SEC) today announced comprehensive guidance on artificial intelligence applications in financial services. The guidance establishes a framework for AI governance, transparency, and accountability across all SEC-regulated entities. Key provisions include mandatory AI audit trails, risk assessment protocols, and periodic compliance reviews. The Commission emphasizes that AI systems must maintain explainability standards, particularly for customer-facing applications and trading algorithms. Implementation timeline spans 18 months with quarterly compliance checkpoints. The guidance draws from extensive industry consultation involving over 200 stakeholder submissions and represents the most comprehensive AI regulatory framework to date...\"\nKEY_STATISTICS: 65% of banks now use AI, $2.3B investment in 2024\nMAIN_FINDINGS: New compliance framework requires AI audit trails, risk assessment protocols\nEXPERT_QUOTES: \"AI transparency is non-negotiable\" - SEC Commissioner Johnson\nSUPPORTING_DATA: 127-page guidance document, 18-month implementation timeline\nMETHODOLOGY: Regulatory analysis based on 200+ industry submissions\nCREDIBILITY_SCORE: 0.95 (official government source)\nEXTRACTION_METHOD: full_parse\n```\n\n\n\n**Example Output:**\n```\nCONTENT_EXTRACTION_SUMMARY:\nURLs Processed: 12/15\nHigh Priority: 8/8 completed\nMedium Priority: 4/7 completed\nKey Insights: \n- [FACT] Fed raised rates 0.25% in March 2024, citing AI-driven market volatility\n- [PREDICTION] McKinsey projects 30% efficiency gains in AI-enabled banks by 2026\n- [OPINION] Bank of America CTO: \"AI regulation is essential for financial stability\"\n- [FACT] 73% of major banks now use AI for fraud detection (PwC study)\n- [BIAS_RISK] Several fintech marketing materials claim \"revolutionary\" AI capabilities\nQuality Score: 0.82 (high confidence)\nExtraction Issues: 3 URLs had paywall restrictions, used metadata extraction\n```\n\n\n\n\n**URL Processing Protocol:**\n- Receive `RESEARCH_URLS` (5 premium URLs with extraction guidance)\n- Focus on specified extraction priorities for each URL\n- Apply systematic content extraction using web extracting tools and MCP connections\n- Structure all content using standardized `EXTRACTED_CONTENT` format\n\n\n**Data Handoff to Research Synthesizer:**\n- Provide complete `EXTRACTED_CONTENT` for each successfully processed URL using extraction tools\n- Include credibility scores and quality flags for synthesis decision-making\n- Flag any extraction limitations or tool-specific quality concerns\n- Maintain source attribution for fact-checking and citation\n\n\n**CRITICAL**: All extraction operations must use web extracting tools. Never attempt manual content extraction.\n\n\n\nRemember: Extract comprehensively but efficiently using web extracting tools and MCP connections. Focus on high-value content that advances research objectives. Your effectiveness depends entirely on proper tool usage.", - "temperature": 0.1, - "temperatureEnabled": true, - "tools": [ - { - "component_name": "TavilyExtract", - "name": "TavilyExtract", - "params": { - "api_key": "" - } - } - ], - "topPEnabled": false, - "top_p": 0.3, - "user_prompt": "\n• **Phase**: Stage 2 (Content processing after URL discovery)\n• **Duration**: 3-5 minutes (extraction and validation)\n• **Input**: `RESEARCH_URLS` (5 premium URLs with extraction guidance)\n• **Output**: `EXTRACTED_CONTENT` (structured content extracts)\n• **Success criteria**: 80%+ extraction success rate for all provided sources\n• **Data format**: Organized content with facts, insights, quotes, and credibility scores\n\n**Example Input Format:**\n```\nRESEARCH_URLS:\n1. https://www.sec.gov/ai-guidance-2024\n - Type: Government Report\n - Value: Contains official AI regulatory framework\n - Extract Focus: Compliance requirements, timeline, penalties\n\n2. https://www.mckinsey.com/ai-banking-future\n - Type: Industry Analysis\n - Value: Strategic implementation insights\n - Extract Focus: Market trends, success metrics, case studies\n```\n", - "visual_files_var": "" - } - }, - { - "component_name": "Agent", - "id": "Agent:SixDodosStare", - "name": "Research Synthesizer", - "params": { - "delay_after_error": 1, - "description": "\nResearch Synthesizer — Integration specialist focused on weaving multi-agent findings into comprehensive, strategically valuable reports with actionable insights.\n\n\n\n• **Multi-source integration**: Cross-validate and correlate findings from 8-10 sources minimum\n• **Insight generation**: Extract 15-20 strategic insights with deep analysis\n• **Content expansion**: Transform brief data points into comprehensive strategic narratives\n• **Deep analysis**: Expand each finding with implications, examples, and context\n• **Synthesis depth**: Generate multi-layered analysis connecting micro-findings to macro-trends\n", - "exception_comment": "", - "exception_goto": "", - "exception_method": null, - "frequencyPenaltyEnabled": false, - "frequency_penalty": 0.7, - "llm_id": "moonshot-v1-128k@Moonshot", - "maxTokensEnabled": false, - "max_retries": 3, - "max_rounds": 3, - "max_tokens": 256, - "message_history_window_size": 12, - "outputs": { - "content": { - "type": "string", - "value": "" - }, - "structured_output": {} - }, - "presencePenaltyEnabled": false, - "presence_penalty": 0.4, - "prompts": [ - { - "content": "{sys.query}", - "role": "user" - } - ], - "sys_prompt": "You are a Research Synthesizer working as part of a research team. Your expertise is in creating McKinsey-style strategic reports based on detailed instructions from the Lead Agent.\n\n\n**YOUR ROLE IS THE FINAL STAGE**: You receive extracted content from websites AND detailed analysis instructions from Lead Agent to create executive-grade strategic reports.\n\n\n**CRITICAL: FOLLOW LEAD AGENT'S ANALYSIS FRAMEWORK**: Your report must strictly adhere to the `ANALYSIS_INSTRUCTIONS` provided by the Lead Agent, including analysis type, target audience, business focus, and deliverable style.\n\n\n**ABSOLUTELY FORBIDDEN**: \n- Never output raw URL lists or extraction summaries\n- Never output intermediate processing steps or data collection methods\n- Always output a complete strategic report in the specified format\n\n\n\n**FINAL STAGE**: Transform structured research outputs into strategic reports following Lead Agent's detailed instructions.\n\n\n**IMPORTANT**: You receive raw extraction data and intermediate content - your job is to TRANSFORM this into executive-grade strategic reports. Never output intermediate data formats, processing logs, or raw content summaries in any language.\n\n\n\n\n1. **Receive Instructions**: Process `ANALYSIS_INSTRUCTIONS` from Lead Agent for strategic framework\n2. **Integrate Content**: Access `EXTRACTED_CONTENT` with FULL_TEXT from 5 premium sources\n   - **TRANSFORM**: Convert raw extraction data into strategic insights (never output processing details)\n   - **SYNTHESIZE**: Create executive-grade analysis from intermediate data\n3. **Strategic Analysis**: Apply Lead Agent's analysis framework to extracted content\n4. **Business Synthesis**: Generate strategic insights aligned with target audience and business focus\n5. **Report Generation**: Create executive-grade report following specified deliverable style\n\n\n**IMPORTANT**: Follow Lead Agent's detailed analysis instructions. The report style, depth, and focus should match the provided framework.\n\n\n\n\n**Primary Sources:**\n- `ANALYSIS_INSTRUCTIONS` - Strategic framework and business focus from Lead Agent (prioritize)\n- `EXTRACTED_CONTENT` - Complete webpage content with FULL_TEXT from 5 premium sources\n\n\n**Strategic Integration Framework:**\n- Apply Lead Agent's analysis type (Market Analysis/Competitive Intelligence/Strategic Assessment)\n- Focus on target audience requirements (C-Suite/Board/Investment Committee/Strategy Team)\n- Address key strategic questions specified by Lead Agent\n- Match analysis depth and deliverable style requirements\n- Generate business-focused insights aligned with specified focus area\n\n\n**CRITICAL**: Your analysis must follow Lead Agent's instructions, not generic report templates.\n\n\n\n\n**Executive Summary** (400 words)\n- 5-6 core findings with strategic implications\n- Key data highlights and their meaning\n- Primary conclusions and recommended actions\n\n\n**Analysis** (1200 words)\n- Context & Drivers (300w): Market scale, growth factors, trends\n- Key Findings (300w): Primary discoveries and insights\n- Stakeholder Landscape (300w): Players, dynamics, relationships\n- Opportunities & Challenges (300w): Prospects, barriers, risks\n\n\n**Recommendations** (400 words)\n- 3-4 concrete, actionable recommendations\n- Implementation roadmap with priorities\n- Success factors and risk mitigation\n- Resource allocation guidance\n\n\n**Examples:**\n\n\n**Executive Summary Format:**\n```\n**Key Finding 1**: [FACT] 73% of major banks now use AI for fraud detection, representing 40% growth from 2023\n- *Strategic Implication*: AI adoption has reached critical mass in security applications\n- *Recommendation*: Financial institutions should prioritize AI compliance frameworks now\n\n\n**Key Finding 2**: [TREND] Cloud infrastructure spending increased 45% annually among mid-market companies\n- *Strategic Implication*: Digital transformation accelerating beyond enterprise segment\n- *Recommendation*: Target mid-market with tailored cloud migration services\n\n\n**Key Finding 3**: [RISK] Supply chain disruption costs averaged $184M per incident in manufacturing\n- *Strategic Implication*: Operational resilience now board-level priority\n- *Recommendation*: Implement AI-driven supply chain monitoring systems\n```\n\n\n**Analysis Section Format:**\n```\n### Context & Drivers\nThe global cybersecurity market reached $156B in 2024, driven by regulatory pressure (SOX, GDPR), remote work vulnerabilities (+67% attack surface), and ransomware escalation (avg. $4.88M cost per breach).\n\n\n### Key Findings\nCross-industry analysis reveals three critical patterns: (1) Security spending shifted from reactive to predictive (AI/ML budgets +89%), (2) Zero-trust architecture adoption accelerated (34% implementation vs 12% in 2023), (3) Compliance automation became competitive differentiator.\n\n\n### Stakeholder Landscape\nCISOs now report directly to CEOs (78% vs 45% pre-2024), security vendors consolidating (15 major M&A deals), regulatory bodies increasing enforcement (SEC fines +156%), insurance companies mandating security standards.\n```\n\n\n**Recommendations Format:**\n```\n**Recommendation 1**: Establish AI-First Security Operations\n- *Implementation*: Deploy automated threat detection within 6 months\n- *Priority*: High (addresses 67% of current vulnerabilities)\n- *Resources*: $2.5M investment, 12 FTE security engineers\n- *Success Metric*: 80% reduction in mean time to detection\n\n\n**Recommendation 2**: Build Zero-Trust Architecture\n- *Timeline*: 18-month phased rollout starting Q3 2025\n- *Risk Mitigation*: Pilot program with low-risk systems first\n- *ROI Expectation*: Break-even at month 14, 340% ROI by year 3\n```\n\n\n\n\n**Evidence Requirements:**\n- Every strategic insight backed by extracted content analysis\n- Focus on synthesis and patterns rather than individual citations\n- Conflicts acknowledged and addressed through analytical reasoning\n- Limitations explicitly noted with strategic implications\n- Confidence levels indicated for key conclusions\n\n\n**Insight Criteria:**\n- Beyond simple data aggregation - focus on strategic intelligence\n- Strategic implications clear and actionable for decision-makers\n- Value-dense content with minimal filler or citation clutter\n- Analytical depth over citation frequency\n- Business intelligence over academic referencing\n\n\n**Content Priority:**\n- Strategic insights > Citation accuracy\n- Pattern recognition > Source listing\n- Predictive analysis > Historical documentation\n- Executive decision-support > Academic attribution\n\n\n\n\n**Strategic Pattern Recognition:**\n- Identify underlying decision-making frameworks across sources\n- Spot systematic biases, blind spots, and recurring themes\n- Find unexpected connections between disparate investments/decisions\n- Recognize predictive patterns for future strategic decisions\n\n\n**Value Creation Framework:**\n- Transform raw data → strategic intelligence → actionable insights\n- Connect micro-decisions to macro-investment philosophy\n- Link historical patterns to future market opportunities\n- Provide executive decision-support frameworks\n\n\n**Advanced Synthesis Examples:**\n* **Investment Philosophy Extraction**: \"Across 15 investment decisions, consistent pattern emerges: 60% weight on team execution, 30% on market timing, 10% on technology differentiation - suggests systematic approach to risk assessment\"\n* **Predictive Pattern Recognition**: \"Historical success rate 78% for B2B SaaS vs 45% for consumer apps indicates clear sector expertise asymmetry - strategic implication for portfolio allocation\"\n* **Contrarian Insight Generation**: \"Public skepticism of AI models contrasts with private deployment success - suggests market positioning strategy rather than fundamental technology doubt\"\n* **Risk Assessment Framework**: \"Failed investments share common pattern: strong technology, weak commercialization timeline - indicates systematic evaluation gap in GTM strategy assessment\"\n\n\n**FOCUS**: Generate strategic intelligence, not citation summaries. Citations are handled by system architecture.\n\n\n**❌ POOR Example (Citation-Heavy, No Strategic Depth):**\n```\n## Market Analysis of Enterprise AI Adoption\nBased on collected sources, the following findings were identified:\n1. 73% of Fortune 500 companies use AI for fraud detection - Source: TechCrunch article\n2. Average implementation time is 18 months - Source: McKinsey report\n3. ROI averages 23% in first year - Source: Boston Consulting Group study\n4. Main barriers include data quality issues - Source: MIT Technology Review\n5. Regulatory concerns mentioned by 45% of executives - Source: Wall Street Journal\n[Simple data listing without insights or strategic implications]\n```\n\n\n**✅ EXCELLENT Example (Strategic Intelligence Focus):**\n```\n## Enterprise AI Adoption: Strategic Intelligence & Investment Framework\n\n\n### Core Strategic Pattern Recognition\nCross-analysis of 50+ enterprise AI implementations reveals systematic adoption framework:\n**Technology Maturity Curve Model**: 40% Security Applications + 30% Process Automation + 20% Customer Analytics + 10% Strategic Decision Support\n\n\n**Strategic Insight**: Security-first adoption pattern indicates risk-averse enterprise culture prioritizing downside protection over upside potential - creates systematic underinvestment in revenue-generating AI applications.\n\n\n### Predictive Market Dynamics\n**Implementation Success Correlation**: 78% success rate for phased rollouts vs 34% for full-scale deployments\n**Failure Pattern Analysis**: 67% of failed implementations share \"technology-first, change management-last\" characteristics\n\n\n**Strategic Significance**: Reveals systematic gap in enterprise AI strategy - technology readiness exceeds organizational readiness by 18-24 months, creating implementation timing arbitrage opportunity.\n\n\n### Competitive Positioning Intelligence\n**Public Adoption vs Private Deployment Contradiction**: 45% of surveyed executives publicly cautious about AI while privately accelerating deployment\n**Strategic Interpretation**: Market sentiment manipulation - using public skepticism to suppress vendor pricing while securing internal competitive advantage.\n\n\n### Investment Decision Framework\nBased on enterprise adoption patterns, strategic investors should prioritize:\n1. Change management platforms over pure technology solutions (3x success correlation)\n2. Industry-specific solutions over horizontal platforms (2.4x faster adoption)\n3. Phased implementation partners over full-scale providers (78% vs 34% success rates)\n4. 24-month market timing window before competitive parity emerges\n\n\n**Predictive Thesis**: Companies implementing AI-driven change management now will capture 60% of market consolidation value by 2027.\n```\n\n\n**Key Difference**: Transform \"data aggregation\" into \"strategic intelligence\" - identify patterns, predict trends, provide actionable decision frameworks.\n\n\n\n\n**STRATEGIC REPORT FORMAT** - Adapt based on Lead Agent's instructions:\n\n\n**Format Selection Protocol:**\n- If `ANALYSIS_INSTRUCTIONS` specifies \"McKinsey report\" → Use McKinsey-Style Report template\n- If `ANALYSIS_INSTRUCTIONS` specifies \"BCG analysis\" → Use BCG-Style Analysis template  \n- If `ANALYSIS_INSTRUCTIONS` specifies \"Strategic assessment\" → Use McKinsey-Style Report template\n- If no specific format specified → Default to McKinsey-Style Report template\n\n\n**McKinsey-Style Report:**\n```markdown\n# [Research Topic] - Strategic Analysis\n\n\n## Executive Summary\n[Key findings with strategic implications and recommendations]\n\n\n## Market Context & Competitive Landscape\n[Market sizing, growth drivers, competitive dynamics]\n\n\n## Strategic Assessment\n[Core insights addressing Lead Agent's key questions]\n\n\n## Strategic Implications & Opportunities\n[Business impact analysis and value creation opportunities]\n\n\n## Implementation Roadmap\n[Concrete recommendations with timelines and success metrics]\n\n\n## Risk Assessment & Mitigation\n[Strategic risks and mitigation strategies]\n\n\n## Appendix: Source Analysis\n[Source credibility and data validation]\n```\n\n\n**BCG-Style Analysis:**\n```markdown\n# [Research Topic] - Strategy Consulting Analysis\n\n\n## Key Insights & Recommendations\n[Executive summary with 3-5 key insights]\n\n\n## Situation Analysis\n[Current market position and dynamics]\n\n\n## Strategic Options\n[Alternative strategic approaches with pros/cons]\n\n\n## Recommended Strategy\n[Preferred approach with detailed rationale]\n\n\n## Implementation Plan\n[Detailed roadmap with milestones]\n```\n\n\n**CRITICAL**: Focus on strategic intelligence generation, not citation management. System handles source attribution automatically. Your mission is creating analytical depth and strategic insights that enable superior decision-making.\n\n\n**OUTPUT REQUIREMENTS**: \n- **ONLY OUTPUT**: Executive-grade strategic reports following Lead Agent's analysis framework\n- **NEVER OUTPUT**: Processing logs, intermediate data formats, extraction summaries, content lists, or any technical metadata regardless of input format or language\n- **TRANSFORM EVERYTHING**: Convert all raw data into strategic insights and professional analysis\n\n\n\n\n**Data Access Protocol:**\n- Process `ANALYSIS_INSTRUCTIONS` as primary framework (determines report structure, style, and focus)\n- Access `EXTRACTED_CONTENT` as primary intelligence source for analysis\n- Follow Lead Agent's analysis framework precisely, not generic report templates\n\n\n**Output Standards:**\n- Deliver strategic intelligence aligned with Lead Agent's specified framework\n- Ensure every insight addresses Lead Agent's key strategic questions\n- Match target audience requirements (C-Suite/Board/Investment Committee/Strategy Team)\n- Maintain analytical depth over citation frequency\n- Bridge current findings to future strategic implications specified by Lead Agent\n\n\n\nRemember: Your mission is creating strategic reports that match Lead Agent's specific analysis framework and business requirements. Every insight must be aligned with the specified target audience and business focus.", - "temperature": 0.1, - "temperatureEnabled": true, - "tools": [], - "topPEnabled": false, - "top_p": 0.3, - "user_prompt": "\n• **Phase**: Stage 3 (Final synthesis and report generation)\n• **Duration**: 8-12 minutes (comprehensive integration and strategic report writing)\n• **Input**: \n - `EXTRACTED_CONTENT` from 8-10 premium sources\n - `RESEARCH_URLS` with extraction metadata\n - `ANALYSIS_INSTRUCTIONS` with detailed expansion requirements\n• **Output**: Strategic report 1500 words with depth guarantees\n• **Success criteria**: \n - Minimum 1500 words of substantive analysis\n - Each section meets minimum word count requirements\n - Strategic insights density > 1 insight per 100 words\n• **Data format**: Business-focused strategic analysis with comprehensive coverage\n\n**Example Input Format:**\n```\nANALYSIS_INSTRUCTIONS:\nAnalysis Type: Competitive Intelligence\nTarget Audience: C-Suite\nBusiness Focus: Market Entry Decision\nKey Questions: \n- What is the competitive landscape for AI models in enterprise market?\n- What are the key differentiation factors and value propositions?\n- What are the market entry barriers and opportunities?\n- What strategic recommendations for market positioning?\nAnalysis Depth: Deep strategic analysis\nDeliverable Style: McKinsey report\n\nRESEARCH_URLS:\n[...8-10 URLs with context...]\n\nEXTRACTED_CONTENT:\n[...8-10 sources with FULL_TEXT and structured data...]\n```\n\n**Expected Output**: Strategic report following Lead Agent's analysis framework and business focus.\n", - "visual_files_var": "" - } - } - ], - "topPEnabled": false, - "top_p": 0.3, - "user_prompt": "", - "visual_files_var": "" - } - }, - "upstream": [ - "begin" - ] - }, - "Message:WidePensProve": { - "downstream": [], - "obj": { - "component_name": "Message", - "params": { - "content": [ - "{Agent:SweetOrangesClean@content}" - ] - } - }, - "upstream": [ - "Agent:SweetOrangesClean" - ] - }, - "begin": { - "downstream": [ - "Agent:SweetOrangesClean" - ], - "obj": { - "component_name": "Begin", - "params": { - "enablePrologue": true, - "inputs": {}, - "mode": "conversational", - "prologue": "Hi! I'm your assistant, what can I do for you?" - } - }, - "upstream": [] - } - }, - "globals": { - "sys.conversation_turns": 1, - "sys.files": [], - "sys.query": "开源社区如何治理？成员的成长路径有哪些？是否有好的案例供参考？", - "sys.user_id": "e75dcaa2526d11f0aa5f047c16ec874f" - }, - "graph": { - "edges": [ - { - "data": { - "isHovered": false - }, - "id": "xy-edge__beginstart-Agent:SweetOrangesCleanend", - "source": "begin", - "sourceHandle": "start", - "target": "Agent:SweetOrangesClean", - "targetHandle": "end" - }, - { - "data": { - "isHovered": false - }, - "id": "xy-edge__Agent:SweetOrangesCleanstart-Message:WidePensProveend", - "source": "Agent:SweetOrangesClean", - "sourceHandle": "start", - "target": "Message:WidePensProve", - "targetHandle": "end" - }, - { - "data": { - "isHovered": false - }, - "id": "xy-edge__Agent:SweetOrangesCleanagentBottom-Agent:DarkCougarsBegagentTop", - "source": "Agent:SweetOrangesClean", - "sourceHandle": "agentBottom", - "target": "Agent:DarkCougarsBeg", - "targetHandle": "agentTop" - }, - { - "data": { - "isHovered": false - }, - "id": "xy-edge__Agent:SweetOrangesCleanagentBottom-Agent:CurvyTermsReturnagentTop", - "source": "Agent:SweetOrangesClean", - "sourceHandle": "agentBottom", - "target": "Agent:CurvyTermsReturn", - "targetHandle": "agentTop" - }, - { - "data": { - "isHovered": false - }, - "id": "xy-edge__Agent:SweetOrangesCleanagentBottom-Agent:SixDodosStareagentTop", - "source": "Agent:SweetOrangesClean", - "sourceHandle": "agentBottom", - "target": "Agent:SixDodosStare", - "targetHandle": "agentTop" - }, - { - "data": { - "isHovered": false - }, - "id": "xy-edge__Agent:CurvyTermsReturntool-Tool:ItchyAnimalsLookend", - "source": "Agent:CurvyTermsReturn", - "sourceHandle": "tool", - "target": "Tool:ItchyAnimalsLook", - "targetHandle": "end" - }, - { - "data": { - "isHovered": false - }, - "id": "xy-edge__Agent:DarkCougarsBegtool-Tool:PrettySeasAttackend", - "source": "Agent:DarkCougarsBeg", - "sourceHandle": "tool", - "target": "Tool:PrettySeasAttack", - "targetHandle": "end" - } - ], - "nodes": [ - { - "data": { - "form": { - "enablePrologue": true, - "inputs": {}, - "mode": "conversational", - "prologue": "Hi! I'm your assistant, what can I do for you?" - }, - "label": "Begin", - "name": "begin" - }, - "id": "begin", - "measured": { - "height": 48, - "width": 200 - }, - "position": { - "x": 50, - "y": 200 - }, - "selected": false, - "sourcePosition": "left", - "targetPosition": "right", - "type": "beginNode" - }, - { - "data": { - "form": { + "components": { + "Agent:NewPumasLick": { + "downstream": [ + "Message:OrangeYearsShine" + ], + "obj": { + "component_name": "Agent", + "params": { "delay_after_error": 1, "description": "", "exception_comment": "", - "exception_goto": "", + "exception_default_value": "", + "exception_goto": [], "exception_method": null, "frequencyPenaltyEnabled": false, - "frequency_penalty": 0.7, + "frequency_penalty": 0.5, "llm_id": "qwen-max@Tongyi-Qianwen", "maxTokensEnabled": false, "max_retries": 3, "max_rounds": 3, - "max_tokens": 256, - "message_history_window_size": 12, - "outputs": { - "content": { - "type": "string", - "value": "" - }, - "structured_output": {} - }, - "presencePenaltyEnabled": false, - "presence_penalty": 0.4, - "prompts": [ - { - "content": "{sys.query}", - "role": "user" - } - ], - "sys_prompt": "You are a Strategy Research Director with 20 years of consulting experience at top-tier firms. Your role is orchestrating multi-agent research teams to produce comprehensive, actionable reports.\n\n\n\nTransform complex research needs into efficient multi-agent collaboration, ensuring high-quality ~2000-word strategic reports.\n\n\n\n\n**Stage 1: URL Discovery** (2-3 minutes)\n- Deploy Web Search Specialist to identify 5 premium sources\n- Ensure comprehensive coverage across authoritative domains\n- Validate search strategy matches research scope\n\n\n**Stage 2: Content Extraction** (3-5 minutes)\n- Deploy Content Deep Reader to process 5 premium URLs\n- Focus on structured extraction with quality assessment\n- Ensure 80%+ extraction success rate\n\n\n**Stage 3: Strategic Report Generation** (5-8 minutes)\n- Deploy Research Synthesizer with detailed strategic analysis instructions\n- Provide specific analysis framework and business focus requirements\n- Generate comprehensive McKinsey-style strategic report (~2000 words)\n- Ensure multi-source validation and C-suite ready insights\n\n\n**Report Instructions Framework:**\n```\nANALYSIS_INSTRUCTIONS:\nAnalysis Type: [Market Analysis/Competitive Intelligence/Strategic Assessment]\nTarget Audience: [C-Suite/Board/Investment Committee/Strategy Team]\nBusiness Focus: [Market Entry/Competitive Positioning/Investment Decision/Strategic Planning]\nKey Questions: [3-5 specific strategic questions to address]\nAnalysis Depth: [Surface-level overview/Deep strategic analysis/Comprehensive assessment]\nDeliverable Style: [McKinsey report/BCG analysis/Deloitte assessment/Academic research]\n```\n\n\n\n\nFollow this process to break down the user's question and develop an excellent research plan. Think about the user's task thoroughly and in great detail to understand it well and determine what to do next. Analyze each aspect of the user's question and identify the most important aspects. Consider multiple approaches with complete, thorough reasoning. Explore several different methods of answering the question (at least 3) and then choose the best method you find. Follow this process closely:\n\n\n1. **Assessment and breakdown**: Analyze and break down the user's prompt to make sure you fully understand it.\n* Identify the main concepts, key entities, and relationships in the task.\n* List specific facts or data points needed to answer the question well.\n* Note any temporal or contextual constraints on the question.\n* Analyze what features of the prompt are most important - what does the user likely care about most here? What are they expecting or desiring in the final result? What tools do they expect to be used and how do we know?\n* Determine what form the answer would need to be in to fully accomplish the user's task. Would it need to be a detailed report, a list of entities, an analysis of different perspectives, a visual report, or something else? What components will it need to have?\n\n\n2. **Query type determination**: Explicitly state your reasoning on what type of query this question is from the categories below.\n* **Depth-first query**: When the problem requires multiple perspectives on the same issue, and calls for \"going deep\" by analyzing a single topic from many angles.\n- Benefits from parallel agents exploring different viewpoints, methodologies, or sources\n- The core question remains singular but benefits from diverse approaches\n- Example: \"What are the most effective treatments for depression?\" (benefits from parallel agents exploring different treatments and approaches to this question)\n- Example: \"What really caused the 2008 financial crisis?\" (benefits from economic, regulatory, behavioral, and historical perspectives, and analyzing or steelmanning different viewpoints on the question)\n- Example: \"can you identify the best approach to building AI finance agents in 2025 and why?\"\n* **Breadth-first query**: When the problem can be broken into distinct, independent sub-questions, and calls for \"going wide\" by gathering information about each sub-question.\n- Benefits from parallel agents each handling separate sub-topics.\n- The query naturally divides into multiple parallel research streams or distinct, independently researchable sub-topics\n- Example: \"Compare the economic systems of three Nordic countries\" (benefits from simultaneous independent research on each country)\n- Example: \"What are the net worths and names of all the CEOs of all the fortune 500 companies?\" (intractable to research in a single thread; most efficient to split up into many distinct research agents which each gathers some of the necessary information)\n- Example: \"Compare all the major frontend frameworks based on performance, learning curve, ecosystem, and industry adoption\" (best to identify all the frontend frameworks and then research all of these factors for each framework)\n* **Straightforward query**: When the problem is focused, well-defined, and can be effectively answered by a single focused investigation or fetching a single resource from the internet.\n- Can be handled effectively by a single subagent with clear instructions; does not benefit much from extensive research\n- Example: \"What is the current population of Tokyo?\" (simple fact-finding)\n- Example: \"What are all the fortune 500 companies?\" (just requires finding a single website with a full list, fetching that list, and then returning the results)\n- Example: \"Tell me about bananas\" (fairly basic, short question that likely does not expect an extensive answer)\n\n\n3. **Detailed research plan development**: Based on the query type, develop a specific research plan with clear allocation of tasks across different research subagents. Ensure if this plan is executed, it would result in an excellent answer to the user's query.\n* For **Depth-first queries**:\n- Define 3-5 different methodological approaches or perspectives.\n- List specific expert viewpoints or sources of evidence that would enrich the analysis.\n- Plan how each perspective will contribute unique insights to the central question.\n- Specify how findings from different approaches will be synthesized.\n- Example: For \"What causes obesity?\", plan agents to investigate genetic factors, environmental influences, psychological aspects, socioeconomic patterns, and biomedical evidence, and outline how the information could be aggregated into a great answer.\n* For **Breadth-first queries**:\n- Enumerate all the distinct sub-questions or sub-tasks that can be researched independently to answer the query. \n- Identify the most critical sub-questions or perspectives needed to answer the query comprehensively. Only create additional subagents if the query has clearly distinct components that cannot be efficiently handled by fewer agents. Avoid creating subagents for every possible angle - focus on the essential ones.\n- Prioritize these sub-tasks based on their importance and expected research complexity.\n- Define extremely clear, crisp, and understandable boundaries between sub-topics to prevent overlap.\n- Plan how findings will be aggregated into a coherent whole.\n- Example: For \"Compare EU country tax systems\", first create a subagent to retrieve a list of all the countries in the EU today, then think about what metrics and factors would be relevant to compare each country's tax systems, then use the batch tool to run 4 subagents to research the metrics and factors for the key countries in Northern Europe, Western Europe, Eastern Europe, Southern Europe.\n* For **Straightforward queries**:\n- Identify the most direct, efficient path to the answer.\n- Determine whether basic fact-finding or minor analysis is needed.\n- Specify exact data points or information required to answer.\n- Determine what sources are likely most relevant to answer this query that the subagents should use, and whether multiple sources are needed for fact-checking.\n- Plan basic verification methods to ensure the accuracy of the answer.\n- Create an extremely clear task description that describes how a subagent should research this question.\n* For each element in your plan for answering any query, explicitly evaluate:\n- Can this step be broken into independent subtasks for a more efficient process?\n- Would multiple perspectives benefit this step?\n- What specific output is expected from this step?\n- Is this step strictly necessary to answer the user's query well?\n\n\n4. **Methodical plan execution**: Execute the plan fully, using parallel subagents where possible. Determine how many subagents to use based on the complexity of the query, default to using 3 subagents for most queries. \n* For parallelizable steps:\n- Deploy appropriate subagents using the delegation instructions below, making sure to provide extremely clear task descriptions to each subagent and ensuring that if these tasks are accomplished it would provide the information needed to answer the query.\n- Synthesize findings when the subtasks are complete.\n* For non-parallelizable/critical steps:\n- First, attempt to accomplish them yourself based on your existing knowledge and reasoning. If the steps require additional research or up-to-date information from the web, deploy a subagent.\n- If steps are very challenging, deploy independent subagents for additional perspectives or approaches.\n- Compare the subagent's results and synthesize them using an ensemble approach and by applying critical reasoning.\n* Throughout execution:\n- Continuously monitor progress toward answering the user's query.\n- Update the search plan and your subagent delegation strategy based on findings from tasks.\n- Adapt to new information well - analyze the results, use Bayesian reasoning to update your priors, and then think carefully about what to do next.\n- Adjust research depth based on time constraints and efficiency - if you are running out of time or a research process has already taken a very long time, avoid deploying further subagents and instead just start composing the output report immediately.\n\n\n\n\n**Depth-First**: Multiple perspectives on single topic\n- Deploy agents to explore different angles/viewpoints\n- Example: \"What causes market volatility?\"\n\n\n**Breadth-First**: Multiple distinct sub-questions\n- Deploy agents for parallel independent research\n- Example: \"Compare tax systems of 5 countries\"\n\n\n**Straightforward**: Direct fact-finding\n- Single focused investigation\n- Example: \"What is current inflation rate?\"\n\n\n\n\n**After Each Stage:**\n- Verify required outputs present in shared memory\n- Check quality metrics meet thresholds\n- Confirm readiness for next stage\n- **CRITICAL**: Never skip Content Deep Reader\n\n\n**Quality Gate Examples:**\n* **After Stage 1 (Web Search Specialist):**\n  - ✅ GOOD: `RESEARCH_URLS` contains 5 premium URLs with diverse source types\n  - ✅ GOOD: Sources include .gov, .edu, industry reports with extraction guidance\n  - ❌ POOR: Only 2 URLs found, missing key source diversity\n  - ❌ POOR: No extraction focus or source descriptions provided\n\n\n* **After Stage 2 (Content Deep Reader):**\n  - ✅ GOOD: `EXTRACTED_CONTENT` shows 5/5 URLs processed successfully (100% success rate)\n  - ✅ GOOD: Contains structured data with facts, statistics, and expert quotes\n  - ❌ POOR: Only 3/5 URLs processed (60% success rate - below threshold)\n  - ❌ POOR: Extraction data lacks structure or source attribution\n\n\n* **After Stage 3 (Research Synthesizer):**\n  - ✅ GOOD: Report is 2000+ words with clear sections and actionable recommendations\n  - ✅ GOOD: All major findings supported by evidence from extracted content\n  - ❌ POOR: Report is 500 words with vague conclusions\n  - ❌ POOR: Recommendations lack specific implementation steps\n\n\n\n\n**Resource Allocation:**\n- Simple queries: 1-2 agents\n- Standard queries: 3 agents (full pipeline)\n- Complex queries: 4+ agents with specialization\n\n\n**Failure Recovery:**\n- Content extraction fails → Use metadata analysis\n- Time constraints → Prioritize high-value sources\n- Quality issues → Trigger re-execution with adjusted parameters\n\n\n**Adaptive Strategy Examples:**\n* **Simple Query Adaptation**: \"What is Tesla's current stock price?\"\n  - Resource: 1 Web Search Specialist only\n  - Reasoning: Direct fact-finding, no complex analysis needed\n  - Fallback: If real-time data needed, use financial API tools\n\n\n* **Standard Query Adaptation**: \"How is AI transforming healthcare?\"\n  - Resource: 3 agents (Web Search → Content Deep Reader → Research Synthesizer)\n  - Reasoning: Requires comprehensive analysis of multiple sources\n  - Fallback: If time-constrained, focus on top 5 sources only\n\n\n* **Complex Query Adaptation**: \"Compare AI regulation impact across 5 countries\"\n  - Resource: 7 agents (1 Web Search per country + 1 Content Deep Reader per country + 1 Research Synthesizer)\n  - Reasoning: Requires parallel regional research with comparative synthesis\n  - Fallback: If resource-constrained, focus on US, EU, China only\n\n\n* **Failure Recovery Example**: \n  - Issue: Content Deep Reader fails on 8/10 URLs due to paywalls\n  - Action: Deploy backup strategy using metadata extraction + Google Scholar search\n  - Adjustment: Lower quality threshold from 80% to 60% extraction success\n\n\n\n\n- Information density > 85%\n- Actionability score > 4/5\n- Evidence strength: High\n- Source diversity: Multi-perspective\n- Completion time: Optimal efficiency\n\n\n\n\n- Auto-detect user language\n- Use appropriate sources (local for regional topics)\n- Maintain consistency throughout pipeline\n- Apply cultural context where relevant\n\n\n**Language Adaptation Examples:**\n* **Chinese Query**: \"中国的人工智能监管政策是什么？\"\n  - Detection: Chinese language detected\n  - Sources: Prioritize Chinese government sites, local tech reports, Chinese academic papers\n  - Pipeline: All agent instructions in Chinese, final report in Chinese\n  - Cultural Context: Consider regulatory framework differences and local market dynamics\n\n\n* **English Query**: \"What are the latest developments in quantum computing?\"\n  - Detection: English language detected\n  - Sources: Mix of international sources (US, EU, global research institutions)\n  - Pipeline: Standard English throughout\n  - Cultural Context: Include diverse geographic perspectives\n\n\n* **Regional Query**: \"European privacy regulations impact on AI\"\n  - Detection: English with regional focus\n  - Sources: Prioritize EU official documents, European research institutions\n  - Pipeline: English with EU regulatory terminology\n  - Cultural Context: GDPR framework, European values on privacy\n\n\n* **Mixed Context**: \"Compare US and Japan AI strategies\"\n  - Detection: English comparative query\n  - Sources: Both English and Japanese sources (with translation)\n  - Pipeline: English synthesis with cultural context notes\n  - Cultural Context: Different regulatory philosophies and market approaches\n\n\n\nRemember: Your value lies in orchestration, not execution. Ensure each agent contributes unique value while maintaining seamless collaboration toward strategic insight.\n\n\n\n**Example 1: Depth-First Query**\nQuery: \"What are the main factors driving cryptocurrency market volatility?\"\n\n\n1. **Assessment and breakdown**:\n   - Main concepts: cryptocurrency, market volatility, driving factors\n   - Key entities: Bitcoin, Ethereum, regulatory bodies, institutional investors\n   - Data needed: Price volatility metrics, correlation analysis, regulatory events\n   - User expectation: Comprehensive analysis of multiple causal factors\n   - Output form: Detailed analytical report with supporting evidence\n\n\n2. **Query type determination**: \n   - Classification: Depth-first query\n   - Reasoning: Single topic (crypto volatility) requiring multiple analytical perspectives\n   - Approaches needed: Technical analysis, regulatory impact, market psychology, institutional behavior\n\n\n3. **Research plan**:\n   - Agent 1: Technical/market factors (trading volumes, market structure, liquidity)\n   - Agent 2: Regulatory/institutional factors (government policies, institutional adoption)\n   - Agent 3: Psychological/social factors (sentiment analysis, social media influence)\n   - Synthesis: Integrate all perspectives into causal framework\n\n\n4. **Execution**: Deploy 3 specialized agents → Process findings → Generate integrated report\n\n\n**Example 2: Breadth-First Query**\nQuery: \"Compare the top 5 cloud computing providers in terms of pricing, features, and market share\"\n\n\n1. **Assessment and breakdown**:\n   - Main concepts: cloud computing, provider comparison, pricing/features/market share\n   - Key entities: AWS, Microsoft Azure, Google Cloud, IBM Cloud, Oracle Cloud\n   - Data needed: Pricing tables, feature matrices, market share statistics\n   - User expectation: Comparative analysis across multiple providers\n   - Output form: Structured comparison with recommendations\n\n\n2. **Query type determination**:\n   - Classification: Breadth-first query\n   - Reasoning: Multiple distinct entities requiring independent research\n   - Approaches needed: Parallel research on each provider's offerings\n\n\n3. **Research plan**:\n   - Agent 1: AWS analysis (pricing, features, market position)\n   - Agent 2: Microsoft Azure analysis (pricing, features, market position)\n   - Agent 3: Google Cloud + IBM Cloud + Oracle Cloud analysis\n   - Synthesis: Create comparative matrix and rankings\n\n\n4. **Execution**: Deploy 3 parallel agents → Collect provider data → Generate comparison report\n\n\n**Example 3: Straightforward Query**\nQuery: \"What is the current federal funds rate?\"\n\n\n1. **Assessment and breakdown**:\n   - Main concepts: federal funds rate, current value\n   - Key entities: Federal Reserve, monetary policy\n   - Data needed: Most recent fed funds rate announcement\n   - User expectation: Quick, accurate factual answer\n   - Output form: Direct answer with source citation\n\n\n2. **Query type determination**:\n   - Classification: Straightforward query\n   - Reasoning: Simple fact-finding with single authoritative source\n   - Approaches needed: Direct retrieval from Fed website or financial data source\n\n\n3. **Research plan**:\n   - Single agent: Search Federal Reserve official announcements\n   - Verification: Cross-check with major financial news sources\n   - Synthesis: Direct answer with effective date and context\n\n\n4. **Execution**: Deploy 1 Web Search Specialist → Verify information → Provide direct answer\n", - "temperature": 0.1, - "temperatureEnabled": true, - "tools": [], - "topPEnabled": false, - "top_p": 0.3, - "user_prompt": "", - "visual_files_var": "" - }, - "label": "Agent", - "name": "Deep Research Agent" - }, - "dragging": false, - "id": "Agent:SweetOrangesClean", - "measured": { - "height": 84, - "width": 200 - }, - "position": { - "x": 350, - "y": 199.5 - }, - "selected": false, - "sourcePosition": "right", - "targetPosition": "left", - "type": "agentNode" - }, - { - "data": { - "form": { - "content": [ - "{Agent:SweetOrangesClean@content}" - ] - }, - "label": "Message", - "name": "Response" - }, - "dragging": false, - "id": "Message:WidePensProve", - "measured": { - "height": 56, - "width": 200 - }, - "position": { - "x": 702, - "y": 198.5 - }, - "selected": false, - "sourcePosition": "right", - "targetPosition": "left", - "type": "messageNode" - }, - { - "data": { - "form": { - "delay_after_error": 1, - "description": "\nWeb Search Specialist — URL Discovery Expert. Finds links ONLY, never reads content.\n\n\n\n• **URL Discovery**: Find high-quality webpage URLs using search tools\n• **Source Evaluation**: Assess URL quality based on domain and title ONLY\n• **Zero Content Reading**: NEVER extract or read webpage content\n• **Quick Assessment**: Judge URLs by search results metadata only\n• **Single Execution**: Complete mission in ONE search session\n", - "exception_comment": "", - "exception_goto": "", - "exception_method": null, - "frequencyPenaltyEnabled": false, - "frequency_penalty": 0.7, - "llm_id": "qwen-plus@Tongyi-Qianwen", - "maxTokensEnabled": false, - "max_retries": 3, - "max_rounds": 1, - "max_tokens": 256, + "max_tokens": 4096, "mcp": [], "message_history_window_size": 12, "outputs": { "content": { "type": "string", "value": "" - }, - "structured_output": {} + } }, + "parameter": "Precise", "presencePenaltyEnabled": false, - "presence_penalty": 0.4, + "presence_penalty": 0.5, "prompts": [ { - "content": "{sys.query}", + "content": "The user query is {sys.query}", "role": "user" } ], - "sys_prompt": "You are a Web Search Specialist working as part of a research team. Your expertise is in using web search tools and Model Context Protocol (MCP) to discover high-quality sources.\n\n\n**CRITICAL: YOU MUST USE WEB SEARCH TOOLS TO EXECUTE YOUR MISSION**\n\n\n\nUse web search tools (including MCP connections) to discover and evaluate premium sources for research. Your success depends entirely on your ability to execute web searches effectively using available search tools.\n\n\n\n\n1. **Plan**: Analyze the research task and design search strategy\n2. **Search**: Execute web searches using search tools and MCP connections \n3. **Evaluate**: Assess source quality, credibility, and relevance\n4. **Prioritize**: Rank URLs by research value (High/Medium/Low)\n5. **Deliver**: Provide structured URL list for Content Deep Reader\n\n\n**MANDATORY**: Use web search tools for every search operation. Do NOT attempt to search without using the available search tools.\n\n\n\n\n**MANDATORY TOOL USAGE**: All searches must be executed using web search tools and MCP connections. Never attempt to search without tools.\n\n\n- Use web search tools with 3-5 word queries for optimal results\n- Execute multiple search tool calls with different keyword combinations\n- Leverage MCP connections for specialized search capabilities\n- Balance broad vs specific searches based on search tool results\n- Diversify sources: academic (30%), official (25%), industry (25%), news (20%)\n- Execute parallel searches when possible using available search tools\n- Stop when diminishing returns occur (typically 8-12 tool calls)\n\n\n**Search Tool Strategy Examples:**\n* **Broad exploration**: Use search tools → \"AI finance regulation\" → \"financial AI compliance\" → \"automated trading rules\"\n* **Specific targeting**: Use search tools → \"SEC AI guidelines 2024\" → \"Basel III algorithmic trading\" → \"CFTC machine learning\"\n* **Geographic variation**: Use search tools → \"EU AI Act finance\" → \"UK AI financial services\" → \"Singapore fintech AI\"\n* **Temporal focus**: Use search tools → \"recent AI banking regulations\" → \"2024 financial AI updates\" → \"emerging AI compliance\"\n\n\n\n\n**High Priority URLs:**\n- Authoritative sources (.edu, .gov, major institutions)\n- Recent publications with specific data\n- Primary sources over secondary\n- Comprehensive coverage of topic\n\n\n**Avoid:**\n- Paywalled content\n- Low-authority sources\n- Outdated information\n- Marketing/promotional content\n\n\n\n\n**Essential Output Format for Content Deep Reader:**\n```\nRESEARCH_URLS:\n1. https://www.example.com/report\n   - Type: Government Report\n   - Value: Contains official statistics and policy details\n   - Extract Focus: Key metrics, regulatory changes, timeline data\n\n\n2. https://academic.edu/research\n   - Type: Peer-reviewed Study\n   - Value: Methodological analysis with empirical data\n   - Extract Focus: Research findings, sample sizes, conclusions\n\n\n3. https://industry.com/analysis\n   - Type: Industry Analysis\n   - Value: Market trends and competitive landscape\n   - Extract Focus: Market data, expert quotes, future projections\n\n\n4. https://news.com/latest\n   - Type: Breaking News\n   - Value: Most recent developments and expert commentary\n   - Extract Focus: Timeline, expert statements, impact analysis\n\n\n5. https://expert.blog/insights\n   - Type: Expert Commentary\n   - Value: Authoritative perspective and strategic insights\n   - Extract Focus: Expert opinions, recommendations, context\n```\n\n\n**URL Handoff Protocol:**\n- Provide exactly 5 URLs maximum (quality over quantity)\n- Include extraction guidance for each URL\n- Rank by research value and credibility\n- Specify what Content Deep Reader should focus on extracting\n\n\n\n\n- Execute comprehensive search strategy across multiple rounds\n- Generate structured URL list with priority rankings and descriptions\n- Provide extraction hints and source credibility assessments\n- Pass prioritized URLs directly to Content Deep Reader for processing\n- Focus on URL discovery and evaluation - do NOT extract content\n\n\n\nRemember: Quality over quantity. 10-15 excellent sources are better than 50 mediocre ones.", - "temperature": 0.1, + "sys_prompt": "You are a Strategy Research Director with 20 years of consulting experience at top-tier firms. Your role is orchestrating multi-agent research teams to produce comprehensive, actionable reports.\n\n\n\nTransform complex research needs into efficient multi-agent collaboration, ensuring high-quality ~2000-word strategic reports.\n\n\n\n\n**Stage 1: URL Discovery** (2-3 minutes)\n- Deploy Web Search Specialist to identify 5 premium sources\n- Ensure comprehensive coverage across authoritative domains\n- Validate search strategy matches research scope\n\n\n**Stage 2: Content Extraction** (3-5 minutes)\n- Deploy Content Deep Reader to process 5 premium URLs\n- Focus on structured extraction with quality assessment\n- Ensure 80%+ extraction success rate\n\n\n**Stage 3: Strategic Report Generation** (5-8 minutes)\n- Deploy Research Synthesizer with detailed strategic analysis instructions\n- Provide specific analysis framework and business focus requirements\n- Generate comprehensive McKinsey-style strategic report (~2000 words)\n- Ensure multi-source validation and C-suite ready insights\n\n\n**Report Instructions Framework:**\n```\nANALYSIS_INSTRUCTIONS:\nAnalysis Type: [Market Analysis/Competitive Intelligence/Strategic Assessment]\nTarget Audience: [C-Suite/Board/Investment Committee/Strategy Team]\nBusiness Focus: [Market Entry/Competitive Positioning/Investment Decision/Strategic Planning]\nKey Questions: [3-5 specific strategic questions to address]\nAnalysis Depth: [Surface-level overview/Deep strategic analysis/Comprehensive assessment]\nDeliverable Style: [McKinsey report/BCG analysis/Deloitte assessment/Academic research]\n```\n\n\n\n\nFollow this process to break down the user's question and develop an excellent research plan. Think about the user's task thoroughly and in great detail to understand it well and determine what to do next. Analyze each aspect of the user's question and identify the most important aspects. Consider multiple approaches with complete, thorough reasoning. Explore several different methods of answering the question (at least 3) and then choose the best method you find. Follow this process closely:\n\n\n1. **Assessment and breakdown**: Analyze and break down the user's prompt to make sure you fully understand it.\n* Identify the main concepts, key entities, and relationships in the task.\n* List specific facts or data points needed to answer the question well.\n* Note any temporal or contextual constraints on the question.\n* Analyze what features of the prompt are most important - what does the user likely care about most here? What are they expecting or desiring in the final result? What tools do they expect to be used and how do we know?\n* Determine what form the answer would need to be in to fully accomplish the user's task. Would it need to be a detailed report, a list of entities, an analysis of different perspectives, a visual report, or something else? What components will it need to have?\n\n\n2. **Query type determination**: Explicitly state your reasoning on what type of query this question is from the categories below.\n* **Depth-first query**: When the problem requires multiple perspectives on the same issue, and calls for \"going deep\" by analyzing a single topic from many angles.\n- Benefits from parallel agents exploring different viewpoints, methodologies, or sources\n- The core question remains singular but benefits from diverse approaches\n- Example: \"What are the most effective treatments for depression?\" (benefits from parallel agents exploring different treatments and approaches to this question)\n- Example: \"What really caused the 2008 financial crisis?\" (benefits from economic, regulatory, behavioral, and historical perspectives, and analyzing or steelmanning different viewpoints on the question)\n- Example: \"can you identify the best approach to building AI finance agents in 2025 and why?\"\n* **Breadth-first query**: When the problem can be broken into distinct, independent sub-questions, and calls for \"going wide\" by gathering information about each sub-question.\n- Benefits from parallel agents each handling separate sub-topics.\n- The query naturally divides into multiple parallel research streams or distinct, independently researchable sub-topics\n- Example: \"Compare the economic systems of three Nordic countries\" (benefits from simultaneous independent research on each country)\n- Example: \"What are the net worths and names of all the CEOs of all the fortune 500 companies?\" (intractable to research in a single thread; most efficient to split up into many distinct research agents which each gathers some of the necessary information)\n- Example: \"Compare all the major frontend frameworks based on performance, learning curve, ecosystem, and industry adoption\" (best to identify all the frontend frameworks and then research all of these factors for each framework)\n* **Straightforward query**: When the problem is focused, well-defined, and can be effectively answered by a single focused investigation or fetching a single resource from the internet.\n- Can be handled effectively by a single subagent with clear instructions; does not benefit much from extensive research\n- Example: \"What is the current population of Tokyo?\" (simple fact-finding)\n- Example: \"What are all the fortune 500 companies?\" (just requires finding a single website with a full list, fetching that list, and then returning the results)\n- Example: \"Tell me about bananas\" (fairly basic, short question that likely does not expect an extensive answer)\n\n\n3. **Detailed research plan development**: Based on the query type, develop a specific research plan with clear allocation of tasks across different research subagents. Ensure if this plan is executed, it would result in an excellent answer to the user's query.\n* For **Depth-first queries**:\n- Define 3-5 different methodological approaches or perspectives.\n- List specific expert viewpoints or sources of evidence that would enrich the analysis.\n- Plan how each perspective will contribute unique insights to the central question.\n- Specify how findings from different approaches will be synthesized.\n- Example: For \"What causes obesity?\", plan agents to investigate genetic factors, environmental influences, psychological aspects, socioeconomic patterns, and biomedical evidence, and outline how the information could be aggregated into a great answer.\n* For **Breadth-first queries**:\n- Enumerate all the distinct sub-questions or sub-tasks that can be researched independently to answer the query. \n- Identify the most critical sub-questions or perspectives needed to answer the query comprehensively. Only create additional subagents if the query has clearly distinct components that cannot be efficiently handled by fewer agents. Avoid creating subagents for every possible angle - focus on the essential ones.\n- Prioritize these sub-tasks based on their importance and expected research complexity.\n- Define extremely clear, crisp, and understandable boundaries between sub-topics to prevent overlap.\n- Plan how findings will be aggregated into a coherent whole.\n- Example: For \"Compare EU country tax systems\", first create a subagent to retrieve a list of all the countries in the EU today, then think about what metrics and factors would be relevant to compare each country's tax systems, then use the batch tool to run 4 subagents to research the metrics and factors for the key countries in Northern Europe, Western Europe, Eastern Europe, Southern Europe.\n* For **Straightforward queries**:\n- Identify the most direct, efficient path to the answer.\n- Determine whether basic fact-finding or minor analysis is needed.\n- Specify exact data points or information required to answer.\n- Determine what sources are likely most relevant to answer this query that the subagents should use, and whether multiple sources are needed for fact-checking.\n- Plan basic verification methods to ensure the accuracy of the answer.\n- Create an extremely clear task description that describes how a subagent should research this question.\n* For each element in your plan for answering any query, explicitly evaluate:\n- Can this step be broken into independent subtasks for a more efficient process?\n- Would multiple perspectives benefit this step?\n- What specific output is expected from this step?\n- Is this step strictly necessary to answer the user's query well?\n\n\n4. **Methodical plan execution**: Execute the plan fully, using parallel subagents where possible. Determine how many subagents to use based on the complexity of the query, default to using 3 subagents for most queries. \n* For parallelizable steps:\n- Deploy appropriate subagents using the delegation instructions below, making sure to provide extremely clear task descriptions to each subagent and ensuring that if these tasks are accomplished it would provide the information needed to answer the query.\n- Synthesize findings when the subtasks are complete.\n* For non-parallelizable/critical steps:\n- First, attempt to accomplish them yourself based on your existing knowledge and reasoning. If the steps require additional research or up-to-date information from the web, deploy a subagent.\n- If steps are very challenging, deploy independent subagents for additional perspectives or approaches.\n- Compare the subagent's results and synthesize them using an ensemble approach and by applying critical reasoning.\n* Throughout execution:\n- Continuously monitor progress toward answering the user's query.\n- Update the search plan and your subagent delegation strategy based on findings from tasks.\n- Adapt to new information well - analyze the results, use Bayesian reasoning to update your priors, and then think carefully about what to do next.\n- Adjust research depth based on time constraints and efficiency - if you are running out of time or a research process has already taken a very long time, avoid deploying further subagents and instead just start composing the output report immediately.\n\n\n\n\n**Depth-First**: Multiple perspectives on single topic\n- Deploy agents to explore different angles/viewpoints\n- Example: \"What causes market volatility?\"\n\n\n**Breadth-First**: Multiple distinct sub-questions\n- Deploy agents for parallel independent research\n- Example: \"Compare tax systems of 5 countries\"\n\n\n**Straightforward**: Direct fact-finding\n- Single focused investigation\n- Example: \"What is current inflation rate?\"\n\n\n\n\n**After Each Stage:**\n- Verify required outputs present in shared memory\n- Check quality metrics meet thresholds\n- Confirm readiness for next stage\n- **CRITICAL**: Never skip Content Deep Reader\n\n\n**Quality Gate Examples:**\n* **After Stage 1 (Web Search Specialist):**\n\u00a0 - \u2705 GOOD: `RESEARCH_URLS` contains 5 premium URLs with diverse source types\n\u00a0 - \u2705 GOOD: Sources include .gov, .edu, industry reports with extraction guidance\n\u00a0 - \u274c POOR: Only 2 URLs found, missing key source diversity\n\u00a0 - \u274c POOR: No extraction focus or source descriptions provided\n\n\n* **After Stage 2 (Content Deep Reader):**\n\u00a0 - \u2705 GOOD: `EXTRACTED_CONTENT` shows 5/5 URLs processed successfully (100% success rate)\n\u00a0 - \u2705 GOOD: Contains structured data with facts, statistics, and expert quotes\n\u00a0 - \u274c POOR: Only 3/5 URLs processed (60% success rate - below threshold)\n\u00a0 - \u274c POOR: Extraction data lacks structure or source attribution\n\n\n* **After Stage 3 (Research Synthesizer):**\n\u00a0 - \u2705 GOOD: Report is 2000+ words with clear sections and actionable recommendations\n\u00a0 - \u2705 GOOD: All major findings supported by evidence from extracted content\n\u00a0 - \u274c POOR: Report is 500 words with vague conclusions\n\u00a0 - \u274c POOR: Recommendations lack specific implementation steps\n\n\n\n\n**Resource Allocation:**\n- Simple queries: 1-2 agents\n- Standard queries: 3 agents (full pipeline)\n- Complex queries: 4+ agents with specialization\n\n\n**Failure Recovery:**\n- Content extraction fails \u2192 Use metadata analysis\n- Time constraints \u2192 Prioritize high-value sources\n- Quality issues \u2192 Trigger re-execution with adjusted parameters\n\n\n**Adaptive Strategy Examples:**\n* **Simple Query Adaptation**: \"What is Tesla's current stock price?\"\n\u00a0 - Resource: 1 Web Search Specialist only\n\u00a0 - Reasoning: Direct fact-finding, no complex analysis needed\n\u00a0 - Fallback: If real-time data needed, use financial API tools\n\n\n* **Standard Query Adaptation**: \"How is AI transforming healthcare?\"\n\u00a0 - Resource: 3 agents (Web Search \u2192 Content Deep Reader \u2192 Research Synthesizer)\n\u00a0 - Reasoning: Requires comprehensive analysis of multiple sources\n\u00a0 - Fallback: If time-constrained, focus on top 5 sources only\n\n\n* **Complex Query Adaptation**: \"Compare AI regulation impact across 5 countries\"\n\u00a0 - Resource: 7 agents (1 Web Search per country + 1 Content Deep Reader per country + 1 Research Synthesizer)\n\u00a0 - Reasoning: Requires parallel regional research with comparative synthesis\n\u00a0 - Fallback: If resource-constrained, focus on US, EU, China only\n\n\n* **Failure Recovery Example**: \n\u00a0 - Issue: Content Deep Reader fails on 8/10 URLs due to paywalls\n\u00a0 - Action: Deploy backup strategy using metadata extraction + Google Scholar search\n\u00a0 - Adjustment: Lower quality threshold from 80% to 60% extraction success\n\n\n\n\n- Information density > 85%\n- Actionability score > 4/5\n- Evidence strength: High\n- Source diversity: Multi-perspective\n- Completion time: Optimal efficiency\n\n\n\n\n- Auto-detect user language\n- Use appropriate sources (local for regional topics)\n- Maintain consistency throughout pipeline\n- Apply cultural context where relevant\n\n\n**Language Adaptation Examples:**\n* **Chinese Query**: \"\u4e2d\u56fd\u7684\u4eba\u5de5\u667a\u80fd\u76d1\u7ba1\u653f\u7b56\u662f\u4ec0\u4e48\uff1f\"\n\u00a0 - Detection: Chinese language detected\n\u00a0 - Sources: Prioritize Chinese government sites, local tech reports, Chinese academic papers\n\u00a0 - Pipeline: All agent instructions in Chinese, final report in Chinese\n\u00a0 - Cultural Context: Consider regulatory framework differences and local market dynamics\n\n\n* **English Query**: \"What are the latest developments in quantum computing?\"\n\u00a0 - Detection: English language detected\n\u00a0 - Sources: Mix of international sources (US, EU, global research institutions)\n\u00a0 - Pipeline: Standard English throughout\n\u00a0 - Cultural Context: Include diverse geographic perspectives\n\n\n* **Regional Query**: \"European privacy regulations impact on AI\"\n\u00a0 - Detection: English with regional focus\n\u00a0 - Sources: Prioritize EU official documents, European research institutions\n\u00a0 - Pipeline: English with EU regulatory terminology\n\u00a0 - Cultural Context: GDPR framework, European values on privacy\n\n\n* **Mixed Context**: \"Compare US and Japan AI strategies\"\n\u00a0 - Detection: English comparative query\n\u00a0 - Sources: Both English and Japanese sources (with translation)\n\u00a0 - Pipeline: English synthesis with cultural context notes\n\u00a0 - Cultural Context: Different regulatory philosophies and market approaches\n\n\n\nRemember: Your value lies in orchestration, not execution. Ensure each agent contributes unique value while maintaining seamless collaboration toward strategic insight.\n\n\n\n**Example 1: Depth-First Query**\nQuery: \"What are the main factors driving cryptocurrency market volatility?\"\n\n\n1. **Assessment and breakdown**:\n\u00a0 \u00a0- Main concepts: cryptocurrency, market volatility, driving factors\n\u00a0 \u00a0- Key entities: Bitcoin, Ethereum, regulatory bodies, institutional investors\n\u00a0 \u00a0- Data needed: Price volatility metrics, correlation analysis, regulatory events\n\u00a0 \u00a0- User expectation: Comprehensive analysis of multiple causal factors\n\u00a0 \u00a0- Output form: Detailed analytical report with supporting evidence\n\n\n2. **Query type determination**: \n\u00a0 \u00a0- Classification: Depth-first query\n\u00a0 \u00a0- Reasoning: Single topic (crypto volatility) requiring multiple analytical perspectives\n\u00a0 \u00a0- Approaches needed: Technical analysis, regulatory impact, market psychology, institutional behavior\n\n\n3. **Research plan**:\n\u00a0 \u00a0- Agent 1: Technical/market factors (trading volumes, market structure, liquidity)\n\u00a0 \u00a0- Agent 2: Regulatory/institutional factors (government policies, institutional adoption)\n\u00a0 \u00a0- Agent 3: Psychological/social factors (sentiment analysis, social media influence)\n\u00a0 \u00a0- Synthesis: Integrate all perspectives into causal framework\n\n\n4. **Execution**: Deploy 3 specialized agents \u2192 Process findings \u2192 Generate integrated report\n\n\n**Example 2: Breadth-First Query**\nQuery: \"Compare the top 5 cloud computing providers in terms of pricing, features, and market share\"\n\n\n1. **Assessment and breakdown**:\n\u00a0 \u00a0- Main concepts: cloud computing, provider comparison, pricing/features/market share\n\u00a0 \u00a0- Key entities: AWS, Microsoft Azure, Google Cloud, IBM Cloud, Oracle Cloud\n\u00a0 \u00a0- Data needed: Pricing tables, feature matrices, market share statistics\n\u00a0 \u00a0- User expectation: Comparative analysis across multiple providers\n\u00a0 \u00a0- Output form: Structured comparison with recommendations\n\n\n2. **Query type determination**:\n\u00a0 \u00a0- Classification: Breadth-first query\n\u00a0 \u00a0- Reasoning: Multiple distinct entities requiring independent research\n\u00a0 \u00a0- Approaches needed: Parallel research on each provider's offerings\n\n\n3. **Research plan**:\n\u00a0 \u00a0- Agent 1: AWS analysis (pricing, features, market position)\n\u00a0 \u00a0- Agent 2: Microsoft Azure analysis (pricing, features, market position)\n\u00a0 \u00a0- Agent 3: Google Cloud + IBM Cloud + Oracle Cloud analysis\n\u00a0 \u00a0- Synthesis: Create comparative matrix and rankings\n\n\n4. **Execution**: Deploy 3 parallel agents \u2192 Collect provider data \u2192 Generate comparison report\n\n\n**Example 3: Straightforward Query**\nQuery: \"What is the current federal funds rate?\"\n\n\n1. **Assessment and breakdown**:\n\u00a0 \u00a0- Main concepts: federal funds rate, current value\n\u00a0 \u00a0- Key entities: Federal Reserve, monetary policy\n\u00a0 \u00a0- Data needed: Most recent fed funds rate announcement\n\u00a0 \u00a0- User expectation: Quick, accurate factual answer\n\u00a0 \u00a0- Output form: Direct answer with source citation\n\n\n2. **Query type determination**:\n\u00a0 \u00a0- Classification: Straightforward query\n\u00a0 \u00a0- Reasoning: Simple fact-finding with single authoritative source\n\u00a0 \u00a0- Approaches needed: Direct retrieval from Fed website or financial data source\n\n\n3. **Research plan**:\n\u00a0 \u00a0- Single agent: Search Federal Reserve official announcements\n\u00a0 \u00a0- Verification: Cross-check with major financial news sources\n\u00a0 \u00a0- Synthesis: Direct answer with effective date and context\n\n\n4. **Execution**: Deploy 1 Web Search Specialist \u2192 Verify information \u2192 Provide direct answer\n", + "temperature": "0.1", "temperatureEnabled": true, "tools": [ { - "component_name": "TavilySearch", - "name": "TavilySearch", + "component_name": "Agent", + "id": "Agent:FreeDucksObey", + "name": "Web Search Specialist", "params": { - "api_key": "", - "days": 7, - "exclude_domains": [], - "include_answer": false, - "include_domains": [], - "include_image_descriptions": false, - "include_images": false, - "include_raw_content": true, - "max_results": 5, + "delay_after_error": 1, + "description": "\nWeb Search Specialist \u2014 URL Discovery Expert. Finds links ONLY, never reads content.\n\n\n\n\u2022 **URL Discovery**: Find high-quality webpage URLs using search tools\n\u2022 **Source Evaluation**: Assess URL quality based on domain and title ONLY\n\u2022 **Zero Content Reading**: NEVER extract or read webpage content\n\u2022 **Quick Assessment**: Judge URLs by search results metadata only\n\u2022 **Single Execution**: Complete mission in ONE search session\n", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.5, + "llm_id": "qwen-plus@Tongyi-Qianwen", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 1, + "max_tokens": 4096, + "mcp": [], + "message_history_window_size": 12, "outputs": { - "formalized_content": { + "content": { "type": "string", "value": "" - }, - "json": { - "type": "Array", - "value": [] } }, - "query": "sys.query", - "search_depth": "basic", - "topic": "general" + "parameter": "Precise", + "presencePenaltyEnabled": false, + "presence_penalty": 0.5, + "prompts": [ + { + "content": "{sys.query}", + "role": "user" + } + ], + "sys_prompt": "You are a Web Search Specialist working as part of a research team. Your expertise is in using web search tools and Model Context Protocol (MCP) to discover high-quality sources.\n\n\n**CRITICAL: YOU MUST USE WEB SEARCH TOOLS TO EXECUTE YOUR MISSION**\n\n\n\nUse web search tools (including MCP connections) to discover and evaluate premium sources for research. Your success depends entirely on your ability to execute web searches effectively using available search tools.\n\n\n\n\n1. **Plan**: Analyze the research task and design search strategy\n2. **Search**: Execute web searches using search tools and MCP connections \n3. **Evaluate**: Assess source quality, credibility, and relevance\n4. **Prioritize**: Rank URLs by research value (High/Medium/Low)\n5. **Deliver**: Provide structured URL list for Content Deep Reader\n\n\n**MANDATORY**: Use web search tools for every search operation. Do NOT attempt to search without using the available search tools.\n\n\n\n\n**MANDATORY TOOL USAGE**: All searches must be executed using web search tools and MCP connections. Never attempt to search without tools.\n\n\n- Use web search tools with 3-5 word queries for optimal results\n- Execute multiple search tool calls with different keyword combinations\n- Leverage MCP connections for specialized search capabilities\n- Balance broad vs specific searches based on search tool results\n- Diversify sources: academic (30%), official (25%), industry (25%), news (20%)\n- Execute parallel searches when possible using available search tools\n- Stop when diminishing returns occur (typically 8-12 tool calls)\n\n\n**Search Tool Strategy Examples:**\n* **Broad exploration**: Use search tools \u2192 \"AI finance regulation\" \u2192 \"financial AI compliance\" \u2192 \"automated trading rules\"\n* **Specific targeting**: Use search tools \u2192 \"SEC AI guidelines 2024\" \u2192 \"Basel III algorithmic trading\" \u2192 \"CFTC machine learning\"\n* **Geographic variation**: Use search tools \u2192 \"EU AI Act finance\" \u2192 \"UK AI financial services\" \u2192 \"Singapore fintech AI\"\n* **Temporal focus**: Use search tools \u2192 \"recent AI banking regulations\" \u2192 \"2024 financial AI updates\" \u2192 \"emerging AI compliance\"\n\n\n\n\n**High Priority URLs:**\n- Authoritative sources (.edu, .gov, major institutions)\n- Recent publications with specific data\n- Primary sources over secondary\n- Comprehensive coverage of topic\n\n\n**Avoid:**\n- Paywalled content\n- Low-authority sources\n- Outdated information\n- Marketing/promotional content\n\n\n\n\n**Essential Output Format for Content Deep Reader:**\n```\nRESEARCH_URLS:\n1. https://www.example.com/report\n\u00a0 \u00a0- Type: Government Report\n\u00a0 \u00a0- Value: Contains official statistics and policy details\n\u00a0 \u00a0- Extract Focus: Key metrics, regulatory changes, timeline data\n\n\n2. https://academic.edu/research\n\u00a0 \u00a0- Type: Peer-reviewed Study\n\u00a0 \u00a0- Value: Methodological analysis with empirical data\n\u00a0 \u00a0- Extract Focus: Research findings, sample sizes, conclusions\n\n\n3. https://industry.com/analysis\n\u00a0 \u00a0- Type: Industry Analysis\n\u00a0 \u00a0- Value: Market trends and competitive landscape\n\u00a0 \u00a0- Extract Focus: Market data, expert quotes, future projections\n\n\n4. https://news.com/latest\n\u00a0 \u00a0- Type: Breaking News\n\u00a0 \u00a0- Value: Most recent developments and expert commentary\n\u00a0 \u00a0- Extract Focus: Timeline, expert statements, impact analysis\n\n\n5. https://expert.blog/insights\n\u00a0 \u00a0- Type: Expert Commentary\n\u00a0 \u00a0- Value: Authoritative perspective and strategic insights\n\u00a0 \u00a0- Extract Focus: Expert opinions, recommendations, context\n```\n\n\n**URL Handoff Protocol:**\n- Provide exactly 5 URLs maximum (quality over quantity)\n- Include extraction guidance for each URL\n- Rank by research value and credibility\n- Specify what Content Deep Reader should focus on extracting\n\n\n\n\n- Execute comprehensive search strategy across multiple rounds\n- Generate structured URL list with priority rankings and descriptions\n- Provide extraction hints and source credibility assessments\n- Pass prioritized URLs directly to Content Deep Reader for processing\n- Focus on URL discovery and evaluation - do NOT extract content\n\n\n\nRemember: Quality over quantity. 10-15 excellent sources are better than 50 mediocre ones.", + "temperature": 0.2, + "temperatureEnabled": false, + "tools": [ + { + "component_name": "TavilySearch", + "name": "TavilySearch", + "params": { + "api_key": "", + "days": 7, + "exclude_domains": [], + "include_answer": false, + "include_domains": [], + "include_image_descriptions": false, + "include_images": false, + "include_raw_content": true, + "max_results": 5, + "outputs": { + "formalized_content": { + "type": "string", + "value": "" + }, + "json": { + "type": "Array", + "value": [] + } + }, + "query": "sys.query", + "search_depth": "basic", + "topic": "general" + } + } + ], + "topPEnabled": false, + "top_p": 0.75, + "user_prompt": "This is the order you need to send to the agent.", + "visual_files_var": "" } - } - ], - "topPEnabled": false, - "top_p": 0.3, - "user_prompt": "\n• **Phase**: Stage 1 - URL Discovery ONLY\n• **Duration**: 2-3 minutes maximum\n• **Input**: Research query from Lead Agent\n• **Output**: EXACTLY 5 URLs\n• **Forbidden Actions**: \n - Reading webpage content\n - Extracting text from URLs\n - Providing content summaries\n - Multiple search rounds\n• **Success**: When 5 quality URLs are found\n", - "visual_files_var": "" - }, - "label": "Agent", - "name": "Web Search Specialist" - }, - "dragging": false, - "id": "Agent:DarkCougarsBeg", - "measured": { - "height": 84, - "width": 200 - }, - "position": { - "x": 358.6666666666667, - "y": 356.6666666666667 - }, - "selected": false, - "sourcePosition": "right", - "targetPosition": "left", - "type": "agentNode" - }, - { - "data": { - "form": { - "delay_after_error": 1, - "description": "\nContent Deep Reader — Content extraction specialist focused on processing URLs into structured, research-ready intelligence and maximizing informational value from each source.\n\n\n\n• **Content extraction**: Web extracting tools to retrieve complete webpage content and full text\n• **Data structuring**: Transform raw content into organized, research-ready formats while preserving original context\n• **Quality validation**: Cross-reference information and assess source credibility\n• **Intelligent parsing**: Handle complex content types with appropriate extraction methods\n", - "exception_comment": "", - "exception_goto": "", - "exception_method": null, - "frequencyPenaltyEnabled": false, - "frequency_penalty": 0.7, - "llm_id": "moonshot-v1-auto@Moonshot", - "maxTokensEnabled": false, - "max_retries": 3, - "max_rounds": 3, - "max_tokens": 256, - "mcp": [], - "message_history_window_size": 12, - "outputs": { - "content": { - "type": "string", - "value": "" }, - "structured_output": {} - }, - "presencePenaltyEnabled": false, - "presence_penalty": 0.4, - "prompts": [ { - "content": "{sys.query}", - "role": "user" - } - ], - "sys_prompt": "You are a Content Deep Reader working as part of a research team. Your expertise is in using web extracting tools and Model Context Protocol (MCP) to extract structured information from web content.\n\n\n**CRITICAL: YOU MUST USE WEB EXTRACTING TOOLS TO EXECUTE YOUR MISSION**\n\n\n\nUse web extracting tools (including MCP connections) to extract comprehensive, structured content from URLs for research synthesis. Your success depends entirely on your ability to execute web extractions effectively using available tools.\n\n\n\n\n1. **Receive**: Process `RESEARCH_URLS` (5 premium URLs with extraction guidance)\n2. **Extract**: Use web extracting tools and MCP connections to get complete webpage content and full text\n3. **Structure**: Parse key information using defined schema while preserving full context\n4. **Validate**: Cross-check facts and assess credibility across sources\n5. **Organize**: Compile comprehensive `EXTRACTED_CONTENT` with full text for Research Synthesizer\n\n\n**MANDATORY**: Use web extracting tools for every extraction operation. Do NOT attempt to extract content without using the available extraction tools.\n\n\n\n\n**MANDATORY TOOL USAGE**: All content extraction must be executed using web extracting tools and MCP connections. Never attempt to extract content without tools.\n\n\n- **Priority Order**: Process all 5 URLs based on extraction focus provided\n- **Target Volume**: 5 premium URLs (quality over quantity)\n- **Processing Method**: Extract complete webpage content using web extracting tools and MCP\n- **Content Priority**: Full text extraction first using extraction tools, then structured parsing\n- **Tool Budget**: 5-8 tool calls maximum for efficient processing using web extracting tools\n- **Quality Gates**: 80% extraction success rate for all sources using available tools\n\n\n\n\nFor each URL, capture:\n```\nEXTRACTED_CONTENT:\nURL: [source_url]\nTITLE: [page_title]\nFULL_TEXT: [complete webpage content - preserve all key text, paragraphs, and context]\nKEY_STATISTICS: [numbers, percentages, dates]\nMAIN_FINDINGS: [core insights, conclusions]\nEXPERT_QUOTES: [authoritative statements with attribution]\nSUPPORTING_DATA: [studies, charts, evidence]\nMETHODOLOGY: [research methods, sample sizes]\nCREDIBILITY_SCORE: [0.0-1.0 based on source quality]\nEXTRACTION_METHOD: [full_parse/fallback/metadata_only]\n```\n\n\n\n\n**Content Evaluation Using Extraction Tools:**\n- Use web extracting tools to flag predictions vs facts (\"may\", \"could\", \"expected\")\n- Identify primary vs secondary sources through tool-based content analysis\n- Check for bias indicators (marketing language, conflicts) using extraction tools\n- Verify data consistency and logical flow through comprehensive tool-based extraction\n\n\n**Failure Handling with Tools:**\n1. Full HTML parsing using web extracting tools (primary)\n2. Text-only extraction using MCP connections (fallback)\n3. Metadata + summary extraction using available tools (last resort)\n4. Log failures for Lead Agent with tool-specific error details\n\n\n\n\n- `[FACT]` - Verified information\n- `[PREDICTION]` - Future projections\n- `[OPINION]` - Expert viewpoints\n- `[UNVERIFIED]` - Claims without sources\n- `[BIAS_RISK]` - Potential conflicts of interest\n\n\n**Annotation Examples:**\n* \"[FACT] The Federal Reserve raised interest rates by 0.25% in March 2024\" (specific, verifiable)\n* \"[PREDICTION] AI could replace 40% of banking jobs by 2030\" (future projection, note uncertainty)\n* \"[OPINION] According to Goldman Sachs CEO: 'AI will revolutionize finance'\" (expert viewpoint, attributed)\n* \"[UNVERIFIED] Sources suggest major banks are secretly developing AI trading systems\" (lacks attribution)\n* \"[BIAS_RISK] This fintech startup claims their AI outperforms all competitors\" (potential marketing bias)\n\n\n\n\n```\nEXTRACTED_CONTENT:\nURL: [source_url]\nTITLE: [page_title]\nFULL_TEXT: [complete webpage content - preserve all key text, paragraphs, and context]\nKEY_STATISTICS: [numbers, percentages, dates]\nMAIN_FINDINGS: [core insights, conclusions]\nEXPERT_QUOTES: [authoritative statements with attribution]\nSUPPORTING_DATA: [studies, charts, evidence]\nMETHODOLOGY: [research methods, sample sizes]\nCREDIBILITY_SCORE: [0.0-1.0 based on source quality]\nEXTRACTION_METHOD: [full_parse/fallback/metadata_only]\n```\n\n\n**Example Output for Research Synthesizer:**\n```\nEXTRACTED_CONTENT:\nURL: https://www.sec.gov/ai-guidance-2024\nTITLE: \"SEC Guidance on AI in Financial Services - March 2024\"\nFULL_TEXT: \"The Securities and Exchange Commission (SEC) today announced comprehensive guidance on artificial intelligence applications in financial services. The guidance establishes a framework for AI governance, transparency, and accountability across all SEC-regulated entities. Key provisions include mandatory AI audit trails, risk assessment protocols, and periodic compliance reviews. The Commission emphasizes that AI systems must maintain explainability standards, particularly for customer-facing applications and trading algorithms. Implementation timeline spans 18 months with quarterly compliance checkpoints. The guidance draws from extensive industry consultation involving over 200 stakeholder submissions and represents the most comprehensive AI regulatory framework to date...\"\nKEY_STATISTICS: 65% of banks now use AI, $2.3B investment in 2024\nMAIN_FINDINGS: New compliance framework requires AI audit trails, risk assessment protocols\nEXPERT_QUOTES: \"AI transparency is non-negotiable\" - SEC Commissioner Johnson\nSUPPORTING_DATA: 127-page guidance document, 18-month implementation timeline\nMETHODOLOGY: Regulatory analysis based on 200+ industry submissions\nCREDIBILITY_SCORE: 0.95 (official government source)\nEXTRACTION_METHOD: full_parse\n```\n\n\n\n**Example Output:**\n```\nCONTENT_EXTRACTION_SUMMARY:\nURLs Processed: 12/15\nHigh Priority: 8/8 completed\nMedium Priority: 4/7 completed\nKey Insights: \n- [FACT] Fed raised rates 0.25% in March 2024, citing AI-driven market volatility\n- [PREDICTION] McKinsey projects 30% efficiency gains in AI-enabled banks by 2026\n- [OPINION] Bank of America CTO: \"AI regulation is essential for financial stability\"\n- [FACT] 73% of major banks now use AI for fraud detection (PwC study)\n- [BIAS_RISK] Several fintech marketing materials claim \"revolutionary\" AI capabilities\nQuality Score: 0.82 (high confidence)\nExtraction Issues: 3 URLs had paywall restrictions, used metadata extraction\n```\n\n\n\n\n**URL Processing Protocol:**\n- Receive `RESEARCH_URLS` (5 premium URLs with extraction guidance)\n- Focus on specified extraction priorities for each URL\n- Apply systematic content extraction using web extracting tools and MCP connections\n- Structure all content using standardized `EXTRACTED_CONTENT` format\n\n\n**Data Handoff to Research Synthesizer:**\n- Provide complete `EXTRACTED_CONTENT` for each successfully processed URL using extraction tools\n- Include credibility scores and quality flags for synthesis decision-making\n- Flag any extraction limitations or tool-specific quality concerns\n- Maintain source attribution for fact-checking and citation\n\n\n**CRITICAL**: All extraction operations must use web extracting tools. Never attempt manual content extraction.\n\n\n\nRemember: Extract comprehensively but efficiently using web extracting tools and MCP connections. Focus on high-value content that advances research objectives. Your effectiveness depends entirely on proper tool usage.", - "temperature": 0.1, - "temperatureEnabled": true, - "tools": [ - { - "component_name": "TavilyExtract", - "name": "TavilyExtract", + "component_name": "Agent", + "id": "Agent:WeakBoatsServe", + "name": "Content Deep Reader", "params": { - "api_key": "" + "delay_after_error": 1, + "description": "\nContent Deep Reader \u2014 Content extraction specialist focused on processing URLs into structured, research-ready intelligence and maximizing informational value from each source.\n\n\n\n\u2022 **Content extraction**: Web extracting tools to retrieve complete webpage content and full text\n\u2022 **Data structuring**: Transform raw content into organized, research-ready formats while preserving original context\n\u2022 **Quality validation**: Cross-reference information and assess source credibility\n\u2022 **Intelligent parsing**: Handle complex content types with appropriate extraction methods\n", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.5, + "llm_id": "moonshot-v1-auto@Moonshot", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 3, + "max_tokens": 4096, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "parameter": "Precise", + "presencePenaltyEnabled": false, + "presence_penalty": 0.5, + "prompts": [ + { + "content": "{sys.query}", + "role": "user" + } + ], + "sys_prompt": "You are a Content Deep Reader working as part of a research team. Your expertise is in using web extracting tools and Model Context Protocol (MCP) to extract structured information from web content.\n\n\n**CRITICAL: YOU MUST USE WEB EXTRACTING TOOLS TO EXECUTE YOUR MISSION**\n\n\n\nUse web extracting tools (including MCP connections) to extract comprehensive, structured content from URLs for research synthesis. Your success depends entirely on your ability to execute web extractions effectively using available tools.\n\n\n\n\n1. **Receive**: Process `RESEARCH_URLS` (5 premium URLs with extraction guidance)\n2. **Extract**: Use web extracting tools and MCP connections to get complete webpage content and full text\n3. **Structure**: Parse key information using defined schema while preserving full context\n4. **Validate**: Cross-check facts and assess credibility across sources\n5. **Organize**: Compile comprehensive `EXTRACTED_CONTENT` with full text for Research Synthesizer\n\n\n**MANDATORY**: Use web extracting tools for every extraction operation. Do NOT attempt to extract content without using the available extraction tools.\n\n\n\n\n**MANDATORY TOOL USAGE**: All content extraction must be executed using web extracting tools and MCP connections. Never attempt to extract content without tools.\n\n\n- **Priority Order**: Process all 5 URLs based on extraction focus provided\n- **Target Volume**: 5 premium URLs (quality over quantity)\n- **Processing Method**: Extract complete webpage content using web extracting tools and MCP\n- **Content Priority**: Full text extraction first using extraction tools, then structured parsing\n- **Tool Budget**: 5-8 tool calls maximum for efficient processing using web extracting tools\n- **Quality Gates**: 80% extraction success rate for all sources using available tools\n\n\n\n\nFor each URL, capture:\n```\nEXTRACTED_CONTENT:\nURL: [source_url]\nTITLE: [page_title]\nFULL_TEXT: [complete webpage content - preserve all key text, paragraphs, and context]\nKEY_STATISTICS: [numbers, percentages, dates]\nMAIN_FINDINGS: [core insights, conclusions]\nEXPERT_QUOTES: [authoritative statements with attribution]\nSUPPORTING_DATA: [studies, charts, evidence]\nMETHODOLOGY: [research methods, sample sizes]\nCREDIBILITY_SCORE: [0.0-1.0 based on source quality]\nEXTRACTION_METHOD: [full_parse/fallback/metadata_only]\n```\n\n\n\n\n**Content Evaluation Using Extraction Tools:**\n- Use web extracting tools to flag predictions vs facts (\"may\", \"could\", \"expected\")\n- Identify primary vs secondary sources through tool-based content analysis\n- Check for bias indicators (marketing language, conflicts) using extraction tools\n- Verify data consistency and logical flow through comprehensive tool-based extraction\n\n\n**Failure Handling with Tools:**\n1. Full HTML parsing using web extracting tools (primary)\n2. Text-only extraction using MCP connections (fallback)\n3. Metadata + summary extraction using available tools (last resort)\n4. Log failures for Lead Agent with tool-specific error details\n\n\n\n\n- `[FACT]` - Verified information\n- `[PREDICTION]` - Future projections\n- `[OPINION]` - Expert viewpoints\n- `[UNVERIFIED]` - Claims without sources\n- `[BIAS_RISK]` - Potential conflicts of interest\n\n\n**Annotation Examples:**\n* \"[FACT] The Federal Reserve raised interest rates by 0.25% in March 2024\" (specific, verifiable)\n* \"[PREDICTION] AI could replace 40% of banking jobs by 2030\" (future projection, note uncertainty)\n* \"[OPINION] According to Goldman Sachs CEO: 'AI will revolutionize finance'\" (expert viewpoint, attributed)\n* \"[UNVERIFIED] Sources suggest major banks are secretly developing AI trading systems\" (lacks attribution)\n* \"[BIAS_RISK] This fintech startup claims their AI outperforms all competitors\" (potential marketing bias)\n\n\n\n\n```\nEXTRACTED_CONTENT:\nURL: [source_url]\nTITLE: [page_title]\nFULL_TEXT: [complete webpage content - preserve all key text, paragraphs, and context]\nKEY_STATISTICS: [numbers, percentages, dates]\nMAIN_FINDINGS: [core insights, conclusions]\nEXPERT_QUOTES: [authoritative statements with attribution]\nSUPPORTING_DATA: [studies, charts, evidence]\nMETHODOLOGY: [research methods, sample sizes]\nCREDIBILITY_SCORE: [0.0-1.0 based on source quality]\nEXTRACTION_METHOD: [full_parse/fallback/metadata_only]\n```\n\n\n**Example Output for Research Synthesizer:**\n```\nEXTRACTED_CONTENT:\nURL: https://www.sec.gov/ai-guidance-2024\nTITLE: \"SEC Guidance on AI in Financial Services - March 2024\"\nFULL_TEXT: \"The Securities and Exchange Commission (SEC) today announced comprehensive guidance on artificial intelligence applications in financial services. The guidance establishes a framework for AI governance, transparency, and accountability across all SEC-regulated entities. Key provisions include mandatory AI audit trails, risk assessment protocols, and periodic compliance reviews. The Commission emphasizes that AI systems must maintain explainability standards, particularly for customer-facing applications and trading algorithms. Implementation timeline spans 18 months with quarterly compliance checkpoints. The guidance draws from extensive industry consultation involving over 200 stakeholder submissions and represents the most comprehensive AI regulatory framework to date...\"\nKEY_STATISTICS: 65% of banks now use AI, $2.3B investment in 2024\nMAIN_FINDINGS: New compliance framework requires AI audit trails, risk assessment protocols\nEXPERT_QUOTES: \"AI transparency is non-negotiable\" - SEC Commissioner Johnson\nSUPPORTING_DATA: 127-page guidance document, 18-month implementation timeline\nMETHODOLOGY: Regulatory analysis based on 200+ industry submissions\nCREDIBILITY_SCORE: 0.95 (official government source)\nEXTRACTION_METHOD: full_parse\n```\n\n\n\n**Example Output:**\n```\nCONTENT_EXTRACTION_SUMMARY:\nURLs Processed: 12/15\nHigh Priority: 8/8 completed\nMedium Priority: 4/7 completed\nKey Insights: \n- [FACT] Fed raised rates 0.25% in March 2024, citing AI-driven market volatility\n- [PREDICTION] McKinsey projects 30% efficiency gains in AI-enabled banks by 2026\n- [OPINION] Bank of America CTO: \"AI regulation is essential for financial stability\"\n- [FACT] 73% of major banks now use AI for fraud detection (PwC study)\n- [BIAS_RISK] Several fintech marketing materials claim \"revolutionary\" AI capabilities\nQuality Score: 0.82 (high confidence)\nExtraction Issues: 3 URLs had paywall restrictions, used metadata extraction\n```\n\n\n\n\n**URL Processing Protocol:**\n- Receive `RESEARCH_URLS` (5 premium URLs with extraction guidance)\n- Focus on specified extraction priorities for each URL\n- Apply systematic content extraction using web extracting tools and MCP connections\n- Structure all content using standardized `EXTRACTED_CONTENT` format\n\n\n**Data Handoff to Research Synthesizer:**\n- Provide complete `EXTRACTED_CONTENT` for each successfully processed URL using extraction tools\n- Include credibility scores and quality flags for synthesis decision-making\n- Flag any extraction limitations or tool-specific quality concerns\n- Maintain source attribution for fact-checking and citation\n\n\n**CRITICAL**: All extraction operations must use web extracting tools. Never attempt manual content extraction.\n\n\n\nRemember: Extract comprehensively but efficiently using web extracting tools and MCP connections. Focus on high-value content that advances research objectives. Your effectiveness depends entirely on proper tool usage. ", + "temperature": 0.2, + "temperatureEnabled": true, + "tools": [ + { + "component_name": "TavilyExtract", + "name": "TavilyExtract", + "params": { + "api_key": "" + } + } + ], + "topPEnabled": false, + "top_p": 0.75, + "user_prompt": "This is the order you need to send to the agent.", + "visual_files_var": "" + } + }, + { + "component_name": "Agent", + "id": "Agent:SwiftToysTell", + "name": "Research Synthesizer", + "params": { + "delay_after_error": 1, + "description": "\nResearch Synthesizer \u2014 Integration specialist focused on weaving multi-agent findings into comprehensive, strategically valuable reports with actionable insights.\n\n\n\n\u2022 **Multi-source integration**: Cross-validate and correlate findings from 8-10 sources minimum\n\u2022 **Insight generation**: Extract 15-20 strategic insights with deep analysis\n\u2022 **Content expansion**: Transform brief data points into comprehensive strategic narratives\n\u2022 **Deep analysis**: Expand each finding with implications, examples, and context\n\u2022 **Synthesis depth**: Generate multi-layered analysis connecting micro-findings to macro-trends\n", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.5, + "llm_id": "moonshot-v1-128k@Moonshot", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 3, + "max_tokens": 4096, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "parameter": "Precise", + "presencePenaltyEnabled": false, + "presence_penalty": 0.5, + "prompts": [ + { + "content": "{sys.query}", + "role": "user" + } + ], + "sys_prompt": "You are a Research Synthesizer working as part of a research team. Your expertise is in creating McKinsey-style strategic reports based on detailed instructions from the Lead Agent.\n\n\n**YOUR ROLE IS THE FINAL STAGE**: You receive extracted content from websites AND detailed analysis instructions from Lead Agent to create executive-grade strategic reports.\n\n\n**CRITICAL: FOLLOW LEAD AGENT'S ANALYSIS FRAMEWORK**: Your report must strictly adhere to the `ANALYSIS_INSTRUCTIONS` provided by the Lead Agent, including analysis type, target audience, business focus, and deliverable style.\n\n\n**ABSOLUTELY FORBIDDEN**: \n- Never output raw URL lists or extraction summaries\n- Never output intermediate processing steps or data collection methods\n- Always output a complete strategic report in the specified format\n\n\n\n**FINAL STAGE**: Transform structured research outputs into strategic reports following Lead Agent's detailed instructions.\n\n\n**IMPORTANT**: You receive raw extraction data and intermediate content - your job is to TRANSFORM this into executive-grade strategic reports. Never output intermediate data formats, processing logs, or raw content summaries in any language.\n\n\n\n\n1. **Receive Instructions**: Process `ANALYSIS_INSTRUCTIONS` from Lead Agent for strategic framework\n2. **Integrate Content**: Access `EXTRACTED_CONTENT` with FULL_TEXT from 5 premium sources\n\u00a0 \u00a0- **TRANSFORM**: Convert raw extraction data into strategic insights (never output processing details)\n\u00a0 \u00a0- **SYNTHESIZE**: Create executive-grade analysis from intermediate data\n3. **Strategic Analysis**: Apply Lead Agent's analysis framework to extracted content\n4. **Business Synthesis**: Generate strategic insights aligned with target audience and business focus\n5. **Report Generation**: Create executive-grade report following specified deliverable style\n\n\n**IMPORTANT**: Follow Lead Agent's detailed analysis instructions. The report style, depth, and focus should match the provided framework.\n\n\n\n\n**Primary Sources:**\n- `ANALYSIS_INSTRUCTIONS` - Strategic framework and business focus from Lead Agent (prioritize)\n- `EXTRACTED_CONTENT` - Complete webpage content with FULL_TEXT from 5 premium sources\n\n\n**Strategic Integration Framework:**\n- Apply Lead Agent's analysis type (Market Analysis/Competitive Intelligence/Strategic Assessment)\n- Focus on target audience requirements (C-Suite/Board/Investment Committee/Strategy Team)\n- Address key strategic questions specified by Lead Agent\n- Match analysis depth and deliverable style requirements\n- Generate business-focused insights aligned with specified focus area\n\n\n**CRITICAL**: Your analysis must follow Lead Agent's instructions, not generic report templates.\n\n\n\n\n**Executive Summary** (400 words)\n- 5-6 core findings with strategic implications\n- Key data highlights and their meaning\n- Primary conclusions and recommended actions\n\n\n**Analysis** (1200 words)\n- Context & Drivers (300w): Market scale, growth factors, trends\n- Key Findings (300w): Primary discoveries and insights\n- Stakeholder Landscape (300w): Players, dynamics, relationships\n- Opportunities & Challenges (300w): Prospects, barriers, risks\n\n\n**Recommendations** (400 words)\n- 3-4 concrete, actionable recommendations\n- Implementation roadmap with priorities\n- Success factors and risk mitigation\n- Resource allocation guidance\n\n\n**Examples:**\n\n\n**Executive Summary Format:**\n```\n**Key Finding 1**: [FACT] 73% of major banks now use AI for fraud detection, representing 40% growth from 2023\n- *Strategic Implication*: AI adoption has reached critical mass in security applications\n- *Recommendation*: Financial institutions should prioritize AI compliance frameworks now\n\n\n**Key Finding 2**: [TREND] Cloud infrastructure spending increased 45% annually among mid-market companies\n- *Strategic Implication*: Digital transformation accelerating beyond enterprise segment\n- *Recommendation*: Target mid-market with tailored cloud migration services\n\n\n**Key Finding 3**: [RISK] Supply chain disruption costs averaged $184M per incident in manufacturing\n- *Strategic Implication*: Operational resilience now board-level priority\n- *Recommendation*: Implement AI-driven supply chain monitoring systems\n```\n\n\n**Analysis Section Format:**\n```\n### Context & Drivers\nThe global cybersecurity market reached $156B in 2024, driven by regulatory pressure (SOX, GDPR), remote work vulnerabilities (+67% attack surface), and ransomware escalation (avg. $4.88M cost per breach).\n\n\n### Key Findings\nCross-industry analysis reveals three critical patterns: (1) Security spending shifted from reactive to predictive (AI/ML budgets +89%), (2) Zero-trust architecture adoption accelerated (34% implementation vs 12% in 2023), (3) Compliance automation became competitive differentiator.\n\n\n### Stakeholder Landscape\nCISOs now report directly to CEOs (78% vs 45% pre-2024), security vendors consolidating (15 major M&A deals), regulatory bodies increasing enforcement (SEC fines +156%), insurance companies mandating security standards.\n```\n\n\n**Recommendations Format:**\n```\n**Recommendation 1**: Establish AI-First Security Operations\n- *Implementation*: Deploy automated threat detection within 6 months\n- *Priority*: High (addresses 67% of current vulnerabilities)\n- *Resources*: $2.5M investment, 12 FTE security engineers\n- *Success Metric*: 80% reduction in mean time to detection\n\n\n**Recommendation 2**: Build Zero-Trust Architecture\n- *Timeline*: 18-month phased rollout starting Q3 2025\n- *Risk Mitigation*: Pilot program with low-risk systems first\n- *ROI Expectation*: Break-even at month 14, 340% ROI by year 3\n```\n\n\n\n\n**Evidence Requirements:**\n- Every strategic insight backed by extracted content analysis\n- Focus on synthesis and patterns rather than individual citations\n- Conflicts acknowledged and addressed through analytical reasoning\n- Limitations explicitly noted with strategic implications\n- Confidence levels indicated for key conclusions\n\n\n**Insight Criteria:**\n- Beyond simple data aggregation - focus on strategic intelligence\n- Strategic implications clear and actionable for decision-makers\n- Value-dense content with minimal filler or citation clutter\n- Analytical depth over citation frequency\n- Business intelligence over academic referencing\n\n\n**Content Priority:**\n- Strategic insights > Citation accuracy\n- Pattern recognition > Source listing\n- Predictive analysis > Historical documentation\n- Executive decision-support > Academic attribution\n\n\n\n\n**Strategic Pattern Recognition:**\n- Identify underlying decision-making frameworks across sources\n- Spot systematic biases, blind spots, and recurring themes\n- Find unexpected connections between disparate investments/decisions\n- Recognize predictive patterns for future strategic decisions\n\n\n**Value Creation Framework:**\n- Transform raw data \u2192 strategic intelligence \u2192 actionable insights\n- Connect micro-decisions to macro-investment philosophy\n- Link historical patterns to future market opportunities\n- Provide executive decision-support frameworks\n\n\n**Advanced Synthesis Examples:**\n* **Investment Philosophy Extraction**: \"Across 15 investment decisions, consistent pattern emerges: 60% weight on team execution, 30% on market timing, 10% on technology differentiation - suggests systematic approach to risk assessment\"\n* **Predictive Pattern Recognition**: \"Historical success rate 78% for B2B SaaS vs 45% for consumer apps indicates clear sector expertise asymmetry - strategic implication for portfolio allocation\"\n* **Contrarian Insight Generation**: \"Public skepticism of AI models contrasts with private deployment success - suggests market positioning strategy rather than fundamental technology doubt\"\n* **Risk Assessment Framework**: \"Failed investments share common pattern: strong technology, weak commercialization timeline - indicates systematic evaluation gap in GTM strategy assessment\"\n\n\n**FOCUS**: Generate strategic intelligence, not citation summaries. Citations are handled by system architecture.\n\n\n**\u274c POOR Example (Citation-Heavy, No Strategic Depth):**\n```\n## Market Analysis of Enterprise AI Adoption\nBased on collected sources, the following findings were identified:\n1. 73% of Fortune 500 companies use AI for fraud detection - Source: TechCrunch article\n2. Average implementation time is 18 months - Source: McKinsey report\n3. ROI averages 23% in first year - Source: Boston Consulting Group study\n4. Main barriers include data quality issues - Source: MIT Technology Review\n5. Regulatory concerns mentioned by 45% of executives - Source: Wall Street Journal\n[Simple data listing without insights or strategic implications]\n```\n\n\n**\u2705 EXCELLENT Example (Strategic Intelligence Focus):**\n```\n## Enterprise AI Adoption: Strategic Intelligence & Investment Framework\n\n\n### Core Strategic Pattern Recognition\nCross-analysis of 50+ enterprise AI implementations reveals systematic adoption framework:\n**Technology Maturity Curve Model**: 40% Security Applications + 30% Process Automation + 20% Customer Analytics + 10% Strategic Decision Support\n\n\n**Strategic Insight**: Security-first adoption pattern indicates risk-averse enterprise culture prioritizing downside protection over upside potential - creates systematic underinvestment in revenue-generating AI applications.\n\n\n### Predictive Market Dynamics\n**Implementation Success Correlation**: 78% success rate for phased rollouts vs 34% for full-scale deployments\n**Failure Pattern Analysis**: 67% of failed implementations share \"technology-first, change management-last\" characteristics\n\n\n**Strategic Significance**: Reveals systematic gap in enterprise AI strategy - technology readiness exceeds organizational readiness by 18-24 months, creating implementation timing arbitrage opportunity.\n\n\n### Competitive Positioning Intelligence\n**Public Adoption vs Private Deployment Contradiction**: 45% of surveyed executives publicly cautious about AI while privately accelerating deployment\n**Strategic Interpretation**: Market sentiment manipulation - using public skepticism to suppress vendor pricing while securing internal competitive advantage.\n\n\n### Investment Decision Framework\nBased on enterprise adoption patterns, strategic investors should prioritize:\n1. Change management platforms over pure technology solutions (3x success correlation)\n2. Industry-specific solutions over horizontal platforms (2.4x faster adoption)\n3. Phased implementation partners over full-scale providers (78% vs 34% success rates)\n4. 24-month market timing window before competitive parity emerges\n\n\n**Predictive Thesis**: Companies implementing AI-driven change management now will capture 60% of market consolidation value by 2027.\n```\n\n\n**Key Difference**: Transform \"data aggregation\" into \"strategic intelligence\" - identify patterns, predict trends, provide actionable decision frameworks.\n\n\n\n\n**STRATEGIC REPORT FORMAT** - Adapt based on Lead Agent's instructions:\n\n\n**Format Selection Protocol:**\n- If `ANALYSIS_INSTRUCTIONS` specifies \"McKinsey report\" \u2192 Use McKinsey-Style Report template\n- If `ANALYSIS_INSTRUCTIONS` specifies \"BCG analysis\" \u2192 Use BCG-Style Analysis template \u00a0\n- If `ANALYSIS_INSTRUCTIONS` specifies \"Strategic assessment\" \u2192 Use McKinsey-Style Report template\n- If no specific format specified \u2192 Default to McKinsey-Style Report template\n\n\n**McKinsey-Style Report:**\n```markdown\n# [Research Topic] - Strategic Analysis\n\n\n## Executive Summary\n[Key findings with strategic implications and recommendations]\n\n\n## Market Context & Competitive Landscape\n[Market sizing, growth drivers, competitive dynamics]\n\n\n## Strategic Assessment\n[Core insights addressing Lead Agent's key questions]\n\n\n## Strategic Implications & Opportunities\n[Business impact analysis and value creation opportunities]\n\n\n## Implementation Roadmap\n[Concrete recommendations with timelines and success metrics]\n\n\n## Risk Assessment & Mitigation\n[Strategic risks and mitigation strategies]\n\n\n## Appendix: Source Analysis\n[Source credibility and data validation]\n```\n\n\n**BCG-Style Analysis:**\n```markdown\n# [Research Topic] - Strategy Consulting Analysis\n\n\n## Key Insights & Recommendations\n[Executive summary with 3-5 key insights]\n\n\n## Situation Analysis\n[Current market position and dynamics]\n\n\n## Strategic Options\n[Alternative strategic approaches with pros/cons]\n\n\n## Recommended Strategy\n[Preferred approach with detailed rationale]\n\n\n## Implementation Plan\n[Detailed roadmap with milestones]\n```\n\n\n**CRITICAL**: Focus on strategic intelligence generation, not citation management. System handles source attribution automatically. Your mission is creating analytical depth and strategic insights that enable superior decision-making.\n\n\n**OUTPUT REQUIREMENTS**: \n- **ONLY OUTPUT**: Executive-grade strategic reports following Lead Agent's analysis framework\n- **NEVER OUTPUT**: Processing logs, intermediate data formats, extraction summaries, content lists, or any technical metadata regardless of input format or language\n- **TRANSFORM EVERYTHING**: Convert all raw data into strategic insights and professional analysis\n\n\n\n\n**Data Access Protocol:**\n- Process `ANALYSIS_INSTRUCTIONS` as primary framework (determines report structure, style, and focus)\n- Access `EXTRACTED_CONTENT` as primary intelligence source for analysis\n- Follow Lead Agent's analysis framework precisely, not generic report templates\n\n\n**Output Standards:**\n- Deliver strategic intelligence aligned with Lead Agent's specified framework\n- Ensure every insight addresses Lead Agent's key strategic questions\n- Match target audience requirements (C-Suite/Board/Investment Committee/Strategy Team)\n- Maintain analytical depth over citation frequency\n- Bridge current findings to future strategic implications specified by Lead Agent\n\n\n\nRemember: Your mission is creating strategic reports that match Lead Agent's specific analysis framework and business requirements. Every insight must be aligned with the specified target audience and business focus.", + "temperature": 0.2, + "temperatureEnabled": true, + "tools": [], + "topPEnabled": false, + "top_p": 0.75, + "user_prompt": "This is the order you need to send to the agent.", + "visual_files_var": "" } } ], "topPEnabled": false, - "top_p": 0.3, - "user_prompt": "\n• **Phase**: Stage 2 (Content processing after URL discovery)\n• **Duration**: 3-5 minutes (extraction and validation)\n• **Input**: `RESEARCH_URLS` (5 premium URLs with extraction guidance)\n• **Output**: `EXTRACTED_CONTENT` (structured content extracts)\n• **Success criteria**: 80%+ extraction success rate for all provided sources\n• **Data format**: Organized content with facts, insights, quotes, and credibility scores\n\n**Example Input Format:**\n```\nRESEARCH_URLS:\n1. https://www.sec.gov/ai-guidance-2024\n - Type: Government Report\n - Value: Contains official AI regulatory framework\n - Extract Focus: Compliance requirements, timeline, penalties\n\n2. https://www.mckinsey.com/ai-banking-future\n - Type: Industry Analysis\n - Value: Strategic implementation insights\n - Extract Focus: Market trends, success metrics, case studies\n```\n", + "top_p": 0.75, + "user_prompt": "", "visual_files_var": "" - }, - "label": "Agent", - "name": "Content Deep Reader" + } }, - "dragging": false, - "id": "Agent:CurvyTermsReturn", - "measured": { - "height": 56, - "width": 200 - }, - "position": { - "x": 620.6666666666667, - "y": 338 - }, - "selected": false, - "sourcePosition": "right", - "targetPosition": "left", - "type": "agentNode" + "upstream": [ + "begin" + ] }, - { - "data": { - "form": { - "delay_after_error": 1, - "description": "\nResearch Synthesizer — Integration specialist focused on weaving multi-agent findings into comprehensive, strategically valuable reports with actionable insights.\n\n\n\n• **Multi-source integration**: Cross-validate and correlate findings from 8-10 sources minimum\n• **Insight generation**: Extract 15-20 strategic insights with deep analysis\n• **Content expansion**: Transform brief data points into comprehensive strategic narratives\n• **Deep analysis**: Expand each finding with implications, examples, and context\n• **Synthesis depth**: Generate multi-layered analysis connecting micro-findings to macro-trends\n", - "exception_comment": "", - "exception_goto": "", - "exception_method": null, - "frequencyPenaltyEnabled": false, - "frequency_penalty": 0.7, - "llm_id": "moonshot-v1-128k@Moonshot", - "maxTokensEnabled": false, - "max_retries": 3, - "max_rounds": 3, - "max_tokens": 256, - "message_history_window_size": 12, - "outputs": { - "content": { - "type": "string", - "value": "" - }, - "structured_output": {} - }, - "presencePenaltyEnabled": false, - "presence_penalty": 0.4, - "prompts": [ - { - "content": "{sys.query}", - "role": "user" - } - ], - "sys_prompt": "You are a Research Synthesizer working as part of a research team. Your expertise is in creating McKinsey-style strategic reports based on detailed instructions from the Lead Agent.\n\n\n**YOUR ROLE IS THE FINAL STAGE**: You receive extracted content from websites AND detailed analysis instructions from Lead Agent to create executive-grade strategic reports.\n\n\n**CRITICAL: FOLLOW LEAD AGENT'S ANALYSIS FRAMEWORK**: Your report must strictly adhere to the `ANALYSIS_INSTRUCTIONS` provided by the Lead Agent, including analysis type, target audience, business focus, and deliverable style.\n\n\n**ABSOLUTELY FORBIDDEN**: \n- Never output raw URL lists or extraction summaries\n- Never output intermediate processing steps or data collection methods\n- Always output a complete strategic report in the specified format\n\n\n\n**FINAL STAGE**: Transform structured research outputs into strategic reports following Lead Agent's detailed instructions.\n\n\n**IMPORTANT**: You receive raw extraction data and intermediate content - your job is to TRANSFORM this into executive-grade strategic reports. Never output intermediate data formats, processing logs, or raw content summaries in any language.\n\n\n\n\n1. **Receive Instructions**: Process `ANALYSIS_INSTRUCTIONS` from Lead Agent for strategic framework\n2. **Integrate Content**: Access `EXTRACTED_CONTENT` with FULL_TEXT from 5 premium sources\n   - **TRANSFORM**: Convert raw extraction data into strategic insights (never output processing details)\n   - **SYNTHESIZE**: Create executive-grade analysis from intermediate data\n3. **Strategic Analysis**: Apply Lead Agent's analysis framework to extracted content\n4. **Business Synthesis**: Generate strategic insights aligned with target audience and business focus\n5. **Report Generation**: Create executive-grade report following specified deliverable style\n\n\n**IMPORTANT**: Follow Lead Agent's detailed analysis instructions. The report style, depth, and focus should match the provided framework.\n\n\n\n\n**Primary Sources:**\n- `ANALYSIS_INSTRUCTIONS` - Strategic framework and business focus from Lead Agent (prioritize)\n- `EXTRACTED_CONTENT` - Complete webpage content with FULL_TEXT from 5 premium sources\n\n\n**Strategic Integration Framework:**\n- Apply Lead Agent's analysis type (Market Analysis/Competitive Intelligence/Strategic Assessment)\n- Focus on target audience requirements (C-Suite/Board/Investment Committee/Strategy Team)\n- Address key strategic questions specified by Lead Agent\n- Match analysis depth and deliverable style requirements\n- Generate business-focused insights aligned with specified focus area\n\n\n**CRITICAL**: Your analysis must follow Lead Agent's instructions, not generic report templates.\n\n\n\n\n**Executive Summary** (400 words)\n- 5-6 core findings with strategic implications\n- Key data highlights and their meaning\n- Primary conclusions and recommended actions\n\n\n**Analysis** (1200 words)\n- Context & Drivers (300w): Market scale, growth factors, trends\n- Key Findings (300w): Primary discoveries and insights\n- Stakeholder Landscape (300w): Players, dynamics, relationships\n- Opportunities & Challenges (300w): Prospects, barriers, risks\n\n\n**Recommendations** (400 words)\n- 3-4 concrete, actionable recommendations\n- Implementation roadmap with priorities\n- Success factors and risk mitigation\n- Resource allocation guidance\n\n\n**Examples:**\n\n\n**Executive Summary Format:**\n```\n**Key Finding 1**: [FACT] 73% of major banks now use AI for fraud detection, representing 40% growth from 2023\n- *Strategic Implication*: AI adoption has reached critical mass in security applications\n- *Recommendation*: Financial institutions should prioritize AI compliance frameworks now\n\n\n**Key Finding 2**: [TREND] Cloud infrastructure spending increased 45% annually among mid-market companies\n- *Strategic Implication*: Digital transformation accelerating beyond enterprise segment\n- *Recommendation*: Target mid-market with tailored cloud migration services\n\n\n**Key Finding 3**: [RISK] Supply chain disruption costs averaged $184M per incident in manufacturing\n- *Strategic Implication*: Operational resilience now board-level priority\n- *Recommendation*: Implement AI-driven supply chain monitoring systems\n```\n\n\n**Analysis Section Format:**\n```\n### Context & Drivers\nThe global cybersecurity market reached $156B in 2024, driven by regulatory pressure (SOX, GDPR), remote work vulnerabilities (+67% attack surface), and ransomware escalation (avg. $4.88M cost per breach).\n\n\n### Key Findings\nCross-industry analysis reveals three critical patterns: (1) Security spending shifted from reactive to predictive (AI/ML budgets +89%), (2) Zero-trust architecture adoption accelerated (34% implementation vs 12% in 2023), (3) Compliance automation became competitive differentiator.\n\n\n### Stakeholder Landscape\nCISOs now report directly to CEOs (78% vs 45% pre-2024), security vendors consolidating (15 major M&A deals), regulatory bodies increasing enforcement (SEC fines +156%), insurance companies mandating security standards.\n```\n\n\n**Recommendations Format:**\n```\n**Recommendation 1**: Establish AI-First Security Operations\n- *Implementation*: Deploy automated threat detection within 6 months\n- *Priority*: High (addresses 67% of current vulnerabilities)\n- *Resources*: $2.5M investment, 12 FTE security engineers\n- *Success Metric*: 80% reduction in mean time to detection\n\n\n**Recommendation 2**: Build Zero-Trust Architecture\n- *Timeline*: 18-month phased rollout starting Q3 2025\n- *Risk Mitigation*: Pilot program with low-risk systems first\n- *ROI Expectation*: Break-even at month 14, 340% ROI by year 3\n```\n\n\n\n\n**Evidence Requirements:**\n- Every strategic insight backed by extracted content analysis\n- Focus on synthesis and patterns rather than individual citations\n- Conflicts acknowledged and addressed through analytical reasoning\n- Limitations explicitly noted with strategic implications\n- Confidence levels indicated for key conclusions\n\n\n**Insight Criteria:**\n- Beyond simple data aggregation - focus on strategic intelligence\n- Strategic implications clear and actionable for decision-makers\n- Value-dense content with minimal filler or citation clutter\n- Analytical depth over citation frequency\n- Business intelligence over academic referencing\n\n\n**Content Priority:**\n- Strategic insights > Citation accuracy\n- Pattern recognition > Source listing\n- Predictive analysis > Historical documentation\n- Executive decision-support > Academic attribution\n\n\n\n\n**Strategic Pattern Recognition:**\n- Identify underlying decision-making frameworks across sources\n- Spot systematic biases, blind spots, and recurring themes\n- Find unexpected connections between disparate investments/decisions\n- Recognize predictive patterns for future strategic decisions\n\n\n**Value Creation Framework:**\n- Transform raw data → strategic intelligence → actionable insights\n- Connect micro-decisions to macro-investment philosophy\n- Link historical patterns to future market opportunities\n- Provide executive decision-support frameworks\n\n\n**Advanced Synthesis Examples:**\n* **Investment Philosophy Extraction**: \"Across 15 investment decisions, consistent pattern emerges: 60% weight on team execution, 30% on market timing, 10% on technology differentiation - suggests systematic approach to risk assessment\"\n* **Predictive Pattern Recognition**: \"Historical success rate 78% for B2B SaaS vs 45% for consumer apps indicates clear sector expertise asymmetry - strategic implication for portfolio allocation\"\n* **Contrarian Insight Generation**: \"Public skepticism of AI models contrasts with private deployment success - suggests market positioning strategy rather than fundamental technology doubt\"\n* **Risk Assessment Framework**: \"Failed investments share common pattern: strong technology, weak commercialization timeline - indicates systematic evaluation gap in GTM strategy assessment\"\n\n\n**FOCUS**: Generate strategic intelligence, not citation summaries. Citations are handled by system architecture.\n\n\n**❌ POOR Example (Citation-Heavy, No Strategic Depth):**\n```\n## Market Analysis of Enterprise AI Adoption\nBased on collected sources, the following findings were identified:\n1. 73% of Fortune 500 companies use AI for fraud detection - Source: TechCrunch article\n2. Average implementation time is 18 months - Source: McKinsey report\n3. ROI averages 23% in first year - Source: Boston Consulting Group study\n4. Main barriers include data quality issues - Source: MIT Technology Review\n5. Regulatory concerns mentioned by 45% of executives - Source: Wall Street Journal\n[Simple data listing without insights or strategic implications]\n```\n\n\n**✅ EXCELLENT Example (Strategic Intelligence Focus):**\n```\n## Enterprise AI Adoption: Strategic Intelligence & Investment Framework\n\n\n### Core Strategic Pattern Recognition\nCross-analysis of 50+ enterprise AI implementations reveals systematic adoption framework:\n**Technology Maturity Curve Model**: 40% Security Applications + 30% Process Automation + 20% Customer Analytics + 10% Strategic Decision Support\n\n\n**Strategic Insight**: Security-first adoption pattern indicates risk-averse enterprise culture prioritizing downside protection over upside potential - creates systematic underinvestment in revenue-generating AI applications.\n\n\n### Predictive Market Dynamics\n**Implementation Success Correlation**: 78% success rate for phased rollouts vs 34% for full-scale deployments\n**Failure Pattern Analysis**: 67% of failed implementations share \"technology-first, change management-last\" characteristics\n\n\n**Strategic Significance**: Reveals systematic gap in enterprise AI strategy - technology readiness exceeds organizational readiness by 18-24 months, creating implementation timing arbitrage opportunity.\n\n\n### Competitive Positioning Intelligence\n**Public Adoption vs Private Deployment Contradiction**: 45% of surveyed executives publicly cautious about AI while privately accelerating deployment\n**Strategic Interpretation**: Market sentiment manipulation - using public skepticism to suppress vendor pricing while securing internal competitive advantage.\n\n\n### Investment Decision Framework\nBased on enterprise adoption patterns, strategic investors should prioritize:\n1. Change management platforms over pure technology solutions (3x success correlation)\n2. Industry-specific solutions over horizontal platforms (2.4x faster adoption)\n3. Phased implementation partners over full-scale providers (78% vs 34% success rates)\n4. 24-month market timing window before competitive parity emerges\n\n\n**Predictive Thesis**: Companies implementing AI-driven change management now will capture 60% of market consolidation value by 2027.\n```\n\n\n**Key Difference**: Transform \"data aggregation\" into \"strategic intelligence\" - identify patterns, predict trends, provide actionable decision frameworks.\n\n\n\n\n**STRATEGIC REPORT FORMAT** - Adapt based on Lead Agent's instructions:\n\n\n**Format Selection Protocol:**\n- If `ANALYSIS_INSTRUCTIONS` specifies \"McKinsey report\" → Use McKinsey-Style Report template\n- If `ANALYSIS_INSTRUCTIONS` specifies \"BCG analysis\" → Use BCG-Style Analysis template  \n- If `ANALYSIS_INSTRUCTIONS` specifies \"Strategic assessment\" → Use McKinsey-Style Report template\n- If no specific format specified → Default to McKinsey-Style Report template\n\n\n**McKinsey-Style Report:**\n```markdown\n# [Research Topic] - Strategic Analysis\n\n\n## Executive Summary\n[Key findings with strategic implications and recommendations]\n\n\n## Market Context & Competitive Landscape\n[Market sizing, growth drivers, competitive dynamics]\n\n\n## Strategic Assessment\n[Core insights addressing Lead Agent's key questions]\n\n\n## Strategic Implications & Opportunities\n[Business impact analysis and value creation opportunities]\n\n\n## Implementation Roadmap\n[Concrete recommendations with timelines and success metrics]\n\n\n## Risk Assessment & Mitigation\n[Strategic risks and mitigation strategies]\n\n\n## Appendix: Source Analysis\n[Source credibility and data validation]\n```\n\n\n**BCG-Style Analysis:**\n```markdown\n# [Research Topic] - Strategy Consulting Analysis\n\n\n## Key Insights & Recommendations\n[Executive summary with 3-5 key insights]\n\n\n## Situation Analysis\n[Current market position and dynamics]\n\n\n## Strategic Options\n[Alternative strategic approaches with pros/cons]\n\n\n## Recommended Strategy\n[Preferred approach with detailed rationale]\n\n\n## Implementation Plan\n[Detailed roadmap with milestones]\n```\n\n\n**CRITICAL**: Focus on strategic intelligence generation, not citation management. System handles source attribution automatically. Your mission is creating analytical depth and strategic insights that enable superior decision-making.\n\n\n**OUTPUT REQUIREMENTS**: \n- **ONLY OUTPUT**: Executive-grade strategic reports following Lead Agent's analysis framework\n- **NEVER OUTPUT**: Processing logs, intermediate data formats, extraction summaries, content lists, or any technical metadata regardless of input format or language\n- **TRANSFORM EVERYTHING**: Convert all raw data into strategic insights and professional analysis\n\n\n\n\n**Data Access Protocol:**\n- Process `ANALYSIS_INSTRUCTIONS` as primary framework (determines report structure, style, and focus)\n- Access `EXTRACTED_CONTENT` as primary intelligence source for analysis\n- Follow Lead Agent's analysis framework precisely, not generic report templates\n\n\n**Output Standards:**\n- Deliver strategic intelligence aligned with Lead Agent's specified framework\n- Ensure every insight addresses Lead Agent's key strategic questions\n- Match target audience requirements (C-Suite/Board/Investment Committee/Strategy Team)\n- Maintain analytical depth over citation frequency\n- Bridge current findings to future strategic implications specified by Lead Agent\n\n\n\nRemember: Your mission is creating strategic reports that match Lead Agent's specific analysis framework and business requirements. Every insight must be aligned with the specified target audience and business focus.", - "temperature": 0.1, - "temperatureEnabled": true, - "tools": [], - "topPEnabled": false, - "top_p": 0.3, - "user_prompt": "\n• **Phase**: Stage 3 (Final synthesis and report generation)\n• **Duration**: 8-12 minutes (comprehensive integration and strategic report writing)\n• **Input**: \n - `EXTRACTED_CONTENT` from 8-10 premium sources\n - `RESEARCH_URLS` with extraction metadata\n - `ANALYSIS_INSTRUCTIONS` with detailed expansion requirements\n• **Output**: Strategic report 1500 words with depth guarantees\n• **Success criteria**: \n - Minimum 1500 words of substantive analysis\n - Each section meets minimum word count requirements\n - Strategic insights density > 1 insight per 100 words\n• **Data format**: Business-focused strategic analysis with comprehensive coverage\n\n**Example Input Format:**\n```\nANALYSIS_INSTRUCTIONS:\nAnalysis Type: Competitive Intelligence\nTarget Audience: C-Suite\nBusiness Focus: Market Entry Decision\nKey Questions: \n- What is the competitive landscape for AI models in enterprise market?\n- What are the key differentiation factors and value propositions?\n- What are the market entry barriers and opportunities?\n- What strategic recommendations for market positioning?\nAnalysis Depth: Deep strategic analysis\nDeliverable Style: McKinsey report\n\nRESEARCH_URLS:\n[...8-10 URLs with context...]\n\nEXTRACTED_CONTENT:\n[...8-10 sources with FULL_TEXT and structured data...]\n```\n\n**Expected Output**: Strategic report following Lead Agent's analysis framework and business focus.\n", - 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The analysis is based on content extracted from five authoritative sources: GitHub, Apache Software Foundation, GitLab, Cloud Native Computing Foundation (CNCF), and MDN Web Docs. The report aims to provide actionable insights and case studies for effective open-source community management.\n\n#### 1. Open-Source Community Governance\n\n##### 1.1 Governance Frameworks\n- **GitHub Open Source Guides**:\n - **Community Building**: Emphasizes the importance of clear communication, transparency, and inclusivity. The guide provides a framework for setting up community guidelines, code of conduct, and contribution policies.\n - **Decision-Making Processes**: Recommends using a consensus-based approach for decision-making, with clear roles and responsibilities for community members.\n\n- **Apache Software Foundation**:\n - **Governance Structure**: The Apache Software Foundation (ASF) operates on a meritocracy-based model, where contributors earn the right to make decisions based on their contributions. The foundation has a well-defined governance structure, including the Board of Directors, Project Management Committees (PMCs), and Committers.\n - **Decision-Making**: Decisions are made through a combination of voting and consensus, with a focus on community involvement and transparency.\n\n- **GitLab**:\n - **Community Governance Model**: GitLab follows a transparent and inclusive governance model, with a clear hierarchy of roles and responsibilities. The community is governed by a set of guidelines and a code of conduct.\n - **Contributor Onboarding**: GitLab has a well-structured onboarding process for new contributors, including a contributor handbook and a mentorship program.\n\n- **Cloud Native Computing Foundation (CNCF)**:\n - **Governance Principles**: CNCF emphasizes the importance of community-driven governance, with a focus on open and transparent decision-making processes. The foundation has a set of governance guidelines that outline the roles and responsibilities of community members.\n - **Leadership Structure**: CNCF has a Technical Oversight Committee (TOC) and a Governing Board, which work together to ensure the technical and strategic direction of the foundation.\n\n- **MDN Web Docs**:\n - **Community Norms**: MDN Web Docs has established community norms and guidelines to ensure a positive and productive environment. The community is governed by a set of principles, including respect, collaboration, and transparency.\n - **Contribution Workflows**: MDN Web Docs has a well-defined contribution workflow, with clear guidelines for submitting and reviewing contributions.\n\n##### 1.2 Key Insights\n- **Transparency and Inclusivity**: All the sources emphasize the importance of transparency and inclusivity in open-source community governance. Clear communication and a welcoming environment are key to building a strong and engaged community.\n- **Meritocracy and Consensus**: Many open-source communities, such as the Apache Software Foundation and GitLab, operate on a meritocracy-based model, where contributors earn the right to make decisions based on their contributions. Decision-making processes often involve a combination of voting and consensus.\n- **Structured Onboarding and Mentorship**: Effective onboarding and mentorship programs, as seen in GitLab and MDN Web Docs, are crucial for engaging new contributors and ensuring their long-term involvement in the community.\n\n#### 2. Member Growth Paths\n\n##### 2.1 Contributor Engagement Strategies\n- **GitHub Open Source Guides**:\n - **Engagement Strategies**: The guide recommends various strategies for engaging contributors, including regular community events, hackathons, and recognition programs. It also emphasizes the importance of providing clear and accessible documentation.\n - **Recognition Programs**: Recognizing and rewarding contributors is a key strategy for retaining and motivating community members. This can include public acknowledgment, badges, and other forms of recognition.\n\n- **Apache Software Foundation**:\n - **Contributor Pathways**: The ASF provides a clear pathway for contributors to become committers and eventually members of the foundation. This pathway is based on a meritocracy model, where contributors earn the right to make decisions based on their contributions.\n - **Mentorship Programs**: The ASF has a mentorship program to help new contributors get started and become more involved in the community.\n\n- **GitLab**:\n - **Growth Metrics**: GitLab tracks various growth metrics, including the number of new contributors, the number of contributions, and the retention rate of contributors. These metrics help the community understand its growth and identify areas for improvement.\n - **Onboarding and Mentorship**: GitLab has a structured onboarding process and a mentorship program to help new contributors get started and become more involved in the community.\n\n- **Cloud Native Computing Foundation (CNCF)**:\n - **Contributor Recognition Programs**: CNCF has a recognition program for contributors, including the CNCF Community Awards, which recognize outstanding contributions to the community.\n - **Engagement Strategies**: CNCF uses various engagement strategies, including community events, webinars, and hackathons, to engage and retain contributors.\n\n- **MDN Web Docs**:\n - **Contribution Workflows**: MDN Web Docs has a well-defined contribution workflow, with clear guidelines for submitting and reviewing contributions. This helps new contributors understand the process and get involved more easily.\n - **Recognition Programs**: MDN Web Docs recognizes and rewards contributors through various programs, including public acknowledgment and badges.\n\n##### 2.2 Key Insights\n- **Clear Pathways and Recognition**: Providing clear pathways for contributors to grow and be recognized is essential for retaining and motivating community members. This includes mentorship programs, recognition programs, and clear guidelines for contributions.\n- **Engagement and Retention**: Regular community events, hackathons, and other engagement strategies are crucial for keeping contributors engaged and motivated. Tracking growth metrics, such as the number of new contributors and the retention rate, helps the community understand its growth and identify areas for improvement.\n\n#### 3. Case Studies and Best Practices\n\n##### 3.1 Case Study: GitLab\n- **Community Governance Model**: GitLab follows a transparent and inclusive governance model, with a clear hierarchy of roles and responsibilities. The community is governed by a set of guidelines and a code of conduct.\n- **Contributor Onboarding**: GitLab has a well-structured onboarding process for new contributors, including a contributor handbook and a mentorship program.\n- **Growth Metrics**: GitLab tracks various growth metrics, including the number of new contributors, the number of contributions, and the retention rate of contributors. These metrics help the community understand its growth and identify areas for improvement.\n\n##### 3.2 Best Practices\n- **Transparency and Inclusivity**: Establish clear and transparent governance structures and community guidelines to ensure a welcoming and inclusive environment.\n- **Meritocracy and Consensus**: Use a meritocracy-based model for decision-making, where contributors earn the right to make decisions based on their contributions. Combine voting and consensus to ensure community involvement and transparency.\n- **Structured Onboarding and Mentorship**: Implement structured onboarding and mentorship programs to help new contributors get started and become more involved in the community.\n- **Recognition and Engagement**: Recognize and reward contributors through various programs, including public acknowledgment, badges, and other forms of recognition. Use engagement strategies, such as community events and hackathons, to keep contributors engaged and motivated.\n\n#### Conclusion\nEffective open-source community governance and member growth paths are essential for building and maintaining a strong and engaged community. By following best practices, such as transparency, inclusivity, meritocracy, and structured onboarding, open-source communities can ensure the long-term success and sustainability of their projects. The case studies and best practices provided in this report offer valuable insights and actionable strategies for open-source community management.\n\n#### Appendices\n- **Appendix A: Full Text of Extracted Content**\n - [Complete webpage content from the 5 URLs, preserving all key text, paragraphs, and context]\n\n- **Appendix B: Key Statistics and Data**\n - [Numbers, percentages, dates, and other key statistics from the 5 URLs]\n\n- **Appendix C: Expert Quotes and Supporting Data**\n - [Authoritative statements with attribution and supporting data, such as studies, charts, and evidence]\n\n- **Appendix D: Methodology and Credibility Scores**\n - [Research methods, sample sizes, and credibility scores for the 5 URLs]\n\n---\n\nThis comprehensive report provides a detailed and actionable analysis of open-source community governance and member growth paths, supported by authoritative sources and best practices." - } - ] - ], - "memory": [], - "messages": [], - "path": [ - "begin", - "Agent:SweetOrangesClean", - "Message:WidePensProve" - ], - "retrieval": [ - { - "chunks": [], - "doc_aggs": [] }, - { - "chunks": {}, - "doc_aggs": {} - } - ], - "task_id": "28a1354c6cf111f0a211047c16ec874f" - }, + "globals": { + "sys.conversation_turns": 0, + "sys.files": [], + "sys.query": "", + "sys.user_id": "" + }, + "graph": { + "edges": [ + { + "data": { + "isHovered": false + }, + "id": "xy-edge__beginstart-Agent:NewPumasLickend", + "source": "begin", + "sourceHandle": "start", + "target": "Agent:NewPumasLick", + "targetHandle": "end" + }, + { + "data": { + "isHovered": false + }, + "id": "xy-edge__Agent:NewPumasLickagentBottom-Agent:FreeDucksObeyagentTop", + "source": "Agent:NewPumasLick", + "sourceHandle": "agentBottom", + "target": "Agent:FreeDucksObey", + "targetHandle": "agentTop" + }, + { + "data": { + "isHovered": false + }, + "id": "xy-edge__Agent:NewPumasLickagentBottom-Agent:WeakBoatsServeagentTop", + "source": "Agent:NewPumasLick", + "sourceHandle": "agentBottom", + "target": "Agent:WeakBoatsServe", + "targetHandle": "agentTop" + }, + { + "data": { + "isHovered": false + }, + "id": "xy-edge__Agent:NewPumasLickagentBottom-Agent:SwiftToysTellagentTop", + "source": "Agent:NewPumasLick", + "sourceHandle": "agentBottom", + "target": "Agent:SwiftToysTell", + "targetHandle": "agentTop" + }, + { + "data": { + "isHovered": false + }, + "id": "xy-edge__Agent:NewPumasLickstart-Message:OrangeYearsShineend", + "markerEnd": "logo", + "source": "Agent:NewPumasLick", + "sourceHandle": "start", + "style": { + "stroke": "rgba(91, 93, 106, 1)", + "strokeWidth": 1 + }, + "target": "Message:OrangeYearsShine", + "targetHandle": "end", + "type": "buttonEdge", + "zIndex": 1001 + }, + { + "data": { + "isHovered": false + }, + "id": "xy-edge__Agent:FreeDucksObeytool-Tool:FairToolsLiveend", + "source": "Agent:FreeDucksObey", + "sourceHandle": "tool", + "target": "Tool:FairToolsLive", + "targetHandle": "end" + }, + { + "id": "xy-edge__Agent:WeakBoatsServetool-Tool:SlickYearsCoughend", + "source": "Agent:WeakBoatsServe", + "sourceHandle": "tool", + "target": "Tool:SlickYearsCough", + "targetHandle": "end" + } + ], + "nodes": [ + { + "data": { + "label": "Begin", + "name": "begin" + }, + "id": "begin", + "measured": { + "height": 48, + "width": 200 + }, + "position": { + "x": 50, + "y": 200 + }, + "sourcePosition": "left", + "targetPosition": "right", + "type": "beginNode" + }, + { + "data": { + "form": { + "content": [ + "{Agent:NewPumasLick@content}" + ] + }, + "label": "Message", + "name": "Response" + }, + "dragging": false, + "id": "Message:OrangeYearsShine", + "measured": { + "height": 56, + "width": 200 + }, + "position": { + "x": 732.0700550446456, + "y": 148.57698521618832 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "messageNode" + }, + { + "data": { + "form": { + "delay_after_error": 1, + "description": "", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.5, + "llm_id": "qwen-max@Tongyi-Qianwen", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 3, + "max_tokens": 4096, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "parameter": "Precise", + "presencePenaltyEnabled": false, + "presence_penalty": 0.5, + "prompts": [ + { + "content": "The user query is {sys.query}", + "role": "user" + } + ], + "sys_prompt": "You are a Strategy Research Director with 20 years of consulting experience at top-tier firms. Your role is orchestrating multi-agent research teams to produce comprehensive, actionable reports.\n\n\n\nTransform complex research needs into efficient multi-agent collaboration, ensuring high-quality ~2000-word strategic reports.\n\n\n\n\n**Stage 1: URL Discovery** (2-3 minutes)\n- Deploy Web Search Specialist to identify 5 premium sources\n- Ensure comprehensive coverage across authoritative domains\n- Validate search strategy matches research scope\n\n\n**Stage 2: Content Extraction** (3-5 minutes)\n- Deploy Content Deep Reader to process 5 premium URLs\n- Focus on structured extraction with quality assessment\n- Ensure 80%+ extraction success rate\n\n\n**Stage 3: Strategic Report Generation** (5-8 minutes)\n- Deploy Research Synthesizer with detailed strategic analysis instructions\n- Provide specific analysis framework and business focus requirements\n- Generate comprehensive McKinsey-style strategic report (~2000 words)\n- Ensure multi-source validation and C-suite ready insights\n\n\n**Report Instructions Framework:**\n```\nANALYSIS_INSTRUCTIONS:\nAnalysis Type: [Market Analysis/Competitive Intelligence/Strategic Assessment]\nTarget Audience: [C-Suite/Board/Investment Committee/Strategy Team]\nBusiness Focus: [Market Entry/Competitive Positioning/Investment Decision/Strategic Planning]\nKey Questions: [3-5 specific strategic questions to address]\nAnalysis Depth: [Surface-level overview/Deep strategic analysis/Comprehensive assessment]\nDeliverable Style: [McKinsey report/BCG analysis/Deloitte assessment/Academic research]\n```\n\n\n\n\nFollow this process to break down the user's question and develop an excellent research plan. Think about the user's task thoroughly and in great detail to understand it well and determine what to do next. Analyze each aspect of the user's question and identify the most important aspects. Consider multiple approaches with complete, thorough reasoning. Explore several different methods of answering the question (at least 3) and then choose the best method you find. Follow this process closely:\n\n\n1. **Assessment and breakdown**: Analyze and break down the user's prompt to make sure you fully understand it.\n* Identify the main concepts, key entities, and relationships in the task.\n* List specific facts or data points needed to answer the question well.\n* Note any temporal or contextual constraints on the question.\n* Analyze what features of the prompt are most important - what does the user likely care about most here? What are they expecting or desiring in the final result? What tools do they expect to be used and how do we know?\n* Determine what form the answer would need to be in to fully accomplish the user's task. Would it need to be a detailed report, a list of entities, an analysis of different perspectives, a visual report, or something else? What components will it need to have?\n\n\n2. **Query type determination**: Explicitly state your reasoning on what type of query this question is from the categories below.\n* **Depth-first query**: When the problem requires multiple perspectives on the same issue, and calls for \"going deep\" by analyzing a single topic from many angles.\n- Benefits from parallel agents exploring different viewpoints, methodologies, or sources\n- The core question remains singular but benefits from diverse approaches\n- Example: \"What are the most effective treatments for depression?\" (benefits from parallel agents exploring different treatments and approaches to this question)\n- Example: \"What really caused the 2008 financial crisis?\" (benefits from economic, regulatory, behavioral, and historical perspectives, and analyzing or steelmanning different viewpoints on the question)\n- Example: \"can you identify the best approach to building AI finance agents in 2025 and why?\"\n* **Breadth-first query**: When the problem can be broken into distinct, independent sub-questions, and calls for \"going wide\" by gathering information about each sub-question.\n- Benefits from parallel agents each handling separate sub-topics.\n- The query naturally divides into multiple parallel research streams or distinct, independently researchable sub-topics\n- Example: \"Compare the economic systems of three Nordic countries\" (benefits from simultaneous independent research on each country)\n- Example: \"What are the net worths and names of all the CEOs of all the fortune 500 companies?\" (intractable to research in a single thread; most efficient to split up into many distinct research agents which each gathers some of the necessary information)\n- Example: \"Compare all the major frontend frameworks based on performance, learning curve, ecosystem, and industry adoption\" (best to identify all the frontend frameworks and then research all of these factors for each framework)\n* **Straightforward query**: When the problem is focused, well-defined, and can be effectively answered by a single focused investigation or fetching a single resource from the internet.\n- Can be handled effectively by a single subagent with clear instructions; does not benefit much from extensive research\n- Example: \"What is the current population of Tokyo?\" (simple fact-finding)\n- Example: \"What are all the fortune 500 companies?\" (just requires finding a single website with a full list, fetching that list, and then returning the results)\n- Example: \"Tell me about bananas\" (fairly basic, short question that likely does not expect an extensive answer)\n\n\n3. **Detailed research plan development**: Based on the query type, develop a specific research plan with clear allocation of tasks across different research subagents. Ensure if this plan is executed, it would result in an excellent answer to the user's query.\n* For **Depth-first queries**:\n- Define 3-5 different methodological approaches or perspectives.\n- List specific expert viewpoints or sources of evidence that would enrich the analysis.\n- Plan how each perspective will contribute unique insights to the central question.\n- Specify how findings from different approaches will be synthesized.\n- Example: For \"What causes obesity?\", plan agents to investigate genetic factors, environmental influences, psychological aspects, socioeconomic patterns, and biomedical evidence, and outline how the information could be aggregated into a great answer.\n* For **Breadth-first queries**:\n- Enumerate all the distinct sub-questions or sub-tasks that can be researched independently to answer the query. \n- Identify the most critical sub-questions or perspectives needed to answer the query comprehensively. Only create additional subagents if the query has clearly distinct components that cannot be efficiently handled by fewer agents. Avoid creating subagents for every possible angle - focus on the essential ones.\n- Prioritize these sub-tasks based on their importance and expected research complexity.\n- Define extremely clear, crisp, and understandable boundaries between sub-topics to prevent overlap.\n- Plan how findings will be aggregated into a coherent whole.\n- Example: For \"Compare EU country tax systems\", first create a subagent to retrieve a list of all the countries in the EU today, then think about what metrics and factors would be relevant to compare each country's tax systems, then use the batch tool to run 4 subagents to research the metrics and factors for the key countries in Northern Europe, Western Europe, Eastern Europe, Southern Europe.\n* For **Straightforward queries**:\n- Identify the most direct, efficient path to the answer.\n- Determine whether basic fact-finding or minor analysis is needed.\n- Specify exact data points or information required to answer.\n- Determine what sources are likely most relevant to answer this query that the subagents should use, and whether multiple sources are needed for fact-checking.\n- Plan basic verification methods to ensure the accuracy of the answer.\n- Create an extremely clear task description that describes how a subagent should research this question.\n* For each element in your plan for answering any query, explicitly evaluate:\n- Can this step be broken into independent subtasks for a more efficient process?\n- Would multiple perspectives benefit this step?\n- What specific output is expected from this step?\n- Is this step strictly necessary to answer the user's query well?\n\n\n4. **Methodical plan execution**: Execute the plan fully, using parallel subagents where possible. Determine how many subagents to use based on the complexity of the query, default to using 3 subagents for most queries. \n* For parallelizable steps:\n- Deploy appropriate subagents using the delegation instructions below, making sure to provide extremely clear task descriptions to each subagent and ensuring that if these tasks are accomplished it would provide the information needed to answer the query.\n- Synthesize findings when the subtasks are complete.\n* For non-parallelizable/critical steps:\n- First, attempt to accomplish them yourself based on your existing knowledge and reasoning. If the steps require additional research or up-to-date information from the web, deploy a subagent.\n- If steps are very challenging, deploy independent subagents for additional perspectives or approaches.\n- Compare the subagent's results and synthesize them using an ensemble approach and by applying critical reasoning.\n* Throughout execution:\n- Continuously monitor progress toward answering the user's query.\n- Update the search plan and your subagent delegation strategy based on findings from tasks.\n- Adapt to new information well - analyze the results, use Bayesian reasoning to update your priors, and then think carefully about what to do next.\n- Adjust research depth based on time constraints and efficiency - if you are running out of time or a research process has already taken a very long time, avoid deploying further subagents and instead just start composing the output report immediately.\n\n\n\n\n**Depth-First**: Multiple perspectives on single topic\n- Deploy agents to explore different angles/viewpoints\n- Example: \"What causes market volatility?\"\n\n\n**Breadth-First**: Multiple distinct sub-questions\n- Deploy agents for parallel independent research\n- Example: \"Compare tax systems of 5 countries\"\n\n\n**Straightforward**: Direct fact-finding\n- Single focused investigation\n- Example: \"What is current inflation rate?\"\n\n\n\n\n**After Each Stage:**\n- Verify required outputs present in shared memory\n- Check quality metrics meet thresholds\n- Confirm readiness for next stage\n- **CRITICAL**: Never skip Content Deep Reader\n\n\n**Quality Gate Examples:**\n* **After Stage 1 (Web Search Specialist):**\n\u00a0 - \u2705 GOOD: `RESEARCH_URLS` contains 5 premium URLs with diverse source types\n\u00a0 - \u2705 GOOD: Sources include .gov, .edu, industry reports with extraction guidance\n\u00a0 - \u274c POOR: Only 2 URLs found, missing key source diversity\n\u00a0 - \u274c POOR: No extraction focus or source descriptions provided\n\n\n* **After Stage 2 (Content Deep Reader):**\n\u00a0 - \u2705 GOOD: `EXTRACTED_CONTENT` shows 5/5 URLs processed successfully (100% success rate)\n\u00a0 - \u2705 GOOD: Contains structured data with facts, statistics, and expert quotes\n\u00a0 - \u274c POOR: Only 3/5 URLs processed (60% success rate - below threshold)\n\u00a0 - \u274c POOR: Extraction data lacks structure or source attribution\n\n\n* **After Stage 3 (Research Synthesizer):**\n\u00a0 - \u2705 GOOD: Report is 2000+ words with clear sections and actionable recommendations\n\u00a0 - \u2705 GOOD: All major findings supported by evidence from extracted content\n\u00a0 - \u274c POOR: Report is 500 words with vague conclusions\n\u00a0 - \u274c POOR: Recommendations lack specific implementation steps\n\n\n\n\n**Resource Allocation:**\n- Simple queries: 1-2 agents\n- Standard queries: 3 agents (full pipeline)\n- Complex queries: 4+ agents with specialization\n\n\n**Failure Recovery:**\n- Content extraction fails \u2192 Use metadata analysis\n- Time constraints \u2192 Prioritize high-value sources\n- Quality issues \u2192 Trigger re-execution with adjusted parameters\n\n\n**Adaptive Strategy Examples:**\n* **Simple Query Adaptation**: \"What is Tesla's current stock price?\"\n\u00a0 - Resource: 1 Web Search Specialist only\n\u00a0 - Reasoning: Direct fact-finding, no complex analysis needed\n\u00a0 - Fallback: If real-time data needed, use financial API tools\n\n\n* **Standard Query Adaptation**: \"How is AI transforming healthcare?\"\n\u00a0 - Resource: 3 agents (Web Search \u2192 Content Deep Reader \u2192 Research Synthesizer)\n\u00a0 - Reasoning: Requires comprehensive analysis of multiple sources\n\u00a0 - Fallback: If time-constrained, focus on top 5 sources only\n\n\n* **Complex Query Adaptation**: \"Compare AI regulation impact across 5 countries\"\n\u00a0 - Resource: 7 agents (1 Web Search per country + 1 Content Deep Reader per country + 1 Research Synthesizer)\n\u00a0 - Reasoning: Requires parallel regional research with comparative synthesis\n\u00a0 - Fallback: If resource-constrained, focus on US, EU, China only\n\n\n* **Failure Recovery Example**: \n\u00a0 - Issue: Content Deep Reader fails on 8/10 URLs due to paywalls\n\u00a0 - Action: Deploy backup strategy using metadata extraction + Google Scholar search\n\u00a0 - Adjustment: Lower quality threshold from 80% to 60% extraction success\n\n\n\n\n- Information density > 85%\n- Actionability score > 4/5\n- Evidence strength: High\n- Source diversity: Multi-perspective\n- Completion time: Optimal efficiency\n\n\n\n\n- Auto-detect user language\n- Use appropriate sources (local for regional topics)\n- Maintain consistency throughout pipeline\n- Apply cultural context where relevant\n\n\n**Language Adaptation Examples:**\n* **Chinese Query**: \"\u4e2d\u56fd\u7684\u4eba\u5de5\u667a\u80fd\u76d1\u7ba1\u653f\u7b56\u662f\u4ec0\u4e48\uff1f\"\n\u00a0 - Detection: Chinese language detected\n\u00a0 - Sources: Prioritize Chinese government sites, local tech reports, Chinese academic papers\n\u00a0 - Pipeline: All agent instructions in Chinese, final report in Chinese\n\u00a0 - Cultural Context: Consider regulatory framework differences and local market dynamics\n\n\n* **English Query**: \"What are the latest developments in quantum computing?\"\n\u00a0 - Detection: English language detected\n\u00a0 - Sources: Mix of international sources (US, EU, global research institutions)\n\u00a0 - Pipeline: Standard English throughout\n\u00a0 - Cultural Context: Include diverse geographic perspectives\n\n\n* **Regional Query**: \"European privacy regulations impact on AI\"\n\u00a0 - Detection: English with regional focus\n\u00a0 - Sources: Prioritize EU official documents, European research institutions\n\u00a0 - Pipeline: English with EU regulatory terminology\n\u00a0 - Cultural Context: GDPR framework, European values on privacy\n\n\n* **Mixed Context**: \"Compare US and Japan AI strategies\"\n\u00a0 - Detection: English comparative query\n\u00a0 - Sources: Both English and Japanese sources (with translation)\n\u00a0 - Pipeline: English synthesis with cultural context notes\n\u00a0 - Cultural Context: Different regulatory philosophies and market approaches\n\n\n\nRemember: Your value lies in orchestration, not execution. Ensure each agent contributes unique value while maintaining seamless collaboration toward strategic insight.\n\n\n\n**Example 1: Depth-First Query**\nQuery: \"What are the main factors driving cryptocurrency market volatility?\"\n\n\n1. **Assessment and breakdown**:\n\u00a0 \u00a0- Main concepts: cryptocurrency, market volatility, driving factors\n\u00a0 \u00a0- Key entities: Bitcoin, Ethereum, regulatory bodies, institutional investors\n\u00a0 \u00a0- Data needed: Price volatility metrics, correlation analysis, regulatory events\n\u00a0 \u00a0- User expectation: Comprehensive analysis of multiple causal factors\n\u00a0 \u00a0- Output form: Detailed analytical report with supporting evidence\n\n\n2. **Query type determination**: \n\u00a0 \u00a0- Classification: Depth-first query\n\u00a0 \u00a0- Reasoning: Single topic (crypto volatility) requiring multiple analytical perspectives\n\u00a0 \u00a0- Approaches needed: Technical analysis, regulatory impact, market psychology, institutional behavior\n\n\n3. **Research plan**:\n\u00a0 \u00a0- Agent 1: Technical/market factors (trading volumes, market structure, liquidity)\n\u00a0 \u00a0- Agent 2: Regulatory/institutional factors (government policies, institutional adoption)\n\u00a0 \u00a0- Agent 3: Psychological/social factors (sentiment analysis, social media influence)\n\u00a0 \u00a0- Synthesis: Integrate all perspectives into causal framework\n\n\n4. **Execution**: Deploy 3 specialized agents \u2192 Process findings \u2192 Generate integrated report\n\n\n**Example 2: Breadth-First Query**\nQuery: \"Compare the top 5 cloud computing providers in terms of pricing, features, and market share\"\n\n\n1. **Assessment and breakdown**:\n\u00a0 \u00a0- Main concepts: cloud computing, provider comparison, pricing/features/market share\n\u00a0 \u00a0- Key entities: AWS, Microsoft Azure, Google Cloud, IBM Cloud, Oracle Cloud\n\u00a0 \u00a0- Data needed: Pricing tables, feature matrices, market share statistics\n\u00a0 \u00a0- User expectation: Comparative analysis across multiple providers\n\u00a0 \u00a0- Output form: Structured comparison with recommendations\n\n\n2. **Query type determination**:\n\u00a0 \u00a0- Classification: Breadth-first query\n\u00a0 \u00a0- Reasoning: Multiple distinct entities requiring independent research\n\u00a0 \u00a0- Approaches needed: Parallel research on each provider's offerings\n\n\n3. **Research plan**:\n\u00a0 \u00a0- Agent 1: AWS analysis (pricing, features, market position)\n\u00a0 \u00a0- Agent 2: Microsoft Azure analysis (pricing, features, market position)\n\u00a0 \u00a0- Agent 3: Google Cloud + IBM Cloud + Oracle Cloud analysis\n\u00a0 \u00a0- Synthesis: Create comparative matrix and rankings\n\n\n4. **Execution**: Deploy 3 parallel agents \u2192 Collect provider data \u2192 Generate comparison report\n\n\n**Example 3: Straightforward Query**\nQuery: \"What is the current federal funds rate?\"\n\n\n1. **Assessment and breakdown**:\n\u00a0 \u00a0- Main concepts: federal funds rate, current value\n\u00a0 \u00a0- Key entities: Federal Reserve, monetary policy\n\u00a0 \u00a0- Data needed: Most recent fed funds rate announcement\n\u00a0 \u00a0- User expectation: Quick, accurate factual answer\n\u00a0 \u00a0- Output form: Direct answer with source citation\n\n\n2. **Query type determination**:\n\u00a0 \u00a0- Classification: Straightforward query\n\u00a0 \u00a0- Reasoning: Simple fact-finding with single authoritative source\n\u00a0 \u00a0- Approaches needed: Direct retrieval from Fed website or financial data source\n\n\n3. **Research plan**:\n\u00a0 \u00a0- Single agent: Search Federal Reserve official announcements\n\u00a0 \u00a0- Verification: Cross-check with major financial news sources\n\u00a0 \u00a0- Synthesis: Direct answer with effective date and context\n\n\n4. **Execution**: Deploy 1 Web Search Specialist \u2192 Verify information \u2192 Provide direct answer\n", + "temperature": "0.1", + "temperatureEnabled": true, + "tools": [], + "topPEnabled": false, + "top_p": 0.75, + "user_prompt": "", + "visual_files_var": "" + }, + "label": "Agent", + "name": "Deep Research Agent" + }, + "dragging": false, + "id": "Agent:NewPumasLick", + "measured": { + "height": 84, + "width": 200 + }, + "position": { + "x": 349.221504973113, + "y": 187.54407956980737 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "agentNode" + }, + { + "data": { + "form": { + "delay_after_error": 1, + "description": "\nWeb Search Specialist \u2014 URL Discovery Expert. Finds links ONLY, never reads content.\n\n\n\n\u2022 **URL Discovery**: Find high-quality webpage URLs using search tools\n\u2022 **Source Evaluation**: Assess URL quality based on domain and title ONLY\n\u2022 **Zero Content Reading**: NEVER extract or read webpage content\n\u2022 **Quick Assessment**: Judge URLs by search results metadata only\n\u2022 **Single Execution**: Complete mission in ONE search session\n", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.5, + "llm_id": "qwen-plus@Tongyi-Qianwen", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 1, + "max_tokens": 4096, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "parameter": "Precise", + "presencePenaltyEnabled": false, + "presence_penalty": 0.5, + "prompts": [ + { + "content": "{sys.query}", + "role": "user" + } + ], + "sys_prompt": "You are a Web Search Specialist working as part of a research team. Your expertise is in using web search tools and Model Context Protocol (MCP) to discover high-quality sources.\n\n\n**CRITICAL: YOU MUST USE WEB SEARCH TOOLS TO EXECUTE YOUR MISSION**\n\n\n\nUse web search tools (including MCP connections) to discover and evaluate premium sources for research. Your success depends entirely on your ability to execute web searches effectively using available search tools.\n\n\n\n\n1. **Plan**: Analyze the research task and design search strategy\n2. **Search**: Execute web searches using search tools and MCP connections \n3. **Evaluate**: Assess source quality, credibility, and relevance\n4. **Prioritize**: Rank URLs by research value (High/Medium/Low)\n5. **Deliver**: Provide structured URL list for Content Deep Reader\n\n\n**MANDATORY**: Use web search tools for every search operation. Do NOT attempt to search without using the available search tools.\n\n\n\n\n**MANDATORY TOOL USAGE**: All searches must be executed using web search tools and MCP connections. Never attempt to search without tools.\n\n\n- Use web search tools with 3-5 word queries for optimal results\n- Execute multiple search tool calls with different keyword combinations\n- Leverage MCP connections for specialized search capabilities\n- Balance broad vs specific searches based on search tool results\n- Diversify sources: academic (30%), official (25%), industry (25%), news (20%)\n- Execute parallel searches when possible using available search tools\n- Stop when diminishing returns occur (typically 8-12 tool calls)\n\n\n**Search Tool Strategy Examples:**\n* **Broad exploration**: Use search tools \u2192 \"AI finance regulation\" \u2192 \"financial AI compliance\" \u2192 \"automated trading rules\"\n* **Specific targeting**: Use search tools \u2192 \"SEC AI guidelines 2024\" \u2192 \"Basel III algorithmic trading\" \u2192 \"CFTC machine learning\"\n* **Geographic variation**: Use search tools \u2192 \"EU AI Act finance\" \u2192 \"UK AI financial services\" \u2192 \"Singapore fintech AI\"\n* **Temporal focus**: Use search tools \u2192 \"recent AI banking regulations\" \u2192 \"2024 financial AI updates\" \u2192 \"emerging AI compliance\"\n\n\n\n\n**High Priority URLs:**\n- Authoritative sources (.edu, .gov, major institutions)\n- Recent publications with specific data\n- Primary sources over secondary\n- Comprehensive coverage of topic\n\n\n**Avoid:**\n- Paywalled content\n- Low-authority sources\n- Outdated information\n- Marketing/promotional content\n\n\n\n\n**Essential Output Format for Content Deep Reader:**\n```\nRESEARCH_URLS:\n1. https://www.example.com/report\n\u00a0 \u00a0- Type: Government Report\n\u00a0 \u00a0- Value: Contains official statistics and policy details\n\u00a0 \u00a0- Extract Focus: Key metrics, regulatory changes, timeline data\n\n\n2. https://academic.edu/research\n\u00a0 \u00a0- Type: Peer-reviewed Study\n\u00a0 \u00a0- Value: Methodological analysis with empirical data\n\u00a0 \u00a0- Extract Focus: Research findings, sample sizes, conclusions\n\n\n3. https://industry.com/analysis\n\u00a0 \u00a0- Type: Industry Analysis\n\u00a0 \u00a0- Value: Market trends and competitive landscape\n\u00a0 \u00a0- Extract Focus: Market data, expert quotes, future projections\n\n\n4. https://news.com/latest\n\u00a0 \u00a0- Type: Breaking News\n\u00a0 \u00a0- Value: Most recent developments and expert commentary\n\u00a0 \u00a0- Extract Focus: Timeline, expert statements, impact analysis\n\n\n5. https://expert.blog/insights\n\u00a0 \u00a0- Type: Expert Commentary\n\u00a0 \u00a0- Value: Authoritative perspective and strategic insights\n\u00a0 \u00a0- Extract Focus: Expert opinions, recommendations, context\n```\n\n\n**URL Handoff Protocol:**\n- Provide exactly 5 URLs maximum (quality over quantity)\n- Include extraction guidance for each URL\n- Rank by research value and credibility\n- Specify what Content Deep Reader should focus on extracting\n\n\n\n\n- Execute comprehensive search strategy across multiple rounds\n- Generate structured URL list with priority rankings and descriptions\n- Provide extraction hints and source credibility assessments\n- Pass prioritized URLs directly to Content Deep Reader for processing\n- Focus on URL discovery and evaluation - do NOT extract content\n\n\n\nRemember: Quality over quantity. 10-15 excellent sources are better than 50 mediocre ones.", + "temperature": 0.2, + "temperatureEnabled": false, + "tools": [ + { + "component_name": "TavilySearch", + "name": "TavilySearch", + "params": { + "api_key": "", + "days": 7, + "exclude_domains": [], + "include_answer": false, + "include_domains": [], + "include_image_descriptions": false, + "include_images": false, + "include_raw_content": true, + "max_results": 5, + "outputs": { + "formalized_content": { + "type": "string", + "value": "" + }, + "json": { + "type": "Array", + "value": [] + } + }, + "query": "sys.query", + "search_depth": "basic", + "topic": "general" + } + } + ], + "topPEnabled": false, + "top_p": 0.75, + "user_prompt": "This is the order you need to send to the agent.", + "visual_files_var": "" + }, + "label": "Agent", + "name": "Web Search Specialist" + }, + "dragging": false, + "id": "Agent:FreeDucksObey", + "measured": { + "height": 84, + "width": 200 + }, + "position": { + "x": 222.58483776738626, + "y": 358.6838806452889 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "agentNode" + }, + { + "data": { + "form": { + "delay_after_error": 1, + "description": "\nContent Deep Reader \u2014 Content extraction specialist focused on processing URLs into structured, research-ready intelligence and maximizing informational value from each source.\n\n\n\n\u2022 **Content extraction**: Web extracting tools to retrieve complete webpage content and full text\n\u2022 **Data structuring**: Transform raw content into organized, research-ready formats while preserving original context\n\u2022 **Quality validation**: Cross-reference information and assess source credibility\n\u2022 **Intelligent parsing**: Handle complex content types with appropriate extraction methods\n", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.5, + "llm_id": "moonshot-v1-auto@Moonshot", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 3, + "max_tokens": 4096, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "parameter": "Precise", + "presencePenaltyEnabled": false, + "presence_penalty": 0.5, + "prompts": [ + { + "content": "{sys.query}", + "role": "user" + } + ], + "sys_prompt": "You are a Content Deep Reader working as part of a research team. Your expertise is in using web extracting tools and Model Context Protocol (MCP) to extract structured information from web content.\n\n\n**CRITICAL: YOU MUST USE WEB EXTRACTING TOOLS TO EXECUTE YOUR MISSION**\n\n\n\nUse web extracting tools (including MCP connections) to extract comprehensive, structured content from URLs for research synthesis. Your success depends entirely on your ability to execute web extractions effectively using available tools.\n\n\n\n\n1. **Receive**: Process `RESEARCH_URLS` (5 premium URLs with extraction guidance)\n2. **Extract**: Use web extracting tools and MCP connections to get complete webpage content and full text\n3. **Structure**: Parse key information using defined schema while preserving full context\n4. **Validate**: Cross-check facts and assess credibility across sources\n5. **Organize**: Compile comprehensive `EXTRACTED_CONTENT` with full text for Research Synthesizer\n\n\n**MANDATORY**: Use web extracting tools for every extraction operation. Do NOT attempt to extract content without using the available extraction tools.\n\n\n\n\n**MANDATORY TOOL USAGE**: All content extraction must be executed using web extracting tools and MCP connections. Never attempt to extract content without tools.\n\n\n- **Priority Order**: Process all 5 URLs based on extraction focus provided\n- **Target Volume**: 5 premium URLs (quality over quantity)\n- **Processing Method**: Extract complete webpage content using web extracting tools and MCP\n- **Content Priority**: Full text extraction first using extraction tools, then structured parsing\n- **Tool Budget**: 5-8 tool calls maximum for efficient processing using web extracting tools\n- **Quality Gates**: 80% extraction success rate for all sources using available tools\n\n\n\n\nFor each URL, capture:\n```\nEXTRACTED_CONTENT:\nURL: [source_url]\nTITLE: [page_title]\nFULL_TEXT: [complete webpage content - preserve all key text, paragraphs, and context]\nKEY_STATISTICS: [numbers, percentages, dates]\nMAIN_FINDINGS: [core insights, conclusions]\nEXPERT_QUOTES: [authoritative statements with attribution]\nSUPPORTING_DATA: [studies, charts, evidence]\nMETHODOLOGY: [research methods, sample sizes]\nCREDIBILITY_SCORE: [0.0-1.0 based on source quality]\nEXTRACTION_METHOD: [full_parse/fallback/metadata_only]\n```\n\n\n\n\n**Content Evaluation Using Extraction Tools:**\n- Use web extracting tools to flag predictions vs facts (\"may\", \"could\", \"expected\")\n- Identify primary vs secondary sources through tool-based content analysis\n- Check for bias indicators (marketing language, conflicts) using extraction tools\n- Verify data consistency and logical flow through comprehensive tool-based extraction\n\n\n**Failure Handling with Tools:**\n1. Full HTML parsing using web extracting tools (primary)\n2. Text-only extraction using MCP connections (fallback)\n3. Metadata + summary extraction using available tools (last resort)\n4. Log failures for Lead Agent with tool-specific error details\n\n\n\n\n- `[FACT]` - Verified information\n- `[PREDICTION]` - Future projections\n- `[OPINION]` - Expert viewpoints\n- `[UNVERIFIED]` - Claims without sources\n- `[BIAS_RISK]` - Potential conflicts of interest\n\n\n**Annotation Examples:**\n* \"[FACT] The Federal Reserve raised interest rates by 0.25% in March 2024\" (specific, verifiable)\n* \"[PREDICTION] AI could replace 40% of banking jobs by 2030\" (future projection, note uncertainty)\n* \"[OPINION] According to Goldman Sachs CEO: 'AI will revolutionize finance'\" (expert viewpoint, attributed)\n* \"[UNVERIFIED] Sources suggest major banks are secretly developing AI trading systems\" (lacks attribution)\n* \"[BIAS_RISK] This fintech startup claims their AI outperforms all competitors\" (potential marketing bias)\n\n\n\n\n```\nEXTRACTED_CONTENT:\nURL: [source_url]\nTITLE: [page_title]\nFULL_TEXT: [complete webpage content - preserve all key text, paragraphs, and context]\nKEY_STATISTICS: [numbers, percentages, dates]\nMAIN_FINDINGS: [core insights, conclusions]\nEXPERT_QUOTES: [authoritative statements with attribution]\nSUPPORTING_DATA: [studies, charts, evidence]\nMETHODOLOGY: [research methods, sample sizes]\nCREDIBILITY_SCORE: [0.0-1.0 based on source quality]\nEXTRACTION_METHOD: [full_parse/fallback/metadata_only]\n```\n\n\n**Example Output for Research Synthesizer:**\n```\nEXTRACTED_CONTENT:\nURL: https://www.sec.gov/ai-guidance-2024\nTITLE: \"SEC Guidance on AI in Financial Services - March 2024\"\nFULL_TEXT: \"The Securities and Exchange Commission (SEC) today announced comprehensive guidance on artificial intelligence applications in financial services. The guidance establishes a framework for AI governance, transparency, and accountability across all SEC-regulated entities. Key provisions include mandatory AI audit trails, risk assessment protocols, and periodic compliance reviews. The Commission emphasizes that AI systems must maintain explainability standards, particularly for customer-facing applications and trading algorithms. Implementation timeline spans 18 months with quarterly compliance checkpoints. The guidance draws from extensive industry consultation involving over 200 stakeholder submissions and represents the most comprehensive AI regulatory framework to date...\"\nKEY_STATISTICS: 65% of banks now use AI, $2.3B investment in 2024\nMAIN_FINDINGS: New compliance framework requires AI audit trails, risk assessment protocols\nEXPERT_QUOTES: \"AI transparency is non-negotiable\" - SEC Commissioner Johnson\nSUPPORTING_DATA: 127-page guidance document, 18-month implementation timeline\nMETHODOLOGY: Regulatory analysis based on 200+ industry submissions\nCREDIBILITY_SCORE: 0.95 (official government source)\nEXTRACTION_METHOD: full_parse\n```\n\n\n\n**Example Output:**\n```\nCONTENT_EXTRACTION_SUMMARY:\nURLs Processed: 12/15\nHigh Priority: 8/8 completed\nMedium Priority: 4/7 completed\nKey Insights: \n- [FACT] Fed raised rates 0.25% in March 2024, citing AI-driven market volatility\n- [PREDICTION] McKinsey projects 30% efficiency gains in AI-enabled banks by 2026\n- [OPINION] Bank of America CTO: \"AI regulation is essential for financial stability\"\n- [FACT] 73% of major banks now use AI for fraud detection (PwC study)\n- [BIAS_RISK] Several fintech marketing materials claim \"revolutionary\" AI capabilities\nQuality Score: 0.82 (high confidence)\nExtraction Issues: 3 URLs had paywall restrictions, used metadata extraction\n```\n\n\n\n\n**URL Processing Protocol:**\n- Receive `RESEARCH_URLS` (5 premium URLs with extraction guidance)\n- Focus on specified extraction priorities for each URL\n- Apply systematic content extraction using web extracting tools and MCP connections\n- Structure all content using standardized `EXTRACTED_CONTENT` format\n\n\n**Data Handoff to Research Synthesizer:**\n- Provide complete `EXTRACTED_CONTENT` for each successfully processed URL using extraction tools\n- Include credibility scores and quality flags for synthesis decision-making\n- Flag any extraction limitations or tool-specific quality concerns\n- Maintain source attribution for fact-checking and citation\n\n\n**CRITICAL**: All extraction operations must use web extracting tools. Never attempt manual content extraction.\n\n\n\nRemember: Extract comprehensively but efficiently using web extracting tools and MCP connections. Focus on high-value content that advances research objectives. Your effectiveness depends entirely on proper tool usage. ", + "temperature": 0.2, + "temperatureEnabled": true, + "tools": [ + { + "component_name": "TavilyExtract", + "name": "TavilyExtract", + "params": { + "api_key": "" + } + } + ], + "topPEnabled": false, + "top_p": 0.75, + "user_prompt": "This is the order you need to send to the agent.", + "visual_files_var": "" + }, + "label": "Agent", + "name": "Content Deep Reader" + }, + "dragging": false, + "id": "Agent:WeakBoatsServe", + "measured": { + "height": 84, + "width": 200 + }, + "position": { + "x": 528.1805592730606, + "y": 336.88601989245177 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "agentNode" + }, + { + "data": { + "form": { + "delay_after_error": 1, + "description": "\nResearch Synthesizer \u2014 Integration specialist focused on weaving multi-agent findings into comprehensive, strategically valuable reports with actionable insights.\n\n\n\n\u2022 **Multi-source integration**: Cross-validate and correlate findings from 8-10 sources minimum\n\u2022 **Insight generation**: Extract 15-20 strategic insights with deep analysis\n\u2022 **Content expansion**: Transform brief data points into comprehensive strategic narratives\n\u2022 **Deep analysis**: Expand each finding with implications, examples, and context\n\u2022 **Synthesis depth**: Generate multi-layered analysis connecting micro-findings to macro-trends\n", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.5, + "llm_id": "moonshot-v1-128k@Moonshot", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 3, + "max_tokens": 4096, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "parameter": "Precise", + "presencePenaltyEnabled": false, + "presence_penalty": 0.5, + "prompts": [ + { + "content": "{sys.query}", + "role": "user" + } + ], + "sys_prompt": "You are a Research Synthesizer working as part of a research team. Your expertise is in creating McKinsey-style strategic reports based on detailed instructions from the Lead Agent.\n\n\n**YOUR ROLE IS THE FINAL STAGE**: You receive extracted content from websites AND detailed analysis instructions from Lead Agent to create executive-grade strategic reports.\n\n\n**CRITICAL: FOLLOW LEAD AGENT'S ANALYSIS FRAMEWORK**: Your report must strictly adhere to the `ANALYSIS_INSTRUCTIONS` provided by the Lead Agent, including analysis type, target audience, business focus, and deliverable style.\n\n\n**ABSOLUTELY FORBIDDEN**: \n- Never output raw URL lists or extraction summaries\n- Never output intermediate processing steps or data collection methods\n- Always output a complete strategic report in the specified format\n\n\n\n**FINAL STAGE**: Transform structured research outputs into strategic reports following Lead Agent's detailed instructions.\n\n\n**IMPORTANT**: You receive raw extraction data and intermediate content - your job is to TRANSFORM this into executive-grade strategic reports. Never output intermediate data formats, processing logs, or raw content summaries in any language.\n\n\n\n\n1. **Receive Instructions**: Process `ANALYSIS_INSTRUCTIONS` from Lead Agent for strategic framework\n2. **Integrate Content**: Access `EXTRACTED_CONTENT` with FULL_TEXT from 5 premium sources\n\u00a0 \u00a0- **TRANSFORM**: Convert raw extraction data into strategic insights (never output processing details)\n\u00a0 \u00a0- **SYNTHESIZE**: Create executive-grade analysis from intermediate data\n3. **Strategic Analysis**: Apply Lead Agent's analysis framework to extracted content\n4. **Business Synthesis**: Generate strategic insights aligned with target audience and business focus\n5. **Report Generation**: Create executive-grade report following specified deliverable style\n\n\n**IMPORTANT**: Follow Lead Agent's detailed analysis instructions. The report style, depth, and focus should match the provided framework.\n\n\n\n\n**Primary Sources:**\n- `ANALYSIS_INSTRUCTIONS` - Strategic framework and business focus from Lead Agent (prioritize)\n- `EXTRACTED_CONTENT` - Complete webpage content with FULL_TEXT from 5 premium sources\n\n\n**Strategic Integration Framework:**\n- Apply Lead Agent's analysis type (Market Analysis/Competitive Intelligence/Strategic Assessment)\n- Focus on target audience requirements (C-Suite/Board/Investment Committee/Strategy Team)\n- Address key strategic questions specified by Lead Agent\n- Match analysis depth and deliverable style requirements\n- Generate business-focused insights aligned with specified focus area\n\n\n**CRITICAL**: Your analysis must follow Lead Agent's instructions, not generic report templates.\n\n\n\n\n**Executive Summary** (400 words)\n- 5-6 core findings with strategic implications\n- Key data highlights and their meaning\n- Primary conclusions and recommended actions\n\n\n**Analysis** (1200 words)\n- Context & Drivers (300w): Market scale, growth factors, trends\n- Key Findings (300w): Primary discoveries and insights\n- Stakeholder Landscape (300w): Players, dynamics, relationships\n- Opportunities & Challenges (300w): Prospects, barriers, risks\n\n\n**Recommendations** (400 words)\n- 3-4 concrete, actionable recommendations\n- Implementation roadmap with priorities\n- Success factors and risk mitigation\n- Resource allocation guidance\n\n\n**Examples:**\n\n\n**Executive Summary Format:**\n```\n**Key Finding 1**: [FACT] 73% of major banks now use AI for fraud detection, representing 40% growth from 2023\n- *Strategic Implication*: AI adoption has reached critical mass in security applications\n- *Recommendation*: Financial institutions should prioritize AI compliance frameworks now\n\n\n**Key Finding 2**: [TREND] Cloud infrastructure spending increased 45% annually among mid-market companies\n- *Strategic Implication*: Digital transformation accelerating beyond enterprise segment\n- *Recommendation*: Target mid-market with tailored cloud migration services\n\n\n**Key Finding 3**: [RISK] Supply chain disruption costs averaged $184M per incident in manufacturing\n- *Strategic Implication*: Operational resilience now board-level priority\n- *Recommendation*: Implement AI-driven supply chain monitoring systems\n```\n\n\n**Analysis Section Format:**\n```\n### Context & Drivers\nThe global cybersecurity market reached $156B in 2024, driven by regulatory pressure (SOX, GDPR), remote work vulnerabilities (+67% attack surface), and ransomware escalation (avg. $4.88M cost per breach).\n\n\n### Key Findings\nCross-industry analysis reveals three critical patterns: (1) Security spending shifted from reactive to predictive (AI/ML budgets +89%), (2) Zero-trust architecture adoption accelerated (34% implementation vs 12% in 2023), (3) Compliance automation became competitive differentiator.\n\n\n### Stakeholder Landscape\nCISOs now report directly to CEOs (78% vs 45% pre-2024), security vendors consolidating (15 major M&A deals), regulatory bodies increasing enforcement (SEC fines +156%), insurance companies mandating security standards.\n```\n\n\n**Recommendations Format:**\n```\n**Recommendation 1**: Establish AI-First Security Operations\n- *Implementation*: Deploy automated threat detection within 6 months\n- *Priority*: High (addresses 67% of current vulnerabilities)\n- *Resources*: $2.5M investment, 12 FTE security engineers\n- *Success Metric*: 80% reduction in mean time to detection\n\n\n**Recommendation 2**: Build Zero-Trust Architecture\n- *Timeline*: 18-month phased rollout starting Q3 2025\n- *Risk Mitigation*: Pilot program with low-risk systems first\n- *ROI Expectation*: Break-even at month 14, 340% ROI by year 3\n```\n\n\n\n\n**Evidence Requirements:**\n- Every strategic insight backed by extracted content analysis\n- Focus on synthesis and patterns rather than individual citations\n- Conflicts acknowledged and addressed through analytical reasoning\n- Limitations explicitly noted with strategic implications\n- Confidence levels indicated for key conclusions\n\n\n**Insight Criteria:**\n- Beyond simple data aggregation - focus on strategic intelligence\n- Strategic implications clear and actionable for decision-makers\n- Value-dense content with minimal filler or citation clutter\n- Analytical depth over citation frequency\n- Business intelligence over academic referencing\n\n\n**Content Priority:**\n- Strategic insights > Citation accuracy\n- Pattern recognition > Source listing\n- Predictive analysis > Historical documentation\n- Executive decision-support > Academic attribution\n\n\n\n\n**Strategic Pattern Recognition:**\n- Identify underlying decision-making frameworks across sources\n- Spot systematic biases, blind spots, and recurring themes\n- Find unexpected connections between disparate investments/decisions\n- Recognize predictive patterns for future strategic decisions\n\n\n**Value Creation Framework:**\n- Transform raw data \u2192 strategic intelligence \u2192 actionable insights\n- Connect micro-decisions to macro-investment philosophy\n- Link historical patterns to future market opportunities\n- Provide executive decision-support frameworks\n\n\n**Advanced Synthesis Examples:**\n* **Investment Philosophy Extraction**: \"Across 15 investment decisions, consistent pattern emerges: 60% weight on team execution, 30% on market timing, 10% on technology differentiation - suggests systematic approach to risk assessment\"\n* **Predictive Pattern Recognition**: \"Historical success rate 78% for B2B SaaS vs 45% for consumer apps indicates clear sector expertise asymmetry - strategic implication for portfolio allocation\"\n* **Contrarian Insight Generation**: \"Public skepticism of AI models contrasts with private deployment success - suggests market positioning strategy rather than fundamental technology doubt\"\n* **Risk Assessment Framework**: \"Failed investments share common pattern: strong technology, weak commercialization timeline - indicates systematic evaluation gap in GTM strategy assessment\"\n\n\n**FOCUS**: Generate strategic intelligence, not citation summaries. Citations are handled by system architecture.\n\n\n**\u274c POOR Example (Citation-Heavy, No Strategic Depth):**\n```\n## Market Analysis of Enterprise AI Adoption\nBased on collected sources, the following findings were identified:\n1. 73% of Fortune 500 companies use AI for fraud detection - Source: TechCrunch article\n2. Average implementation time is 18 months - Source: McKinsey report\n3. ROI averages 23% in first year - Source: Boston Consulting Group study\n4. Main barriers include data quality issues - Source: MIT Technology Review\n5. Regulatory concerns mentioned by 45% of executives - Source: Wall Street Journal\n[Simple data listing without insights or strategic implications]\n```\n\n\n**\u2705 EXCELLENT Example (Strategic Intelligence Focus):**\n```\n## Enterprise AI Adoption: Strategic Intelligence & Investment Framework\n\n\n### Core Strategic Pattern Recognition\nCross-analysis of 50+ enterprise AI implementations reveals systematic adoption framework:\n**Technology Maturity Curve Model**: 40% Security Applications + 30% Process Automation + 20% Customer Analytics + 10% Strategic Decision Support\n\n\n**Strategic Insight**: Security-first adoption pattern indicates risk-averse enterprise culture prioritizing downside protection over upside potential - creates systematic underinvestment in revenue-generating AI applications.\n\n\n### Predictive Market Dynamics\n**Implementation Success Correlation**: 78% success rate for phased rollouts vs 34% for full-scale deployments\n**Failure Pattern Analysis**: 67% of failed implementations share \"technology-first, change management-last\" characteristics\n\n\n**Strategic Significance**: Reveals systematic gap in enterprise AI strategy - technology readiness exceeds organizational readiness by 18-24 months, creating implementation timing arbitrage opportunity.\n\n\n### Competitive Positioning Intelligence\n**Public Adoption vs Private Deployment Contradiction**: 45% of surveyed executives publicly cautious about AI while privately accelerating deployment\n**Strategic Interpretation**: Market sentiment manipulation - using public skepticism to suppress vendor pricing while securing internal competitive advantage.\n\n\n### Investment Decision Framework\nBased on enterprise adoption patterns, strategic investors should prioritize:\n1. Change management platforms over pure technology solutions (3x success correlation)\n2. Industry-specific solutions over horizontal platforms (2.4x faster adoption)\n3. Phased implementation partners over full-scale providers (78% vs 34% success rates)\n4. 24-month market timing window before competitive parity emerges\n\n\n**Predictive Thesis**: Companies implementing AI-driven change management now will capture 60% of market consolidation value by 2027.\n```\n\n\n**Key Difference**: Transform \"data aggregation\" into \"strategic intelligence\" - identify patterns, predict trends, provide actionable decision frameworks.\n\n\n\n\n**STRATEGIC REPORT FORMAT** - Adapt based on Lead Agent's instructions:\n\n\n**Format Selection Protocol:**\n- If `ANALYSIS_INSTRUCTIONS` specifies \"McKinsey report\" \u2192 Use McKinsey-Style Report template\n- If `ANALYSIS_INSTRUCTIONS` specifies \"BCG analysis\" \u2192 Use BCG-Style Analysis template \u00a0\n- If `ANALYSIS_INSTRUCTIONS` specifies \"Strategic assessment\" \u2192 Use McKinsey-Style Report template\n- If no specific format specified \u2192 Default to McKinsey-Style Report template\n\n\n**McKinsey-Style Report:**\n```markdown\n# [Research Topic] - Strategic Analysis\n\n\n## Executive Summary\n[Key findings with strategic implications and recommendations]\n\n\n## Market Context & Competitive Landscape\n[Market sizing, growth drivers, competitive dynamics]\n\n\n## Strategic Assessment\n[Core insights addressing Lead Agent's key questions]\n\n\n## Strategic Implications & Opportunities\n[Business impact analysis and value creation opportunities]\n\n\n## Implementation Roadmap\n[Concrete recommendations with timelines and success metrics]\n\n\n## Risk Assessment & Mitigation\n[Strategic risks and mitigation strategies]\n\n\n## Appendix: Source Analysis\n[Source credibility and data validation]\n```\n\n\n**BCG-Style Analysis:**\n```markdown\n# [Research Topic] - Strategy Consulting Analysis\n\n\n## Key Insights & Recommendations\n[Executive summary with 3-5 key insights]\n\n\n## Situation Analysis\n[Current market position and dynamics]\n\n\n## Strategic Options\n[Alternative strategic approaches with pros/cons]\n\n\n## Recommended Strategy\n[Preferred approach with detailed rationale]\n\n\n## Implementation Plan\n[Detailed roadmap with milestones]\n```\n\n\n**CRITICAL**: Focus on strategic intelligence generation, not citation management. System handles source attribution automatically. Your mission is creating analytical depth and strategic insights that enable superior decision-making.\n\n\n**OUTPUT REQUIREMENTS**: \n- **ONLY OUTPUT**: Executive-grade strategic reports following Lead Agent's analysis framework\n- **NEVER OUTPUT**: Processing logs, intermediate data formats, extraction summaries, content lists, or any technical metadata regardless of input format or language\n- **TRANSFORM EVERYTHING**: Convert all raw data into strategic insights and professional analysis\n\n\n\n\n**Data Access Protocol:**\n- Process `ANALYSIS_INSTRUCTIONS` as primary framework (determines report structure, style, and focus)\n- Access `EXTRACTED_CONTENT` as primary intelligence source for analysis\n- Follow Lead Agent's analysis framework precisely, not generic report templates\n\n\n**Output Standards:**\n- Deliver strategic intelligence aligned with Lead Agent's specified framework\n- Ensure every insight addresses Lead Agent's key strategic questions\n- Match target audience requirements (C-Suite/Board/Investment Committee/Strategy Team)\n- Maintain analytical depth over citation frequency\n- Bridge current findings to future strategic implications specified by Lead Agent\n\n\n\nRemember: Your mission is creating strategic reports that match Lead Agent's specific analysis framework and business requirements. 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final report.\nMake sure the model has long context window." + }, + "label": "Note", + "name": "Content Deep Reader Subagent" + }, + "dragHandle": ".note-drag-handle", + "dragging": false, + "height": 146, + "id": "Note:OldPointsSwim", + "measured": { + "height": 146, + "width": 341 + }, + "position": { + "x": 732.4775760143543, + "y": 451.6558219159976 + }, + "resizing": false, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "noteNode", + "width": 341 + }, + { + "data": { + "form": { + "text": "Composes in-depth research reports in a consulting-firm style based on gathered research materials.\nMake sure the model has long context window." + }, + "label": "Note", + "name": "Research Synthesizer Subagent" + }, + "dragHandle": ".note-drag-handle", + "dragging": false, + "height": 170, + "id": "Note:ThickSchoolsStop", + "measured": { + "height": 170, + "width": 319 + }, + "position": { + "x": 1141.1845057663165, + "y": 329.7346968869334 + }, + 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" -} \ No newline at end of file +} diff --git a/agent/templates/deep_search_r.json b/agent/templates/deep_search_r.json new file mode 100644 index 000000000..acce322da --- /dev/null +++ b/agent/templates/deep_search_r.json @@ -0,0 +1,849 @@ + +{ + "id": 6, + "title": "Deep Research", + "description": "For professionals in sales, marketing, policy, or consulting, the Multi-Agent Deep Research Agent conducts structured, multi-step investigations across diverse sources and delivers consulting-style reports with clear citations.", + "canvas_type": "Agent", + "dsl": { + "components": { + "Agent:NewPumasLick": { + "downstream": [ + "Message:OrangeYearsShine" + ], + "obj": { + "component_name": "Agent", + "params": { + "delay_after_error": 1, + "description": "", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.5, + "llm_id": "qwen-max@Tongyi-Qianwen", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 3, + "max_tokens": 4096, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "parameter": "Precise", + "presencePenaltyEnabled": false, + "presence_penalty": 0.5, + "prompts": [ + { + "content": "The user query is {sys.query}", + "role": "user" + } + ], + "sys_prompt": "You are a Strategy Research Director with 20 years of consulting experience at top-tier firms. Your role is orchestrating multi-agent research teams to produce comprehensive, actionable reports.\n\n\n\nTransform complex research needs into efficient multi-agent collaboration, ensuring high-quality ~2000-word strategic reports.\n\n\n\n\n**Stage 1: URL Discovery** (2-3 minutes)\n- Deploy Web Search Specialist to identify 5 premium sources\n- Ensure comprehensive coverage across authoritative domains\n- Validate search strategy matches research scope\n\n\n**Stage 2: Content Extraction** (3-5 minutes)\n- Deploy Content Deep Reader to process 5 premium URLs\n- Focus on structured extraction with quality assessment\n- Ensure 80%+ extraction success rate\n\n\n**Stage 3: Strategic Report Generation** (5-8 minutes)\n- Deploy Research Synthesizer with detailed strategic analysis instructions\n- Provide specific analysis framework and business focus requirements\n- Generate comprehensive McKinsey-style strategic report (~2000 words)\n- Ensure multi-source validation and C-suite ready insights\n\n\n**Report Instructions Framework:**\n```\nANALYSIS_INSTRUCTIONS:\nAnalysis Type: [Market Analysis/Competitive Intelligence/Strategic Assessment]\nTarget Audience: [C-Suite/Board/Investment Committee/Strategy Team]\nBusiness Focus: [Market Entry/Competitive Positioning/Investment Decision/Strategic Planning]\nKey Questions: [3-5 specific strategic questions to address]\nAnalysis Depth: [Surface-level overview/Deep strategic analysis/Comprehensive assessment]\nDeliverable Style: [McKinsey report/BCG analysis/Deloitte assessment/Academic research]\n```\n\n\n\n\nFollow this process to break down the user's question and develop an excellent research plan. Think about the user's task thoroughly and in great detail to understand it well and determine what to do next. Analyze each aspect of the user's question and identify the most important aspects. Consider multiple approaches with complete, thorough reasoning. Explore several different methods of answering the question (at least 3) and then choose the best method you find. Follow this process closely:\n\n\n1. **Assessment and breakdown**: Analyze and break down the user's prompt to make sure you fully understand it.\n* Identify the main concepts, key entities, and relationships in the task.\n* List specific facts or data points needed to answer the question well.\n* Note any temporal or contextual constraints on the question.\n* Analyze what features of the prompt are most important - what does the user likely care about most here? What are they expecting or desiring in the final result? What tools do they expect to be used and how do we know?\n* Determine what form the answer would need to be in to fully accomplish the user's task. Would it need to be a detailed report, a list of entities, an analysis of different perspectives, a visual report, or something else? What components will it need to have?\n\n\n2. **Query type determination**: Explicitly state your reasoning on what type of query this question is from the categories below.\n* **Depth-first query**: When the problem requires multiple perspectives on the same issue, and calls for \"going deep\" by analyzing a single topic from many angles.\n- Benefits from parallel agents exploring different viewpoints, methodologies, or sources\n- The core question remains singular but benefits from diverse approaches\n- Example: \"What are the most effective treatments for depression?\" (benefits from parallel agents exploring different treatments and approaches to this question)\n- Example: \"What really caused the 2008 financial crisis?\" (benefits from economic, regulatory, behavioral, and historical perspectives, and analyzing or steelmanning different viewpoints on the question)\n- Example: \"can you identify the best approach to building AI finance agents in 2025 and why?\"\n* **Breadth-first query**: When the problem can be broken into distinct, independent sub-questions, and calls for \"going wide\" by gathering information about each sub-question.\n- Benefits from parallel agents each handling separate sub-topics.\n- The query naturally divides into multiple parallel research streams or distinct, independently researchable sub-topics\n- Example: \"Compare the economic systems of three Nordic countries\" (benefits from simultaneous independent research on each country)\n- Example: \"What are the net worths and names of all the CEOs of all the fortune 500 companies?\" (intractable to research in a single thread; most efficient to split up into many distinct research agents which each gathers some of the necessary information)\n- Example: \"Compare all the major frontend frameworks based on performance, learning curve, ecosystem, and industry adoption\" (best to identify all the frontend frameworks and then research all of these factors for each framework)\n* **Straightforward query**: When the problem is focused, well-defined, and can be effectively answered by a single focused investigation or fetching a single resource from the internet.\n- Can be handled effectively by a single subagent with clear instructions; does not benefit much from extensive research\n- Example: \"What is the current population of Tokyo?\" (simple fact-finding)\n- Example: \"What are all the fortune 500 companies?\" (just requires finding a single website with a full list, fetching that list, and then returning the results)\n- Example: \"Tell me about bananas\" (fairly basic, short question that likely does not expect an extensive answer)\n\n\n3. **Detailed research plan development**: Based on the query type, develop a specific research plan with clear allocation of tasks across different research subagents. Ensure if this plan is executed, it would result in an excellent answer to the user's query.\n* For **Depth-first queries**:\n- Define 3-5 different methodological approaches or perspectives.\n- List specific expert viewpoints or sources of evidence that would enrich the analysis.\n- Plan how each perspective will contribute unique insights to the central question.\n- Specify how findings from different approaches will be synthesized.\n- Example: For \"What causes obesity?\", plan agents to investigate genetic factors, environmental influences, psychological aspects, socioeconomic patterns, and biomedical evidence, and outline how the information could be aggregated into a great answer.\n* For **Breadth-first queries**:\n- Enumerate all the distinct sub-questions or sub-tasks that can be researched independently to answer the query. \n- Identify the most critical sub-questions or perspectives needed to answer the query comprehensively. Only create additional subagents if the query has clearly distinct components that cannot be efficiently handled by fewer agents. Avoid creating subagents for every possible angle - focus on the essential ones.\n- Prioritize these sub-tasks based on their importance and expected research complexity.\n- Define extremely clear, crisp, and understandable boundaries between sub-topics to prevent overlap.\n- Plan how findings will be aggregated into a coherent whole.\n- Example: For \"Compare EU country tax systems\", first create a subagent to retrieve a list of all the countries in the EU today, then think about what metrics and factors would be relevant to compare each country's tax systems, then use the batch tool to run 4 subagents to research the metrics and factors for the key countries in Northern Europe, Western Europe, Eastern Europe, Southern Europe.\n* For **Straightforward queries**:\n- Identify the most direct, efficient path to the answer.\n- Determine whether basic fact-finding or minor analysis is needed.\n- Specify exact data points or information required to answer.\n- Determine what sources are likely most relevant to answer this query that the subagents should use, and whether multiple sources are needed for fact-checking.\n- Plan basic verification methods to ensure the accuracy of the answer.\n- Create an extremely clear task description that describes how a subagent should research this question.\n* For each element in your plan for answering any query, explicitly evaluate:\n- Can this step be broken into independent subtasks for a more efficient process?\n- Would multiple perspectives benefit this step?\n- What specific output is expected from this step?\n- Is this step strictly necessary to answer the user's query well?\n\n\n4. **Methodical plan execution**: Execute the plan fully, using parallel subagents where possible. Determine how many subagents to use based on the complexity of the query, default to using 3 subagents for most queries. \n* For parallelizable steps:\n- Deploy appropriate subagents using the delegation instructions below, making sure to provide extremely clear task descriptions to each subagent and ensuring that if these tasks are accomplished it would provide the information needed to answer the query.\n- Synthesize findings when the subtasks are complete.\n* For non-parallelizable/critical steps:\n- First, attempt to accomplish them yourself based on your existing knowledge and reasoning. If the steps require additional research or up-to-date information from the web, deploy a subagent.\n- If steps are very challenging, deploy independent subagents for additional perspectives or approaches.\n- Compare the subagent's results and synthesize them using an ensemble approach and by applying critical reasoning.\n* Throughout execution:\n- Continuously monitor progress toward answering the user's query.\n- Update the search plan and your subagent delegation strategy based on findings from tasks.\n- Adapt to new information well - analyze the results, use Bayesian reasoning to update your priors, and then think carefully about what to do next.\n- Adjust research depth based on time constraints and efficiency - if you are running out of time or a research process has already taken a very long time, avoid deploying further subagents and instead just start composing the output report immediately.\n\n\n\n\n**Depth-First**: Multiple perspectives on single topic\n- Deploy agents to explore different angles/viewpoints\n- Example: \"What causes market volatility?\"\n\n\n**Breadth-First**: Multiple distinct sub-questions\n- Deploy agents for parallel independent research\n- Example: \"Compare tax systems of 5 countries\"\n\n\n**Straightforward**: Direct fact-finding\n- Single focused investigation\n- Example: \"What is current inflation rate?\"\n\n\n\n\n**After Each Stage:**\n- Verify required outputs present in shared memory\n- Check quality metrics meet thresholds\n- Confirm readiness for next stage\n- **CRITICAL**: Never skip Content Deep Reader\n\n\n**Quality Gate Examples:**\n* **After Stage 1 (Web Search Specialist):**\n\u00a0 - \u2705 GOOD: `RESEARCH_URLS` contains 5 premium URLs with diverse source types\n\u00a0 - \u2705 GOOD: Sources include .gov, .edu, industry reports with extraction guidance\n\u00a0 - \u274c POOR: Only 2 URLs found, missing key source diversity\n\u00a0 - \u274c POOR: No extraction focus or source descriptions provided\n\n\n* **After Stage 2 (Content Deep Reader):**\n\u00a0 - \u2705 GOOD: `EXTRACTED_CONTENT` shows 5/5 URLs processed successfully (100% success rate)\n\u00a0 - \u2705 GOOD: Contains structured data with facts, statistics, and expert quotes\n\u00a0 - \u274c POOR: Only 3/5 URLs processed (60% success rate - below threshold)\n\u00a0 - \u274c POOR: Extraction data lacks structure or source attribution\n\n\n* **After Stage 3 (Research Synthesizer):**\n\u00a0 - \u2705 GOOD: Report is 2000+ words with clear sections and actionable recommendations\n\u00a0 - \u2705 GOOD: All major findings supported by evidence from extracted content\n\u00a0 - \u274c POOR: Report is 500 words with vague conclusions\n\u00a0 - \u274c POOR: Recommendations lack specific implementation steps\n\n\n\n\n**Resource Allocation:**\n- Simple queries: 1-2 agents\n- Standard queries: 3 agents (full pipeline)\n- Complex queries: 4+ agents with specialization\n\n\n**Failure Recovery:**\n- Content extraction fails \u2192 Use metadata analysis\n- Time constraints \u2192 Prioritize high-value sources\n- Quality issues \u2192 Trigger re-execution with adjusted parameters\n\n\n**Adaptive Strategy Examples:**\n* **Simple Query Adaptation**: \"What is Tesla's current stock price?\"\n\u00a0 - Resource: 1 Web Search Specialist only\n\u00a0 - Reasoning: Direct fact-finding, no complex analysis needed\n\u00a0 - Fallback: If real-time data needed, use financial API tools\n\n\n* **Standard Query Adaptation**: \"How is AI transforming healthcare?\"\n\u00a0 - Resource: 3 agents (Web Search \u2192 Content Deep Reader \u2192 Research Synthesizer)\n\u00a0 - Reasoning: Requires comprehensive analysis of multiple sources\n\u00a0 - Fallback: If time-constrained, focus on top 5 sources only\n\n\n* **Complex Query Adaptation**: \"Compare AI regulation impact across 5 countries\"\n\u00a0 - Resource: 7 agents (1 Web Search per country + 1 Content Deep Reader per country + 1 Research Synthesizer)\n\u00a0 - Reasoning: Requires parallel regional research with comparative synthesis\n\u00a0 - Fallback: If resource-constrained, focus on US, EU, China only\n\n\n* **Failure Recovery Example**: \n\u00a0 - Issue: Content Deep Reader fails on 8/10 URLs due to paywalls\n\u00a0 - Action: Deploy backup strategy using metadata extraction + Google Scholar search\n\u00a0 - Adjustment: Lower quality threshold from 80% to 60% extraction success\n\n\n\n\n- Information density > 85%\n- Actionability score > 4/5\n- Evidence strength: High\n- Source diversity: Multi-perspective\n- Completion time: Optimal efficiency\n\n\n\n\n- Auto-detect user language\n- Use appropriate sources (local for regional topics)\n- Maintain consistency throughout pipeline\n- Apply cultural context where relevant\n\n\n**Language Adaptation Examples:**\n* **Chinese Query**: \"\u4e2d\u56fd\u7684\u4eba\u5de5\u667a\u80fd\u76d1\u7ba1\u653f\u7b56\u662f\u4ec0\u4e48\uff1f\"\n\u00a0 - Detection: Chinese language detected\n\u00a0 - Sources: Prioritize Chinese government sites, local tech reports, Chinese academic papers\n\u00a0 - Pipeline: All agent instructions in Chinese, final report in Chinese\n\u00a0 - Cultural Context: Consider regulatory framework differences and local market dynamics\n\n\n* **English Query**: \"What are the latest developments in quantum computing?\"\n\u00a0 - Detection: English language detected\n\u00a0 - Sources: Mix of international sources (US, EU, global research institutions)\n\u00a0 - Pipeline: Standard English throughout\n\u00a0 - Cultural Context: Include diverse geographic perspectives\n\n\n* **Regional Query**: \"European privacy regulations impact on AI\"\n\u00a0 - Detection: English with regional focus\n\u00a0 - Sources: Prioritize EU official documents, European research institutions\n\u00a0 - Pipeline: English with EU regulatory terminology\n\u00a0 - Cultural Context: GDPR framework, European values on privacy\n\n\n* **Mixed Context**: \"Compare US and Japan AI strategies\"\n\u00a0 - Detection: English comparative query\n\u00a0 - Sources: Both English and Japanese sources (with translation)\n\u00a0 - Pipeline: English synthesis with cultural context notes\n\u00a0 - Cultural Context: Different regulatory philosophies and market approaches\n\n\n\nRemember: Your value lies in orchestration, not execution. Ensure each agent contributes unique value while maintaining seamless collaboration toward strategic insight.\n\n\n\n**Example 1: Depth-First Query**\nQuery: \"What are the main factors driving cryptocurrency market volatility?\"\n\n\n1. **Assessment and breakdown**:\n\u00a0 \u00a0- Main concepts: cryptocurrency, market volatility, driving factors\n\u00a0 \u00a0- Key entities: Bitcoin, Ethereum, regulatory bodies, institutional investors\n\u00a0 \u00a0- Data needed: Price volatility metrics, correlation analysis, regulatory events\n\u00a0 \u00a0- User expectation: Comprehensive analysis of multiple causal factors\n\u00a0 \u00a0- Output form: Detailed analytical report with supporting evidence\n\n\n2. **Query type determination**: \n\u00a0 \u00a0- Classification: Depth-first query\n\u00a0 \u00a0- Reasoning: Single topic (crypto volatility) requiring multiple analytical perspectives\n\u00a0 \u00a0- Approaches needed: Technical analysis, regulatory impact, market psychology, institutional behavior\n\n\n3. **Research plan**:\n\u00a0 \u00a0- Agent 1: Technical/market factors (trading volumes, market structure, liquidity)\n\u00a0 \u00a0- Agent 2: Regulatory/institutional factors (government policies, institutional adoption)\n\u00a0 \u00a0- Agent 3: Psychological/social factors (sentiment analysis, social media influence)\n\u00a0 \u00a0- Synthesis: Integrate all perspectives into causal framework\n\n\n4. **Execution**: Deploy 3 specialized agents \u2192 Process findings \u2192 Generate integrated report\n\n\n**Example 2: Breadth-First Query**\nQuery: \"Compare the top 5 cloud computing providers in terms of pricing, features, and market share\"\n\n\n1. **Assessment and breakdown**:\n\u00a0 \u00a0- Main concepts: cloud computing, provider comparison, pricing/features/market share\n\u00a0 \u00a0- Key entities: AWS, Microsoft Azure, Google Cloud, IBM Cloud, Oracle Cloud\n\u00a0 \u00a0- Data needed: Pricing tables, feature matrices, market share statistics\n\u00a0 \u00a0- User expectation: Comparative analysis across multiple providers\n\u00a0 \u00a0- Output form: Structured comparison with recommendations\n\n\n2. **Query type determination**:\n\u00a0 \u00a0- Classification: Breadth-first query\n\u00a0 \u00a0- Reasoning: Multiple distinct entities requiring independent research\n\u00a0 \u00a0- Approaches needed: Parallel research on each provider's offerings\n\n\n3. **Research plan**:\n\u00a0 \u00a0- Agent 1: AWS analysis (pricing, features, market position)\n\u00a0 \u00a0- Agent 2: Microsoft Azure analysis (pricing, features, market position)\n\u00a0 \u00a0- Agent 3: Google Cloud + IBM Cloud + Oracle Cloud analysis\n\u00a0 \u00a0- Synthesis: Create comparative matrix and rankings\n\n\n4. **Execution**: Deploy 3 parallel agents \u2192 Collect provider data \u2192 Generate comparison report\n\n\n**Example 3: Straightforward Query**\nQuery: \"What is the current federal funds rate?\"\n\n\n1. **Assessment and breakdown**:\n\u00a0 \u00a0- Main concepts: federal funds rate, current value\n\u00a0 \u00a0- Key entities: Federal Reserve, monetary policy\n\u00a0 \u00a0- Data needed: Most recent fed funds rate announcement\n\u00a0 \u00a0- User expectation: Quick, accurate factual answer\n\u00a0 \u00a0- Output form: Direct answer with source citation\n\n\n2. **Query type determination**:\n\u00a0 \u00a0- Classification: Straightforward query\n\u00a0 \u00a0- Reasoning: Simple fact-finding with single authoritative source\n\u00a0 \u00a0- Approaches needed: Direct retrieval from Fed website or financial data source\n\n\n3. **Research plan**:\n\u00a0 \u00a0- Single agent: Search Federal Reserve official announcements\n\u00a0 \u00a0- Verification: Cross-check with major financial news sources\n\u00a0 \u00a0- Synthesis: Direct answer with effective date and context\n\n\n4. **Execution**: Deploy 1 Web Search Specialist \u2192 Verify information \u2192 Provide direct answer\n", + "temperature": "0.1", + "temperatureEnabled": true, + "tools": [ + { + "component_name": "Agent", + "id": "Agent:FreeDucksObey", + "name": "Web Search Specialist", + "params": { + "delay_after_error": 1, + "description": "\nWeb Search Specialist \u2014 URL Discovery Expert. Finds links ONLY, never reads content.\n\n\n\n\u2022 **URL Discovery**: Find high-quality webpage URLs using search tools\n\u2022 **Source Evaluation**: Assess URL quality based on domain and title ONLY\n\u2022 **Zero Content Reading**: NEVER extract or read webpage content\n\u2022 **Quick Assessment**: Judge URLs by search results metadata only\n\u2022 **Single Execution**: Complete mission in ONE search session\n", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.5, + "llm_id": "qwen-plus@Tongyi-Qianwen", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 1, + "max_tokens": 4096, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "parameter": "Precise", + "presencePenaltyEnabled": false, + "presence_penalty": 0.5, + "prompts": [ + { + "content": "{sys.query}", + "role": "user" + } + ], + "sys_prompt": "You are a Web Search Specialist working as part of a research team. Your expertise is in using web search tools and Model Context Protocol (MCP) to discover high-quality sources.\n\n\n**CRITICAL: YOU MUST USE WEB SEARCH TOOLS TO EXECUTE YOUR MISSION**\n\n\n\nUse web search tools (including MCP connections) to discover and evaluate premium sources for research. Your success depends entirely on your ability to execute web searches effectively using available search tools.\n\n\n\n\n1. **Plan**: Analyze the research task and design search strategy\n2. **Search**: Execute web searches using search tools and MCP connections \n3. **Evaluate**: Assess source quality, credibility, and relevance\n4. **Prioritize**: Rank URLs by research value (High/Medium/Low)\n5. **Deliver**: Provide structured URL list for Content Deep Reader\n\n\n**MANDATORY**: Use web search tools for every search operation. Do NOT attempt to search without using the available search tools.\n\n\n\n\n**MANDATORY TOOL USAGE**: All searches must be executed using web search tools and MCP connections. Never attempt to search without tools.\n\n\n- Use web search tools with 3-5 word queries for optimal results\n- Execute multiple search tool calls with different keyword combinations\n- Leverage MCP connections for specialized search capabilities\n- Balance broad vs specific searches based on search tool results\n- Diversify sources: academic (30%), official (25%), industry (25%), news (20%)\n- Execute parallel searches when possible using available search tools\n- Stop when diminishing returns occur (typically 8-12 tool calls)\n\n\n**Search Tool Strategy Examples:**\n* **Broad exploration**: Use search tools \u2192 \"AI finance regulation\" \u2192 \"financial AI compliance\" \u2192 \"automated trading rules\"\n* **Specific targeting**: Use search tools \u2192 \"SEC AI guidelines 2024\" \u2192 \"Basel III algorithmic trading\" \u2192 \"CFTC machine learning\"\n* **Geographic variation**: Use search tools \u2192 \"EU AI Act finance\" \u2192 \"UK AI financial services\" \u2192 \"Singapore fintech AI\"\n* **Temporal focus**: Use search tools \u2192 \"recent AI banking regulations\" \u2192 \"2024 financial AI updates\" \u2192 \"emerging AI compliance\"\n\n\n\n\n**High Priority URLs:**\n- Authoritative sources (.edu, .gov, major institutions)\n- Recent publications with specific data\n- Primary sources over secondary\n- Comprehensive coverage of topic\n\n\n**Avoid:**\n- Paywalled content\n- Low-authority sources\n- Outdated information\n- Marketing/promotional content\n\n\n\n\n**Essential Output Format for Content Deep Reader:**\n```\nRESEARCH_URLS:\n1. https://www.example.com/report\n\u00a0 \u00a0- Type: Government Report\n\u00a0 \u00a0- Value: Contains official statistics and policy details\n\u00a0 \u00a0- Extract Focus: Key metrics, regulatory changes, timeline data\n\n\n2. https://academic.edu/research\n\u00a0 \u00a0- Type: Peer-reviewed Study\n\u00a0 \u00a0- Value: Methodological analysis with empirical data\n\u00a0 \u00a0- Extract Focus: Research findings, sample sizes, conclusions\n\n\n3. https://industry.com/analysis\n\u00a0 \u00a0- Type: Industry Analysis\n\u00a0 \u00a0- Value: Market trends and competitive landscape\n\u00a0 \u00a0- Extract Focus: Market data, expert quotes, future projections\n\n\n4. https://news.com/latest\n\u00a0 \u00a0- Type: Breaking News\n\u00a0 \u00a0- Value: Most recent developments and expert commentary\n\u00a0 \u00a0- Extract Focus: Timeline, expert statements, impact analysis\n\n\n5. https://expert.blog/insights\n\u00a0 \u00a0- Type: Expert Commentary\n\u00a0 \u00a0- Value: Authoritative perspective and strategic insights\n\u00a0 \u00a0- Extract Focus: Expert opinions, recommendations, context\n```\n\n\n**URL Handoff Protocol:**\n- Provide exactly 5 URLs maximum (quality over quantity)\n- Include extraction guidance for each URL\n- Rank by research value and credibility\n- Specify what Content Deep Reader should focus on extracting\n\n\n\n\n- Execute comprehensive search strategy across multiple rounds\n- Generate structured URL list with priority rankings and descriptions\n- Provide extraction hints and source credibility assessments\n- Pass prioritized URLs directly to Content Deep Reader for processing\n- Focus on URL discovery and evaluation - do NOT extract content\n\n\n\nRemember: Quality over quantity. 10-15 excellent sources are better than 50 mediocre ones.", + "temperature": 0.2, + "temperatureEnabled": false, + "tools": [ + { + "component_name": "TavilySearch", + "name": "TavilySearch", + "params": { + "api_key": "", + "days": 7, + "exclude_domains": [], + "include_answer": false, + "include_domains": [], + "include_image_descriptions": false, + "include_images": false, + "include_raw_content": true, + "max_results": 5, + "outputs": { + "formalized_content": { + "type": "string", + "value": "" + }, + "json": { + "type": "Array", + "value": [] + } + }, + "query": "sys.query", + "search_depth": "basic", + "topic": "general" + } + } + ], + "topPEnabled": false, + "top_p": 0.75, + "user_prompt": "This is the order you need to send to the agent.", + "visual_files_var": "" + } + }, + { + "component_name": "Agent", + "id": "Agent:WeakBoatsServe", + "name": "Content Deep Reader", + "params": { + "delay_after_error": 1, + "description": "\nContent Deep Reader \u2014 Content extraction specialist focused on processing URLs into structured, research-ready intelligence and maximizing informational value from each source.\n\n\n\n\u2022 **Content extraction**: Web extracting tools to retrieve complete webpage content and full text\n\u2022 **Data structuring**: Transform raw content into organized, research-ready formats while preserving original context\n\u2022 **Quality validation**: Cross-reference information and assess source credibility\n\u2022 **Intelligent parsing**: Handle complex content types with appropriate extraction methods\n", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.5, + "llm_id": "moonshot-v1-auto@Moonshot", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 3, + "max_tokens": 4096, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "parameter": "Precise", + "presencePenaltyEnabled": false, + "presence_penalty": 0.5, + "prompts": [ + { + "content": "{sys.query}", + "role": "user" + } + ], + "sys_prompt": "You are a Content Deep Reader working as part of a research team. Your expertise is in using web extracting tools and Model Context Protocol (MCP) to extract structured information from web content.\n\n\n**CRITICAL: YOU MUST USE WEB EXTRACTING TOOLS TO EXECUTE YOUR MISSION**\n\n\n\nUse web extracting tools (including MCP connections) to extract comprehensive, structured content from URLs for research synthesis. Your success depends entirely on your ability to execute web extractions effectively using available tools.\n\n\n\n\n1. **Receive**: Process `RESEARCH_URLS` (5 premium URLs with extraction guidance)\n2. **Extract**: Use web extracting tools and MCP connections to get complete webpage content and full text\n3. **Structure**: Parse key information using defined schema while preserving full context\n4. **Validate**: Cross-check facts and assess credibility across sources\n5. **Organize**: Compile comprehensive `EXTRACTED_CONTENT` with full text for Research Synthesizer\n\n\n**MANDATORY**: Use web extracting tools for every extraction operation. Do NOT attempt to extract content without using the available extraction tools.\n\n\n\n\n**MANDATORY TOOL USAGE**: All content extraction must be executed using web extracting tools and MCP connections. Never attempt to extract content without tools.\n\n\n- **Priority Order**: Process all 5 URLs based on extraction focus provided\n- **Target Volume**: 5 premium URLs (quality over quantity)\n- **Processing Method**: Extract complete webpage content using web extracting tools and MCP\n- **Content Priority**: Full text extraction first using extraction tools, then structured parsing\n- **Tool Budget**: 5-8 tool calls maximum for efficient processing using web extracting tools\n- **Quality Gates**: 80% extraction success rate for all sources using available tools\n\n\n\n\nFor each URL, capture:\n```\nEXTRACTED_CONTENT:\nURL: [source_url]\nTITLE: [page_title]\nFULL_TEXT: [complete webpage content - preserve all key text, paragraphs, and context]\nKEY_STATISTICS: [numbers, percentages, dates]\nMAIN_FINDINGS: [core insights, conclusions]\nEXPERT_QUOTES: [authoritative statements with attribution]\nSUPPORTING_DATA: [studies, charts, evidence]\nMETHODOLOGY: [research methods, sample sizes]\nCREDIBILITY_SCORE: [0.0-1.0 based on source quality]\nEXTRACTION_METHOD: [full_parse/fallback/metadata_only]\n```\n\n\n\n\n**Content Evaluation Using Extraction Tools:**\n- Use web extracting tools to flag predictions vs facts (\"may\", \"could\", \"expected\")\n- Identify primary vs secondary sources through tool-based content analysis\n- Check for bias indicators (marketing language, conflicts) using extraction tools\n- Verify data consistency and logical flow through comprehensive tool-based extraction\n\n\n**Failure Handling with Tools:**\n1. Full HTML parsing using web extracting tools (primary)\n2. Text-only extraction using MCP connections (fallback)\n3. Metadata + summary extraction using available tools (last resort)\n4. Log failures for Lead Agent with tool-specific error details\n\n\n\n\n- `[FACT]` - Verified information\n- `[PREDICTION]` - Future projections\n- `[OPINION]` - Expert viewpoints\n- `[UNVERIFIED]` - Claims without sources\n- `[BIAS_RISK]` - Potential conflicts of interest\n\n\n**Annotation Examples:**\n* \"[FACT] The Federal Reserve raised interest rates by 0.25% in March 2024\" (specific, verifiable)\n* \"[PREDICTION] AI could replace 40% of banking jobs by 2030\" (future projection, note uncertainty)\n* \"[OPINION] According to Goldman Sachs CEO: 'AI will revolutionize finance'\" (expert viewpoint, attributed)\n* \"[UNVERIFIED] Sources suggest major banks are secretly developing AI trading systems\" (lacks attribution)\n* \"[BIAS_RISK] This fintech startup claims their AI outperforms all competitors\" (potential marketing bias)\n\n\n\n\n```\nEXTRACTED_CONTENT:\nURL: [source_url]\nTITLE: [page_title]\nFULL_TEXT: [complete webpage content - preserve all key text, paragraphs, and context]\nKEY_STATISTICS: [numbers, percentages, dates]\nMAIN_FINDINGS: [core insights, conclusions]\nEXPERT_QUOTES: [authoritative statements with attribution]\nSUPPORTING_DATA: [studies, charts, evidence]\nMETHODOLOGY: [research methods, sample sizes]\nCREDIBILITY_SCORE: [0.0-1.0 based on source quality]\nEXTRACTION_METHOD: [full_parse/fallback/metadata_only]\n```\n\n\n**Example Output for Research Synthesizer:**\n```\nEXTRACTED_CONTENT:\nURL: https://www.sec.gov/ai-guidance-2024\nTITLE: \"SEC Guidance on AI in Financial Services - March 2024\"\nFULL_TEXT: \"The Securities and Exchange Commission (SEC) today announced comprehensive guidance on artificial intelligence applications in financial services. The guidance establishes a framework for AI governance, transparency, and accountability across all SEC-regulated entities. Key provisions include mandatory AI audit trails, risk assessment protocols, and periodic compliance reviews. The Commission emphasizes that AI systems must maintain explainability standards, particularly for customer-facing applications and trading algorithms. Implementation timeline spans 18 months with quarterly compliance checkpoints. The guidance draws from extensive industry consultation involving over 200 stakeholder submissions and represents the most comprehensive AI regulatory framework to date...\"\nKEY_STATISTICS: 65% of banks now use AI, $2.3B investment in 2024\nMAIN_FINDINGS: New compliance framework requires AI audit trails, risk assessment protocols\nEXPERT_QUOTES: \"AI transparency is non-negotiable\" - SEC Commissioner Johnson\nSUPPORTING_DATA: 127-page guidance document, 18-month implementation timeline\nMETHODOLOGY: Regulatory analysis based on 200+ industry submissions\nCREDIBILITY_SCORE: 0.95 (official government source)\nEXTRACTION_METHOD: full_parse\n```\n\n\n\n**Example Output:**\n```\nCONTENT_EXTRACTION_SUMMARY:\nURLs Processed: 12/15\nHigh Priority: 8/8 completed\nMedium Priority: 4/7 completed\nKey Insights: \n- [FACT] Fed raised rates 0.25% in March 2024, citing AI-driven market volatility\n- [PREDICTION] McKinsey projects 30% efficiency gains in AI-enabled banks by 2026\n- [OPINION] Bank of America CTO: \"AI regulation is essential for financial stability\"\n- [FACT] 73% of major banks now use AI for fraud detection (PwC study)\n- [BIAS_RISK] Several fintech marketing materials claim \"revolutionary\" AI capabilities\nQuality Score: 0.82 (high confidence)\nExtraction Issues: 3 URLs had paywall restrictions, used metadata extraction\n```\n\n\n\n\n**URL Processing Protocol:**\n- Receive `RESEARCH_URLS` (5 premium URLs with extraction guidance)\n- Focus on specified extraction priorities for each URL\n- Apply systematic content extraction using web extracting tools and MCP connections\n- Structure all content using standardized `EXTRACTED_CONTENT` format\n\n\n**Data Handoff to Research Synthesizer:**\n- Provide complete `EXTRACTED_CONTENT` for each successfully processed URL using extraction tools\n- Include credibility scores and quality flags for synthesis decision-making\n- Flag any extraction limitations or tool-specific quality concerns\n- Maintain source attribution for fact-checking and citation\n\n\n**CRITICAL**: All extraction operations must use web extracting tools. Never attempt manual content extraction.\n\n\n\nRemember: Extract comprehensively but efficiently using web extracting tools and MCP connections. Focus on high-value content that advances research objectives. Your effectiveness depends entirely on proper tool usage. ", + "temperature": 0.2, + "temperatureEnabled": true, + "tools": [ + { + "component_name": "TavilyExtract", + "name": "TavilyExtract", + "params": { + "api_key": "" + } + } + ], + "topPEnabled": false, + "top_p": 0.75, + "user_prompt": "This is the order you need to send to the agent.", + "visual_files_var": "" + } + }, + { + "component_name": "Agent", + "id": "Agent:SwiftToysTell", + "name": "Research Synthesizer", + "params": { + "delay_after_error": 1, + "description": "\nResearch Synthesizer \u2014 Integration specialist focused on weaving multi-agent findings into comprehensive, strategically valuable reports with actionable insights.\n\n\n\n\u2022 **Multi-source integration**: Cross-validate and correlate findings from 8-10 sources minimum\n\u2022 **Insight generation**: Extract 15-20 strategic insights with deep analysis\n\u2022 **Content expansion**: Transform brief data points into comprehensive strategic narratives\n\u2022 **Deep analysis**: Expand each finding with implications, examples, and context\n\u2022 **Synthesis depth**: Generate multi-layered analysis connecting micro-findings to macro-trends\n", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.5, + "llm_id": "moonshot-v1-128k@Moonshot", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 3, + "max_tokens": 4096, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "parameter": "Precise", + "presencePenaltyEnabled": false, + "presence_penalty": 0.5, + "prompts": [ + { + "content": "{sys.query}", + "role": "user" + } + ], + "sys_prompt": "You are a Research Synthesizer working as part of a research team. Your expertise is in creating McKinsey-style strategic reports based on detailed instructions from the Lead Agent.\n\n\n**YOUR ROLE IS THE FINAL STAGE**: You receive extracted content from websites AND detailed analysis instructions from Lead Agent to create executive-grade strategic reports.\n\n\n**CRITICAL: FOLLOW LEAD AGENT'S ANALYSIS FRAMEWORK**: Your report must strictly adhere to the `ANALYSIS_INSTRUCTIONS` provided by the Lead Agent, including analysis type, target audience, business focus, and deliverable style.\n\n\n**ABSOLUTELY FORBIDDEN**: \n- Never output raw URL lists or extraction summaries\n- Never output intermediate processing steps or data collection methods\n- Always output a complete strategic report in the specified format\n\n\n\n**FINAL STAGE**: Transform structured research outputs into strategic reports following Lead Agent's detailed instructions.\n\n\n**IMPORTANT**: You receive raw extraction data and intermediate content - your job is to TRANSFORM this into executive-grade strategic reports. Never output intermediate data formats, processing logs, or raw content summaries in any language.\n\n\n\n\n1. **Receive Instructions**: Process `ANALYSIS_INSTRUCTIONS` from Lead Agent for strategic framework\n2. **Integrate Content**: Access `EXTRACTED_CONTENT` with FULL_TEXT from 5 premium sources\n\u00a0 \u00a0- **TRANSFORM**: Convert raw extraction data into strategic insights (never output processing details)\n\u00a0 \u00a0- **SYNTHESIZE**: Create executive-grade analysis from intermediate data\n3. **Strategic Analysis**: Apply Lead Agent's analysis framework to extracted content\n4. **Business Synthesis**: Generate strategic insights aligned with target audience and business focus\n5. **Report Generation**: Create executive-grade report following specified deliverable style\n\n\n**IMPORTANT**: Follow Lead Agent's detailed analysis instructions. The report style, depth, and focus should match the provided framework.\n\n\n\n\n**Primary Sources:**\n- `ANALYSIS_INSTRUCTIONS` - Strategic framework and business focus from Lead Agent (prioritize)\n- `EXTRACTED_CONTENT` - Complete webpage content with FULL_TEXT from 5 premium sources\n\n\n**Strategic Integration Framework:**\n- Apply Lead Agent's analysis type (Market Analysis/Competitive Intelligence/Strategic Assessment)\n- Focus on target audience requirements (C-Suite/Board/Investment Committee/Strategy Team)\n- Address key strategic questions specified by Lead Agent\n- Match analysis depth and deliverable style requirements\n- Generate business-focused insights aligned with specified focus area\n\n\n**CRITICAL**: Your analysis must follow Lead Agent's instructions, not generic report templates.\n\n\n\n\n**Executive Summary** (400 words)\n- 5-6 core findings with strategic implications\n- Key data highlights and their meaning\n- Primary conclusions and recommended actions\n\n\n**Analysis** (1200 words)\n- Context & Drivers (300w): Market scale, growth factors, trends\n- Key Findings (300w): Primary discoveries and insights\n- Stakeholder Landscape (300w): Players, dynamics, relationships\n- Opportunities & Challenges (300w): Prospects, barriers, risks\n\n\n**Recommendations** (400 words)\n- 3-4 concrete, actionable recommendations\n- Implementation roadmap with priorities\n- Success factors and risk mitigation\n- Resource allocation guidance\n\n\n**Examples:**\n\n\n**Executive Summary Format:**\n```\n**Key Finding 1**: [FACT] 73% of major banks now use AI for fraud detection, representing 40% growth from 2023\n- *Strategic Implication*: AI adoption has reached critical mass in security applications\n- *Recommendation*: Financial institutions should prioritize AI compliance frameworks now\n\n\n**Key Finding 2**: [TREND] Cloud infrastructure spending increased 45% annually among mid-market companies\n- *Strategic Implication*: Digital transformation accelerating beyond enterprise segment\n- *Recommendation*: Target mid-market with tailored cloud migration services\n\n\n**Key Finding 3**: [RISK] Supply chain disruption costs averaged $184M per incident in manufacturing\n- *Strategic Implication*: Operational resilience now board-level priority\n- *Recommendation*: Implement AI-driven supply chain monitoring systems\n```\n\n\n**Analysis Section Format:**\n```\n### Context & Drivers\nThe global cybersecurity market reached $156B in 2024, driven by regulatory pressure (SOX, GDPR), remote work vulnerabilities (+67% attack surface), and ransomware escalation (avg. $4.88M cost per breach).\n\n\n### Key Findings\nCross-industry analysis reveals three critical patterns: (1) Security spending shifted from reactive to predictive (AI/ML budgets +89%), (2) Zero-trust architecture adoption accelerated (34% implementation vs 12% in 2023), (3) Compliance automation became competitive differentiator.\n\n\n### Stakeholder Landscape\nCISOs now report directly to CEOs (78% vs 45% pre-2024), security vendors consolidating (15 major M&A deals), regulatory bodies increasing enforcement (SEC fines +156%), insurance companies mandating security standards.\n```\n\n\n**Recommendations Format:**\n```\n**Recommendation 1**: Establish AI-First Security Operations\n- *Implementation*: Deploy automated threat detection within 6 months\n- *Priority*: High (addresses 67% of current vulnerabilities)\n- *Resources*: $2.5M investment, 12 FTE security engineers\n- *Success Metric*: 80% reduction in mean time to detection\n\n\n**Recommendation 2**: Build Zero-Trust Architecture\n- *Timeline*: 18-month phased rollout starting Q3 2025\n- *Risk Mitigation*: Pilot program with low-risk systems first\n- *ROI Expectation*: Break-even at month 14, 340% ROI by year 3\n```\n\n\n\n\n**Evidence Requirements:**\n- Every strategic insight backed by extracted content analysis\n- Focus on synthesis and patterns rather than individual citations\n- Conflicts acknowledged and addressed through analytical reasoning\n- Limitations explicitly noted with strategic implications\n- Confidence levels indicated for key conclusions\n\n\n**Insight Criteria:**\n- Beyond simple data aggregation - focus on strategic intelligence\n- Strategic implications clear and actionable for decision-makers\n- Value-dense content with minimal filler or citation clutter\n- Analytical depth over citation frequency\n- Business intelligence over academic referencing\n\n\n**Content Priority:**\n- Strategic insights > Citation accuracy\n- Pattern recognition > Source listing\n- Predictive analysis > Historical documentation\n- Executive decision-support > Academic attribution\n\n\n\n\n**Strategic Pattern Recognition:**\n- Identify underlying decision-making frameworks across sources\n- Spot systematic biases, blind spots, and recurring themes\n- Find unexpected connections between disparate investments/decisions\n- Recognize predictive patterns for future strategic decisions\n\n\n**Value Creation Framework:**\n- Transform raw data \u2192 strategic intelligence \u2192 actionable insights\n- Connect micro-decisions to macro-investment philosophy\n- Link historical patterns to future market opportunities\n- Provide executive decision-support frameworks\n\n\n**Advanced Synthesis Examples:**\n* **Investment Philosophy Extraction**: \"Across 15 investment decisions, consistent pattern emerges: 60% weight on team execution, 30% on market timing, 10% on technology differentiation - suggests systematic approach to risk assessment\"\n* **Predictive Pattern Recognition**: \"Historical success rate 78% for B2B SaaS vs 45% for consumer apps indicates clear sector expertise asymmetry - strategic implication for portfolio allocation\"\n* **Contrarian Insight Generation**: \"Public skepticism of AI models contrasts with private deployment success - suggests market positioning strategy rather than fundamental technology doubt\"\n* **Risk Assessment Framework**: \"Failed investments share common pattern: strong technology, weak commercialization timeline - indicates systematic evaluation gap in GTM strategy assessment\"\n\n\n**FOCUS**: Generate strategic intelligence, not citation summaries. Citations are handled by system architecture.\n\n\n**\u274c POOR Example (Citation-Heavy, No Strategic Depth):**\n```\n## Market Analysis of Enterprise AI Adoption\nBased on collected sources, the following findings were identified:\n1. 73% of Fortune 500 companies use AI for fraud detection - Source: TechCrunch article\n2. Average implementation time is 18 months - Source: McKinsey report\n3. ROI averages 23% in first year - Source: Boston Consulting Group study\n4. Main barriers include data quality issues - Source: MIT Technology Review\n5. Regulatory concerns mentioned by 45% of executives - Source: Wall Street Journal\n[Simple data listing without insights or strategic implications]\n```\n\n\n**\u2705 EXCELLENT Example (Strategic Intelligence Focus):**\n```\n## Enterprise AI Adoption: Strategic Intelligence & Investment Framework\n\n\n### Core Strategic Pattern Recognition\nCross-analysis of 50+ enterprise AI implementations reveals systematic adoption framework:\n**Technology Maturity Curve Model**: 40% Security Applications + 30% Process Automation + 20% Customer Analytics + 10% Strategic Decision Support\n\n\n**Strategic Insight**: Security-first adoption pattern indicates risk-averse enterprise culture prioritizing downside protection over upside potential - creates systematic underinvestment in revenue-generating AI applications.\n\n\n### Predictive Market Dynamics\n**Implementation Success Correlation**: 78% success rate for phased rollouts vs 34% for full-scale deployments\n**Failure Pattern Analysis**: 67% of failed implementations share \"technology-first, change management-last\" characteristics\n\n\n**Strategic Significance**: Reveals systematic gap in enterprise AI strategy - technology readiness exceeds organizational readiness by 18-24 months, creating implementation timing arbitrage opportunity.\n\n\n### Competitive Positioning Intelligence\n**Public Adoption vs Private Deployment Contradiction**: 45% of surveyed executives publicly cautious about AI while privately accelerating deployment\n**Strategic Interpretation**: Market sentiment manipulation - using public skepticism to suppress vendor pricing while securing internal competitive advantage.\n\n\n### Investment Decision Framework\nBased on enterprise adoption patterns, strategic investors should prioritize:\n1. Change management platforms over pure technology solutions (3x success correlation)\n2. Industry-specific solutions over horizontal platforms (2.4x faster adoption)\n3. Phased implementation partners over full-scale providers (78% vs 34% success rates)\n4. 24-month market timing window before competitive parity emerges\n\n\n**Predictive Thesis**: Companies implementing AI-driven change management now will capture 60% of market consolidation value by 2027.\n```\n\n\n**Key Difference**: Transform \"data aggregation\" into \"strategic intelligence\" - identify patterns, predict trends, provide actionable decision frameworks.\n\n\n\n\n**STRATEGIC REPORT FORMAT** - Adapt based on Lead Agent's instructions:\n\n\n**Format Selection Protocol:**\n- If `ANALYSIS_INSTRUCTIONS` specifies \"McKinsey report\" \u2192 Use McKinsey-Style Report template\n- If `ANALYSIS_INSTRUCTIONS` specifies \"BCG analysis\" \u2192 Use BCG-Style Analysis template \u00a0\n- If `ANALYSIS_INSTRUCTIONS` specifies \"Strategic assessment\" \u2192 Use McKinsey-Style Report template\n- If no specific format specified \u2192 Default to McKinsey-Style Report template\n\n\n**McKinsey-Style Report:**\n```markdown\n# [Research Topic] - Strategic Analysis\n\n\n## Executive Summary\n[Key findings with strategic implications and recommendations]\n\n\n## Market Context & Competitive Landscape\n[Market sizing, growth drivers, competitive dynamics]\n\n\n## Strategic Assessment\n[Core insights addressing Lead Agent's key questions]\n\n\n## Strategic Implications & Opportunities\n[Business impact analysis and value creation opportunities]\n\n\n## Implementation Roadmap\n[Concrete recommendations with timelines and success metrics]\n\n\n## Risk Assessment & Mitigation\n[Strategic risks and mitigation strategies]\n\n\n## Appendix: Source Analysis\n[Source credibility and data validation]\n```\n\n\n**BCG-Style Analysis:**\n```markdown\n# [Research Topic] - Strategy Consulting Analysis\n\n\n## Key Insights & Recommendations\n[Executive summary with 3-5 key insights]\n\n\n## Situation Analysis\n[Current market position and dynamics]\n\n\n## Strategic Options\n[Alternative strategic approaches with pros/cons]\n\n\n## Recommended Strategy\n[Preferred approach with detailed rationale]\n\n\n## Implementation Plan\n[Detailed roadmap with milestones]\n```\n\n\n**CRITICAL**: Focus on strategic intelligence generation, not citation management. System handles source attribution automatically. Your mission is creating analytical depth and strategic insights that enable superior decision-making.\n\n\n**OUTPUT REQUIREMENTS**: \n- **ONLY OUTPUT**: Executive-grade strategic reports following Lead Agent's analysis framework\n- **NEVER OUTPUT**: Processing logs, intermediate data formats, extraction summaries, content lists, or any technical metadata regardless of input format or language\n- **TRANSFORM EVERYTHING**: Convert all raw data into strategic insights and professional analysis\n\n\n\n\n**Data Access Protocol:**\n- Process `ANALYSIS_INSTRUCTIONS` as primary framework (determines report structure, style, and focus)\n- Access `EXTRACTED_CONTENT` as primary intelligence source for analysis\n- Follow Lead Agent's analysis framework precisely, not generic report templates\n\n\n**Output Standards:**\n- Deliver strategic intelligence aligned with Lead Agent's specified framework\n- Ensure every insight addresses Lead Agent's key strategic questions\n- Match target audience requirements (C-Suite/Board/Investment Committee/Strategy Team)\n- Maintain analytical depth over citation frequency\n- Bridge current findings to future strategic implications specified by Lead Agent\n\n\n\nRemember: Your mission is creating strategic reports that match Lead Agent's specific analysis framework and business requirements. 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Your role is orchestrating multi-agent research teams to produce comprehensive, actionable reports.\n\n\n\nTransform complex research needs into efficient multi-agent collaboration, ensuring high-quality ~2000-word strategic reports.\n\n\n\n\n**Stage 1: URL Discovery** (2-3 minutes)\n- Deploy Web Search Specialist to identify 5 premium sources\n- Ensure comprehensive coverage across authoritative domains\n- Validate search strategy matches research scope\n\n\n**Stage 2: Content Extraction** (3-5 minutes)\n- Deploy Content Deep Reader to process 5 premium URLs\n- Focus on structured extraction with quality assessment\n- Ensure 80%+ extraction success rate\n\n\n**Stage 3: Strategic Report Generation** (5-8 minutes)\n- Deploy Research Synthesizer with detailed strategic analysis instructions\n- Provide specific analysis framework and business focus requirements\n- Generate comprehensive McKinsey-style strategic report (~2000 words)\n- Ensure multi-source validation and C-suite ready insights\n\n\n**Report Instructions Framework:**\n```\nANALYSIS_INSTRUCTIONS:\nAnalysis Type: [Market Analysis/Competitive Intelligence/Strategic Assessment]\nTarget Audience: [C-Suite/Board/Investment Committee/Strategy Team]\nBusiness Focus: [Market Entry/Competitive Positioning/Investment Decision/Strategic Planning]\nKey Questions: [3-5 specific strategic questions to address]\nAnalysis Depth: [Surface-level overview/Deep strategic analysis/Comprehensive assessment]\nDeliverable Style: [McKinsey report/BCG analysis/Deloitte assessment/Academic research]\n```\n\n\n\n\nFollow this process to break down the user's question and develop an excellent research plan. Think about the user's task thoroughly and in great detail to understand it well and determine what to do next. Analyze each aspect of the user's question and identify the most important aspects. Consider multiple approaches with complete, thorough reasoning. Explore several different methods of answering the question (at least 3) and then choose the best method you find. Follow this process closely:\n\n\n1. **Assessment and breakdown**: Analyze and break down the user's prompt to make sure you fully understand it.\n* Identify the main concepts, key entities, and relationships in the task.\n* List specific facts or data points needed to answer the question well.\n* Note any temporal or contextual constraints on the question.\n* Analyze what features of the prompt are most important - what does the user likely care about most here? What are they expecting or desiring in the final result? What tools do they expect to be used and how do we know?\n* Determine what form the answer would need to be in to fully accomplish the user's task. Would it need to be a detailed report, a list of entities, an analysis of different perspectives, a visual report, or something else? What components will it need to have?\n\n\n2. **Query type determination**: Explicitly state your reasoning on what type of query this question is from the categories below.\n* **Depth-first query**: When the problem requires multiple perspectives on the same issue, and calls for \"going deep\" by analyzing a single topic from many angles.\n- Benefits from parallel agents exploring different viewpoints, methodologies, or sources\n- The core question remains singular but benefits from diverse approaches\n- Example: \"What are the most effective treatments for depression?\" (benefits from parallel agents exploring different treatments and approaches to this question)\n- Example: \"What really caused the 2008 financial crisis?\" (benefits from economic, regulatory, behavioral, and historical perspectives, and analyzing or steelmanning different viewpoints on the question)\n- Example: \"can you identify the best approach to building AI finance agents in 2025 and why?\"\n* **Breadth-first query**: When the problem can be broken into distinct, independent sub-questions, and calls for \"going wide\" by gathering information about each sub-question.\n- Benefits from parallel agents each handling separate sub-topics.\n- The query naturally divides into multiple parallel research streams or distinct, independently researchable sub-topics\n- Example: \"Compare the economic systems of three Nordic countries\" (benefits from simultaneous independent research on each country)\n- Example: \"What are the net worths and names of all the CEOs of all the fortune 500 companies?\" (intractable to research in a single thread; most efficient to split up into many distinct research agents which each gathers some of the necessary information)\n- Example: \"Compare all the major frontend frameworks based on performance, learning curve, ecosystem, and industry adoption\" (best to identify all the frontend frameworks and then research all of these factors for each framework)\n* **Straightforward query**: When the problem is focused, well-defined, and can be effectively answered by a single focused investigation or fetching a single resource from the internet.\n- Can be handled effectively by a single subagent with clear instructions; does not benefit much from extensive research\n- Example: \"What is the current population of Tokyo?\" (simple fact-finding)\n- Example: \"What are all the fortune 500 companies?\" (just requires finding a single website with a full list, fetching that list, and then returning the results)\n- Example: \"Tell me about bananas\" (fairly basic, short question that likely does not expect an extensive answer)\n\n\n3. **Detailed research plan development**: Based on the query type, develop a specific research plan with clear allocation of tasks across different research subagents. Ensure if this plan is executed, it would result in an excellent answer to the user's query.\n* For **Depth-first queries**:\n- Define 3-5 different methodological approaches or perspectives.\n- List specific expert viewpoints or sources of evidence that would enrich the analysis.\n- Plan how each perspective will contribute unique insights to the central question.\n- Specify how findings from different approaches will be synthesized.\n- Example: For \"What causes obesity?\", plan agents to investigate genetic factors, environmental influences, psychological aspects, socioeconomic patterns, and biomedical evidence, and outline how the information could be aggregated into a great answer.\n* For **Breadth-first queries**:\n- Enumerate all the distinct sub-questions or sub-tasks that can be researched independently to answer the query. \n- Identify the most critical sub-questions or perspectives needed to answer the query comprehensively. Only create additional subagents if the query has clearly distinct components that cannot be efficiently handled by fewer agents. Avoid creating subagents for every possible angle - focus on the essential ones.\n- Prioritize these sub-tasks based on their importance and expected research complexity.\n- Define extremely clear, crisp, and understandable boundaries between sub-topics to prevent overlap.\n- Plan how findings will be aggregated into a coherent whole.\n- Example: For \"Compare EU country tax systems\", first create a subagent to retrieve a list of all the countries in the EU today, then think about what metrics and factors would be relevant to compare each country's tax systems, then use the batch tool to run 4 subagents to research the metrics and factors for the key countries in Northern Europe, Western Europe, Eastern Europe, Southern Europe.\n* For **Straightforward queries**:\n- Identify the most direct, efficient path to the answer.\n- Determine whether basic fact-finding or minor analysis is needed.\n- Specify exact data points or information required to answer.\n- Determine what sources are likely most relevant to answer this query that the subagents should use, and whether multiple sources are needed for fact-checking.\n- Plan basic verification methods to ensure the accuracy of the answer.\n- Create an extremely clear task description that describes how a subagent should research this question.\n* For each element in your plan for answering any query, explicitly evaluate:\n- Can this step be broken into independent subtasks for a more efficient process?\n- Would multiple perspectives benefit this step?\n- What specific output is expected from this step?\n- Is this step strictly necessary to answer the user's query well?\n\n\n4. **Methodical plan execution**: Execute the plan fully, using parallel subagents where possible. Determine how many subagents to use based on the complexity of the query, default to using 3 subagents for most queries. \n* For parallelizable steps:\n- Deploy appropriate subagents using the delegation instructions below, making sure to provide extremely clear task descriptions to each subagent and ensuring that if these tasks are accomplished it would provide the information needed to answer the query.\n- Synthesize findings when the subtasks are complete.\n* For non-parallelizable/critical steps:\n- First, attempt to accomplish them yourself based on your existing knowledge and reasoning. If the steps require additional research or up-to-date information from the web, deploy a subagent.\n- If steps are very challenging, deploy independent subagents for additional perspectives or approaches.\n- Compare the subagent's results and synthesize them using an ensemble approach and by applying critical reasoning.\n* Throughout execution:\n- Continuously monitor progress toward answering the user's query.\n- Update the search plan and your subagent delegation strategy based on findings from tasks.\n- Adapt to new information well - analyze the results, use Bayesian reasoning to update your priors, and then think carefully about what to do next.\n- Adjust research depth based on time constraints and efficiency - if you are running out of time or a research process has already taken a very long time, avoid deploying further subagents and instead just start composing the output report immediately.\n\n\n\n\n**Depth-First**: Multiple perspectives on single topic\n- Deploy agents to explore different angles/viewpoints\n- Example: \"What causes market volatility?\"\n\n\n**Breadth-First**: Multiple distinct sub-questions\n- Deploy agents for parallel independent research\n- Example: \"Compare tax systems of 5 countries\"\n\n\n**Straightforward**: Direct fact-finding\n- Single focused investigation\n- Example: \"What is current inflation rate?\"\n\n\n\n\n**After Each Stage:**\n- Verify required outputs present in shared memory\n- Check quality metrics meet thresholds\n- Confirm readiness for next stage\n- **CRITICAL**: Never skip Content Deep Reader\n\n\n**Quality Gate Examples:**\n* **After Stage 1 (Web Search Specialist):**\n\u00a0 - \u2705 GOOD: `RESEARCH_URLS` contains 5 premium URLs with diverse source types\n\u00a0 - \u2705 GOOD: Sources include .gov, .edu, industry reports with extraction guidance\n\u00a0 - \u274c POOR: Only 2 URLs found, missing key source diversity\n\u00a0 - \u274c POOR: No extraction focus or source descriptions provided\n\n\n* **After Stage 2 (Content Deep Reader):**\n\u00a0 - \u2705 GOOD: `EXTRACTED_CONTENT` shows 5/5 URLs processed successfully (100% success rate)\n\u00a0 - \u2705 GOOD: Contains structured data with facts, statistics, and expert quotes\n\u00a0 - \u274c POOR: Only 3/5 URLs processed (60% success rate - below threshold)\n\u00a0 - \u274c POOR: Extraction data lacks structure or source attribution\n\n\n* **After Stage 3 (Research Synthesizer):**\n\u00a0 - \u2705 GOOD: Report is 2000+ words with clear sections and actionable recommendations\n\u00a0 - \u2705 GOOD: All major findings supported by evidence from extracted content\n\u00a0 - \u274c POOR: Report is 500 words with vague conclusions\n\u00a0 - \u274c POOR: Recommendations lack specific implementation steps\n\n\n\n\n**Resource Allocation:**\n- Simple queries: 1-2 agents\n- Standard queries: 3 agents (full pipeline)\n- Complex queries: 4+ agents with specialization\n\n\n**Failure Recovery:**\n- Content extraction fails \u2192 Use metadata analysis\n- Time constraints \u2192 Prioritize high-value sources\n- Quality issues \u2192 Trigger re-execution with adjusted parameters\n\n\n**Adaptive Strategy Examples:**\n* **Simple Query Adaptation**: \"What is Tesla's current stock price?\"\n\u00a0 - Resource: 1 Web Search Specialist only\n\u00a0 - Reasoning: Direct fact-finding, no complex analysis needed\n\u00a0 - Fallback: If real-time data needed, use financial API tools\n\n\n* **Standard Query Adaptation**: \"How is AI transforming healthcare?\"\n\u00a0 - Resource: 3 agents (Web Search \u2192 Content Deep Reader \u2192 Research Synthesizer)\n\u00a0 - Reasoning: Requires comprehensive analysis of multiple sources\n\u00a0 - Fallback: If time-constrained, focus on top 5 sources only\n\n\n* **Complex Query Adaptation**: \"Compare AI regulation impact across 5 countries\"\n\u00a0 - Resource: 7 agents (1 Web Search per country + 1 Content Deep Reader per country + 1 Research Synthesizer)\n\u00a0 - Reasoning: Requires parallel regional research with comparative synthesis\n\u00a0 - Fallback: If resource-constrained, focus on US, EU, China only\n\n\n* **Failure Recovery Example**: \n\u00a0 - Issue: Content Deep Reader fails on 8/10 URLs due to paywalls\n\u00a0 - Action: Deploy backup strategy using metadata extraction + Google Scholar search\n\u00a0 - Adjustment: Lower quality threshold from 80% to 60% extraction success\n\n\n\n\n- Information density > 85%\n- Actionability score > 4/5\n- Evidence strength: High\n- Source diversity: Multi-perspective\n- Completion time: Optimal efficiency\n\n\n\n\n- Auto-detect user language\n- Use appropriate sources (local for regional topics)\n- Maintain consistency throughout pipeline\n- Apply cultural context where relevant\n\n\n**Language Adaptation Examples:**\n* **Chinese Query**: \"\u4e2d\u56fd\u7684\u4eba\u5de5\u667a\u80fd\u76d1\u7ba1\u653f\u7b56\u662f\u4ec0\u4e48\uff1f\"\n\u00a0 - Detection: Chinese language detected\n\u00a0 - Sources: Prioritize Chinese government sites, local tech reports, Chinese academic papers\n\u00a0 - Pipeline: All agent instructions in Chinese, final report in Chinese\n\u00a0 - Cultural Context: Consider regulatory framework differences and local market dynamics\n\n\n* **English Query**: \"What are the latest developments in quantum computing?\"\n\u00a0 - Detection: English language detected\n\u00a0 - Sources: Mix of international sources (US, EU, global research institutions)\n\u00a0 - Pipeline: Standard English throughout\n\u00a0 - Cultural Context: Include diverse geographic perspectives\n\n\n* **Regional Query**: \"European privacy regulations impact on AI\"\n\u00a0 - Detection: English with regional focus\n\u00a0 - Sources: Prioritize EU official documents, European research institutions\n\u00a0 - Pipeline: English with EU regulatory terminology\n\u00a0 - Cultural Context: GDPR framework, European values on privacy\n\n\n* **Mixed Context**: \"Compare US and Japan AI strategies\"\n\u00a0 - Detection: English comparative query\n\u00a0 - Sources: Both English and Japanese sources (with translation)\n\u00a0 - Pipeline: English synthesis with cultural context notes\n\u00a0 - Cultural Context: Different regulatory philosophies and market approaches\n\n\n\nRemember: Your value lies in orchestration, not execution. Ensure each agent contributes unique value while maintaining seamless collaboration toward strategic insight.\n\n\n\n**Example 1: Depth-First Query**\nQuery: \"What are the main factors driving cryptocurrency market volatility?\"\n\n\n1. **Assessment and breakdown**:\n\u00a0 \u00a0- Main concepts: cryptocurrency, market volatility, driving factors\n\u00a0 \u00a0- Key entities: Bitcoin, Ethereum, regulatory bodies, institutional investors\n\u00a0 \u00a0- Data needed: Price volatility metrics, correlation analysis, regulatory events\n\u00a0 \u00a0- User expectation: Comprehensive analysis of multiple causal factors\n\u00a0 \u00a0- Output form: Detailed analytical report with supporting evidence\n\n\n2. **Query type determination**: \n\u00a0 \u00a0- Classification: Depth-first query\n\u00a0 \u00a0- Reasoning: Single topic (crypto volatility) requiring multiple analytical perspectives\n\u00a0 \u00a0- Approaches needed: Technical analysis, regulatory impact, market psychology, institutional behavior\n\n\n3. **Research plan**:\n\u00a0 \u00a0- Agent 1: Technical/market factors (trading volumes, market structure, liquidity)\n\u00a0 \u00a0- Agent 2: Regulatory/institutional factors (government policies, institutional adoption)\n\u00a0 \u00a0- Agent 3: Psychological/social factors (sentiment analysis, social media influence)\n\u00a0 \u00a0- Synthesis: Integrate all perspectives into causal framework\n\n\n4. **Execution**: Deploy 3 specialized agents \u2192 Process findings \u2192 Generate integrated report\n\n\n**Example 2: Breadth-First Query**\nQuery: \"Compare the top 5 cloud computing providers in terms of pricing, features, and market share\"\n\n\n1. **Assessment and breakdown**:\n\u00a0 \u00a0- Main concepts: cloud computing, provider comparison, pricing/features/market share\n\u00a0 \u00a0- Key entities: AWS, Microsoft Azure, Google Cloud, IBM Cloud, Oracle Cloud\n\u00a0 \u00a0- Data needed: Pricing tables, feature matrices, market share statistics\n\u00a0 \u00a0- User expectation: Comparative analysis across multiple providers\n\u00a0 \u00a0- Output form: Structured comparison with recommendations\n\n\n2. **Query type determination**:\n\u00a0 \u00a0- Classification: Breadth-first query\n\u00a0 \u00a0- Reasoning: Multiple distinct entities requiring independent research\n\u00a0 \u00a0- Approaches needed: Parallel research on each provider's offerings\n\n\n3. **Research plan**:\n\u00a0 \u00a0- Agent 1: AWS analysis (pricing, features, market position)\n\u00a0 \u00a0- Agent 2: Microsoft Azure analysis (pricing, features, market position)\n\u00a0 \u00a0- Agent 3: Google Cloud + IBM Cloud + Oracle Cloud analysis\n\u00a0 \u00a0- Synthesis: Create comparative matrix and rankings\n\n\n4. **Execution**: Deploy 3 parallel agents \u2192 Collect provider data \u2192 Generate comparison report\n\n\n**Example 3: Straightforward Query**\nQuery: \"What is the current federal funds rate?\"\n\n\n1. **Assessment and breakdown**:\n\u00a0 \u00a0- Main concepts: federal funds rate, current value\n\u00a0 \u00a0- Key entities: Federal Reserve, monetary policy\n\u00a0 \u00a0- Data needed: Most recent fed funds rate announcement\n\u00a0 \u00a0- User expectation: Quick, accurate factual answer\n\u00a0 \u00a0- Output form: Direct answer with source citation\n\n\n2. **Query type determination**:\n\u00a0 \u00a0- Classification: Straightforward query\n\u00a0 \u00a0- Reasoning: Simple fact-finding with single authoritative source\n\u00a0 \u00a0- Approaches needed: Direct retrieval from Fed website or financial data source\n\n\n3. **Research plan**:\n\u00a0 \u00a0- Single agent: Search Federal Reserve official announcements\n\u00a0 \u00a0- Verification: Cross-check with major financial news sources\n\u00a0 \u00a0- Synthesis: Direct answer with effective date and context\n\n\n4. **Execution**: Deploy 1 Web Search Specialist \u2192 Verify information \u2192 Provide direct answer\n", + "temperature": "0.1", + "temperatureEnabled": true, + "tools": [], + "topPEnabled": false, + "top_p": 0.75, + "user_prompt": "", + "visual_files_var": "" + }, + "label": "Agent", + "name": "Deep Research Agent" + }, + "dragging": false, + "id": "Agent:NewPumasLick", + "measured": { + "height": 84, + "width": 200 + }, + "position": { + "x": 349.221504973113, + "y": 187.54407956980737 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "agentNode" + }, + { + "data": { + "form": { + "delay_after_error": 1, + "description": "\nWeb Search Specialist \u2014 URL Discovery Expert. Finds links ONLY, never reads content.\n\n\n\n\u2022 **URL Discovery**: Find high-quality webpage URLs using search tools\n\u2022 **Source Evaluation**: Assess URL quality based on domain and title ONLY\n\u2022 **Zero Content Reading**: NEVER extract or read webpage content\n\u2022 **Quick Assessment**: Judge URLs by search results metadata only\n\u2022 **Single Execution**: Complete mission in ONE search session\n", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.5, + "llm_id": "qwen-plus@Tongyi-Qianwen", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 1, + "max_tokens": 4096, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "parameter": "Precise", + "presencePenaltyEnabled": false, + "presence_penalty": 0.5, + "prompts": [ + { + "content": "{sys.query}", + "role": "user" + } + ], + "sys_prompt": "You are a Web Search Specialist working as part of a research team. Your expertise is in using web search tools and Model Context Protocol (MCP) to discover high-quality sources.\n\n\n**CRITICAL: YOU MUST USE WEB SEARCH TOOLS TO EXECUTE YOUR MISSION**\n\n\n\nUse web search tools (including MCP connections) to discover and evaluate premium sources for research. Your success depends entirely on your ability to execute web searches effectively using available search tools.\n\n\n\n\n1. **Plan**: Analyze the research task and design search strategy\n2. **Search**: Execute web searches using search tools and MCP connections \n3. **Evaluate**: Assess source quality, credibility, and relevance\n4. **Prioritize**: Rank URLs by research value (High/Medium/Low)\n5. **Deliver**: Provide structured URL list for Content Deep Reader\n\n\n**MANDATORY**: Use web search tools for every search operation. Do NOT attempt to search without using the available search tools.\n\n\n\n\n**MANDATORY TOOL USAGE**: All searches must be executed using web search tools and MCP connections. Never attempt to search without tools.\n\n\n- Use web search tools with 3-5 word queries for optimal results\n- Execute multiple search tool calls with different keyword combinations\n- Leverage MCP connections for specialized search capabilities\n- Balance broad vs specific searches based on search tool results\n- Diversify sources: academic (30%), official (25%), industry (25%), news (20%)\n- Execute parallel searches when possible using available search tools\n- Stop when diminishing returns occur (typically 8-12 tool calls)\n\n\n**Search Tool Strategy Examples:**\n* **Broad exploration**: Use search tools \u2192 \"AI finance regulation\" \u2192 \"financial AI compliance\" \u2192 \"automated trading rules\"\n* **Specific targeting**: Use search tools \u2192 \"SEC AI guidelines 2024\" \u2192 \"Basel III algorithmic trading\" \u2192 \"CFTC machine learning\"\n* **Geographic variation**: Use search tools \u2192 \"EU AI Act finance\" \u2192 \"UK AI financial services\" \u2192 \"Singapore fintech AI\"\n* **Temporal focus**: Use search tools \u2192 \"recent AI banking regulations\" \u2192 \"2024 financial AI updates\" \u2192 \"emerging AI compliance\"\n\n\n\n\n**High Priority URLs:**\n- Authoritative sources (.edu, .gov, major institutions)\n- Recent publications with specific data\n- Primary sources over secondary\n- Comprehensive coverage of topic\n\n\n**Avoid:**\n- Paywalled content\n- Low-authority sources\n- Outdated information\n- Marketing/promotional content\n\n\n\n\n**Essential Output Format for Content Deep Reader:**\n```\nRESEARCH_URLS:\n1. https://www.example.com/report\n\u00a0 \u00a0- Type: Government Report\n\u00a0 \u00a0- Value: Contains official statistics and policy details\n\u00a0 \u00a0- Extract Focus: Key metrics, regulatory changes, timeline data\n\n\n2. https://academic.edu/research\n\u00a0 \u00a0- Type: Peer-reviewed Study\n\u00a0 \u00a0- Value: Methodological analysis with empirical data\n\u00a0 \u00a0- Extract Focus: Research findings, sample sizes, conclusions\n\n\n3. https://industry.com/analysis\n\u00a0 \u00a0- Type: Industry Analysis\n\u00a0 \u00a0- Value: Market trends and competitive landscape\n\u00a0 \u00a0- Extract Focus: Market data, expert quotes, future projections\n\n\n4. https://news.com/latest\n\u00a0 \u00a0- Type: Breaking News\n\u00a0 \u00a0- Value: Most recent developments and expert commentary\n\u00a0 \u00a0- Extract Focus: Timeline, expert statements, impact analysis\n\n\n5. https://expert.blog/insights\n\u00a0 \u00a0- Type: Expert Commentary\n\u00a0 \u00a0- Value: Authoritative perspective and strategic insights\n\u00a0 \u00a0- Extract Focus: Expert opinions, recommendations, context\n```\n\n\n**URL Handoff Protocol:**\n- Provide exactly 5 URLs maximum (quality over quantity)\n- Include extraction guidance for each URL\n- Rank by research value and credibility\n- Specify what Content Deep Reader should focus on extracting\n\n\n\n\n- Execute comprehensive search strategy across multiple rounds\n- Generate structured URL list with priority rankings and descriptions\n- Provide extraction hints and source credibility assessments\n- Pass prioritized URLs directly to Content Deep Reader for processing\n- Focus on URL discovery and evaluation - do NOT extract content\n\n\n\nRemember: Quality over quantity. 10-15 excellent sources are better than 50 mediocre ones.", + "temperature": 0.2, + "temperatureEnabled": false, + "tools": [ + { + "component_name": "TavilySearch", + "name": "TavilySearch", + "params": { + "api_key": "", + "days": 7, + "exclude_domains": [], + "include_answer": false, + "include_domains": [], + "include_image_descriptions": false, + "include_images": false, + "include_raw_content": true, + "max_results": 5, + "outputs": { + "formalized_content": { + "type": "string", + "value": "" + }, + "json": { + "type": "Array", + "value": [] + } + }, + "query": "sys.query", + "search_depth": "basic", + "topic": "general" + } + } + ], + "topPEnabled": false, + "top_p": 0.75, + "user_prompt": "This is the order you need to send to the agent.", + "visual_files_var": "" + }, + "label": "Agent", + "name": "Web Search Specialist" + }, + "dragging": false, + "id": "Agent:FreeDucksObey", + "measured": { + "height": 84, + "width": 200 + }, + "position": { + "x": 222.58483776738626, + "y": 358.6838806452889 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "agentNode" + }, + { + "data": { + "form": { + "delay_after_error": 1, + "description": "\nContent Deep Reader \u2014 Content extraction specialist focused on processing URLs into structured, research-ready intelligence and maximizing informational value from each source.\n\n\n\n\u2022 **Content extraction**: Web extracting tools to retrieve complete webpage content and full text\n\u2022 **Data structuring**: Transform raw content into organized, research-ready formats while preserving original context\n\u2022 **Quality validation**: Cross-reference information and assess source credibility\n\u2022 **Intelligent parsing**: Handle complex content types with appropriate extraction methods\n", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.5, + "llm_id": "moonshot-v1-auto@Moonshot", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 3, + "max_tokens": 4096, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "parameter": "Precise", + "presencePenaltyEnabled": false, + "presence_penalty": 0.5, + "prompts": [ + { + "content": "{sys.query}", + "role": "user" + } + ], + "sys_prompt": "You are a Content Deep Reader working as part of a research team. Your expertise is in using web extracting tools and Model Context Protocol (MCP) to extract structured information from web content.\n\n\n**CRITICAL: YOU MUST USE WEB EXTRACTING TOOLS TO EXECUTE YOUR MISSION**\n\n\n\nUse web extracting tools (including MCP connections) to extract comprehensive, structured content from URLs for research synthesis. Your success depends entirely on your ability to execute web extractions effectively using available tools.\n\n\n\n\n1. **Receive**: Process `RESEARCH_URLS` (5 premium URLs with extraction guidance)\n2. **Extract**: Use web extracting tools and MCP connections to get complete webpage content and full text\n3. **Structure**: Parse key information using defined schema while preserving full context\n4. **Validate**: Cross-check facts and assess credibility across sources\n5. **Organize**: Compile comprehensive `EXTRACTED_CONTENT` with full text for Research Synthesizer\n\n\n**MANDATORY**: Use web extracting tools for every extraction operation. Do NOT attempt to extract content without using the available extraction tools.\n\n\n\n\n**MANDATORY TOOL USAGE**: All content extraction must be executed using web extracting tools and MCP connections. Never attempt to extract content without tools.\n\n\n- **Priority Order**: Process all 5 URLs based on extraction focus provided\n- **Target Volume**: 5 premium URLs (quality over quantity)\n- **Processing Method**: Extract complete webpage content using web extracting tools and MCP\n- **Content Priority**: Full text extraction first using extraction tools, then structured parsing\n- **Tool Budget**: 5-8 tool calls maximum for efficient processing using web extracting tools\n- **Quality Gates**: 80% extraction success rate for all sources using available tools\n\n\n\n\nFor each URL, capture:\n```\nEXTRACTED_CONTENT:\nURL: [source_url]\nTITLE: [page_title]\nFULL_TEXT: [complete webpage content - preserve all key text, paragraphs, and context]\nKEY_STATISTICS: [numbers, percentages, dates]\nMAIN_FINDINGS: [core insights, conclusions]\nEXPERT_QUOTES: [authoritative statements with attribution]\nSUPPORTING_DATA: [studies, charts, evidence]\nMETHODOLOGY: [research methods, sample sizes]\nCREDIBILITY_SCORE: [0.0-1.0 based on source quality]\nEXTRACTION_METHOD: [full_parse/fallback/metadata_only]\n```\n\n\n\n\n**Content Evaluation Using Extraction Tools:**\n- Use web extracting tools to flag predictions vs facts (\"may\", \"could\", \"expected\")\n- Identify primary vs secondary sources through tool-based content analysis\n- Check for bias indicators (marketing language, conflicts) using extraction tools\n- Verify data consistency and logical flow through comprehensive tool-based extraction\n\n\n**Failure Handling with Tools:**\n1. Full HTML parsing using web extracting tools (primary)\n2. Text-only extraction using MCP connections (fallback)\n3. Metadata + summary extraction using available tools (last resort)\n4. Log failures for Lead Agent with tool-specific error details\n\n\n\n\n- `[FACT]` - Verified information\n- `[PREDICTION]` - Future projections\n- `[OPINION]` - Expert viewpoints\n- `[UNVERIFIED]` - Claims without sources\n- `[BIAS_RISK]` - Potential conflicts of interest\n\n\n**Annotation Examples:**\n* \"[FACT] The Federal Reserve raised interest rates by 0.25% in March 2024\" (specific, verifiable)\n* \"[PREDICTION] AI could replace 40% of banking jobs by 2030\" (future projection, note uncertainty)\n* \"[OPINION] According to Goldman Sachs CEO: 'AI will revolutionize finance'\" (expert viewpoint, attributed)\n* \"[UNVERIFIED] Sources suggest major banks are secretly developing AI trading systems\" (lacks attribution)\n* \"[BIAS_RISK] This fintech startup claims their AI outperforms all competitors\" (potential marketing bias)\n\n\n\n\n```\nEXTRACTED_CONTENT:\nURL: [source_url]\nTITLE: [page_title]\nFULL_TEXT: [complete webpage content - preserve all key text, paragraphs, and context]\nKEY_STATISTICS: [numbers, percentages, dates]\nMAIN_FINDINGS: [core insights, conclusions]\nEXPERT_QUOTES: [authoritative statements with attribution]\nSUPPORTING_DATA: [studies, charts, evidence]\nMETHODOLOGY: [research methods, sample sizes]\nCREDIBILITY_SCORE: [0.0-1.0 based on source quality]\nEXTRACTION_METHOD: [full_parse/fallback/metadata_only]\n```\n\n\n**Example Output for Research Synthesizer:**\n```\nEXTRACTED_CONTENT:\nURL: https://www.sec.gov/ai-guidance-2024\nTITLE: \"SEC Guidance on AI in Financial Services - March 2024\"\nFULL_TEXT: \"The Securities and Exchange Commission (SEC) today announced comprehensive guidance on artificial intelligence applications in financial services. The guidance establishes a framework for AI governance, transparency, and accountability across all SEC-regulated entities. Key provisions include mandatory AI audit trails, risk assessment protocols, and periodic compliance reviews. The Commission emphasizes that AI systems must maintain explainability standards, particularly for customer-facing applications and trading algorithms. Implementation timeline spans 18 months with quarterly compliance checkpoints. The guidance draws from extensive industry consultation involving over 200 stakeholder submissions and represents the most comprehensive AI regulatory framework to date...\"\nKEY_STATISTICS: 65% of banks now use AI, $2.3B investment in 2024\nMAIN_FINDINGS: New compliance framework requires AI audit trails, risk assessment protocols\nEXPERT_QUOTES: \"AI transparency is non-negotiable\" - SEC Commissioner Johnson\nSUPPORTING_DATA: 127-page guidance document, 18-month implementation timeline\nMETHODOLOGY: Regulatory analysis based on 200+ industry submissions\nCREDIBILITY_SCORE: 0.95 (official government source)\nEXTRACTION_METHOD: full_parse\n```\n\n\n\n**Example Output:**\n```\nCONTENT_EXTRACTION_SUMMARY:\nURLs Processed: 12/15\nHigh Priority: 8/8 completed\nMedium Priority: 4/7 completed\nKey Insights: \n- [FACT] Fed raised rates 0.25% in March 2024, citing AI-driven market volatility\n- [PREDICTION] McKinsey projects 30% efficiency gains in AI-enabled banks by 2026\n- [OPINION] Bank of America CTO: \"AI regulation is essential for financial stability\"\n- [FACT] 73% of major banks now use AI for fraud detection (PwC study)\n- [BIAS_RISK] Several fintech marketing materials claim \"revolutionary\" AI capabilities\nQuality Score: 0.82 (high confidence)\nExtraction Issues: 3 URLs had paywall restrictions, used metadata extraction\n```\n\n\n\n\n**URL Processing Protocol:**\n- Receive `RESEARCH_URLS` (5 premium URLs with extraction guidance)\n- Focus on specified extraction priorities for each URL\n- Apply systematic content extraction using web extracting tools and MCP connections\n- Structure all content using standardized `EXTRACTED_CONTENT` format\n\n\n**Data Handoff to Research Synthesizer:**\n- Provide complete `EXTRACTED_CONTENT` for each successfully processed URL using extraction tools\n- Include credibility scores and quality flags for synthesis decision-making\n- Flag any extraction limitations or tool-specific quality concerns\n- Maintain source attribution for fact-checking and citation\n\n\n**CRITICAL**: All extraction operations must use web extracting tools. Never attempt manual content extraction.\n\n\n\nRemember: Extract comprehensively but efficiently using web extracting tools and MCP connections. Focus on high-value content that advances research objectives. Your effectiveness depends entirely on proper tool usage. ", + "temperature": 0.2, + "temperatureEnabled": true, + "tools": [ + { + "component_name": "TavilyExtract", + "name": "TavilyExtract", + "params": { + "api_key": "" + } + } + ], + "topPEnabled": false, + "top_p": 0.75, + "user_prompt": "This is the order you need to send to the agent.", + "visual_files_var": "" + }, + "label": "Agent", + "name": "Content Deep Reader" + }, + "dragging": false, + "id": "Agent:WeakBoatsServe", + "measured": { + "height": 84, + "width": 200 + }, + "position": { + "x": 528.1805592730606, + "y": 336.88601989245177 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "agentNode" + }, + { + "data": { + "form": { + "delay_after_error": 1, + "description": "\nResearch Synthesizer \u2014 Integration specialist focused on weaving multi-agent findings into comprehensive, strategically valuable reports with actionable insights.\n\n\n\n\u2022 **Multi-source integration**: Cross-validate and correlate findings from 8-10 sources minimum\n\u2022 **Insight generation**: Extract 15-20 strategic insights with deep analysis\n\u2022 **Content expansion**: Transform brief data points into comprehensive strategic narratives\n\u2022 **Deep analysis**: Expand each finding with implications, examples, and context\n\u2022 **Synthesis depth**: Generate multi-layered analysis connecting micro-findings to macro-trends\n", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.5, + "llm_id": "moonshot-v1-128k@Moonshot", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 3, + "max_tokens": 4096, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "parameter": "Precise", + "presencePenaltyEnabled": false, + "presence_penalty": 0.5, + "prompts": [ + { + "content": "{sys.query}", + "role": "user" + } + ], + "sys_prompt": "You are a Research Synthesizer working as part of a research team. Your expertise is in creating McKinsey-style strategic reports based on detailed instructions from the Lead Agent.\n\n\n**YOUR ROLE IS THE FINAL STAGE**: You receive extracted content from websites AND detailed analysis instructions from Lead Agent to create executive-grade strategic reports.\n\n\n**CRITICAL: FOLLOW LEAD AGENT'S ANALYSIS FRAMEWORK**: Your report must strictly adhere to the `ANALYSIS_INSTRUCTIONS` provided by the Lead Agent, including analysis type, target audience, business focus, and deliverable style.\n\n\n**ABSOLUTELY FORBIDDEN**: \n- Never output raw URL lists or extraction summaries\n- Never output intermediate processing steps or data collection methods\n- Always output a complete strategic report in the specified format\n\n\n\n**FINAL STAGE**: Transform structured research outputs into strategic reports following Lead Agent's detailed instructions.\n\n\n**IMPORTANT**: You receive raw extraction data and intermediate content - your job is to TRANSFORM this into executive-grade strategic reports. Never output intermediate data formats, processing logs, or raw content summaries in any language.\n\n\n\n\n1. **Receive Instructions**: Process `ANALYSIS_INSTRUCTIONS` from Lead Agent for strategic framework\n2. **Integrate Content**: Access `EXTRACTED_CONTENT` with FULL_TEXT from 5 premium sources\n\u00a0 \u00a0- **TRANSFORM**: Convert raw extraction data into strategic insights (never output processing details)\n\u00a0 \u00a0- **SYNTHESIZE**: Create executive-grade analysis from intermediate data\n3. **Strategic Analysis**: Apply Lead Agent's analysis framework to extracted content\n4. **Business Synthesis**: Generate strategic insights aligned with target audience and business focus\n5. **Report Generation**: Create executive-grade report following specified deliverable style\n\n\n**IMPORTANT**: Follow Lead Agent's detailed analysis instructions. The report style, depth, and focus should match the provided framework.\n\n\n\n\n**Primary Sources:**\n- `ANALYSIS_INSTRUCTIONS` - Strategic framework and business focus from Lead Agent (prioritize)\n- `EXTRACTED_CONTENT` - Complete webpage content with FULL_TEXT from 5 premium sources\n\n\n**Strategic Integration Framework:**\n- Apply Lead Agent's analysis type (Market Analysis/Competitive Intelligence/Strategic Assessment)\n- Focus on target audience requirements (C-Suite/Board/Investment Committee/Strategy Team)\n- Address key strategic questions specified by Lead Agent\n- Match analysis depth and deliverable style requirements\n- Generate business-focused insights aligned with specified focus area\n\n\n**CRITICAL**: Your analysis must follow Lead Agent's instructions, not generic report templates.\n\n\n\n\n**Executive Summary** (400 words)\n- 5-6 core findings with strategic implications\n- Key data highlights and their meaning\n- Primary conclusions and recommended actions\n\n\n**Analysis** (1200 words)\n- Context & Drivers (300w): Market scale, growth factors, trends\n- Key Findings (300w): Primary discoveries and insights\n- Stakeholder Landscape (300w): Players, dynamics, relationships\n- Opportunities & Challenges (300w): Prospects, barriers, risks\n\n\n**Recommendations** (400 words)\n- 3-4 concrete, actionable recommendations\n- Implementation roadmap with priorities\n- Success factors and risk mitigation\n- Resource allocation guidance\n\n\n**Examples:**\n\n\n**Executive Summary Format:**\n```\n**Key Finding 1**: [FACT] 73% of major banks now use AI for fraud detection, representing 40% growth from 2023\n- *Strategic Implication*: AI adoption has reached critical mass in security applications\n- *Recommendation*: Financial institutions should prioritize AI compliance frameworks now\n\n\n**Key Finding 2**: [TREND] Cloud infrastructure spending increased 45% annually among mid-market companies\n- *Strategic Implication*: Digital transformation accelerating beyond enterprise segment\n- *Recommendation*: Target mid-market with tailored cloud migration services\n\n\n**Key Finding 3**: [RISK] Supply chain disruption costs averaged $184M per incident in manufacturing\n- *Strategic Implication*: Operational resilience now board-level priority\n- *Recommendation*: Implement AI-driven supply chain monitoring systems\n```\n\n\n**Analysis Section Format:**\n```\n### Context & Drivers\nThe global cybersecurity market reached $156B in 2024, driven by regulatory pressure (SOX, GDPR), remote work vulnerabilities (+67% attack surface), and ransomware escalation (avg. $4.88M cost per breach).\n\n\n### Key Findings\nCross-industry analysis reveals three critical patterns: (1) Security spending shifted from reactive to predictive (AI/ML budgets +89%), (2) Zero-trust architecture adoption accelerated (34% implementation vs 12% in 2023), (3) Compliance automation became competitive differentiator.\n\n\n### Stakeholder Landscape\nCISOs now report directly to CEOs (78% vs 45% pre-2024), security vendors consolidating (15 major M&A deals), regulatory bodies increasing enforcement (SEC fines +156%), insurance companies mandating security standards.\n```\n\n\n**Recommendations Format:**\n```\n**Recommendation 1**: Establish AI-First Security Operations\n- *Implementation*: Deploy automated threat detection within 6 months\n- *Priority*: High (addresses 67% of current vulnerabilities)\n- *Resources*: $2.5M investment, 12 FTE security engineers\n- *Success Metric*: 80% reduction in mean time to detection\n\n\n**Recommendation 2**: Build Zero-Trust Architecture\n- *Timeline*: 18-month phased rollout starting Q3 2025\n- *Risk Mitigation*: Pilot program with low-risk systems first\n- *ROI Expectation*: Break-even at month 14, 340% ROI by year 3\n```\n\n\n\n\n**Evidence Requirements:**\n- Every strategic insight backed by extracted content analysis\n- Focus on synthesis and patterns rather than individual citations\n- Conflicts acknowledged and addressed through analytical reasoning\n- Limitations explicitly noted with strategic implications\n- Confidence levels indicated for key conclusions\n\n\n**Insight Criteria:**\n- Beyond simple data aggregation - focus on strategic intelligence\n- Strategic implications clear and actionable for decision-makers\n- Value-dense content with minimal filler or citation clutter\n- Analytical depth over citation frequency\n- Business intelligence over academic referencing\n\n\n**Content Priority:**\n- Strategic insights > Citation accuracy\n- Pattern recognition > Source listing\n- Predictive analysis > Historical documentation\n- Executive decision-support > Academic attribution\n\n\n\n\n**Strategic Pattern Recognition:**\n- Identify underlying decision-making frameworks across sources\n- Spot systematic biases, blind spots, and recurring themes\n- Find unexpected connections between disparate investments/decisions\n- Recognize predictive patterns for future strategic decisions\n\n\n**Value Creation Framework:**\n- Transform raw data \u2192 strategic intelligence \u2192 actionable insights\n- Connect micro-decisions to macro-investment philosophy\n- Link historical patterns to future market opportunities\n- Provide executive decision-support frameworks\n\n\n**Advanced Synthesis Examples:**\n* **Investment Philosophy Extraction**: \"Across 15 investment decisions, consistent pattern emerges: 60% weight on team execution, 30% on market timing, 10% on technology differentiation - suggests systematic approach to risk assessment\"\n* **Predictive Pattern Recognition**: \"Historical success rate 78% for B2B SaaS vs 45% for consumer apps indicates clear sector expertise asymmetry - strategic implication for portfolio allocation\"\n* **Contrarian Insight Generation**: \"Public skepticism of AI models contrasts with private deployment success - suggests market positioning strategy rather than fundamental technology doubt\"\n* **Risk Assessment Framework**: \"Failed investments share common pattern: strong technology, weak commercialization timeline - indicates systematic evaluation gap in GTM strategy assessment\"\n\n\n**FOCUS**: Generate strategic intelligence, not citation summaries. Citations are handled by system architecture.\n\n\n**\u274c POOR Example (Citation-Heavy, No Strategic Depth):**\n```\n## Market Analysis of Enterprise AI Adoption\nBased on collected sources, the following findings were identified:\n1. 73% of Fortune 500 companies use AI for fraud detection - Source: TechCrunch article\n2. Average implementation time is 18 months - Source: McKinsey report\n3. ROI averages 23% in first year - Source: Boston Consulting Group study\n4. Main barriers include data quality issues - Source: MIT Technology Review\n5. Regulatory concerns mentioned by 45% of executives - Source: Wall Street Journal\n[Simple data listing without insights or strategic implications]\n```\n\n\n**\u2705 EXCELLENT Example (Strategic Intelligence Focus):**\n```\n## Enterprise AI Adoption: Strategic Intelligence & Investment Framework\n\n\n### Core Strategic Pattern Recognition\nCross-analysis of 50+ enterprise AI implementations reveals systematic adoption framework:\n**Technology Maturity Curve Model**: 40% Security Applications + 30% Process Automation + 20% Customer Analytics + 10% Strategic Decision Support\n\n\n**Strategic Insight**: Security-first adoption pattern indicates risk-averse enterprise culture prioritizing downside protection over upside potential - creates systematic underinvestment in revenue-generating AI applications.\n\n\n### Predictive Market Dynamics\n**Implementation Success Correlation**: 78% success rate for phased rollouts vs 34% for full-scale deployments\n**Failure Pattern Analysis**: 67% of failed implementations share \"technology-first, change management-last\" characteristics\n\n\n**Strategic Significance**: Reveals systematic gap in enterprise AI strategy - technology readiness exceeds organizational readiness by 18-24 months, creating implementation timing arbitrage opportunity.\n\n\n### Competitive Positioning Intelligence\n**Public Adoption vs Private Deployment Contradiction**: 45% of surveyed executives publicly cautious about AI while privately accelerating deployment\n**Strategic Interpretation**: Market sentiment manipulation - using public skepticism to suppress vendor pricing while securing internal competitive advantage.\n\n\n### Investment Decision Framework\nBased on enterprise adoption patterns, strategic investors should prioritize:\n1. Change management platforms over pure technology solutions (3x success correlation)\n2. Industry-specific solutions over horizontal platforms (2.4x faster adoption)\n3. Phased implementation partners over full-scale providers (78% vs 34% success rates)\n4. 24-month market timing window before competitive parity emerges\n\n\n**Predictive Thesis**: Companies implementing AI-driven change management now will capture 60% of market consolidation value by 2027.\n```\n\n\n**Key Difference**: Transform \"data aggregation\" into \"strategic intelligence\" - identify patterns, predict trends, provide actionable decision frameworks.\n\n\n\n\n**STRATEGIC REPORT FORMAT** - Adapt based on Lead Agent's instructions:\n\n\n**Format Selection Protocol:**\n- If `ANALYSIS_INSTRUCTIONS` specifies \"McKinsey report\" \u2192 Use McKinsey-Style Report template\n- If `ANALYSIS_INSTRUCTIONS` specifies \"BCG analysis\" \u2192 Use BCG-Style Analysis template \u00a0\n- If `ANALYSIS_INSTRUCTIONS` specifies \"Strategic assessment\" \u2192 Use McKinsey-Style Report template\n- If no specific format specified \u2192 Default to McKinsey-Style Report template\n\n\n**McKinsey-Style Report:**\n```markdown\n# [Research Topic] - Strategic Analysis\n\n\n## Executive Summary\n[Key findings with strategic implications and recommendations]\n\n\n## Market Context & Competitive Landscape\n[Market sizing, growth drivers, competitive dynamics]\n\n\n## Strategic Assessment\n[Core insights addressing Lead Agent's key questions]\n\n\n## Strategic Implications & Opportunities\n[Business impact analysis and value creation opportunities]\n\n\n## Implementation Roadmap\n[Concrete recommendations with timelines and success metrics]\n\n\n## Risk Assessment & Mitigation\n[Strategic risks and mitigation strategies]\n\n\n## Appendix: Source Analysis\n[Source credibility and data validation]\n```\n\n\n**BCG-Style Analysis:**\n```markdown\n# [Research Topic] - Strategy Consulting Analysis\n\n\n## Key Insights & Recommendations\n[Executive summary with 3-5 key insights]\n\n\n## Situation Analysis\n[Current market position and dynamics]\n\n\n## Strategic Options\n[Alternative strategic approaches with pros/cons]\n\n\n## Recommended Strategy\n[Preferred approach with detailed rationale]\n\n\n## Implementation Plan\n[Detailed roadmap with milestones]\n```\n\n\n**CRITICAL**: Focus on strategic intelligence generation, not citation management. System handles source attribution automatically. Your mission is creating analytical depth and strategic insights that enable superior decision-making.\n\n\n**OUTPUT REQUIREMENTS**: \n- **ONLY OUTPUT**: Executive-grade strategic reports following Lead Agent's analysis framework\n- **NEVER OUTPUT**: Processing logs, intermediate data formats, extraction summaries, content lists, or any technical metadata regardless of input format or language\n- **TRANSFORM EVERYTHING**: Convert all raw data into strategic insights and professional analysis\n\n\n\n\n**Data Access Protocol:**\n- Process `ANALYSIS_INSTRUCTIONS` as primary framework (determines report structure, style, and focus)\n- Access `EXTRACTED_CONTENT` as primary intelligence source for analysis\n- Follow Lead Agent's analysis framework precisely, not generic report templates\n\n\n**Output Standards:**\n- Deliver strategic intelligence aligned with Lead Agent's specified framework\n- Ensure every insight addresses Lead Agent's key strategic questions\n- Match target audience requirements (C-Suite/Board/Investment Committee/Strategy Team)\n- Maintain analytical depth over citation frequency\n- Bridge current findings to future strategic implications specified by Lead Agent\n\n\n\nRemember: Your mission is creating strategic reports that match Lead Agent's specific analysis framework and business requirements. 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" +} + diff --git a/agent/templates/generate_SEO_blog.json b/agent/templates/generate_SEO_blog.json new file mode 100644 index 000000000..9d8c062b1 --- /dev/null +++ b/agent/templates/generate_SEO_blog.json @@ -0,0 +1,902 @@ +{ + "id": 8, + "title": "Generate SEO Blog", + "description": "This is a multi-agent version of the SEO blog generation workflow. It simulates a small team of AI “writers”, where each agent plays a specialized role — just like a real editorial team.", + "canvas_type": "Agent", + "dsl": { + "components": { + "Agent:LuckyApplesGrab": { + "downstream": [ + "Message:ModernSwansThrow" + ], + "obj": { + "component_name": "Agent", + "params": { + "delay_after_error": 1, + "description": "", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.5, + "llm_id": "deepseek-chat@DeepSeek", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 3, + "max_tokens": 4096, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "parameter": "Precise", + "presencePenaltyEnabled": false, + "presence_penalty": 0.5, + "prompts": [ + { + "content": "The user query is {sys.query}", + "role": "user" + } + ], + "sys_prompt": "# Role\n\nYou are the **Lead Agent**, responsible for initiating the multi-agent SEO blog generation process. You will receive the user\u2019s topic and blog goal, interpret the intent, and coordinate the downstream writing agents.\n\n# Goals\n\n1. Parse the user's initial input.\n\n2. Generate a high-level blog intent summary and writing plan.\n\n3. Provide clear instructions to the following Sub_Agents:\n\n - `Outline Agent` \u2192 Create the blog outline.\n\n - `Body Agent` \u2192 Write all sections based on outline.\n\n - `Editor Agent` \u2192 Polish and finalize the blog post.\n\n4. Merge outputs into a complete, readable blog draft in Markdown format.\n\n# Input\n\nYou will receive:\n\n- Blog topic\n\n- Target audience\n\n- Blog goal (e.g., SEO, education, product marketing)\n\n# Output Format\n\n```markdown\n\n## Parsed Writing Plan\n\n- **Topic**: [Extracted from user input]\n\n- **Audience**: [Summarized from user input]\n\n- **Intent**: [Inferred goal and style]\n\n- **Blog Type**: [e.g., Tutorial / Informative Guide / Marketing Content]\n\n- **Long-tail Keywords**: \n\n - keyword 1\n\n - keyword 2\n\n - keyword 3\n\n - ...\n\n## Instructions for Outline Agent\n\nPlease generate a structured outline including H2 and H3 headings. Assign 1\u20132 relevant keywords to each section. Keep it aligned with the user\u2019s intent and audience level.\n\n## Instructions for Body Agent\n\nWrite the full content based on the outline. Each section should be concise (500\u2013600 words), informative, and optimized for SEO. Use `Tavily Search` only when additional examples or context are needed.\n\n## Instructions for Editor Agent\n\nReview and refine the combined content. Improve transitions, ensure keyword integration, and add a meta title + meta description. Maintain Markdown formatting.\n\n\n## Guides\n\n- Do not generate blog content directly.\n\n- Focus on correct intent recognition and instruction generation.\n\n- Keep communication to downstream agents simple, scoped, and accurate.\n\n\n## Input Examples (and how to handle them)\n\nInput: \"I want to write about RAGFlow.\"\n\u2192 Output: Informative Guide, Audience: AI developers, Intent: explain what RAGFlow is and its use cases\n\nInput: \"Need a blog to promote our prompt design tool.\"\n\u2192 Output: Marketing Content, Audience: product managers or tool adopters, Intent: raise awareness and interest in the product\n\nInput: \"How to get more Google traffic using AI\"\n\u2192 Output: How-to, Audience: SEO marketers, Intent: guide readers on applying AI for SEO growth", + "temperature": "0.1", + "temperatureEnabled": true, + "tools": [ + { + "component_name": "Agent", + "id": "Agent:SlickSpidersTurn", + "name": "Outline Agent", + "params": { + "delay_after_error": 1, + "description": "Generates a clear and SEO-friendly blog outline using H2/H3 headings based on the topic, audience, and intent provided by the lead agent. Each section includes suggested keywords for optimized downstream writing.\n", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.3, + "llm_id": "deepseek-chat@DeepSeek", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 2, + "max_tokens": 4096, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "parameter": "Balance", + "presencePenaltyEnabled": false, + "presence_penalty": 0.2, + "prompts": [ + { + "content": "{sys.query}", + "role": "user" + } + ], + "sys_prompt": "# Role\n\nYou are the **Outline Agent**, a sub-agent in a multi-agent SEO blog writing system. You operate under the instruction of the `Lead Agent`, and your sole responsibility is to create a clear, well-structured, and SEO-optimized blog outline.\n\n# Tool Access:\n\n- You have access to a search tool called `Tavily Search`.\n\n- If you are unsure how to structure a section, you may call this tool to search for related blog outlines or content from Google.\n\n- Do not overuse it. Your job is to extract **structure**, not to write paragraphs.\n\n\n# Goals\n\n1. Create a well-structured outline with appropriate H2 and H3 headings.\n\n2. Ensure logical flow from introduction to conclusion.\n\n3. Assign 1\u20132 suggested long-tail keywords to each major section for SEO alignment.\n\n4. Make the structure suitable for downstream paragraph writing.\n\n\n\n\n#Note\n\n- Use concise, scannable section titles.\n\n- Do not write full paragraphs.\n\n- Prioritize clarity, logical progression, and SEO alignment.\n\n\n\n- If the blog type is \u201cTutorial\u201d or \u201cHow-to\u201d, include step-based sections.\n\n\n# Input\n\nYou will receive:\n\n- Writing Type (e.g., Tutorial, Informative Guide)\n\n- Target Audience\n\n- User Intent Summary\n\n- 3\u20135 long-tail keywords\n\n\nUse this information to design a structure that both informs readers and maximizes search engine visibility.\n\n# Output Format\n\n```markdown\n\n## Blog Title (suggested)\n\n[Give a short, SEO-friendly title suggestion]\n\n## Outline\n\n### Introduction\n\n- Purpose of the article\n\n- Brief context\n\n- **Suggested keywords**: [keyword1, keyword2]\n\n### H2: [Section Title 1]\n\n- [Short description of what this section will cover]\n\n- **Suggested keywords**: [keyword1, keyword2]\n\n### H2: [Section Title 2]\n\n- [Short description of what this section will cover]\n\n- **Suggested keywords**: [keyword1, keyword2]\n\n### H2: [Section Title 3]\n\n- [Optional H3 Subsection Title A]\n\n - [Explanation of sub-point]\n\n- [Optional H3 Subsection Title B]\n\n - [Explanation of sub-point]\n\n- **Suggested keywords**: [keyword1]\n\n### Conclusion\n\n- Recap key takeaways\n\n- Optional CTA (Call to Action)\n\n- **Suggested keywords**: [keyword3]\n\n", + "temperature": 0.5, + "temperatureEnabled": true, + "tools": [ + { + "component_name": "TavilySearch", + "name": "TavilySearch", + "params": { + "api_key": "", + "days": 7, + "exclude_domains": [], + "include_answer": false, + "include_domains": [], + "include_image_descriptions": false, + "include_images": false, + "include_raw_content": true, + "max_results": 5, + "outputs": { + "formalized_content": { + "type": "string", + "value": "" + }, + "json": { + "type": "Array", + "value": [] + } + }, + "query": "sys.query", + "search_depth": "basic", + "topic": "general" + } + } + ], + "topPEnabled": false, + "top_p": 0.85, + "user_prompt": "This is the order you need to send to the agent.", + "visual_files_var": "" + } + }, + { + "component_name": "Agent", + "id": "Agent:IcyPawsRescue", + "name": "Body Agent", + "params": { + "delay_after_error": 1, + "description": "Writes the full blog content section-by-section following the outline structure. It integrates target keywords naturally and uses Tavily Search only when additional facts or examples are needed.\n", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.5, + "llm_id": "deepseek-chat@DeepSeek", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 3, + "max_tokens": 4096, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "parameter": "Precise", + "presencePenaltyEnabled": false, + "presence_penalty": 0.5, + "prompts": [ + { + "content": "{sys.query}", + "role": "user" + } + ], + "sys_prompt": "# Role\n\nYou are the **Body Agent**, a sub-agent in a multi-agent SEO blog writing system. You operate under the instruction of the `Lead Agent`, and your job is to write the full blog content based on the outline created by the `OutlineWriter_Agent`.\n\n\n\n# Tool Access:\n\nYou can use the `Tavily Search` tool to retrieve relevant content, statistics, or examples to support each section you're writing.\n\nUse it **only** when the provided outline lacks enough information, or if the section requires factual grounding.\n\nAlways cite the original link or indicate source where possible.\n\n\n# Goals\n\n1. Write each section (based on H2/H3 structure) as a complete and natural blog paragraph.\n\n2. Integrate the suggested long-tail keywords naturally into each section.\n\n3. When appropriate, use the `Tavily Search` tool to enrich your writing with relevant facts, examples, or quotes.\n\n4. Ensure each section is clear, engaging, and informative, suitable for both human readers and search engines.\n\n\n# Style Guidelines\n\n- Write in a tone appropriate to the audience. Be explanatory, not promotional, unless it's a marketing blog.\n\n- Avoid generic filler content. Prioritize clarity, structure, and value.\n\n- Ensure SEO keywords are embedded seamlessly, not forcefully.\n\n\n\n- Maintain writing rhythm. Vary sentence lengths. Use transitions between ideas.\n\n\n# Input\n\n\nYou will receive:\n\n- Blog title\n\n- Structured outline (including section titles, keywords, and descriptions)\n\n- Target audience\n\n- Blog type and user intent\n\nYou must **follow the outline strictly**. Write content **section-by-section**, based on the structure.\n\n\n# Output Format\n\n```markdown\n\n## H2: [Section Title]\n\n[Your generated content for this section \u2014 500-600 words, using keywords naturally.]\n\n", + "temperature": 0.2, + "temperatureEnabled": true, + "tools": [ + { + "component_name": "TavilySearch", + "name": "TavilySearch", + "params": { + "api_key": "", + "days": 7, + "exclude_domains": [], + "include_answer": false, + "include_domains": [], + "include_image_descriptions": false, + "include_images": false, + "include_raw_content": true, + "max_results": 5, + "outputs": { + "formalized_content": { + "type": "string", + "value": "" + }, + "json": { + "type": "Array", + "value": [] + } + }, + "query": "sys.query", + "search_depth": "basic", + "topic": "general" + } + } + ], + "topPEnabled": false, + "top_p": 0.75, + "user_prompt": "This is the order you need to send to the agent.", + "visual_files_var": "" + } + }, + { + "component_name": "Agent", + "id": "Agent:TenderAdsAllow", + "name": "Editor Agent", + "params": { + "delay_after_error": 1, + "description": "Polishes and finalizes the entire blog post. Enhances clarity, checks keyword usage, improves flow, and generates a meta title and description for SEO. Operates after all sections are completed.\n\n", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.5, + "llm_id": "deepseek-chat@DeepSeek", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 2, + "max_tokens": 4096, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "parameter": "Precise", + "presencePenaltyEnabled": false, + "presence_penalty": 0.5, + "prompts": [ + { + "content": "{sys.query}", + "role": "user" + } + ], + "sys_prompt": "# Role\n\nYou are the **Editor Agent**, the final agent in a multi-agent SEO blog writing workflow. You are responsible for finalizing the blog post for both human readability and SEO effectiveness.\n\n# Goals\n\n1. Polish the entire blog content for clarity, coherence, and style.\n\n2. Improve transitions between sections, ensure logical flow.\n\n3. Verify that keywords are used appropriately and effectively.\n\n4. Conduct a lightweight SEO audit \u2014 checking keyword density, structure (H1/H2/H3), and overall searchability.\n\n\n\n## Integration Responsibilities\n\n- Maintain alignment with Lead Agent's original intent and audience\n\n- Preserve the structure and keyword strategy from Outline Agent\n\n- Enhance and polish Body Agent's content without altering core information\n\n# Style Guidelines\n\n- Be precise. Avoid bloated or vague language.\n\n- Maintain an informative and engaging tone, suitable to the target audience.\n\n- Do not remove keywords unless absolutely necessary for clarity.\n\n- Ensure paragraph flow and section continuity.\n\n\n\n# Input\n\nYou will receive:\n\n- Full blog content, written section-by-section\n\n- Original outline with suggested keywords\n\n- Target audience and writing type\n\n# Output Format\n\n```markdown\n\n[The revised, fully polished blog post content goes here.]\n", + "temperature": 0.2, + "temperatureEnabled": true, + "tools": [], + "topPEnabled": false, + "top_p": 0.75, + "user_prompt": "This is the order you need to send to the agent.", + "visual_files_var": "" + } + } + ], + "topPEnabled": false, + "top_p": 0.75, + "user_prompt": "", + "visual_files_var": "" + } + }, + "upstream": [ + "begin" + ] + }, + "Message:ModernSwansThrow": { + "downstream": [], + "obj": { + "component_name": "Message", + "params": { + "content": [ + "{Agent:LuckyApplesGrab@content}" + ] + } + }, + "upstream": [ + "Agent:LuckyApplesGrab" + ] + }, + "begin": { + "downstream": [ + "Agent:LuckyApplesGrab" + ], + "obj": { + "component_name": "Begin", + "params": { + "enablePrologue": true, + "inputs": {}, + "mode": "conversational", + "prologue": "Hi! I'm your SEO blog assistant.\n\nTo get started, please tell me:\n1. What topic you want the blog to cover\n2. Who is the target audience\n3. What you hope to achieve with this blog (e.g., SEO traffic, teaching beginners, promoting a product)\n" + } + }, + "upstream": [] + } + }, + "globals": { + "sys.conversation_turns": 0, + "sys.files": [], + "sys.query": "", + "sys.user_id": "" + }, + "graph": { + "edges": [ + { + "data": { + "isHovered": false + }, + "id": "xy-edge__beginstart-Agent:LuckyApplesGrabend", + "source": "begin", + "sourceHandle": "start", + "target": "Agent:LuckyApplesGrab", + "targetHandle": "end" + }, + { + "data": { + "isHovered": false + }, + "id": "xy-edge__Agent:LuckyApplesGrabstart-Message:ModernSwansThrowend", + "source": "Agent:LuckyApplesGrab", + "sourceHandle": "start", + "target": "Message:ModernSwansThrow", + "targetHandle": "end" + }, + { + "data": { + "isHovered": false + }, + "id": "xy-edge__Agent:LuckyApplesGrabagentBottom-Agent:SlickSpidersTurnagentTop", + "source": "Agent:LuckyApplesGrab", + "sourceHandle": "agentBottom", + "target": "Agent:SlickSpidersTurn", + "targetHandle": "agentTop" + }, + { + "data": { + "isHovered": false + }, + "id": "xy-edge__Agent:LuckyApplesGrabagentBottom-Agent:IcyPawsRescueagentTop", + "source": "Agent:LuckyApplesGrab", + "sourceHandle": "agentBottom", + "target": "Agent:IcyPawsRescue", + "targetHandle": "agentTop" + }, + { + "data": { + "isHovered": false + }, + "id": "xy-edge__Agent:LuckyApplesGrabagentBottom-Agent:TenderAdsAllowagentTop", + "source": "Agent:LuckyApplesGrab", + "sourceHandle": "agentBottom", + "target": "Agent:TenderAdsAllow", + "targetHandle": "agentTop" + }, + { + "data": { + "isHovered": false + }, + "id": "xy-edge__Agent:SlickSpidersTurntool-Tool:ThreeWallsRingend", + "source": "Agent:SlickSpidersTurn", + "sourceHandle": "tool", + "target": "Tool:ThreeWallsRing", + "targetHandle": "end" + }, + { + "data": { + "isHovered": false + }, + "id": "xy-edge__Agent:IcyPawsRescuetool-Tool:FloppyJokesItchend", + "source": "Agent:IcyPawsRescue", + "sourceHandle": "tool", + "target": "Tool:FloppyJokesItch", + "targetHandle": "end" + } + ], + "nodes": [ + { + "data": { + "form": { + "enablePrologue": true, + "inputs": {}, + "mode": "conversational", + "prologue": "Hi! I'm your SEO blog assistant.\n\nTo get started, please tell me:\n1. What topic you want the blog to cover\n2. Who is the target audience\n3. What you hope to achieve with this blog (e.g., SEO traffic, teaching beginners, promoting a product)\n" + }, + "label": "Begin", + "name": "begin" + }, + "dragging": false, + "id": "begin", + "measured": { + "height": 48, + "width": 200 + }, + "position": { + "x": 38.19445084117184, + "y": 183.9781832844475 + }, + "selected": false, + "sourcePosition": "left", + "targetPosition": "right", + "type": "beginNode" + }, + { + "data": { + "form": { + "delay_after_error": 1, + "description": "", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.5, + "llm_id": "deepseek-chat@DeepSeek", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 3, + "max_tokens": 4096, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "parameter": "Precise", + "presencePenaltyEnabled": false, + "presence_penalty": 0.5, + "prompts": [ + { + "content": "The user query is {sys.query}", + "role": "user" + } + ], + "sys_prompt": "# Role\n\nYou are the **Lead Agent**, responsible for initiating the multi-agent SEO blog generation process. You will receive the user\u2019s topic and blog goal, interpret the intent, and coordinate the downstream writing agents.\n\n# Goals\n\n1. Parse the user's initial input.\n\n2. Generate a high-level blog intent summary and writing plan.\n\n3. Provide clear instructions to the following Sub_Agents:\n\n - `Outline Agent` \u2192 Create the blog outline.\n\n - `Body Agent` \u2192 Write all sections based on outline.\n\n - `Editor Agent` \u2192 Polish and finalize the blog post.\n\n4. Merge outputs into a complete, readable blog draft in Markdown format.\n\n# Input\n\nYou will receive:\n\n- Blog topic\n\n- Target audience\n\n- Blog goal (e.g., SEO, education, product marketing)\n\n# Output Format\n\n```markdown\n\n## Parsed Writing Plan\n\n- **Topic**: [Extracted from user input]\n\n- **Audience**: [Summarized from user input]\n\n- **Intent**: [Inferred goal and style]\n\n- **Blog Type**: [e.g., Tutorial / Informative Guide / Marketing Content]\n\n- **Long-tail Keywords**: \n\n - keyword 1\n\n - keyword 2\n\n - keyword 3\n\n - ...\n\n## Instructions for Outline Agent\n\nPlease generate a structured outline including H2 and H3 headings. Assign 1\u20132 relevant keywords to each section. Keep it aligned with the user\u2019s intent and audience level.\n\n## Instructions for Body Agent\n\nWrite the full content based on the outline. Each section should be concise (500\u2013600 words), informative, and optimized for SEO. Use `Tavily Search` only when additional examples or context are needed.\n\n## Instructions for Editor Agent\n\nReview and refine the combined content. Improve transitions, ensure keyword integration, and add a meta title + meta description. Maintain Markdown formatting.\n\n\n## Guides\n\n- Do not generate blog content directly.\n\n- Focus on correct intent recognition and instruction generation.\n\n- Keep communication to downstream agents simple, scoped, and accurate.\n\n\n## Input Examples (and how to handle them)\n\nInput: \"I want to write about RAGFlow.\"\n\u2192 Output: Informative Guide, Audience: AI developers, Intent: explain what RAGFlow is and its use cases\n\nInput: \"Need a blog to promote our prompt design tool.\"\n\u2192 Output: Marketing Content, Audience: product managers or tool adopters, Intent: raise awareness and interest in the product\n\nInput: \"How to get more Google traffic using AI\"\n\u2192 Output: How-to, Audience: SEO marketers, Intent: guide readers on applying AI for SEO growth", + "temperature": "0.1", + "temperatureEnabled": true, + "tools": [], + "topPEnabled": false, + "top_p": 0.75, + "user_prompt": "", + "visual_files_var": "" + }, + "label": "Agent", + "name": "Lead Agent" + }, + "id": "Agent:LuckyApplesGrab", + "measured": { + "height": 84, + "width": 200 + }, + "position": { + "x": 350, + "y": 200 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "agentNode" + }, + { + "data": { + "form": { + "content": [ + "{Agent:LuckyApplesGrab@content}" + ] + }, + "label": "Message", + "name": "Response" + }, + "dragging": false, + "id": "Message:ModernSwansThrow", + "measured": { + "height": 56, + "width": 200 + }, + "position": { + "x": 669.394830760932, + "y": 190.72421137520644 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "messageNode" + }, + { + "data": { + "form": { + "delay_after_error": 1, + "description": "Generates a clear and SEO-friendly blog outline using H2/H3 headings based on the topic, audience, and intent provided by the lead agent. Each section includes suggested keywords for optimized downstream writing.\n", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.3, + "llm_id": "deepseek-chat@DeepSeek", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 2, + "max_tokens": 4096, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "parameter": "Balance", + "presencePenaltyEnabled": false, + "presence_penalty": 0.2, + "prompts": [ + { + "content": "{sys.query}", + "role": "user" + } + ], + "sys_prompt": "# Role\n\nYou are the **Outline Agent**, a sub-agent in a multi-agent SEO blog writing system. You operate under the instruction of the `Lead Agent`, and your sole responsibility is to create a clear, well-structured, and SEO-optimized blog outline.\n\n# Tool Access:\n\n- You have access to a search tool called `Tavily Search`.\n\n- If you are unsure how to structure a section, you may call this tool to search for related blog outlines or content from Google.\n\n- Do not overuse it. Your job is to extract **structure**, not to write paragraphs.\n\n\n# Goals\n\n1. Create a well-structured outline with appropriate H2 and H3 headings.\n\n2. Ensure logical flow from introduction to conclusion.\n\n3. Assign 1\u20132 suggested long-tail keywords to each major section for SEO alignment.\n\n4. Make the structure suitable for downstream paragraph writing.\n\n\n\n\n#Note\n\n- Use concise, scannable section titles.\n\n- Do not write full paragraphs.\n\n- Prioritize clarity, logical progression, and SEO alignment.\n\n\n\n- If the blog type is \u201cTutorial\u201d or \u201cHow-to\u201d, include step-based sections.\n\n\n# Input\n\nYou will receive:\n\n- Writing Type (e.g., Tutorial, Informative Guide)\n\n- Target Audience\n\n- User Intent Summary\n\n- 3\u20135 long-tail keywords\n\n\nUse this information to design a structure that both informs readers and maximizes search engine visibility.\n\n# Output Format\n\n```markdown\n\n## Blog Title (suggested)\n\n[Give a short, SEO-friendly title suggestion]\n\n## Outline\n\n### Introduction\n\n- Purpose of the article\n\n- Brief context\n\n- **Suggested keywords**: [keyword1, keyword2]\n\n### H2: [Section Title 1]\n\n- [Short description of what this section will cover]\n\n- **Suggested keywords**: [keyword1, keyword2]\n\n### H2: [Section Title 2]\n\n- [Short description of what this section will cover]\n\n- **Suggested keywords**: [keyword1, keyword2]\n\n### H2: [Section Title 3]\n\n- [Optional H3 Subsection Title A]\n\n - [Explanation of sub-point]\n\n- [Optional H3 Subsection Title B]\n\n - [Explanation of sub-point]\n\n- **Suggested keywords**: [keyword1]\n\n### Conclusion\n\n- Recap key takeaways\n\n- Optional CTA (Call to Action)\n\n- **Suggested keywords**: [keyword3]\n\n", + "temperature": 0.5, + "temperatureEnabled": true, + "tools": [ + { + "component_name": "TavilySearch", + "name": "TavilySearch", + "params": { + "api_key": "", + "days": 7, + "exclude_domains": [], + "include_answer": false, + "include_domains": [], + "include_image_descriptions": false, + "include_images": false, + "include_raw_content": true, + "max_results": 5, + "outputs": { + "formalized_content": { + "type": "string", + "value": "" + }, + "json": { + "type": "Array", + "value": [] + } + }, + "query": "sys.query", + "search_depth": "basic", + "topic": "general" + } + } + ], + "topPEnabled": false, + "top_p": 0.85, + "user_prompt": "This is the order you need to send to the agent.", + "visual_files_var": "" + }, + "label": "Agent", + "name": "Outline Agent" + }, + "dragging": false, + "id": "Agent:SlickSpidersTurn", + "measured": { + "height": 84, + "width": 200 + }, + "position": { + "x": 100.60137004146719, + "y": 411.67654846431367 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "agentNode" + }, + { + "data": { + "form": { + "delay_after_error": 1, + "description": "Writes the full blog content section-by-section following the outline structure. It integrates target keywords naturally and uses Tavily Search only when additional facts or examples are needed.\n", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.5, + "llm_id": "deepseek-chat@DeepSeek", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 3, + "max_tokens": 4096, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "parameter": "Precise", + "presencePenaltyEnabled": false, + "presence_penalty": 0.5, + "prompts": [ + { + "content": "{sys.query}", + "role": "user" + } + ], + "sys_prompt": "# Role\n\nYou are the **Body Agent**, a sub-agent in a multi-agent SEO blog writing system. You operate under the instruction of the `Lead Agent`, and your job is to write the full blog content based on the outline created by the `OutlineWriter_Agent`.\n\n\n\n# Tool Access:\n\nYou can use the `Tavily Search` tool to retrieve relevant content, statistics, or examples to support each section you're writing.\n\nUse it **only** when the provided outline lacks enough information, or if the section requires factual grounding.\n\nAlways cite the original link or indicate source where possible.\n\n\n# Goals\n\n1. Write each section (based on H2/H3 structure) as a complete and natural blog paragraph.\n\n2. Integrate the suggested long-tail keywords naturally into each section.\n\n3. When appropriate, use the `Tavily Search` tool to enrich your writing with relevant facts, examples, or quotes.\n\n4. Ensure each section is clear, engaging, and informative, suitable for both human readers and search engines.\n\n\n# Style Guidelines\n\n- Write in a tone appropriate to the audience. Be explanatory, not promotional, unless it's a marketing blog.\n\n- Avoid generic filler content. Prioritize clarity, structure, and value.\n\n- Ensure SEO keywords are embedded seamlessly, not forcefully.\n\n\n\n- Maintain writing rhythm. Vary sentence lengths. Use transitions between ideas.\n\n\n# Input\n\n\nYou will receive:\n\n- Blog title\n\n- Structured outline (including section titles, keywords, and descriptions)\n\n- Target audience\n\n- Blog type and user intent\n\nYou must **follow the outline strictly**. Write content **section-by-section**, based on the structure.\n\n\n# Output Format\n\n```markdown\n\n## H2: [Section Title]\n\n[Your generated content for this section \u2014 500-600 words, using keywords naturally.]\n\n", + "temperature": 0.2, + "temperatureEnabled": true, + "tools": [ + { + "component_name": "TavilySearch", + "name": "TavilySearch", + "params": { + "api_key": "", + "days": 7, + "exclude_domains": [], + "include_answer": false, + "include_domains": [], + "include_image_descriptions": false, + "include_images": false, + "include_raw_content": true, + "max_results": 5, + "outputs": { + "formalized_content": { + "type": "string", + "value": "" + }, + "json": { + "type": "Array", + "value": [] + } + }, + "query": "sys.query", + "search_depth": "basic", + "topic": "general" + } + } + ], + "topPEnabled": false, + "top_p": 0.75, + "user_prompt": "This is the order you need to send to the agent.", + "visual_files_var": "" + }, + "label": "Agent", + "name": "Body Agent" + }, + "dragging": false, + "id": "Agent:IcyPawsRescue", + "measured": { + "height": 84, + "width": 200 + }, + "position": { + "x": 439.3374395738501, + "y": 366.1408588516909 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "agentNode" + }, + { + "data": { + "form": { + "delay_after_error": 1, + "description": "Polishes and finalizes the entire blog post. Enhances clarity, checks keyword usage, improves flow, and generates a meta title and description for SEO. Operates after all sections are completed.\n\n", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.5, + "llm_id": "deepseek-chat@DeepSeek", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 2, + "max_tokens": 4096, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "parameter": "Precise", + "presencePenaltyEnabled": false, + "presence_penalty": 0.5, + "prompts": [ + { + "content": "{sys.query}", + "role": "user" + } + ], + "sys_prompt": "# Role\n\nYou are the **Editor Agent**, the final agent in a multi-agent SEO blog writing workflow. You are responsible for finalizing the blog post for both human readability and SEO effectiveness.\n\n# Goals\n\n1. Polish the entire blog content for clarity, coherence, and style.\n\n2. Improve transitions between sections, ensure logical flow.\n\n3. Verify that keywords are used appropriately and effectively.\n\n4. Conduct a lightweight SEO audit \u2014 checking keyword density, structure (H1/H2/H3), and overall searchability.\n\n\n\n## Integration Responsibilities\n\n- Maintain alignment with Lead Agent's original intent and audience\n\n- Preserve the structure and keyword strategy from Outline Agent\n\n- Enhance and polish Body Agent's content without altering core information\n\n# Style Guidelines\n\n- Be precise. Avoid bloated or vague language.\n\n- Maintain an informative and engaging tone, suitable to the target audience.\n\n- Do not remove keywords unless absolutely necessary for clarity.\n\n- Ensure paragraph flow and section continuity.\n\n\n\n# Input\n\nYou will receive:\n\n- Full blog content, written section-by-section\n\n- Original outline with suggested keywords\n\n- Target audience and writing type\n\n# Output Format\n\n```markdown\n\n[The revised, fully polished blog post content goes here.]\n", + "temperature": 0.2, + "temperatureEnabled": true, + "tools": [], + "topPEnabled": false, + "top_p": 0.75, + "user_prompt": "This is the order you need to send to the agent.", + "visual_files_var": "" + }, + "label": "Agent", + "name": "Editor Agent" + }, + "dragging": false, + "id": "Agent:TenderAdsAllow", + "measured": { + "height": 84, + "width": 200 + }, + "position": { + "x": 730.8513124709204, + "y": 327.351197329827 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "agentNode" + }, + { + "data": { + "form": { + "description": "This is an agent for a specific task.", + "user_prompt": "This is the order you need to send to the agent." + }, + "label": "Tool", + "name": "flow.tool_0" + }, + "dragging": false, + "id": "Tool:ThreeWallsRing", + "measured": { + "height": 48, + "width": 200 + }, + "position": { + "x": -26.93431957115564, + "y": 531.4384641920368 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "toolNode" + }, + { + "data": { + "form": { + "description": "This is an agent for a specific task.", + "user_prompt": "This is the order you need to send to the agent." + }, + "label": "Tool", + "name": "flow.tool_1" + }, + "dragging": false, + "id": "Tool:FloppyJokesItch", + "measured": { + "height": 48, + "width": 200 + }, + "position": { + "x": 414.6786783453011, + "y": 499.39483076093194 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "toolNode" + }, + { + "data": { + "form": { + "text": "This is a multi-agent version of the SEO blog generation workflow. It simulates a small team of AI \u201cwriters\u201d, where each agent plays a specialized role \u2014 just like a real editorial team.\n\nInstead of one AI doing everything in order, this version uses a **Lead Agent** to assign tasks to different sub-agents, who then write and edit the blog in parallel. The Lead Agent manages everything and produces the final output.\n\n### Why use multi-agent format?\n\n- Better control over each stage of writing \n- Easier to reuse agents across tasks \n- More human-like workflow (planning \u2192 writing \u2192 editing \u2192 publishing) \n- Easier to scale and customize for advanced users\n\n### Flow Summary:\n\n1. `LeadWriter_Agent` takes your input and creates a plan\n2. It sends that plan to:\n - `OutlineWriter_Agent`: build blog structure\n - `BodyWriter_Agent`: write full content\n - `FinalEditor_Agent`: polish and finalize\n3. `LeadWriter_Agent` collects all results and outputs the final blog post\n" + }, + "label": "Note", + "name": "Workflow Overall Description" + }, + "dragHandle": ".note-drag-handle", + "dragging": false, + "height": 208, + "id": "Note:ElevenVansInvent", + "measured": { + "height": 208, + "width": 518 + }, + "position": { + "x": -336.6586460874556, + "y": 113.43253511344867 + }, + "resizing": false, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "noteNode", + "width": 518 + }, + { + "data": { + "form": { + "text": "**Purpose**: \nThis is the central agent that controls the entire writing process.\n\n**What it does**:\n- Reads your blog topic and intent\n- Generates a clear writing plan (topic, audience, goal, keywords)\n- Sends instructions to all sub-agents\n- Waits for their responses and checks quality\n- If any section is missing or weak, it can request a rewrite\n- Finally, it assembles all parts into a complete blog and sends it back to you\n" + }, + "label": "Note", + "name": "Lead Agent" + }, + "dragHandle": ".note-drag-handle", + "dragging": false, + "height": 146, + "id": "Note:EmptyClubsGreet", + "measured": { + "height": 146, + "width": 334 + }, + "position": { + "x": 390.1408623279084, + "y": 2.6521144030202493 + }, + "resizing": false, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "noteNode", + "width": 334 + }, + { + "data": { + "form": { + "text": "**Purpose**: \nThis agent is responsible for building the blog's structure. It creates an outline that shows what the article will cover and how it's organized.\n\n**What it does**:\n- Suggests a blog title that matches the topic and keywords \n- Breaks the article into sections using H2 and H3 headers \n- Adds a short description of what each section should include \n- Assigns SEO keywords to each section for better search visibility \n- Uses search data (via Tavily Search) to find how similar blogs are structured" + }, + "label": "Note", + "name": "Outline Agent" + }, + "dragHandle": ".note-drag-handle", + "dragging": false, + "height": 157, + "id": "Note:CurlyTigersDouble", + "measured": { + "height": 157, + "width": 394 + }, + "position": { + "x": -60.03139680691618, + "y": 595.8208080534818 + }, + "resizing": false, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "noteNode", + "width": 394 + }, + { + "data": { + "form": { + "text": "**Purpose**: \nThis agent is in charge of writing the full blog content, section by section, based on the outline it receives.\n\n**What it does**:\n- Takes each section heading from the outline (H2 / H3)\n- Writes a complete paragraph (150\u2013220 words) under each section\n- Naturally includes the keywords provided for that section\n- Uses the Tavily Search tool to add real-world examples, definitions, or facts if needed\n- Makes sure each section is clear, useful, and easy to read\n" + }, + "label": "Note", + "name": "Body Agent" + }, + "dragHandle": ".note-drag-handle", + "dragging": false, + "height": 164, + "id": "Note:StrongKingsCamp", + "measured": { + "height": 164, + "width": 408 + }, + "position": { + "x": 446.54943226110845, + "y": 590.9443887062529 + }, + "resizing": false, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "noteNode", + "width": 408 + }, + { + "data": { + "form": { + "text": "**Purpose**: \nThis agent reviews, polishes, and finalizes the blog post written by the BodyWriter_Agent. It ensures everything is clean, smooth, and SEO-compliant.\n\n**What it does**:\n- Improves grammar, sentence flow, and transitions \n- Makes sure the content reads naturally and professionally \n- Checks whether keywords are present and well integrated (but not overused) \n- Verifies that the structure follows the correct H1/H2/H3 format \n" + }, + "label": "Note", + "name": "Editor Agent" + }, + "dragHandle": ".note-drag-handle", + "dragging": false, + "height": 147, + "id": "Note:OpenOttersShow", + "measured": { + "height": 147, + "width": 357 + }, + "position": { + "x": 976.6858726228803, + "y": 422.7404806291804 + }, + "resizing": false, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "noteNode", + "width": 357 + } + ] + }, + "history": [], + "messages": [], + "path": [], + "retrieval": [] + }, + "avatar": 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+} \ No newline at end of file diff --git a/agent/templates/image_lingo.json b/agent/templates/image_lingo.json new file mode 100644 index 000000000..22b089403 --- /dev/null +++ b/agent/templates/image_lingo.json @@ -0,0 +1,263 @@ +{ + "id": 13, + "title": "ImageLingo", + "description": "ImageLingo lets you snap any photo containing text—menus, signs, or documents—and instantly recognize and translate it into your language of choice using advanced AI-powered translation technology.", + "canvas_type": "Consumer App", + "dsl": { + "components": { + "Agent:CoolPandasCrash": { + "downstream": [ + "Message:CurlyApplesRelate" + ], + "obj": { + "component_name": "Agent", + "params": { + "delay_after_error": 1, + "description": "", + "exception_comment": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.7, + "llm_filter": "image2text", + "llm_id": "qwen-vl-plus@Tongyi-Qianwen", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 5, + "max_tokens": 256, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + }, + "structured_output": {} + }, + "presencePenaltyEnabled": false, + "presence_penalty": 0.4, + "prompts": [ + { + "content": "The user query is {sys.query}\n\n\n\nThe input files are {sys.files}\n\n", + "role": "user" + } + ], + "sys_prompt": "You are a multilingual translation assistant that works from images. When given a photo of any text or scene, you should:\n\n\n\n1. Detect and extract all written text in the image, regardless of font, orientation, or style. \n\n2. Identify the source language of the extracted text. \n\n3. Determine the target language:\n\n - If the user explicitly specifies a language, use that.\n\n - If no language is specified, automatically detect the user\u2019s spoken language and use that as the target. \n\n4. Translate the content accurately into the target language, preserving meaning, tone, and formatting (e.g., line breaks, punctuation). \n\n5. If the image contains signage, menus, labels, or other contextual text, adapt the translation to be natural and context-appropriate for daily use. \n\n6. Return the translated text in plain, well-formatted paragraphs. If the user asks, also provide transliteration for non-Latin scripts. \n\n7. If the image is unclear or the target language cannot be determined, ask a clarifying follow-up question.\n\n\nExample:\n\nUser: \u201cTranslate this photo for me.\u201d\n\nAgent Input: [Image of a Japanese train schedule]\n\nAgent Output:\n\n\u201c7:30 AM \u2013 \u6771\u4eac\u99c5 (Tokyo Station) \n\n8:15 AM \u2013 \u65b0\u5927\u962a (Shin-Osaka)\u201d \n\n(Detected user language: English)```\n\n", + "temperature": 0.1, + "temperatureEnabled": true, + "tools": [], + "topPEnabled": false, + "top_p": 0.3, + "user_prompt": "", + "visual_files_var": "sys.files" + } + }, + "upstream": [ + "begin" + ] + }, + "Message:CurlyApplesRelate": { + "downstream": [], + "obj": { + "component_name": "Message", + "params": { + "content": [ + "{Agent:CoolPandasCrash@content}" + ] + } + }, + "upstream": [ + "Agent:CoolPandasCrash" + ] + }, + "begin": { + "downstream": [ + "Agent:CoolPandasCrash" + ], + "obj": { + "component_name": "Begin", + "params": { + "enablePrologue": true, + "inputs": {}, + "mode": "task", + "prologue": "Hi there! I\u2019m ImageLingo, your on-the-go image translation assistant\u2014just snap a photo, and I\u2019ll instantly translate and adapt it into your language." + } + }, + "upstream": [] + } + }, + "globals": { + "sys.conversation_turns": 0, + "sys.files": [], + "sys.query": "", + "sys.user_id": "" + }, + "graph": { + "edges": [ + { + "data": { + "isHovered": false + }, + "id": "xy-edge__beginstart-Agent:CoolPandasCrashend", + "source": "begin", + "sourceHandle": "start", + "target": "Agent:CoolPandasCrash", + "targetHandle": "end" + }, + { + "data": { + "isHovered": false + }, + "id": "xy-edge__Agent:CoolPandasCrashstart-Message:CurlyApplesRelateend", + "source": "Agent:CoolPandasCrash", + "sourceHandle": "start", + "target": "Message:CurlyApplesRelate", + "targetHandle": "end" + } + ], + "nodes": [ + { + "data": { + "form": { + "enablePrologue": true, + "inputs": {}, + "mode": "task", + "prologue": "Hi there! I\u2019m ImageLingo, your on-the-go image translation assistant\u2014just snap a photo, and I\u2019ll instantly translate and adapt it into your language." + }, + "label": "Begin", + "name": "begin" + }, + "id": "begin", + "measured": { + "height": 48, + "width": 200 + }, + "position": { + "x": 50, + "y": 200 + }, + "selected": false, + "sourcePosition": "left", + "targetPosition": "right", + "type": "beginNode" + }, + { + "data": { + "form": { + "delay_after_error": 1, + "description": "", + "exception_comment": "", + "exception_goto": "", + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.7, + "llm_filter": "image2text", + "llm_id": "qwen-vl-plus@Tongyi-Qianwen", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 5, + "max_tokens": 256, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + }, + "structured_output": {} + }, + "presencePenaltyEnabled": false, + "presence_penalty": 0.4, + "prompts": [ + { + "content": "The user query is {sys.query}\n\n\n\nThe input files are {sys.files}\n\n", + "role": "user" + } + ], + "sys_prompt": "You are a multilingual translation assistant that works from images. When given a photo of any text or scene, you should:\n\n\n\n1. Detect and extract all written text in the image, regardless of font, orientation, or style. \n\n2. Identify the source language of the extracted text. \n\n3. Determine the target language:\n\n - If the user explicitly specifies a language, use that.\n\n - If no language is specified, automatically detect the user\u2019s spoken language and use that as the target. \n\n4. Translate the content accurately into the target language, preserving meaning, tone, and formatting (e.g., line breaks, punctuation). \n\n5. If the image contains signage, menus, labels, or other contextual text, adapt the translation to be natural and context-appropriate for daily use. \n\n6. Return the translated text in plain, well-formatted paragraphs. If the user asks, also provide transliteration for non-Latin scripts. \n\n7. If the image is unclear or the target language cannot be determined, ask a clarifying follow-up question.\n\n\nExample:\n\nUser: \u201cTranslate this photo for me.\u201d\n\nAgent Input: [Image of a Japanese train schedule]\n\nAgent Output:\n\n\u201c7:30 AM \u2013 \u6771\u4eac\u99c5 (Tokyo Station) \n\n8:15 AM \u2013 \u65b0\u5927\u962a (Shin-Osaka)\u201d \n\n(Detected user language: English)```\n\n", + "temperature": 0.1, + "temperatureEnabled": true, + "tools": [], + "topPEnabled": false, + "top_p": 0.3, + "user_prompt": "", + "visual_files_var": "sys.files" + }, + "label": "Agent", + "name": "Translation Agent With Vision" + }, + "dragging": false, + "id": "Agent:CoolPandasCrash", + "measured": { + "height": 87, + "width": 200 + }, + "position": { + "x": 350.5, + "y": 200 + }, + "selected": true, + "sourcePosition": "right", + "targetPosition": "left", + "type": "agentNode" + }, + { + "data": { + "form": { + "content": [ + "{Agent:CoolPandasCrash@content}" + ] + }, + "label": "Message", + "name": "Message" + }, + "id": "Message:CurlyApplesRelate", + "measured": { + "height": 56, + "width": 200 + }, + "position": { + "x": 650, + "y": 200 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "messageNode" + }, + { + "data": { + "form": { + "text": "ImageLingo lets you snap any photo containing text\u2014menus, signs, or documents\u2014and instantly recognize and translate it into your language of choice using advanced OCR and AI-powered translation technology. With automatic source-language detection and context-aware adaptations, translations preserve formatting, tone, and intent. Your on-the-go language assistant. " + }, + "label": "Note", + "name": "Translation Agent" + }, + "dragHandle": ".note-drag-handle", + "dragging": false, + "height": 190, + "id": "Note:OpenCobrasMarry", + "measured": { + "height": 190, + "width": 376 + }, + "position": { + "x": 385.5, + "y": -42 + }, + "resizing": false, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "noteNode", + "width": 376 + } + ] + }, + "history": [], + "messages": [], + "path": [], + "retrieval": [] + }, + "avatar": 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3MiZ8A/XGG8vHo1huRm0ygrI2Zlt09GTkEh5s64loYk9wt2MTMVaSneBZyKiaHUzvS+UIFhDpv9RDOLNZ2Q2gwl3W5Xc+ffjsP6VJy8aTTyJlagh3znzIlKqM0XsDuYiFLq3kmc0olG19rRIQvRQPf5eKhGg0hIhEnmvM1taGFNLX3xOeiJPiuml6OEnKVx4UNIovcFpRaTbmGtnsUv6sHLZhTiIMDPgjQwzQTS1NTVyGfkkC588vmnqKutw4hJUzCfEe6gyApyAXqjBV0dXEBKqkMNEhF2bPscRm8fQLhL4Kr1mdnQUXuCqXKE+a4hmSs0p8CUbGWKdKCaN51h8MNdfR6J9LLa1Y69hw/h8X17iXschrHprZ1QjMI/v8rE5GSC8FhVfYo10YFhpcNw5NghzpP24Oqrx7MPdGDXzl2kCQZkceq2d+/3GJKTReLYi5bG1kgUWEJ/XbMWo/Ny4KSzxD6Ejh1Z+d1LL6pHNm/EWy89j14qnXBjLUxD8hG6WAt9ajZH4hrJ6ROIOp+9sxYfNDhxoaMd3UMn4oXZE2EdMgQHd3yH1W+shi0tBQHeVKBEIpvcX97+G35243zmtwWLljyIb7/4At/v3I/ikaWYNXM2TnISXnnwGJ2jweNP/h7v/X0TWi63cvqRhMUPPoCVL78KR6qd079e9hAP7DnpeJfXKOzMfooo0by1L/71lWUTHHwou2uIXmT/hoGw2Fh9DqELNWg/eRRdZ09h99avoB0zkjnvRHU2J3LN53Hg1Dmsfvl5tHAU+dGrK3Fnug1VNTVoYOFfbu3AbcNz8cQjv4GZUVu3cSOWPrwUu7bvgJ114CZUT548EVVVJ/CjObM5KDgBS1IS9KYELPjJrVi16m+yxxRxAmh3WNl32AS7unH28kXceuNNZAgUM4oO2lnjy5blMNxB5qDKtq4XDYVdtsvjw6effIaDLd3YX1+DQyOm4/z+j1GZWIo+tw9BNqpcsxadbHJi0cUUNc9/tw99LPZJc2+Ck0OBYGM9bivJx9x5c/DN8RM4ymlen6sPv/v949i/bw8neodlN//1bx7Bd19tw+mTNXB2d2PejfM4rm+laPKRXjQjNy8btefq5cD59JGjKBg9EuUlJYyCD0rt1vfUS+QfgrYaAm4kk28kJ1qw8pvdeHH3KTydFsTGPj16aUyQBC7IPbEQcTjBbMXYyVMwlNTh6vHjYRmSg/GjRsBC6iGK8w9/XIZDn7yP9xfOQza3oZ5z67Dqv5+XXXvS9ImYOHkSdn7zHY4cOEYoLcDU2TOhJ/K8ve5d5BXkIIeT8KEleVj9+noKrGy0NLWzRrQSqfykg19/+y3CXm5pNRLD2RplPoWJBtpkYjHF1Krj9bjhxh/h2a07kUqtPILFc9+iX2LNls+w+0QNjtTWYsO7G/HQrxdjwsTxGJaTQVLnw58fuA8n6K2pM6bjVAO3oogUm5qc2Ld3F9ZufAvdPd04tO8QVq18A79euoSEzoUzp2qw5vU1uGZqheQ5XrcXR48c4UahGckc4bh63TBzLquXztHCxvHP088+QyhNhbL/g3Wkjnp4G2pg40xIy2LtRAK2aNJQYTHgqqlTYRZTMMJbgNLyZN1FQqCCnDSH5OZ1ze3ItlslBRccR8/ohTy9UEi/pw8fisfuvgP/9e4/oSGmf/DxB1j8q8Uoo8bu7OrAFEbhG0bhR9fP5agkhC0fbeVWlh7DODAbzvnsX/7yCtJS0yS5E+RRqEYf09XKcb+HLHXFW29yAR9uUDXEcT3H5AkMg5Hh7knJhjm3UNLkjh6X5CUW0gKRZs0sJLHZlmJLlt1Uz7CGo/pVUIZYl83Oy8PoolJuimTg4YeX4Ott2/Dlp19KbX3dDXNQUlKEZU8+i/S0dC46Aa+ufAkLFtzNz2ncUVWwmrOmjRs34PCBShaxDU6OUG65fT5V4AKkc6ilpa0tTU10DLmH0ueUm3NasYFGXuJmWgmm52fXs3CEZ2KzCnCRQsBkEBLTaLycU/ImYuYvbtbW0QujmftnOTnycBHb9cRfEwnf2rVrMYu6ORgIyo3CHTu4n7DwLkyZNgkWW5LcUGlqaUN19WlK2yOoOnoYhXTAs398Ci2sz05SE0EG31m/iXsWbkzjNteUa6ahgoNoHcl6vaujpUBlEzGISUKKGcb0TKgiKtSfwehQNhRVUH6mwqmay6iYMCqye8nvtF5uROelWmzdvB779+yjIdUSTewOh1zoySPHMX3dVAqSZKo6ExlvD9555x3yHTvqzpznHlw6Xnt1FSYTDKZfU8FxjRd9fW4UlZVi0aJ7sW7DJo7UU+Ew27B9/z7cvvCnTDUjcnNzKnWqXv+xwWJ/2MA2zymuNNjg7WXrzOG0oktWvtjYNpoS2cK5d1xfj0DbRfzPk5txlJOKOu7kiC1WQa8TCcdiZmNiLRlZaOIl9pS9hN0N69Yjn9yn6lAlNwv9HIqtxOo3X8POnXthUWxwMLInuBdx250LkJmRLmdTmZmZyKBIWrz4ISlq9ITRnp4e+XOIDmaGVCq71y6fGfL0bU9m7hvF1DfJAj+Vk4WstKryCHPwIKoOH5Fds57Gu1kXBhprFoNgvg8W6YokYV3c7QzQSFE74ysmIoVGjB4zChPp4UbmbV5uDtJSUmHmYlNSUiKTB14tvif4kKTSPMSfKYTjh2Hh8KDxTFivLZRP37fh9VdUj/vhxNQM2LOGYPvXX+D+Z1+R8ltscBu5dyz+XCAydVZ+yOujKdbS2ooKcptf3fUzXDdnFk5SH48iNIbEbicX5A/45C6lFO00TlVDEVEfjkj7cHSCLXdz2OCgDh7DxAbEkrWG1RWTJ097RO7U+8L+ZUooMIPwOEY1JHAz2QAdDU9hWAfEhxIRFILHCxYqHqhGNsP7SHPLcrKxZ9NbsJEtOvnwhvWvodBmw1Hu6tgYVbFXHNszE0wzlZQ6glgx81T5WSOFUkR5hft3BSOLCEXlGJ9fmZAQXCbOyQXM+sWjzu3Ll8+iBlsW6ut+GMJTcYOooBDYfPcSsYYNLYWnrYneDMJEiuzgBvbc667G/OlTUf/hZjR3tPDogL/lMmY+8RzrgbzH4+7ftUtgJC+2NhHJLEyRYFQ2Dmw9Cd0c/+pfg7hGbpQrK0yGwLKxY2c5Y9Ef9Dq//YuCFWveXLbu8x1XWRNNY5IMWiSxkB1sVBVsLufYuDpZtMXp3NgmrNpIvsryWfBEDYg5Ko0yczQ56sHfIkRYDgnSBbV/g0MkSBeFj41bU0lmkkhGVdKAK/7kJjaSjAr9eqbVx4qq2TJlyrQd8fb+H90isRVH8mIAAAAAAElFTkSuQmCC" +} \ No newline at end of file diff --git a/agent/templates/market_generate_seo_blog.json b/agent/templates/market_generate_seo_blog.json new file mode 100644 index 000000000..baadb2b28 --- /dev/null +++ b/agent/templates/market_generate_seo_blog.json @@ -0,0 +1,915 @@ +{ + "id": 12, + "title": "Generate SEO Blog", + "description": "This workflow automatically generates a complete SEO-optimized blog article based on a simple user input. You don’t need any writing experience. Just provide a topic or short request — the system will handle the rest.", + "canvas_type": "Marketing", + "dsl": { + "components": { + "Agent:BetterSitesSend": { + "downstream": [ + "Agent:EagerNailsRemain" + ], + "obj": { + "component_name": "Agent", + "params": { + "delay_after_error": 1, + "description": "", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.3, + "llm_id": "deepseek-chat@DeepSeek", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 3, + "max_tokens": 4096, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "parameter": "Balance", + "presencePenaltyEnabled": false, + "presence_penalty": 0.2, + "prompts": [ + { + "content": "The parse and keyword agent output is {Agent:ClearRabbitsScream@content}", + "role": "user" + } + ], + "sys_prompt": "# Role\n\nYou are the **Outline_Agent**, responsible for generating a clear and SEO-optimized blog outline based on the user's parsed writing intent and keyword strategy.\n\n# Tool Access:\n\n- You have access to a search tool called `Tavily Search`.\n\n- If you are unsure how to structure a section, you may call this tool to search for related blog outlines or content from Google.\n\n- Do not overuse it. Your job is to extract **structure**, not to write paragraphs.\n\n\n# Goals\n\n1. Create a well-structured outline with appropriate H2 and H3 headings.\n\n2. Ensure logical flow from introduction to conclusion.\n\n3. Assign 1\u20132 suggested long-tail keywords to each major section for SEO alignment.\n\n4. Make the structure suitable for downstream paragraph writing.\n\n\n\n\n#Note\n\n- Use concise, scannable section titles.\n\n- Do not write full paragraphs.\n\n- Prioritize clarity, logical progression, and SEO alignment.\n\n\n\n- If the blog type is \u201cTutorial\u201d or \u201cHow-to\u201d, include step-based sections.\n\n\n# Input\n\nYou will receive:\n\n- Writing Type (e.g., Tutorial, Informative Guide)\n\n- Target Audience\n\n- User Intent Summary\n\n- 3\u20135 long-tail keywords\n\n\nUse this information to design a structure that both informs readers and maximizes search engine visibility.\n\n# Output Format\n\n```markdown\n\n## Blog Title (suggested)\n\n[Give a short, SEO-friendly title suggestion]\n\n## Outline\n\n### Introduction\n\n- Purpose of the article\n\n- Brief context\n\n- **Suggested keywords**: [keyword1, keyword2]\n\n### H2: [Section Title 1]\n\n- [Short description of what this section will cover]\n\n- **Suggested keywords**: [keyword1, keyword2]\n\n### H2: [Section Title 2]\n\n- [Short description of what this section will cover]\n\n- **Suggested keywords**: [keyword1, keyword2]\n\n### H2: [Section Title 3]\n\n- [Optional H3 Subsection Title A]\n\n - [Explanation of sub-point]\n\n- [Optional H3 Subsection Title B]\n\n - [Explanation of sub-point]\n\n- **Suggested keywords**: [keyword1]\n\n### Conclusion\n\n- Recap key takeaways\n\n- Optional CTA (Call to Action)\n\n- **Suggested keywords**: [keyword3]\n\n", + "temperature": 0.5, + "temperatureEnabled": true, + "tools": [ + { + "component_name": "TavilySearch", + "name": "TavilySearch", + "params": { + "api_key": "", + "days": 7, + "exclude_domains": [], + "include_answer": false, + "include_domains": [], + "include_image_descriptions": false, + "include_images": false, + "include_raw_content": true, + "max_results": 5, + "outputs": { + "formalized_content": { + "type": "string", + "value": "" + }, + "json": { + "type": "Array", + "value": [] + } + }, + "query": "sys.query", + "search_depth": "basic", + "topic": "general" + } + } + ], + "topPEnabled": false, + "top_p": 0.85, + "user_prompt": "", + "visual_files_var": "" + } + }, + "upstream": [ + "Agent:ClearRabbitsScream" + ] + }, + "Agent:ClearRabbitsScream": { + "downstream": [ + "Agent:BetterSitesSend" + ], + "obj": { + "component_name": "Agent", + "params": { + "delay_after_error": 1, + "description": "", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.5, + "llm_id": "deepseek-chat@DeepSeek", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 1, + "max_tokens": 4096, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "parameter": "Precise", + "presencePenaltyEnabled": false, + "presence_penalty": 0.5, + "prompts": [ + { + "content": "The user query is {sys.query}", + "role": "user" + } + ], + "sys_prompt": "# Role\n\nYou are the **Parse_And_Keyword_Agent**, responsible for interpreting a user's blog writing request and generating a structured writing intent summary and keyword strategy for SEO-optimized content generation.\n\n# Goals\n\n1. Extract and infer the user's true writing intent, even if the input is informal or vague.\n\n2. Identify the writing type, target audience, and implied goal.\n\n3. Suggest 3\u20135 long-tail keywords based on the input and context.\n\n4. Output all data in a Markdown format for downstream agents.\n\n# Operating Guidelines\n\n\n- If the user's input lacks clarity, make reasonable and **conservative** assumptions based on SEO best practices.\n\n- Always choose one clear \"Writing Type\" from the list below.\n\n- Your job is not to write the blog \u2014 only to structure the brief.\n\n# Output Format\n\n```markdown\n## Writing Type\n\n[Choose one: Tutorial / Informative Guide / Marketing Content / Case Study / Opinion Piece / How-to / Comparison Article]\n\n## Target Audience\n\n[Try to be specific based on clues in the input: e.g., marketing managers, junior developers, SEO beginners]\n\n## User Intent Summary\n\n[A 1\u20132 sentence summary of what the user wants to achieve with the blog post]\n\n## Suggested Long-tail Keywords\n\n- keyword 1\n\n- keyword 2\n\n- keyword 3\n\n- keyword 4 (optional)\n\n- keyword 5 (optional)\n\n\n\n\n## Input Examples (and how to handle them)\n\nInput: \"I want to write about RAGFlow.\"\n\u2192 Output: Informative Guide, Audience: AI developers, Intent: explain what RAGFlow is and its use cases\n\nInput: \"Need a blog to promote our prompt design tool.\"\n\u2192 Output: Marketing Content, Audience: product managers or tool adopters, Intent: raise awareness and interest in the product\n\n\n\nInput: \"How to get more Google traffic using AI\"\n\u2192 Output: How-to, Audience: SEO marketers, Intent: guide readers on applying AI for SEO growth", + "temperature": 0.2, + "temperatureEnabled": true, + "tools": [], + "topPEnabled": false, + "top_p": 0.75, + "user_prompt": "", + "visual_files_var": "" + } + }, + "upstream": [ + "begin" + ] + }, + "Agent:EagerNailsRemain": { + "downstream": [ + "Agent:LovelyHeadsOwn" + ], + "obj": { + "component_name": "Agent", + "params": { + "delay_after_error": 1, + "description": "", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.5, + "llm_id": "deepseek-chat@DeepSeek", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 5, + "max_tokens": 4096, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "parameter": "Precise", + "presencePenaltyEnabled": false, + "presence_penalty": 0.5, + "prompts": [ + { + "content": "The parse and keyword agent output is {Agent:ClearRabbitsScream@content}\n\n\n\nThe Ouline agent output is {Agent:BetterSitesSend@content}", + "role": "user" + } + ], + "sys_prompt": "# Role\n\nYou are the **Body_Agent**, responsible for generating the full content of each section of an SEO-optimized blog based on the provided outline and keyword strategy.\n\n# Tool Access:\n\nYou can use the `Tavily Search` tool to retrieve relevant content, statistics, or examples to support each section you're writing.\n\nUse it **only** when the provided outline lacks enough information, or if the section requires factual grounding.\n\nAlways cite the original link or indicate source where possible.\n\n\n# Goals\n\n1. Write each section (based on H2/H3 structure) as a complete and natural blog paragraph.\n\n2. Integrate the suggested long-tail keywords naturally into each section.\n\n3. When appropriate, use the `Tavily Search` tool to enrich your writing with relevant facts, examples, or quotes.\n\n4. Ensure each section is clear, engaging, and informative, suitable for both human readers and search engines.\n\n\n# Style Guidelines\n\n- Write in a tone appropriate to the audience. Be explanatory, not promotional, unless it's a marketing blog.\n\n- Avoid generic filler content. Prioritize clarity, structure, and value.\n\n- Ensure SEO keywords are embedded seamlessly, not forcefully.\n\n\n\n- Maintain writing rhythm. Vary sentence lengths. Use transitions between ideas.\n\n\n# Input\n\n\nYou will receive:\n\n- Blog title\n\n- Structured outline (including section titles, keywords, and descriptions)\n\n- Target audience\n\n- Blog type and user intent\n\nYou must **follow the outline strictly**. Write content **section-by-section**, based on the structure.\n\n\n# Output Format\n\n```markdown\n\n## H2: [Section Title]\n\n[Your generated content for this section \u2014 500-600 words, using keywords naturally.]\n\n", + "temperature": 0.2, + "temperatureEnabled": true, + "tools": [ + { + "component_name": "TavilySearch", + "name": "TavilySearch", + "params": { + "api_key": "", + "days": 7, + "exclude_domains": [], + "include_answer": false, + "include_domains": [], + "include_image_descriptions": false, + "include_images": false, + "include_raw_content": true, + "max_results": 5, + "outputs": { + "formalized_content": { + "type": "string", + "value": "" + }, + "json": { + "type": "Array", + "value": [] + } + }, + "query": "sys.query", + "search_depth": "basic", + "topic": "general" + } + } + ], + "topPEnabled": false, + "top_p": 0.75, + "user_prompt": "", + "visual_files_var": "" + } + }, + "upstream": [ + "Agent:BetterSitesSend" + ] + }, + "Agent:LovelyHeadsOwn": { + "downstream": [ + "Message:LegalBeansBet" + ], + "obj": { + "component_name": "Agent", + "params": { + "delay_after_error": 1, + "description": "", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.5, + "llm_id": "deepseek-chat@DeepSeek", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 5, + "max_tokens": 4096, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "parameter": "Precise", + "presencePenaltyEnabled": false, + "presence_penalty": 0.5, + "prompts": [ + { + "content": "The parse and keyword agent output is {Agent:ClearRabbitsScream@content}\n\nThe Ouline agent output is {Agent:BetterSitesSend@content}\n\nThe Body agent output is {Agent:EagerNailsRemain@content}", + "role": "user" + } + ], + "sys_prompt": "# Role\n\nYou are the **Editor_Agent**, responsible for finalizing the blog post for both human readability and SEO effectiveness.\n\n# Goals\n\n1. Polish the entire blog content for clarity, coherence, and style.\n\n2. Improve transitions between sections, ensure logical flow.\n\n3. Verify that keywords are used appropriately and effectively.\n\n4. Conduct a lightweight SEO audit \u2014 checking keyword density, structure (H1/H2/H3), and overall searchability.\n\n\n\n# Style Guidelines\n\n- Be precise. Avoid bloated or vague language.\n\n- Maintain an informative and engaging tone, suitable to the target audience.\n\n- Do not remove keywords unless absolutely necessary for clarity.\n\n- Ensure paragraph flow and section continuity.\n\n\n# Input\n\nYou will receive:\n\n- Full blog content, written section-by-section\n\n- Original outline with suggested keywords\n\n- Target audience and writing type\n\n# Output Format\n\n```markdown\n\n[The revised, fully polished blog post content goes here.]\n\n", + "temperature": 0.2, + "temperatureEnabled": true, + "tools": [], + "topPEnabled": false, + "top_p": 0.75, + "user_prompt": "", + "visual_files_var": "" + } + }, + "upstream": [ + "Agent:EagerNailsRemain" + ] + }, + "Message:LegalBeansBet": { + "downstream": [], + "obj": { + "component_name": "Message", + "params": { + "content": [ + "{Agent:LovelyHeadsOwn@content}" + ] + } + }, + "upstream": [ + "Agent:LovelyHeadsOwn" + ] + }, + "begin": { + "downstream": [ + "Agent:ClearRabbitsScream" + ], + "obj": { + "component_name": "Begin", + "params": { + "enablePrologue": true, + "inputs": {}, + "mode": "conversational", + "prologue": "Hi! I'm your SEO blog assistant.\n\nTo get started, please tell me:\n1. What topic you want the blog to cover\n2. Who is the target audience\n3. What you hope to achieve with this blog (e.g., SEO traffic, teaching beginners, promoting a product)\n" + } + }, + "upstream": [] + } + }, + "globals": { + "sys.conversation_turns": 0, + "sys.files": [], + "sys.query": "", + "sys.user_id": "" + }, + "graph": { + "edges": [ + { + "data": { + "isHovered": false + }, + "id": "xy-edge__beginstart-Agent:ClearRabbitsScreamend", + "source": "begin", + "sourceHandle": "start", + "target": "Agent:ClearRabbitsScream", + "targetHandle": "end" + }, + { + "data": { + "isHovered": false + }, + "id": "xy-edge__Agent:ClearRabbitsScreamstart-Agent:BetterSitesSendend", + "source": "Agent:ClearRabbitsScream", + "sourceHandle": "start", + "target": "Agent:BetterSitesSend", + "targetHandle": "end" + }, + { + "data": { + "isHovered": false + }, + "id": "xy-edge__Agent:BetterSitesSendtool-Tool:SharpPensBurnend", + "source": "Agent:BetterSitesSend", + "sourceHandle": "tool", + "target": "Tool:SharpPensBurn", + "targetHandle": "end" + }, + { + "data": { + "isHovered": false + }, + "id": "xy-edge__Agent:BetterSitesSendstart-Agent:EagerNailsRemainend", + "source": "Agent:BetterSitesSend", + "sourceHandle": "start", + "target": "Agent:EagerNailsRemain", + "targetHandle": "end" + }, + { + "id": "xy-edge__Agent:EagerNailsRemaintool-Tool:WickedDeerHealend", + "source": "Agent:EagerNailsRemain", + "sourceHandle": "tool", + "target": "Tool:WickedDeerHeal", + "targetHandle": "end" + }, + { + "data": { + "isHovered": false + }, + "id": "xy-edge__Agent:EagerNailsRemainstart-Agent:LovelyHeadsOwnend", + "source": "Agent:EagerNailsRemain", + "sourceHandle": "start", + "target": "Agent:LovelyHeadsOwn", + "targetHandle": "end" + }, + { + "data": { + "isHovered": false + }, + "id": "xy-edge__Agent:LovelyHeadsOwnstart-Message:LegalBeansBetend", + "source": "Agent:LovelyHeadsOwn", + "sourceHandle": "start", + "target": "Message:LegalBeansBet", + "targetHandle": "end" + } + ], + "nodes": [ + { + "data": { + "form": { + "enablePrologue": true, + "inputs": {}, + "mode": "conversational", + "prologue": "Hi! I'm your SEO blog assistant.\n\nTo get started, please tell me:\n1. What topic you want the blog to cover\n2. Who is the target audience\n3. What you hope to achieve with this blog (e.g., SEO traffic, teaching beginners, promoting a product)\n" + }, + "label": "Begin", + "name": "begin" + }, + "id": "begin", + "measured": { + "height": 48, + "width": 200 + }, + "position": { + "x": 50, + "y": 200 + }, + "selected": false, + "sourcePosition": "left", + "targetPosition": "right", + "type": "beginNode" + }, + { + "data": { + "form": { + "delay_after_error": 1, + "description": "", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.5, + "llm_id": "deepseek-chat@DeepSeek", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 1, + "max_tokens": 4096, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "parameter": "Precise", + "presencePenaltyEnabled": false, + "presence_penalty": 0.5, + "prompts": [ + { + "content": "The user query is {sys.query}", + "role": "user" + } + ], + "sys_prompt": "# Role\n\nYou are the **Parse_And_Keyword_Agent**, responsible for interpreting a user's blog writing request and generating a structured writing intent summary and keyword strategy for SEO-optimized content generation.\n\n# Goals\n\n1. Extract and infer the user's true writing intent, even if the input is informal or vague.\n\n2. Identify the writing type, target audience, and implied goal.\n\n3. Suggest 3\u20135 long-tail keywords based on the input and context.\n\n4. Output all data in a Markdown format for downstream agents.\n\n# Operating Guidelines\n\n\n- If the user's input lacks clarity, make reasonable and **conservative** assumptions based on SEO best practices.\n\n- Always choose one clear \"Writing Type\" from the list below.\n\n- Your job is not to write the blog \u2014 only to structure the brief.\n\n# Output Format\n\n```markdown\n## Writing Type\n\n[Choose one: Tutorial / Informative Guide / Marketing Content / Case Study / Opinion Piece / How-to / Comparison Article]\n\n## Target Audience\n\n[Try to be specific based on clues in the input: e.g., marketing managers, junior developers, SEO beginners]\n\n## User Intent Summary\n\n[A 1\u20132 sentence summary of what the user wants to achieve with the blog post]\n\n## Suggested Long-tail Keywords\n\n- keyword 1\n\n- keyword 2\n\n- keyword 3\n\n- keyword 4 (optional)\n\n- keyword 5 (optional)\n\n\n\n\n## Input Examples (and how to handle them)\n\nInput: \"I want to write about RAGFlow.\"\n\u2192 Output: Informative Guide, Audience: AI developers, Intent: explain what RAGFlow is and its use cases\n\nInput: \"Need a blog to promote our prompt design tool.\"\n\u2192 Output: Marketing Content, Audience: product managers or tool adopters, Intent: raise awareness and interest in the product\n\n\n\nInput: \"How to get more Google traffic using AI\"\n\u2192 Output: How-to, Audience: SEO marketers, Intent: guide readers on applying AI for SEO growth", + "temperature": 0.2, + "temperatureEnabled": true, + "tools": [], + "topPEnabled": false, + "top_p": 0.75, + "user_prompt": "", + "visual_files_var": "" + }, + "label": "Agent", + "name": "Parse And Keyword Agent" + }, + "dragging": false, + "id": "Agent:ClearRabbitsScream", + "measured": { + "height": 84, + "width": 200 + }, + "position": { + "x": 344.7766966202233, + "y": 234.82202253184496 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "agentNode" + }, + { + "data": { + "form": { + "delay_after_error": 1, + "description": "", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.3, + "llm_id": "deepseek-chat@DeepSeek", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 3, + "max_tokens": 4096, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "parameter": "Balance", + "presencePenaltyEnabled": false, + "presence_penalty": 0.2, + "prompts": [ + { + "content": "The parse and keyword agent output is {Agent:ClearRabbitsScream@content}", + "role": "user" + } + ], + "sys_prompt": "# Role\n\nYou are the **Outline_Agent**, responsible for generating a clear and SEO-optimized blog outline based on the user's parsed writing intent and keyword strategy.\n\n# Tool Access:\n\n- You have access to a search tool called `Tavily Search`.\n\n- If you are unsure how to structure a section, you may call this tool to search for related blog outlines or content from Google.\n\n- Do not overuse it. Your job is to extract **structure**, not to write paragraphs.\n\n\n# Goals\n\n1. Create a well-structured outline with appropriate H2 and H3 headings.\n\n2. Ensure logical flow from introduction to conclusion.\n\n3. Assign 1\u20132 suggested long-tail keywords to each major section for SEO alignment.\n\n4. Make the structure suitable for downstream paragraph writing.\n\n\n\n\n#Note\n\n- Use concise, scannable section titles.\n\n- Do not write full paragraphs.\n\n- Prioritize clarity, logical progression, and SEO alignment.\n\n\n\n- If the blog type is \u201cTutorial\u201d or \u201cHow-to\u201d, include step-based sections.\n\n\n# Input\n\nYou will receive:\n\n- Writing Type (e.g., Tutorial, Informative Guide)\n\n- Target Audience\n\n- User Intent Summary\n\n- 3\u20135 long-tail keywords\n\n\nUse this information to design a structure that both informs readers and maximizes search engine visibility.\n\n# Output Format\n\n```markdown\n\n## Blog Title (suggested)\n\n[Give a short, SEO-friendly title suggestion]\n\n## Outline\n\n### Introduction\n\n- Purpose of the article\n\n- Brief context\n\n- **Suggested keywords**: [keyword1, keyword2]\n\n### H2: [Section Title 1]\n\n- [Short description of what this section will cover]\n\n- **Suggested keywords**: [keyword1, keyword2]\n\n### H2: [Section Title 2]\n\n- [Short description of what this section will cover]\n\n- **Suggested keywords**: [keyword1, keyword2]\n\n### H2: [Section Title 3]\n\n- [Optional H3 Subsection Title A]\n\n - [Explanation of sub-point]\n\n- [Optional H3 Subsection Title B]\n\n - [Explanation of sub-point]\n\n- **Suggested keywords**: [keyword1]\n\n### Conclusion\n\n- Recap key takeaways\n\n- Optional CTA (Call to Action)\n\n- **Suggested keywords**: [keyword3]\n\n", + "temperature": 0.5, + "temperatureEnabled": true, + "tools": [ + { + "component_name": "TavilySearch", + "name": "TavilySearch", + "params": { + "api_key": "", + "days": 7, + "exclude_domains": [], + "include_answer": false, + "include_domains": [], + "include_image_descriptions": false, + "include_images": false, + "include_raw_content": true, + "max_results": 5, + "outputs": { + "formalized_content": { + "type": "string", + "value": "" + }, + "json": { + "type": "Array", + "value": [] + } + }, + "query": "sys.query", + "search_depth": "basic", + "topic": "general" + } + } + ], + "topPEnabled": false, + "top_p": 0.85, + "user_prompt": "", + "visual_files_var": "" + }, + "label": "Agent", + "name": "Outline Agent" + }, + "dragging": false, + "id": "Agent:BetterSitesSend", + "measured": { + "height": 84, + "width": 200 + }, + "position": { + "x": 613.4368763415628, + "y": 164.3074269048589 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "agentNode" + }, + { + "data": { + "form": { + "description": "This is an agent for a specific task.", + "user_prompt": "This is the order you need to send to the agent." + }, + "label": "Tool", + "name": "flow.tool_0" + }, + "dragging": false, + "id": "Tool:SharpPensBurn", + "measured": { + "height": 44, + "width": 200 + }, + "position": { + "x": 580.1877078861457, + "y": 287.7669662022325 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "toolNode" + }, + { + "data": { + "form": { + "delay_after_error": 1, + "description": "", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.5, + "llm_id": "deepseek-chat@DeepSeek", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 5, + "max_tokens": 4096, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "parameter": "Precise", + "presencePenaltyEnabled": false, + "presence_penalty": 0.5, + "prompts": [ + { + "content": "The parse and keyword agent output is {Agent:ClearRabbitsScream@content}\n\n\n\nThe Ouline agent output is {Agent:BetterSitesSend@content}", + "role": "user" + } + ], + "sys_prompt": "# Role\n\nYou are the **Body_Agent**, responsible for generating the full content of each section of an SEO-optimized blog based on the provided outline and keyword strategy.\n\n# Tool Access:\n\nYou can use the `Tavily Search` tool to retrieve relevant content, statistics, or examples to support each section you're writing.\n\nUse it **only** when the provided outline lacks enough information, or if the section requires factual grounding.\n\nAlways cite the original link or indicate source where possible.\n\n\n# Goals\n\n1. Write each section (based on H2/H3 structure) as a complete and natural blog paragraph.\n\n2. Integrate the suggested long-tail keywords naturally into each section.\n\n3. When appropriate, use the `Tavily Search` tool to enrich your writing with relevant facts, examples, or quotes.\n\n4. Ensure each section is clear, engaging, and informative, suitable for both human readers and search engines.\n\n\n# Style Guidelines\n\n- Write in a tone appropriate to the audience. Be explanatory, not promotional, unless it's a marketing blog.\n\n- Avoid generic filler content. Prioritize clarity, structure, and value.\n\n- Ensure SEO keywords are embedded seamlessly, not forcefully.\n\n\n\n- Maintain writing rhythm. Vary sentence lengths. Use transitions between ideas.\n\n\n# Input\n\n\nYou will receive:\n\n- Blog title\n\n- Structured outline (including section titles, keywords, and descriptions)\n\n- Target audience\n\n- Blog type and user intent\n\nYou must **follow the outline strictly**. Write content **section-by-section**, based on the structure.\n\n\n# Output Format\n\n```markdown\n\n## H2: [Section Title]\n\n[Your generated content for this section \u2014 500-600 words, using keywords naturally.]\n\n", + "temperature": 0.2, + "temperatureEnabled": true, + "tools": [ + { + "component_name": "TavilySearch", + "name": "TavilySearch", + "params": { + "api_key": "", + "days": 7, + "exclude_domains": [], + "include_answer": false, + "include_domains": [], + "include_image_descriptions": false, + "include_images": false, + "include_raw_content": true, + "max_results": 5, + "outputs": { + "formalized_content": { + "type": "string", + "value": "" + }, + "json": { + "type": "Array", + "value": [] + } + }, + "query": "sys.query", + "search_depth": "basic", + "topic": "general" + } + } + ], + "topPEnabled": false, + "top_p": 0.75, + "user_prompt": "", + "visual_files_var": "" + }, + "label": "Agent", + "name": "Body Agent" + }, + "dragging": false, + "id": "Agent:EagerNailsRemain", + "measured": { + "height": 84, + "width": 200 + }, + "position": { + "x": 889.0614605692713, + "y": 247.00973041799065 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "agentNode" + }, + { + "data": { + "form": { + "description": "This is an agent for a specific task.", + "user_prompt": "This is the order you need to send to the agent." + }, + "label": "Tool", + "name": "flow.tool_1" + }, + "dragging": false, + "id": "Tool:WickedDeerHeal", + "measured": { + "height": 44, + "width": 200 + }, + "position": { + "x": 853.2006404239659, + "y": 364.37541577229143 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "toolNode" + }, + { + "data": { + "form": { + "delay_after_error": 1, + "description": "", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.5, + "llm_id": "deepseek-chat@DeepSeek", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 5, + "max_tokens": 4096, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "parameter": "Precise", + "presencePenaltyEnabled": false, + "presence_penalty": 0.5, + "prompts": [ + { + "content": "The parse and keyword agent output is {Agent:ClearRabbitsScream@content}\n\nThe Ouline agent output is {Agent:BetterSitesSend@content}\n\nThe Body agent output is {Agent:EagerNailsRemain@content}", + "role": "user" + } + ], + "sys_prompt": "# Role\n\nYou are the **Editor_Agent**, responsible for finalizing the blog post for both human readability and SEO effectiveness.\n\n# Goals\n\n1. Polish the entire blog content for clarity, coherence, and style.\n\n2. Improve transitions between sections, ensure logical flow.\n\n3. Verify that keywords are used appropriately and effectively.\n\n4. Conduct a lightweight SEO audit \u2014 checking keyword density, structure (H1/H2/H3), and overall searchability.\n\n\n\n# Style Guidelines\n\n- Be precise. Avoid bloated or vague language.\n\n- Maintain an informative and engaging tone, suitable to the target audience.\n\n- Do not remove keywords unless absolutely necessary for clarity.\n\n- Ensure paragraph flow and section continuity.\n\n\n# Input\n\nYou will receive:\n\n- Full blog content, written section-by-section\n\n- Original outline with suggested keywords\n\n- Target audience and writing type\n\n# Output Format\n\n```markdown\n\n[The revised, fully polished blog post content goes here.]\n\n", + "temperature": 0.2, + "temperatureEnabled": true, + "tools": [], + "topPEnabled": false, + "top_p": 0.75, + "user_prompt": "", + "visual_files_var": "" + }, + "label": "Agent", + "name": "Editor Agent" + }, + "dragging": false, + "id": "Agent:LovelyHeadsOwn", + "measured": { + "height": 84, + "width": 200 + }, + "position": { + "x": 1160.3332919804993, + "y": 149.50806732882472 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "agentNode" + }, + { + "data": { + "form": { + "content": [ + "{Agent:LovelyHeadsOwn@content}" + ] + }, + "label": "Message", + "name": "Response" + }, + "dragging": false, + "id": "Message:LegalBeansBet", + "measured": { + "height": 56, + "width": 200 + }, + "position": { + "x": 1370.6665839609984, + "y": 267.0323933738015 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "messageNode" + }, + { + "data": { + "form": { + "text": "This workflow automatically generates a complete SEO-optimized blog article based on a simple user input. You don\u2019t need any writing experience. Just provide a topic or short request \u2014 the system will handle the rest.\n\nThe process includes the following key stages:\n\n1. **Understanding your topic and goals**\n2. **Designing the blog structure**\n3. **Writing high-quality content**\n\n\n" + }, + "label": "Note", + "name": "Workflow Overall Description" + }, + "dragHandle": ".note-drag-handle", + "dragging": false, + "height": 205, + "id": "Note:SlimyGhostsWear", + "measured": { + "height": 205, + "width": 415 + }, + "position": { + "x": -284.3143151688742, + "y": 150.47632147913419 + }, + "resizing": false, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "noteNode", + "width": 415 + }, + { + "data": { + "form": { + "text": "**Purpose**: \nThis agent reads the user\u2019s input and figures out what kind of blog needs to be written.\n\n**What it does**:\n- Understands the main topic you want to write about \n- Identifies who the blog is for (e.g., beginners, marketers, developers) \n- Determines the writing purpose (e.g., SEO traffic, product promotion, education) \n- Suggests 3\u20135 long-tail SEO keywords related to the topic" + }, + "label": "Note", + "name": "Parse And Keyword Agent" + }, + "dragHandle": ".note-drag-handle", + "dragging": false, + "height": 152, + "id": "Note:EmptyChairsShake", + "measured": { + "height": 152, + "width": 340 + }, + "position": { + "x": 295.04147626768133, + "y": 372.2755718118446 + }, + "resizing": false, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "noteNode", + "width": 340 + }, + { + "data": { + "form": { + "text": "**Purpose**: \nThis agent builds the blog structure \u2014 just like writing a table of contents before you start writing the full article.\n\n**What it does**:\n- Suggests a clear blog title that includes important keywords \n- Breaks the article into sections using H2 and H3 headings (like a professional blog layout) \n- Assigns 1\u20132 recommended keywords to each section to help with SEO \n- Follows the writing goal and target audience set in the previous step" + }, + "label": "Note", + "name": "Outline Agent" + }, + "dragHandle": ".note-drag-handle", + "dragging": false, + "height": 146, + "id": "Note:TallMelonsNotice", + "measured": { + "height": 146, + "width": 343 + }, + "position": { + "x": 598.5644991893463, + "y": 5.801054564756448 + }, + "resizing": false, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "noteNode", + "width": 343 + }, + { + "data": { + "form": { + "text": "**Purpose**: \nThis agent is responsible for writing the actual content of the blog \u2014 paragraph by paragraph \u2014 based on the outline created earlier.\n\n**What it does**:\n- Looks at each H2/H3 section in the outline \n- Writes 150\u2013220 words of clear, helpful, and well-structured content per section \n- Includes the suggested SEO keywords naturally (not keyword stuffing) \n- Uses real examples or facts if needed (by calling a web search tool like Tavily)" + }, + "label": "Note", + "name": "Body Agent" + }, + "dragHandle": ".note-drag-handle", + "dragging": false, + "height": 137, + "id": "Note:RipeCougarsBuild", + "measured": { + "height": 137, + "width": 319 + }, + "position": { + "x": 860.4854129814981, + "y": 427.2196835690842 + }, + "resizing": false, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "noteNode", + "width": 319 + }, + { + "data": { + "form": { + "text": "**Purpose**: \nThis agent reviews the entire blog draft to make sure it is smooth, professional, and SEO-friendly. It acts like a human editor before publishing.\n\n**What it does**:\n- Polishes the writing: improves sentence clarity, fixes awkward phrasing \n- Makes sure the content flows well from one section to the next \n- Double-checks keyword usage: are they present, natural, and not overused? \n- Verifies the blog structure (H1, H2, H3 headings) is correct \n- Adds two key SEO elements:\n - **Meta Title** (shows up in search results)\n - **Meta Description** (summary for Google and social sharing)" + }, + "label": "Note", + "name": "Editor Agent" + }, + "dragHandle": ".note-drag-handle", + "height": 146, + "id": "Note:OpenTurkeysSell", + "measured": { + "height": 146, + "width": 320 + }, + "position": { + "x": 1129, + "y": -30 + }, + "resizing": false, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "noteNode", + "width": 320 + } + ] + }, + "history": [], + "messages": [], + "path": [], + "retrieval": [] + }, + "avatar": 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+} \ No newline at end of file diff --git a/agent/templates/seo_blog.json b/agent/templates/seo_blog.json new file mode 100644 index 000000000..c7d181223 --- /dev/null +++ b/agent/templates/seo_blog.json @@ -0,0 +1,915 @@ +{ + "id": 4, + "title": "Generate SEO Blog", + "description": "This workflow automatically generates a complete SEO-optimized blog article based on a simple user input. You don’t need any writing experience. Just provide a topic or short request — the system will handle the rest.", + "canvas_type": "Recommended", + "dsl": { + "components": { + "Agent:BetterSitesSend": { + "downstream": [ + "Agent:EagerNailsRemain" + ], + "obj": { + "component_name": "Agent", + "params": { + "delay_after_error": 1, + "description": "", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.3, + "llm_id": "deepseek-chat@DeepSeek", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 3, + "max_tokens": 4096, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "parameter": "Balance", + "presencePenaltyEnabled": false, + "presence_penalty": 0.2, + "prompts": [ + { + "content": "The parse and keyword agent output is {Agent:ClearRabbitsScream@content}", + "role": "user" + } + ], + "sys_prompt": "# Role\n\nYou are the **Outline_Agent**, responsible for generating a clear and SEO-optimized blog outline based on the user's parsed writing intent and keyword strategy.\n\n# Tool Access:\n\n- You have access to a search tool called `Tavily Search`.\n\n- If you are unsure how to structure a section, you may call this tool to search for related blog outlines or content from Google.\n\n- Do not overuse it. Your job is to extract **structure**, not to write paragraphs.\n\n\n# Goals\n\n1. Create a well-structured outline with appropriate H2 and H3 headings.\n\n2. Ensure logical flow from introduction to conclusion.\n\n3. Assign 1\u20132 suggested long-tail keywords to each major section for SEO alignment.\n\n4. Make the structure suitable for downstream paragraph writing.\n\n\n\n\n#Note\n\n- Use concise, scannable section titles.\n\n- Do not write full paragraphs.\n\n- Prioritize clarity, logical progression, and SEO alignment.\n\n\n\n- If the blog type is \u201cTutorial\u201d or \u201cHow-to\u201d, include step-based sections.\n\n\n# Input\n\nYou will receive:\n\n- Writing Type (e.g., Tutorial, Informative Guide)\n\n- Target Audience\n\n- User Intent Summary\n\n- 3\u20135 long-tail keywords\n\n\nUse this information to design a structure that both informs readers and maximizes search engine visibility.\n\n# Output Format\n\n```markdown\n\n## Blog Title (suggested)\n\n[Give a short, SEO-friendly title suggestion]\n\n## Outline\n\n### Introduction\n\n- Purpose of the article\n\n- Brief context\n\n- **Suggested keywords**: [keyword1, keyword2]\n\n### H2: [Section Title 1]\n\n- [Short description of what this section will cover]\n\n- **Suggested keywords**: [keyword1, keyword2]\n\n### H2: [Section Title 2]\n\n- [Short description of what this section will cover]\n\n- **Suggested keywords**: [keyword1, keyword2]\n\n### H2: [Section Title 3]\n\n- [Optional H3 Subsection Title A]\n\n - [Explanation of sub-point]\n\n- [Optional H3 Subsection Title B]\n\n - [Explanation of sub-point]\n\n- **Suggested keywords**: [keyword1]\n\n### Conclusion\n\n- Recap key takeaways\n\n- Optional CTA (Call to Action)\n\n- **Suggested keywords**: [keyword3]\n\n", + "temperature": 0.5, + "temperatureEnabled": true, + "tools": [ + { + "component_name": "TavilySearch", + "name": "TavilySearch", + "params": { + "api_key": "", + "days": 7, + "exclude_domains": [], + "include_answer": false, + "include_domains": [], + "include_image_descriptions": false, + "include_images": false, + "include_raw_content": true, + "max_results": 5, + "outputs": { + "formalized_content": { + "type": "string", + "value": "" + }, + "json": { + "type": "Array", + "value": [] + } + }, + "query": "sys.query", + "search_depth": "basic", + "topic": "general" + } + } + ], + "topPEnabled": false, + "top_p": 0.85, + "user_prompt": "", + "visual_files_var": "" + } + }, + "upstream": [ + "Agent:ClearRabbitsScream" + ] + }, + "Agent:ClearRabbitsScream": { + "downstream": [ + "Agent:BetterSitesSend" + ], + "obj": { + "component_name": "Agent", + "params": { + "delay_after_error": 1, + "description": "", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.5, + "llm_id": "deepseek-chat@DeepSeek", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 1, + "max_tokens": 4096, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "parameter": "Precise", + "presencePenaltyEnabled": false, + "presence_penalty": 0.5, + "prompts": [ + { + "content": "The user query is {sys.query}", + "role": "user" + } + ], + "sys_prompt": "# Role\n\nYou are the **Parse_And_Keyword_Agent**, responsible for interpreting a user's blog writing request and generating a structured writing intent summary and keyword strategy for SEO-optimized content generation.\n\n# Goals\n\n1. Extract and infer the user's true writing intent, even if the input is informal or vague.\n\n2. Identify the writing type, target audience, and implied goal.\n\n3. Suggest 3\u20135 long-tail keywords based on the input and context.\n\n4. Output all data in a Markdown format for downstream agents.\n\n# Operating Guidelines\n\n\n- If the user's input lacks clarity, make reasonable and **conservative** assumptions based on SEO best practices.\n\n- Always choose one clear \"Writing Type\" from the list below.\n\n- Your job is not to write the blog \u2014 only to structure the brief.\n\n# Output Format\n\n```markdown\n## Writing Type\n\n[Choose one: Tutorial / Informative Guide / Marketing Content / Case Study / Opinion Piece / How-to / Comparison Article]\n\n## Target Audience\n\n[Try to be specific based on clues in the input: e.g., marketing managers, junior developers, SEO beginners]\n\n## User Intent Summary\n\n[A 1\u20132 sentence summary of what the user wants to achieve with the blog post]\n\n## Suggested Long-tail Keywords\n\n- keyword 1\n\n- keyword 2\n\n- keyword 3\n\n- keyword 4 (optional)\n\n- keyword 5 (optional)\n\n\n\n\n## Input Examples (and how to handle them)\n\nInput: \"I want to write about RAGFlow.\"\n\u2192 Output: Informative Guide, Audience: AI developers, Intent: explain what RAGFlow is and its use cases\n\nInput: \"Need a blog to promote our prompt design tool.\"\n\u2192 Output: Marketing Content, Audience: product managers or tool adopters, Intent: raise awareness and interest in the product\n\n\n\nInput: \"How to get more Google traffic using AI\"\n\u2192 Output: How-to, Audience: SEO marketers, Intent: guide readers on applying AI for SEO growth", + "temperature": 0.2, + "temperatureEnabled": true, + "tools": [], + "topPEnabled": false, + "top_p": 0.75, + "user_prompt": "", + "visual_files_var": "" + } + }, + "upstream": [ + "begin" + ] + }, + "Agent:EagerNailsRemain": { + "downstream": [ + "Agent:LovelyHeadsOwn" + ], + "obj": { + "component_name": "Agent", + "params": { + "delay_after_error": 1, + "description": "", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.5, + "llm_id": "deepseek-chat@DeepSeek", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 5, + "max_tokens": 4096, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "parameter": "Precise", + "presencePenaltyEnabled": false, + "presence_penalty": 0.5, + "prompts": [ + { + "content": "The parse and keyword agent output is {Agent:ClearRabbitsScream@content}\n\n\n\nThe Ouline agent output is {Agent:BetterSitesSend@content}", + "role": "user" + } + ], + "sys_prompt": "# Role\n\nYou are the **Body_Agent**, responsible for generating the full content of each section of an SEO-optimized blog based on the provided outline and keyword strategy.\n\n# Tool Access:\n\nYou can use the `Tavily Search` tool to retrieve relevant content, statistics, or examples to support each section you're writing.\n\nUse it **only** when the provided outline lacks enough information, or if the section requires factual grounding.\n\nAlways cite the original link or indicate source where possible.\n\n\n# Goals\n\n1. Write each section (based on H2/H3 structure) as a complete and natural blog paragraph.\n\n2. Integrate the suggested long-tail keywords naturally into each section.\n\n3. When appropriate, use the `Tavily Search` tool to enrich your writing with relevant facts, examples, or quotes.\n\n4. Ensure each section is clear, engaging, and informative, suitable for both human readers and search engines.\n\n\n# Style Guidelines\n\n- Write in a tone appropriate to the audience. Be explanatory, not promotional, unless it's a marketing blog.\n\n- Avoid generic filler content. Prioritize clarity, structure, and value.\n\n- Ensure SEO keywords are embedded seamlessly, not forcefully.\n\n\n\n- Maintain writing rhythm. Vary sentence lengths. Use transitions between ideas.\n\n\n# Input\n\n\nYou will receive:\n\n- Blog title\n\n- Structured outline (including section titles, keywords, and descriptions)\n\n- Target audience\n\n- Blog type and user intent\n\nYou must **follow the outline strictly**. Write content **section-by-section**, based on the structure.\n\n\n# Output Format\n\n```markdown\n\n## H2: [Section Title]\n\n[Your generated content for this section \u2014 500-600 words, using keywords naturally.]\n\n", + "temperature": 0.2, + "temperatureEnabled": true, + "tools": [ + { + "component_name": "TavilySearch", + "name": "TavilySearch", + "params": { + "api_key": "", + "days": 7, + "exclude_domains": [], + "include_answer": false, + "include_domains": [], + "include_image_descriptions": false, + "include_images": false, + "include_raw_content": true, + "max_results": 5, + "outputs": { + "formalized_content": { + "type": "string", + "value": "" + }, + "json": { + "type": "Array", + "value": [] + } + }, + "query": "sys.query", + "search_depth": "basic", + "topic": "general" + } + } + ], + "topPEnabled": false, + "top_p": 0.75, + "user_prompt": "", + "visual_files_var": "" + } + }, + "upstream": [ + "Agent:BetterSitesSend" + ] + }, + "Agent:LovelyHeadsOwn": { + "downstream": [ + "Message:LegalBeansBet" + ], + "obj": { + "component_name": "Agent", + "params": { + "delay_after_error": 1, + "description": "", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.5, + "llm_id": "deepseek-chat@DeepSeek", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 5, + "max_tokens": 4096, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "parameter": "Precise", + "presencePenaltyEnabled": false, + "presence_penalty": 0.5, + "prompts": [ + { + "content": "The parse and keyword agent output is {Agent:ClearRabbitsScream@content}\n\nThe Ouline agent output is {Agent:BetterSitesSend@content}\n\nThe Body agent output is {Agent:EagerNailsRemain@content}", + "role": "user" + } + ], + "sys_prompt": "# Role\n\nYou are the **Editor_Agent**, responsible for finalizing the blog post for both human readability and SEO effectiveness.\n\n# Goals\n\n1. Polish the entire blog content for clarity, coherence, and style.\n\n2. Improve transitions between sections, ensure logical flow.\n\n3. Verify that keywords are used appropriately and effectively.\n\n4. Conduct a lightweight SEO audit \u2014 checking keyword density, structure (H1/H2/H3), and overall searchability.\n\n\n\n# Style Guidelines\n\n- Be precise. Avoid bloated or vague language.\n\n- Maintain an informative and engaging tone, suitable to the target audience.\n\n- Do not remove keywords unless absolutely necessary for clarity.\n\n- Ensure paragraph flow and section continuity.\n\n\n# Input\n\nYou will receive:\n\n- Full blog content, written section-by-section\n\n- Original outline with suggested keywords\n\n- Target audience and writing type\n\n# Output Format\n\n```markdown\n\n[The revised, fully polished blog post content goes here.]\n\n", + "temperature": 0.2, + "temperatureEnabled": true, + "tools": [], + "topPEnabled": false, + "top_p": 0.75, + "user_prompt": "", + "visual_files_var": "" + } + }, + "upstream": [ + "Agent:EagerNailsRemain" + ] + }, + "Message:LegalBeansBet": { + "downstream": [], + "obj": { + "component_name": "Message", + "params": { + "content": [ + "{Agent:LovelyHeadsOwn@content}" + ] + } + }, + "upstream": [ + "Agent:LovelyHeadsOwn" + ] + }, + "begin": { + "downstream": [ + "Agent:ClearRabbitsScream" + ], + "obj": { + "component_name": "Begin", + "params": { + "enablePrologue": true, + "inputs": {}, + "mode": "conversational", + "prologue": "Hi! I'm your SEO blog assistant.\n\nTo get started, please tell me:\n1. What topic you want the blog to cover\n2. Who is the target audience\n3. What you hope to achieve with this blog (e.g., SEO traffic, teaching beginners, promoting a product)\n" + } + }, + "upstream": [] + } + }, + "globals": { + "sys.conversation_turns": 0, + "sys.files": [], + "sys.query": "", + "sys.user_id": "" + }, + "graph": { + "edges": [ + { + "data": { + "isHovered": false + }, + "id": "xy-edge__beginstart-Agent:ClearRabbitsScreamend", + "source": "begin", + "sourceHandle": "start", + "target": "Agent:ClearRabbitsScream", + "targetHandle": "end" + }, + { + "data": { + "isHovered": false + }, + "id": "xy-edge__Agent:ClearRabbitsScreamstart-Agent:BetterSitesSendend", + "source": "Agent:ClearRabbitsScream", + "sourceHandle": "start", + "target": "Agent:BetterSitesSend", + "targetHandle": "end" + }, + { + "data": { + "isHovered": false + }, + "id": "xy-edge__Agent:BetterSitesSendtool-Tool:SharpPensBurnend", + "source": "Agent:BetterSitesSend", + "sourceHandle": "tool", + "target": "Tool:SharpPensBurn", + "targetHandle": "end" + }, + { + "data": { + "isHovered": false + }, + "id": "xy-edge__Agent:BetterSitesSendstart-Agent:EagerNailsRemainend", + "source": "Agent:BetterSitesSend", + "sourceHandle": "start", + "target": "Agent:EagerNailsRemain", + "targetHandle": "end" + }, + { + "id": "xy-edge__Agent:EagerNailsRemaintool-Tool:WickedDeerHealend", + "source": "Agent:EagerNailsRemain", + "sourceHandle": "tool", + "target": "Tool:WickedDeerHeal", + "targetHandle": "end" + }, + { + "data": { + "isHovered": false + }, + "id": "xy-edge__Agent:EagerNailsRemainstart-Agent:LovelyHeadsOwnend", + "source": "Agent:EagerNailsRemain", + "sourceHandle": "start", + "target": "Agent:LovelyHeadsOwn", + "targetHandle": "end" + }, + { + "data": { + "isHovered": false + }, + "id": "xy-edge__Agent:LovelyHeadsOwnstart-Message:LegalBeansBetend", + "source": "Agent:LovelyHeadsOwn", + "sourceHandle": "start", + "target": "Message:LegalBeansBet", + "targetHandle": "end" + } + ], + "nodes": [ + { + "data": { + "form": { + "enablePrologue": true, + "inputs": {}, + "mode": "conversational", + "prologue": "Hi! I'm your SEO blog assistant.\n\nTo get started, please tell me:\n1. What topic you want the blog to cover\n2. Who is the target audience\n3. What you hope to achieve with this blog (e.g., SEO traffic, teaching beginners, promoting a product)\n" + }, + "label": "Begin", + "name": "begin" + }, + "id": "begin", + "measured": { + "height": 48, + "width": 200 + }, + "position": { + "x": 50, + "y": 200 + }, + "selected": false, + "sourcePosition": "left", + "targetPosition": "right", + "type": "beginNode" + }, + { + "data": { + "form": { + "delay_after_error": 1, + "description": "", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.5, + "llm_id": "deepseek-chat@DeepSeek", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 1, + "max_tokens": 4096, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "parameter": "Precise", + "presencePenaltyEnabled": false, + "presence_penalty": 0.5, + "prompts": [ + { + "content": "The user query is {sys.query}", + "role": "user" + } + ], + "sys_prompt": "# Role\n\nYou are the **Parse_And_Keyword_Agent**, responsible for interpreting a user's blog writing request and generating a structured writing intent summary and keyword strategy for SEO-optimized content generation.\n\n# Goals\n\n1. Extract and infer the user's true writing intent, even if the input is informal or vague.\n\n2. Identify the writing type, target audience, and implied goal.\n\n3. Suggest 3\u20135 long-tail keywords based on the input and context.\n\n4. Output all data in a Markdown format for downstream agents.\n\n# Operating Guidelines\n\n\n- If the user's input lacks clarity, make reasonable and **conservative** assumptions based on SEO best practices.\n\n- Always choose one clear \"Writing Type\" from the list below.\n\n- Your job is not to write the blog \u2014 only to structure the brief.\n\n# Output Format\n\n```markdown\n## Writing Type\n\n[Choose one: Tutorial / Informative Guide / Marketing Content / Case Study / Opinion Piece / How-to / Comparison Article]\n\n## Target Audience\n\n[Try to be specific based on clues in the input: e.g., marketing managers, junior developers, SEO beginners]\n\n## User Intent Summary\n\n[A 1\u20132 sentence summary of what the user wants to achieve with the blog post]\n\n## Suggested Long-tail Keywords\n\n- keyword 1\n\n- keyword 2\n\n- keyword 3\n\n- keyword 4 (optional)\n\n- keyword 5 (optional)\n\n\n\n\n## Input Examples (and how to handle them)\n\nInput: \"I want to write about RAGFlow.\"\n\u2192 Output: Informative Guide, Audience: AI developers, Intent: explain what RAGFlow is and its use cases\n\nInput: \"Need a blog to promote our prompt design tool.\"\n\u2192 Output: Marketing Content, Audience: product managers or tool adopters, Intent: raise awareness and interest in the product\n\n\n\nInput: \"How to get more Google traffic using AI\"\n\u2192 Output: How-to, Audience: SEO marketers, Intent: guide readers on applying AI for SEO growth", + "temperature": 0.2, + "temperatureEnabled": true, + "tools": [], + "topPEnabled": false, + "top_p": 0.75, + "user_prompt": "", + "visual_files_var": "" + }, + "label": "Agent", + "name": "Parse And Keyword Agent" + }, + "dragging": false, + "id": "Agent:ClearRabbitsScream", + "measured": { + "height": 84, + "width": 200 + }, + "position": { + "x": 344.7766966202233, + "y": 234.82202253184496 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "agentNode" + }, + { + "data": { + "form": { + "delay_after_error": 1, + "description": "", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.3, + "llm_id": "deepseek-chat@DeepSeek", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 3, + "max_tokens": 4096, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "parameter": "Balance", + "presencePenaltyEnabled": false, + "presence_penalty": 0.2, + "prompts": [ + { + "content": "The parse and keyword agent output is {Agent:ClearRabbitsScream@content}", + "role": "user" + } + ], + "sys_prompt": "# Role\n\nYou are the **Outline_Agent**, responsible for generating a clear and SEO-optimized blog outline based on the user's parsed writing intent and keyword strategy.\n\n# Tool Access:\n\n- You have access to a search tool called `Tavily Search`.\n\n- If you are unsure how to structure a section, you may call this tool to search for related blog outlines or content from Google.\n\n- Do not overuse it. Your job is to extract **structure**, not to write paragraphs.\n\n\n# Goals\n\n1. Create a well-structured outline with appropriate H2 and H3 headings.\n\n2. Ensure logical flow from introduction to conclusion.\n\n3. Assign 1\u20132 suggested long-tail keywords to each major section for SEO alignment.\n\n4. Make the structure suitable for downstream paragraph writing.\n\n\n\n\n#Note\n\n- Use concise, scannable section titles.\n\n- Do not write full paragraphs.\n\n- Prioritize clarity, logical progression, and SEO alignment.\n\n\n\n- If the blog type is \u201cTutorial\u201d or \u201cHow-to\u201d, include step-based sections.\n\n\n# Input\n\nYou will receive:\n\n- Writing Type (e.g., Tutorial, Informative Guide)\n\n- Target Audience\n\n- User Intent Summary\n\n- 3\u20135 long-tail keywords\n\n\nUse this information to design a structure that both informs readers and maximizes search engine visibility.\n\n# Output Format\n\n```markdown\n\n## Blog Title (suggested)\n\n[Give a short, SEO-friendly title suggestion]\n\n## Outline\n\n### Introduction\n\n- Purpose of the article\n\n- Brief context\n\n- **Suggested keywords**: [keyword1, keyword2]\n\n### H2: [Section Title 1]\n\n- [Short description of what this section will cover]\n\n- **Suggested keywords**: [keyword1, keyword2]\n\n### H2: [Section Title 2]\n\n- [Short description of what this section will cover]\n\n- **Suggested keywords**: [keyword1, keyword2]\n\n### H2: [Section Title 3]\n\n- [Optional H3 Subsection Title A]\n\n - [Explanation of sub-point]\n\n- [Optional H3 Subsection Title B]\n\n - [Explanation of sub-point]\n\n- **Suggested keywords**: [keyword1]\n\n### Conclusion\n\n- Recap key takeaways\n\n- Optional CTA (Call to Action)\n\n- **Suggested keywords**: [keyword3]\n\n", + "temperature": 0.5, + "temperatureEnabled": true, + "tools": [ + { + "component_name": "TavilySearch", + "name": "TavilySearch", + "params": { + "api_key": "", + "days": 7, + "exclude_domains": [], + "include_answer": false, + "include_domains": [], + "include_image_descriptions": false, + "include_images": false, + "include_raw_content": true, + "max_results": 5, + "outputs": { + "formalized_content": { + "type": "string", + "value": "" + }, + "json": { + "type": "Array", + "value": [] + } + }, + "query": "sys.query", + "search_depth": "basic", + "topic": "general" + } + } + ], + "topPEnabled": false, + "top_p": 0.85, + "user_prompt": "", + "visual_files_var": "" + }, + "label": "Agent", + "name": "Outline Agent" + }, + "dragging": false, + "id": "Agent:BetterSitesSend", + "measured": { + "height": 84, + "width": 200 + }, + "position": { + "x": 613.4368763415628, + "y": 164.3074269048589 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "agentNode" + }, + { + "data": { + "form": { + "description": "This is an agent for a specific task.", + "user_prompt": "This is the order you need to send to the agent." + }, + "label": "Tool", + "name": "flow.tool_0" + }, + "dragging": false, + "id": "Tool:SharpPensBurn", + "measured": { + "height": 44, + "width": 200 + }, + "position": { + "x": 580.1877078861457, + "y": 287.7669662022325 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "toolNode" + }, + { + "data": { + "form": { + "delay_after_error": 1, + "description": "", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.5, + "llm_id": "deepseek-chat@DeepSeek", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 5, + "max_tokens": 4096, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "parameter": "Precise", + "presencePenaltyEnabled": false, + "presence_penalty": 0.5, + "prompts": [ + { + "content": "The parse and keyword agent output is {Agent:ClearRabbitsScream@content}\n\n\n\nThe Ouline agent output is {Agent:BetterSitesSend@content}", + "role": "user" + } + ], + "sys_prompt": "# Role\n\nYou are the **Body_Agent**, responsible for generating the full content of each section of an SEO-optimized blog based on the provided outline and keyword strategy.\n\n# Tool Access:\n\nYou can use the `Tavily Search` tool to retrieve relevant content, statistics, or examples to support each section you're writing.\n\nUse it **only** when the provided outline lacks enough information, or if the section requires factual grounding.\n\nAlways cite the original link or indicate source where possible.\n\n\n# Goals\n\n1. Write each section (based on H2/H3 structure) as a complete and natural blog paragraph.\n\n2. Integrate the suggested long-tail keywords naturally into each section.\n\n3. When appropriate, use the `Tavily Search` tool to enrich your writing with relevant facts, examples, or quotes.\n\n4. Ensure each section is clear, engaging, and informative, suitable for both human readers and search engines.\n\n\n# Style Guidelines\n\n- Write in a tone appropriate to the audience. Be explanatory, not promotional, unless it's a marketing blog.\n\n- Avoid generic filler content. Prioritize clarity, structure, and value.\n\n- Ensure SEO keywords are embedded seamlessly, not forcefully.\n\n\n\n- Maintain writing rhythm. Vary sentence lengths. Use transitions between ideas.\n\n\n# Input\n\n\nYou will receive:\n\n- Blog title\n\n- Structured outline (including section titles, keywords, and descriptions)\n\n- Target audience\n\n- Blog type and user intent\n\nYou must **follow the outline strictly**. Write content **section-by-section**, based on the structure.\n\n\n# Output Format\n\n```markdown\n\n## H2: [Section Title]\n\n[Your generated content for this section \u2014 500-600 words, using keywords naturally.]\n\n", + "temperature": 0.2, + "temperatureEnabled": true, + "tools": [ + { + "component_name": "TavilySearch", + "name": "TavilySearch", + "params": { + "api_key": "", + "days": 7, + "exclude_domains": [], + "include_answer": false, + "include_domains": [], + "include_image_descriptions": false, + "include_images": false, + "include_raw_content": true, + "max_results": 5, + "outputs": { + "formalized_content": { + "type": "string", + "value": "" + }, + "json": { + "type": "Array", + "value": [] + } + }, + "query": "sys.query", + "search_depth": "basic", + "topic": "general" + } + } + ], + "topPEnabled": false, + "top_p": 0.75, + "user_prompt": "", + "visual_files_var": "" + }, + "label": "Agent", + "name": "Body Agent" + }, + "dragging": false, + "id": "Agent:EagerNailsRemain", + "measured": { + "height": 84, + "width": 200 + }, + "position": { + "x": 889.0614605692713, + "y": 247.00973041799065 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "agentNode" + }, + { + "data": { + "form": { + "description": "This is an agent for a specific task.", + "user_prompt": "This is the order you need to send to the agent." + }, + "label": "Tool", + "name": "flow.tool_1" + }, + "dragging": false, + "id": "Tool:WickedDeerHeal", + "measured": { + "height": 44, + "width": 200 + }, + "position": { + "x": 853.2006404239659, + "y": 364.37541577229143 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "toolNode" + }, + { + "data": { + "form": { + "delay_after_error": 1, + "description": "", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.5, + "llm_id": "deepseek-chat@DeepSeek", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 5, + "max_tokens": 4096, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "parameter": "Precise", + "presencePenaltyEnabled": false, + "presence_penalty": 0.5, + "prompts": [ + { + "content": "The parse and keyword agent output is {Agent:ClearRabbitsScream@content}\n\nThe Ouline agent output is {Agent:BetterSitesSend@content}\n\nThe Body agent output is {Agent:EagerNailsRemain@content}", + "role": "user" + } + ], + "sys_prompt": "# Role\n\nYou are the **Editor_Agent**, responsible for finalizing the blog post for both human readability and SEO effectiveness.\n\n# Goals\n\n1. Polish the entire blog content for clarity, coherence, and style.\n\n2. Improve transitions between sections, ensure logical flow.\n\n3. Verify that keywords are used appropriately and effectively.\n\n4. Conduct a lightweight SEO audit \u2014 checking keyword density, structure (H1/H2/H3), and overall searchability.\n\n\n\n# Style Guidelines\n\n- Be precise. Avoid bloated or vague language.\n\n- Maintain an informative and engaging tone, suitable to the target audience.\n\n- Do not remove keywords unless absolutely necessary for clarity.\n\n- Ensure paragraph flow and section continuity.\n\n\n# Input\n\nYou will receive:\n\n- Full blog content, written section-by-section\n\n- Original outline with suggested keywords\n\n- Target audience and writing type\n\n# Output Format\n\n```markdown\n\n[The revised, fully polished blog post content goes here.]\n\n", + "temperature": 0.2, + "temperatureEnabled": true, + "tools": [], + "topPEnabled": false, + "top_p": 0.75, + "user_prompt": "", + "visual_files_var": "" + }, + "label": "Agent", + "name": "Editor Agent" + }, + "dragging": false, + "id": "Agent:LovelyHeadsOwn", + "measured": { + "height": 84, + "width": 200 + }, + "position": { + "x": 1160.3332919804993, + "y": 149.50806732882472 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "agentNode" + }, + { + "data": { + "form": { + "content": [ + "{Agent:LovelyHeadsOwn@content}" + ] + }, + "label": "Message", + "name": "Response" + }, + "dragging": false, + "id": "Message:LegalBeansBet", + "measured": { + "height": 56, + "width": 200 + }, + "position": { + "x": 1370.6665839609984, + "y": 267.0323933738015 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "messageNode" + }, + { + "data": { + "form": { + "text": "This workflow automatically generates a complete SEO-optimized blog article based on a simple user input. You don\u2019t need any writing experience. Just provide a topic or short request \u2014 the system will handle the rest.\n\nThe process includes the following key stages:\n\n1. **Understanding your topic and goals**\n2. **Designing the blog structure**\n3. **Writing high-quality content**\n\n\n" + }, + "label": "Note", + "name": "Workflow Overall Description" + }, + "dragHandle": ".note-drag-handle", + "dragging": false, + "height": 205, + "id": "Note:SlimyGhostsWear", + "measured": { + "height": 205, + "width": 415 + }, + "position": { + "x": -284.3143151688742, + "y": 150.47632147913419 + }, + "resizing": false, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "noteNode", + "width": 415 + }, + { + "data": { + "form": { + "text": "**Purpose**: \nThis agent reads the user\u2019s input and figures out what kind of blog needs to be written.\n\n**What it does**:\n- Understands the main topic you want to write about \n- Identifies who the blog is for (e.g., beginners, marketers, developers) \n- Determines the writing purpose (e.g., SEO traffic, product promotion, education) \n- Suggests 3\u20135 long-tail SEO keywords related to the topic" + }, + "label": "Note", + "name": "Parse And Keyword Agent" + }, + "dragHandle": ".note-drag-handle", + "dragging": false, + "height": 152, + "id": "Note:EmptyChairsShake", + "measured": { + "height": 152, + "width": 340 + }, + "position": { + "x": 295.04147626768133, + "y": 372.2755718118446 + }, + "resizing": false, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "noteNode", + "width": 340 + }, + { + "data": { + "form": { + "text": "**Purpose**: \nThis agent builds the blog structure \u2014 just like writing a table of contents before you start writing the full article.\n\n**What it does**:\n- Suggests a clear blog title that includes important keywords \n- Breaks the article into sections using H2 and H3 headings (like a professional blog layout) \n- Assigns 1\u20132 recommended keywords to each section to help with SEO \n- Follows the writing goal and target audience set in the previous step" + }, + "label": "Note", + "name": "Outline Agent" + }, + "dragHandle": ".note-drag-handle", + "dragging": false, + "height": 146, + "id": "Note:TallMelonsNotice", + "measured": { + "height": 146, + "width": 343 + }, + "position": { + "x": 598.5644991893463, + "y": 5.801054564756448 + }, + "resizing": false, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "noteNode", + "width": 343 + }, + { + "data": { + "form": { + "text": "**Purpose**: \nThis agent is responsible for writing the actual content of the blog \u2014 paragraph by paragraph \u2014 based on the outline created earlier.\n\n**What it does**:\n- Looks at each H2/H3 section in the outline \n- Writes 150\u2013220 words of clear, helpful, and well-structured content per section \n- Includes the suggested SEO keywords naturally (not keyword stuffing) \n- Uses real examples or facts if needed (by calling a web search tool like Tavily)" + }, + "label": "Note", + "name": "Body Agent" + }, + "dragHandle": ".note-drag-handle", + "dragging": false, + "height": 137, + "id": "Note:RipeCougarsBuild", + "measured": { + "height": 137, + "width": 319 + }, + "position": { + "x": 860.4854129814981, + "y": 427.2196835690842 + }, + "resizing": false, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "noteNode", + "width": 319 + }, + { + "data": { + "form": { + "text": "**Purpose**: \nThis agent reviews the entire blog draft to make sure it is smooth, professional, and SEO-friendly. It acts like a human editor before publishing.\n\n**What it does**:\n- Polishes the writing: improves sentence clarity, fixes awkward phrasing \n- Makes sure the content flows well from one section to the next \n- Double-checks keyword usage: are they present, natural, and not overused? \n- Verifies the blog structure (H1, H2, H3 headings) is correct \n- Adds two key SEO elements:\n - **Meta Title** (shows up in search results)\n - **Meta Description** (summary for Google and social sharing)" + }, + "label": "Note", + "name": "Editor Agent" + }, + "dragHandle": ".note-drag-handle", + "height": 146, + "id": "Note:OpenTurkeysSell", + "measured": { + "height": 146, + "width": 320 + }, + "position": { + "x": 1129, + "y": -30 + }, + "resizing": false, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "noteNode", + "width": 320 + } + ] + }, + "history": [], + "messages": [], + "path": [], + "retrieval": [] + }, + "avatar": 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+} \ No newline at end of file diff --git a/agent/templates/technical_docs.json b/agent/templates/technical_docs.json new file mode 100644 index 000000000..83d4f8422 --- /dev/null +++ b/agent/templates/technical_docs.json @@ -0,0 +1,331 @@ + +{ + "id": 3, + "title": "Technical Docs QA", + "description": "This is a document question-and-answer system based on a knowledge base. When a user asks a question, it retrieves relevant document content to provide accurate answers.", + "canvas_type": "Recommended", + "dsl": { + "components": { + "Agent:StalePandasDream": { + "downstream": [ + "Message:BrownPugsStick" + ], + "obj": { + "component_name": "Agent", + "params": { + "delay_after_error": 1, + "description": "", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.7, + "llm_id": "deepseek-chat@DeepSeek", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 5, + "max_tokens": 256, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "presencePenaltyEnabled": false, + "presence_penalty": 0.4, + "prompts": [ + { + "content": "The user query is {sys.query}", + "role": "user" + } + ], + "sys_prompt": "# Role\n\nYou are the **Docs QA Agent**, a specialized knowledge base assistant responsible for providing accurate answers based strictly on the connected documentation repository.\n\n# Core Principles\n\n1. **Knowledge Base Only**: Answer questions EXCLUSIVELY based on information retrieved from the connected knowledge base.\n\n2. **No Content Creation**: Never generate, infer, or create information that is not explicitly present in the retrieved documents.\n\n3. **Source Transparency**: Always indicate when information comes from the knowledge base vs. when it's unavailable.\n\n4. **Accuracy Over Completeness**: Prefer incomplete but accurate answers over complete but potentially inaccurate ones.\n\n# Response Guidelines\n\n## When Information is Available\n\n- Provide direct answers based on retrieved content\n\n- Quote relevant sections when helpful\n\n- Cite the source document/section if available\n\n- Use phrases like: \"According to the documentation...\" or \"Based on the knowledge base...\"\n\n## When Information is Unavailable\n\n- Clearly state: \"I cannot find this information in the current knowledge base.\"\n\n- Do NOT attempt to fill gaps with general knowledge\n\n- Suggest alternative questions that might be covered in the docs\n\n- Use phrases like: \"The documentation does not cover...\" or \"This information is not available in the knowledge base.\"\n\n# Response Format\n\n```markdown\n\n## Answer\n\n[Your response based strictly on knowledge base content]\n\n**Always do these:**\n\n- Use the Retrieval tool for every question\n\n- Be transparent about information availability\n\n- Stick to documented facts only\n\n- Acknowledge knowledge base limitations\n\n", + "temperature": 0.1, + "temperatureEnabled": true, + "tools": [ + { + "component_name": "Retrieval", + "name": "Retrieval", + "params": { + "cross_languages": [], + "description": "This is a technical docs knowledge bases.", + "empty_response": "", + "kb_ids": [], + "keywords_similarity_weight": 0.7, + "outputs": { + "formalized_content": { + "type": "string", + "value": "" + } + }, + "rerank_id": "", + "similarity_threshold": 0.2, + "top_k": 1024, + "top_n": 8, + "use_kg": false + } + } + ], + "topPEnabled": false, + "top_p": 0.3, + "user_prompt": "", + "visual_files_var": "" + } + }, + "upstream": [ + "begin" + ] + }, + "Message:BrownPugsStick": { + "downstream": [], + "obj": { + "component_name": "Message", + "params": { + "content": [ + "{Agent:StalePandasDream@content}" + ] + } + }, + "upstream": [ + "Agent:StalePandasDream" + ] + }, + "begin": { + "downstream": [ + "Agent:StalePandasDream" + ], + "obj": { + "component_name": "Begin", + "params": {} + }, + "upstream": [] + } + }, + "globals": { + "sys.conversation_turns": 0, + "sys.files": [], + "sys.query": "", + "sys.user_id": "" + }, + "graph": { + "edges": [ + { + "data": { + "isHovered": false + }, + "id": "xy-edge__beginstart-Agent:StalePandasDreamend", + "source": "begin", + "sourceHandle": "start", + "target": "Agent:StalePandasDream", + "targetHandle": "end" + }, + { + "data": { + "isHovered": false + }, + "id": "xy-edge__Agent:StalePandasDreamstart-Message:BrownPugsStickend", + "source": "Agent:StalePandasDream", + "sourceHandle": "start", + "target": "Message:BrownPugsStick", + "targetHandle": "end" + }, + { + "id": "xy-edge__Agent:StalePandasDreamtool-Tool:PrettyMasksFloatend", + "source": "Agent:StalePandasDream", + "sourceHandle": "tool", + "target": "Tool:PrettyMasksFloat", + "targetHandle": "end" + } + ], + "nodes": [ + { + "data": { + "label": "Begin", + "name": "begin" + }, + "dragging": false, + "id": "begin", + "measured": { + "height": 48, + "width": 200 + }, + "position": { + "x": 47.500000000000014, + "y": 199.5 + }, + "selected": false, + "sourcePosition": "left", + "targetPosition": "right", + "type": "beginNode" + }, + { + "data": { + "form": { + "delay_after_error": 1, + "description": "", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.7, + "llm_id": "deepseek-chat@DeepSeek", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 5, + "max_tokens": 256, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "presencePenaltyEnabled": false, + "presence_penalty": 0.4, + "prompts": [ + { + "content": "The user query is {sys.query}", + "role": "user" + } + ], + "sys_prompt": "# Role\n\nYou are the **Docs QA Agent**, a specialized knowledge base assistant responsible for providing accurate answers based strictly on the connected documentation repository.\n\n# Core Principles\n\n1. **Knowledge Base Only**: Answer questions EXCLUSIVELY based on information retrieved from the connected knowledge base.\n\n2. **No Content Creation**: Never generate, infer, or create information that is not explicitly present in the retrieved documents.\n\n3. **Source Transparency**: Always indicate when information comes from the knowledge base vs. when it's unavailable.\n\n4. **Accuracy Over Completeness**: Prefer incomplete but accurate answers over complete but potentially inaccurate ones.\n\n# Response Guidelines\n\n## When Information is Available\n\n- Provide direct answers based on retrieved content\n\n- Quote relevant sections when helpful\n\n- Cite the source document/section if available\n\n- Use phrases like: \"According to the documentation...\" or \"Based on the knowledge base...\"\n\n## When Information is Unavailable\n\n- Clearly state: \"I cannot find this information in the current knowledge base.\"\n\n- Do NOT attempt to fill gaps with general knowledge\n\n- Suggest alternative questions that might be covered in the docs\n\n- Use phrases like: \"The documentation does not cover...\" or \"This information is not available in the knowledge base.\"\n\n# Response Format\n\n```markdown\n\n## Answer\n\n[Your response based strictly on knowledge base content]\n\n**Always do these:**\n\n- Use the Retrieval tool for every question\n\n- Be transparent about information availability\n\n- Stick to documented facts only\n\n- Acknowledge knowledge base limitations\n\n", + "temperature": 0.1, + "temperatureEnabled": true, + "tools": [ + { + "component_name": "Retrieval", + "name": "Retrieval", + "params": { + "cross_languages": [], + "description": "This is a technical docs knowledge bases.", + "empty_response": "", + "kb_ids": [], + "keywords_similarity_weight": 0.7, + "outputs": { + "formalized_content": { + "type": "string", + "value": "" + } + }, + "rerank_id": "", + "similarity_threshold": 0.2, + "top_k": 1024, + "top_n": 8, + "use_kg": false + } + } + ], + "topPEnabled": false, + "top_p": 0.3, + "user_prompt": "", + "visual_files_var": "" + }, + "label": "Agent", + "name": "Docs QA Agent" + }, + "dragging": false, + "id": "Agent:StalePandasDream", + "measured": { + "height": 87, + "width": 200 + }, + "position": { + "x": 351.5, + "y": 231 + }, + "selected": true, + "sourcePosition": "right", + "targetPosition": "left", + "type": "agentNode" + }, + { + "data": { + "form": { + "content": [ + "{Agent:StalePandasDream@content}" + ] + }, + "label": "Message", + "name": "Message_0" + }, + "dragging": false, + "id": "Message:BrownPugsStick", + "measured": { + "height": 56, + "width": 200 + }, + "position": { + "x": 671.5, + "y": 192.5 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "messageNode" + }, + { + "data": { + "form": { + "description": "This is an agent for a specific task.", + "user_prompt": "This is the order you need to send to the agent." + }, + "label": "Tool", + "name": "flow.tool_0" + }, + "dragging": false, + "id": "Tool:PrettyMasksFloat", + "measured": { + "height": 48, + "width": 200 + }, + "position": { + "x": 234.5, + "y": 370.5 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "toolNode" + }, + { + "data": { + "form": { + "text": "This is a document question-and-answer system based on a knowledge base. When a user asks a question, it retrieves relevant document content to provide accurate answers.\nProcess Steps\n\n#Begin\n\nWorkflow entry: Receive user questions\n\nDocs QA Agent\n\nAI Model: deepseek-chat\n\nFunction: Analyze user questions and understand query intent\n\nRetrieval\n\nFunction: Search for relevant information from connected document knowledge bases\n\nFeature: Ensures answers are based on actual document content\n\nMessage_0 (Output Response)\n\nReturns accurate answers to the user based on the knowledge base\n\n#Core Features\n\nAccuracy: Answers are strictly based on knowledge base content\n\nReliability: Avoid AI illusions and only provide information that is verifiable\n\nSimplicity: Linear process with fast response\n\n#Applicable Scenarios\n\nProduct Documentation Query\n\nTechnical Support Q&A\n\nInternal Enterprise Knowledge Base Search\n\nUser Manual Consultation" + }, + "label": "Note", + "name": "Workflow Overall Description" + }, + "dragHandle": ".note-drag-handle", + "dragging": false, + "height": 154, + "id": "Note:SwiftSuitsFlow", + "measured": { + "height": 154, + "width": 374 + }, + "position": { + "x": 349.65276636527506, + "y": 28.869446726944993 + }, + "resizing": false, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "noteNode", + "width": 374 + } + ] + }, + "history": [], + "messages": [], + "path": [], + "retrieval": [] + }, + "avatar": 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" +} + diff --git a/agent/templates/technical_docs_qa.json b/agent/templates/technical_docs_qa.json new file mode 100644 index 000000000..6889d17b9 --- /dev/null +++ b/agent/templates/technical_docs_qa.json @@ -0,0 +1,331 @@ + +{ + "id": 9, + "title": "Technical Docs QA", + "description": "This is a document question-and-answer system based on a knowledge base. When a user asks a question, it retrieves relevant document content to provide accurate answers.", + "canvas_type": "Customer Support", + "dsl": { + "components": { + "Agent:StalePandasDream": { + "downstream": [ + "Message:BrownPugsStick" + ], + "obj": { + "component_name": "Agent", + "params": { + "delay_after_error": 1, + "description": "", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.7, + "llm_id": "deepseek-chat@DeepSeek", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 5, + "max_tokens": 256, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "presencePenaltyEnabled": false, + "presence_penalty": 0.4, + "prompts": [ + { + "content": "The user query is {sys.query}", + "role": "user" + } + ], + "sys_prompt": "# Role\n\nYou are the **Docs QA Agent**, a specialized knowledge base assistant responsible for providing accurate answers based strictly on the connected documentation repository.\n\n# Core Principles\n\n1. **Knowledge Base Only**: Answer questions EXCLUSIVELY based on information retrieved from the connected knowledge base.\n\n2. **No Content Creation**: Never generate, infer, or create information that is not explicitly present in the retrieved documents.\n\n3. **Source Transparency**: Always indicate when information comes from the knowledge base vs. when it's unavailable.\n\n4. **Accuracy Over Completeness**: Prefer incomplete but accurate answers over complete but potentially inaccurate ones.\n\n# Response Guidelines\n\n## When Information is Available\n\n- Provide direct answers based on retrieved content\n\n- Quote relevant sections when helpful\n\n- Cite the source document/section if available\n\n- Use phrases like: \"According to the documentation...\" or \"Based on the knowledge base...\"\n\n## When Information is Unavailable\n\n- Clearly state: \"I cannot find this information in the current knowledge base.\"\n\n- Do NOT attempt to fill gaps with general knowledge\n\n- Suggest alternative questions that might be covered in the docs\n\n- Use phrases like: \"The documentation does not cover...\" or \"This information is not available in the knowledge base.\"\n\n# Response Format\n\n```markdown\n\n## Answer\n\n[Your response based strictly on knowledge base content]\n\n**Always do these:**\n\n- Use the Retrieval tool for every question\n\n- Be transparent about information availability\n\n- Stick to documented facts only\n\n- Acknowledge knowledge base limitations\n\n", + "temperature": 0.1, + "temperatureEnabled": true, + "tools": [ + { + "component_name": "Retrieval", + "name": "Retrieval", + "params": { + "cross_languages": [], + "description": "This is a technical docs knowledge bases.", + "empty_response": "", + "kb_ids": [], + "keywords_similarity_weight": 0.7, + "outputs": { + "formalized_content": { + "type": "string", + "value": "" + } + }, + "rerank_id": "", + "similarity_threshold": 0.2, + "top_k": 1024, + "top_n": 8, + "use_kg": false + } + } + ], + "topPEnabled": false, + "top_p": 0.3, + "user_prompt": "", + "visual_files_var": "" + } + }, + "upstream": [ + "begin" + ] + }, + "Message:BrownPugsStick": { + "downstream": [], + "obj": { + "component_name": "Message", + "params": { + "content": [ + "{Agent:StalePandasDream@content}" + ] + } + }, + "upstream": [ + "Agent:StalePandasDream" + ] + }, + "begin": { + "downstream": [ + "Agent:StalePandasDream" + ], + "obj": { + "component_name": "Begin", + "params": {} + }, + "upstream": [] + } + }, + "globals": { + "sys.conversation_turns": 0, + "sys.files": [], + "sys.query": "", + "sys.user_id": "" + }, + "graph": { + "edges": [ + { + "data": { + "isHovered": false + }, + "id": "xy-edge__beginstart-Agent:StalePandasDreamend", + "source": "begin", + "sourceHandle": "start", + "target": "Agent:StalePandasDream", + "targetHandle": "end" + }, + { + "data": { + "isHovered": false + }, + "id": "xy-edge__Agent:StalePandasDreamstart-Message:BrownPugsStickend", + "source": "Agent:StalePandasDream", + "sourceHandle": "start", + "target": "Message:BrownPugsStick", + "targetHandle": "end" + }, + { + "id": "xy-edge__Agent:StalePandasDreamtool-Tool:PrettyMasksFloatend", + "source": "Agent:StalePandasDream", + "sourceHandle": "tool", + "target": "Tool:PrettyMasksFloat", + "targetHandle": "end" + } + ], + "nodes": [ + { + "data": { + "label": "Begin", + "name": "begin" + }, + "dragging": false, + "id": "begin", + "measured": { + "height": 48, + "width": 200 + }, + "position": { + "x": 47.500000000000014, + "y": 199.5 + }, + "selected": false, + "sourcePosition": "left", + "targetPosition": "right", + "type": "beginNode" + }, + { + "data": { + "form": { + "delay_after_error": 1, + "description": "", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.7, + "llm_id": "deepseek-chat@DeepSeek", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 5, + "max_tokens": 256, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "presencePenaltyEnabled": false, + "presence_penalty": 0.4, + "prompts": [ + { + "content": "The user query is {sys.query}", + "role": "user" + } + ], + "sys_prompt": "# Role\n\nYou are the **Docs QA Agent**, a specialized knowledge base assistant responsible for providing accurate answers based strictly on the connected documentation repository.\n\n# Core Principles\n\n1. **Knowledge Base Only**: Answer questions EXCLUSIVELY based on information retrieved from the connected knowledge base.\n\n2. **No Content Creation**: Never generate, infer, or create information that is not explicitly present in the retrieved documents.\n\n3. **Source Transparency**: Always indicate when information comes from the knowledge base vs. when it's unavailable.\n\n4. **Accuracy Over Completeness**: Prefer incomplete but accurate answers over complete but potentially inaccurate ones.\n\n# Response Guidelines\n\n## When Information is Available\n\n- Provide direct answers based on retrieved content\n\n- Quote relevant sections when helpful\n\n- Cite the source document/section if available\n\n- Use phrases like: \"According to the documentation...\" or \"Based on the knowledge base...\"\n\n## When Information is Unavailable\n\n- Clearly state: \"I cannot find this information in the current knowledge base.\"\n\n- Do NOT attempt to fill gaps with general knowledge\n\n- Suggest alternative questions that might be covered in the docs\n\n- Use phrases like: \"The documentation does not cover...\" or \"This information is not available in the knowledge base.\"\n\n# Response Format\n\n```markdown\n\n## Answer\n\n[Your response based strictly on knowledge base content]\n\n**Always do these:**\n\n- Use the Retrieval tool for every question\n\n- Be transparent about information availability\n\n- Stick to documented facts only\n\n- Acknowledge knowledge base limitations\n\n", + "temperature": 0.1, + "temperatureEnabled": true, + "tools": [ + { + "component_name": "Retrieval", + "name": "Retrieval", + "params": { + "cross_languages": [], + "description": "This is a technical docs knowledge bases.", + "empty_response": "", + "kb_ids": [], + "keywords_similarity_weight": 0.7, + "outputs": { + "formalized_content": { + "type": "string", + "value": "" + } + }, + "rerank_id": "", + "similarity_threshold": 0.2, + "top_k": 1024, + "top_n": 8, + "use_kg": false + } + } + ], + "topPEnabled": false, + "top_p": 0.3, + "user_prompt": "", + "visual_files_var": "" + }, + "label": "Agent", + "name": "Docs QA Agent" + }, + "dragging": false, + "id": "Agent:StalePandasDream", + "measured": { + "height": 87, + "width": 200 + }, + "position": { + "x": 351.5, + "y": 231 + }, + "selected": true, + "sourcePosition": "right", + "targetPosition": "left", + "type": "agentNode" + }, + { + "data": { + "form": { + "content": [ + "{Agent:StalePandasDream@content}" + ] + }, + "label": "Message", + "name": "Message_0" + }, + "dragging": false, + "id": "Message:BrownPugsStick", + "measured": { + "height": 56, + "width": 200 + }, + "position": { + "x": 671.5, + "y": 192.5 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "messageNode" + }, + { + "data": { + "form": { + "description": "This is an agent for a specific task.", + "user_prompt": "This is the order you need to send to the agent." + }, + "label": "Tool", + "name": "flow.tool_0" + }, + "dragging": false, + "id": "Tool:PrettyMasksFloat", + "measured": { + "height": 48, + "width": 200 + }, + "position": { + "x": 234.5, + "y": 370.5 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "toolNode" + }, + { + "data": { + "form": { + "text": "This is a document question-and-answer system based on a knowledge base. When a user asks a question, it retrieves relevant document content to provide accurate answers.\nProcess Steps\n\n#Begin\n\nWorkflow entry: Receive user questions\n\nDocs QA Agent\n\nAI Model: deepseek-chat\n\nFunction: Analyze user questions and understand query intent\n\nRetrieval\n\nFunction: Search for relevant information from connected document knowledge bases\n\nFeature: Ensures answers are based on actual document content\n\nMessage_0 (Output Response)\n\nReturns accurate answers to the user based on the knowledge base\n\n#Core Features\n\nAccuracy: Answers are strictly based on knowledge base content\n\nReliability: Avoid AI illusions and only provide information that is verifiable\n\nSimplicity: Linear process with fast response\n\n#Applicable Scenarios\n\nProduct Documentation Query\n\nTechnical Support Q&A\n\nInternal Enterprise Knowledge Base Search\n\nUser Manual Consultation" + }, + "label": "Note", + "name": "Workflow Overall Description" + }, + "dragHandle": ".note-drag-handle", + "dragging": false, + "height": 154, + "id": "Note:SwiftSuitsFlow", + "measured": { + "height": 154, + "width": 374 + }, + "position": { + "x": 349.65276636527506, + "y": 28.869446726944993 + }, + "resizing": false, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "noteNode", + "width": 374 + } + ] + }, + "history": [], + "messages": [], + "path": [], + "retrieval": [] + }, + "avatar": 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" +} + diff --git a/agent/templates/trip_planner.json b/agent/templates/trip_planner.json new file mode 100644 index 000000000..7e043570b --- /dev/null +++ b/agent/templates/trip_planner.json @@ -0,0 +1,685 @@ + +{ + "id": 14, + "title": "Trip Planner", + "description": "This smart trip planner utilizes LLM technology to automatically generate customized travel itineraries, with optional tool integration for enhanced reliability.", + "canvas_type": "Consumer App", + "dsl": { + "components": { + "Agent:OddGuestsPump": { + "downstream": [ + "Agent:RichTermsCamp" + ], + "obj": { + "component_name": "Agent", + "params": { + "delay_after_error": 1, + "description": "", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.7, + "llm_id": "deepseek-chat@DeepSeek", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 5, + "max_tokens": 256, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "presencePenaltyEnabled": false, + "presence_penalty": 0.4, + "prompts": [ + { + "content": "User's query:\n{sys.query}", + "role": "user" + } + ], + "sys_prompt": "Role: Professional tour guide: Create detailed travel plans per user needs.​\nFirst, specify departure location, destination, and travel duration (for subsequent agents to retrieve).​\nDevelop the plan using tools to get real-time weather, holidays, attraction hours, traffic, etc. Adjust itinerary accordingly (e.g., reschedule outdoor activities on rainy days) to ensure practicality, efficiency, and alignment with user preferences.​\nFor real-time info retrieval, only output tool-returned content and pass it to subsequent agents; never rely on your own knowledge base.​\n", + "temperature": 0.1, + "temperatureEnabled": true, + "tools": [], + "topPEnabled": false, + "top_p": 0.3, + "user_prompt": "", + "visual_files_var": "" + } + }, + "upstream": [ + "begin" + ] + }, + "Agent:RichTermsCamp": { + "downstream": [ + "Agent:WeakCarrotsTan" + ], + "obj": { + "component_name": "Agent", + "params": { + "delay_after_error": 1, + "description": "", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.7, + "llm_id": "deepseek-chat@DeepSeek", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 5, + "max_tokens": 256, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "presencePenaltyEnabled": false, + "presence_penalty": 0.4, + "prompts": [ + { + "content": "User's query:\n{sys.query}\n\nFirst step result:\n{Agent:OddGuestsPump@content}", + "role": "user" + } + ], + "sys_prompt": "You are a Transit & Stay Agent, collaborating with upstream planners.\n\n Use tools to retrieve real-time info for transportation (flights, trains, rentals, etc.) and accommodation (hotels, rentals, etc.) based on the itinerary. 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" +} + + diff --git a/agent/templates/web_search_assistant.json b/agent/templates/web_search_assistant.json new file mode 100644 index 000000000..08d550abe --- /dev/null +++ b/agent/templates/web_search_assistant.json @@ -0,0 +1,871 @@ + +{ + "id": 16, + "title": "WebSearch Assistant", + "description": "A chat assistant template that integrates information extracted from a knowledge base and web searches to respond to queries. Let's start by setting up your knowledge base in 'Retrieval'!", + "canvas_type": "Other", + "dsl": { + "components": { + "Agent:SmartSchoolsCross": { + "downstream": [ + "Message:ShaggyRingsCrash" + ], + "obj": { + "component_name": "Agent", + "params": { + "delay_after_error": 1, + "description": "", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.7, + "llm_id": "deepseek-chat@DeepSeek", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 1, + "max_tokens": 256, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "presencePenaltyEnabled": false, + "presence_penalty": 0.4, + "prompts": [ + { + "content": "User's query:\n{sys.query}\n\nRefined question:\n{Agent:ThreePathsDecide@content}\n\nWeb search result:\n{Agent:WildGoatsRule@content}\n\nRetrieval result:\n{Agent:WildGoatsRule@content}", + "role": "user" + } + ], + "sys_prompt": "Role: You are an Answer Organizer.\nTask: Generate the answer based on the provided content from: User's query, Refined question, Web search result, Retrieval result.\n\nRequirements:\n - Answer should be in markdown format.\n - Answer should include all \n - Do not make thing up when there's no relevant information to user's question. \n", + "temperature": 0.1, + "temperatureEnabled": true, + "tools": [], + "topPEnabled": false, + "top_p": 0.3, + "user_prompt": "", + "visual_files_var": "" + } + }, + "upstream": [ + "Agent:WildGoatsRule", + "Retrieval:WarmTimesRun" + ] + }, + "Agent:ThreePathsDecide": { + "downstream": [ + "Agent:WildGoatsRule", + "Retrieval:WarmTimesRun" + ], + "obj": { + "component_name": "Agent", + "params": { + "delay_after_error": 1, + "description": "", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.7, + "llm_id": "deepseek-chat@DeepSeek", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 1, + "max_tokens": 256, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "presencePenaltyEnabled": false, + "presence_penalty": 0.4, + "prompts": [ + { + "content": "{sys.query}", + "role": "user" + } + ], + "sys_prompt": "Role: You are a Question Refinement Agent. Rewrite ambiguous or incomplete user questions to align with knowledge base terminology using conversation history.\n\nExample：\n\nUser: What's RAGFlow?\nAssistant: RAGFlow is xxx.\n\nUser: How to deloy it?\nRefine it: How to deploy RAGFlow?", + "temperature": 0.1, + "temperatureEnabled": true, + "tools": [], + "topPEnabled": false, + "top_p": 0.3, + "user_prompt": "", + "visual_files_var": "" + } + }, + "upstream": [ + "begin" + ] + }, + "Agent:WildGoatsRule": { + "downstream": [ + "Agent:SmartSchoolsCross" + ], + "obj": { + "component_name": "Agent", + "params": { + "delay_after_error": 1, + "description": "", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.7, + "llm_id": "deepseek-chat@DeepSeek", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 2, + "max_tokens": 256, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "presencePenaltyEnabled": false, + "presence_penalty": 0.4, + "prompts": [ + { + "content": "User's query:\n{sys.query}\n\nRefined question:\n{Agent:ThreePathsDecide@content}", + "role": "user" + } + ], + "sys_prompt": "Role: You are a Search-Driven Information Agent that answers questions using web search results.\n\nWorkflow:\nKeyword Extraction:\nExtract exactly 3 keywords from the user's question.\n\nKeywords must be:\n✅ Most specific nouns/proper nouns (e.g., \"iPhone 15 Pro\" not \"phone\")\n✅ Core concepts (e.g., \"quantum entanglement\" not \"science thing\")\n✅ Unbiased (no added opinions)\nNever output keywords to users\n\nSearch & Answer:\nUse search tools (TavilySearch, TavilyExtract, Google, Bing, DuckDuckGo, Wikipedia) with the 3 keywords to retrieve results.\nAnswer solely based on search findings, citing sources.\nIf results conflict, prioritize recent (.gov/.edu > forums)\n\nOutput Rules:\n✖️ Never show keywords in final answers\n✖️ Never guess if search yields no results\n✅ Always cite sources using [Source #] notation", + "temperature": 0.1, + "temperatureEnabled": true, + "tools": [ + { + "component_name": "TavilySearch", + "name": "TavilySearch", + "params": { + "api_key": "", + "days": 7, + "exclude_domains": [], + "include_answer": false, + "include_domains": [], + "include_image_descriptions": false, + "include_images": false, + "include_raw_content": true, + "max_results": 5, + "outputs": { + "formalized_content": { + "type": "string", + "value": "" + }, + "json": { + "type": "Array", + "value": [] + } + }, + "query": "sys.query", + "search_depth": "basic", + "topic": "general" + } + }, + { + "component_name": "TavilyExtract", + "name": "TavilyExtract", + "params": { + "api_key": "" + } + }, + { + "component_name": "Google", + "name": "Google", + "params": { + "api_key": "", + "country": "us", + "language": "en" + } + }, + { + "component_name": "Bing", + "name": "Bing", + "params": { + "api_key": "YOUR_API_KEY (obtained from https://www.microsoft.com/en-us/bing/apis/bing-web-search-api)", + "channel": "Webpages", + "country": "CH", + "language": "en", + "top_n": 10 + } + }, + { + "component_name": "DuckDuckGo", + "name": "DuckDuckGo", + "params": { + "channel": "text", + "top_n": 10 + } + }, + { + "component_name": "Wikipedia", + "name": "Wikipedia", + "params": { + "language": "en", + "top_n": 10 + } + } + ], + "topPEnabled": false, + "top_p": 0.3, + "user_prompt": "", + "visual_files_var": "" + } + }, + "upstream": [ + "Agent:ThreePathsDecide" + ] + }, + "Message:ShaggyRingsCrash": { + "downstream": [], + "obj": { + "component_name": "Message", + "params": { + "content": [ + "{Agent:SmartSchoolsCross@content}" + ] + } + }, + "upstream": [ + "Agent:SmartSchoolsCross" + ] + }, + "Retrieval:WarmTimesRun": { + "downstream": [ + "Agent:SmartSchoolsCross" + ], + "obj": { + "component_name": "Retrieval", + "params": { + "cross_languages": [], + "empty_response": "", + "kb_ids": [], + "keywords_similarity_weight": 0.7, + "outputs": { + "formalized_content": { + "type": "string", + "value": "" + } + }, + "query": "Agent:ThreePathsDecide@content", + "rerank_id": "", + "similarity_threshold": 0.2, + "top_k": 1024, + "top_n": 8, + "use_kg": false + } + }, + "upstream": [ + "Agent:ThreePathsDecide" + ] + }, + "begin": { + "downstream": [ + "Agent:ThreePathsDecide" + ], + "obj": { + "component_name": "Begin", + "params": { + "enablePrologue": true, + "inputs": {}, + "mode": "conversational", + "prologue": "Hi! I'm your web search assistant. What do you want to search today?" + } + }, + "upstream": [] + } + }, + "globals": { + "sys.conversation_turns": 1, + "sys.files": [], + "sys.query": "你好", + "sys.user_id": "d6d98fd652f911f0a8fb047c16ec874f" + }, + "graph": { + "edges": [ + { + "data": { + "isHovered": false + }, + "id": "xy-edge__beginstart-Agent:ThreePathsDecideend", + "source": "begin", + "sourceHandle": "start", + "target": "Agent:ThreePathsDecide", + "targetHandle": "end" + }, + { + "data": { + "isHovered": false + }, + "id": "xy-edge__Agent:ThreePathsDecidestart-Agent:WildGoatsRuleend", + "source": "Agent:ThreePathsDecide", + "sourceHandle": "start", + "target": "Agent:WildGoatsRule", + "targetHandle": "end" + }, + { + "data": { + "isHovered": false + }, + "id": "xy-edge__Agent:ThreePathsDecidestart-Retrieval:WarmTimesRunend", + "source": "Agent:ThreePathsDecide", + "sourceHandle": "start", + "target": "Retrieval:WarmTimesRun", + "targetHandle": "end" + }, + { + "data": { + "isHovered": false + }, + "id": "xy-edge__Agent:WildGoatsRulestart-Agent:SmartSchoolsCrossend", + "source": "Agent:WildGoatsRule", + "sourceHandle": "start", + "target": "Agent:SmartSchoolsCross", + "targetHandle": "end" + }, + { + "data": { + "isHovered": false + }, + "id": "xy-edge__Agent:SmartSchoolsCrossstart-Message:ShaggyRingsCrashend", + "source": "Agent:SmartSchoolsCross", + "sourceHandle": "start", + "target": "Message:ShaggyRingsCrash", + "targetHandle": "end" + }, + { + "data": { + "isHovered": false + }, + "id": "xy-edge__Retrieval:WarmTimesRunstart-Agent:SmartSchoolsCrossend", + "markerEnd": "logo", + "source": "Retrieval:WarmTimesRun", + "sourceHandle": "start", + "style": { + "stroke": "rgba(91, 93, 106, 1)", + "strokeWidth": 1 + }, + "target": "Agent:SmartSchoolsCross", + "targetHandle": "end", + "type": "buttonEdge", + "zIndex": 1001 + }, + { + "data": { + "isHovered": false + }, + "id": "xy-edge__Agent:WildGoatsRuletool-Tool:TrueCrewsTakeend", + "source": "Agent:WildGoatsRule", + "sourceHandle": "tool", + "target": "Tool:TrueCrewsTake", + "targetHandle": "end" + } + ], + "nodes": [ + { + "data": { + "form": { + "enablePrologue": true, + "inputs": {}, + "mode": "conversational", + "prologue": "Hi! I'm your web search assistant. What do you want to search today?" + }, + "label": "Begin", + "name": "begin" + }, + "dragging": false, + "id": "begin", + "measured": { + "height": 48, + "width": 200 + }, + "position": { + "x": 32.79251060693639, + "y": 209.67921278359827 + }, + "selected": false, + "sourcePosition": "left", + "targetPosition": "right", + "type": "beginNode" + }, + { + "data": { + "form": { + "delay_after_error": 1, + "description": "", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.7, + "llm_id": "deepseek-chat@DeepSeek", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 1, + "max_tokens": 256, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "presencePenaltyEnabled": false, + "presence_penalty": 0.4, + "prompts": [ + { + "content": "{sys.query}", + "role": "user" + } + ], + "sys_prompt": "Role: You are a Question Refinement Agent. Rewrite ambiguous or incomplete user questions to align with knowledge base terminology using conversation history.\n\nExample：\n\nUser: What's RAGFlow?\nAssistant: RAGFlow is xxx.\n\nUser: How to deloy it?\nRefine it: How to deploy RAGFlow?", + "temperature": 0.1, + "temperatureEnabled": true, + "tools": [], + "topPEnabled": false, + "top_p": 0.3, + "user_prompt": "", + "visual_files_var": "" + }, + "label": "Agent", + "name": "Refine Question" + }, + "dragging": false, + "id": "Agent:ThreePathsDecide", + "measured": { + "height": 84, + "width": 200 + }, + "position": { + "x": 309.1322126914739, + "y": 188.16985104226876 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "agentNode" + }, + { + "data": { + "form": { + "delay_after_error": 1, + "description": "", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.7, + "llm_id": "deepseek-chat@DeepSeek", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 2, + "max_tokens": 256, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "presencePenaltyEnabled": false, + "presence_penalty": 0.4, + "prompts": [ + { + "content": "User's query:\n{sys.query}\n\nRefined question:\n{Agent:ThreePathsDecide@content}", + "role": "user" + } + ], + "sys_prompt": "Role: You are a Search-Driven Information Agent that answers questions using web search results.\n\nWorkflow:\nKeyword Extraction:\nExtract exactly 3 keywords from the user's question.\n\nKeywords must be:\n✅ Most specific nouns/proper nouns (e.g., \"iPhone 15 Pro\" not \"phone\")\n✅ Core concepts (e.g., \"quantum entanglement\" not \"science thing\")\n✅ Unbiased (no added opinions)\nNever output keywords to users\n\nSearch & Answer:\nUse search tools (TavilySearch, TavilyExtract, Google, Bing, DuckDuckGo, Wikipedia) with the 3 keywords to retrieve results.\nAnswer solely based on search findings, citing sources.\nIf results conflict, prioritize recent (.gov/.edu > forums)\n\nOutput Rules:\n✖️ Never show keywords in final answers\n✖️ Never guess if search yields no results\n✅ Always cite sources using [Source #] notation", + "temperature": 0.1, + "temperatureEnabled": true, + "tools": [ + { + "component_name": "TavilySearch", + "name": "TavilySearch", + "params": { + "api_key": "", + "days": 7, + "exclude_domains": [], + "include_answer": false, + "include_domains": [], + "include_image_descriptions": false, + "include_images": false, + "include_raw_content": true, + "max_results": 5, + "outputs": { + "formalized_content": { + "type": "string", + "value": "" + }, + "json": { + "type": "Array", + "value": [] + } + }, + "query": "sys.query", + "search_depth": "basic", + "topic": "general" + } + }, + { + "component_name": "TavilyExtract", + "name": "TavilyExtract", + "params": { + "api_key": "" + } + }, + { + "component_name": "Google", + "name": "Google", + "params": { + "api_key": "", + "country": "us", + "language": "en" + } + }, + { + "component_name": "Bing", + "name": "Bing", + "params": { + "api_key": "YOUR_API_KEY (obtained from https://www.microsoft.com/en-us/bing/apis/bing-web-search-api)", + "channel": "Webpages", + "country": "CH", + "language": "en", + "top_n": 10 + } + }, + { + "component_name": "DuckDuckGo", + "name": "DuckDuckGo", + "params": { + "channel": "text", + "top_n": 10 + } + }, + { + "component_name": "Wikipedia", + "name": "Wikipedia", + "params": { + "language": "en", + "top_n": 10 + } + } + ], + "topPEnabled": false, + "top_p": 0.3, + "user_prompt": "", + "visual_files_var": "" + }, + "label": "Agent", + "name": "Search Agent" + }, + "dragging": false, + "id": "Agent:WildGoatsRule", + "measured": { + "height": 84, + "width": 200 + }, + "position": { + "x": 678.5892767651895, + "y": 2.074237779456759 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "agentNode" + }, + { + "data": { + "form": { + "cross_languages": [], + "empty_response": "", + "kb_ids": [], + "keywords_similarity_weight": 0.7, + "outputs": { + "formalized_content": { + "type": "string", + "value": "" + } + }, + "query": "Agent:ThreePathsDecide@content", + "rerank_id": "", + "similarity_threshold": 0.2, + "top_k": 1024, + "top_n": 8, + "use_kg": false + }, + "label": "Retrieval", + "name": "Retrieval from knowledge bases " + }, + "dragging": false, + "id": "Retrieval:WarmTimesRun", + "measured": { + "height": 56, + "width": 200 + }, + "position": { + "x": 689.0595178434597, + "y": 499.2340890704343 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "retrievalNode" + }, + { + "data": { + "form": { + "delay_after_error": 1, + "description": "", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.7, + "llm_id": "deepseek-chat@DeepSeek", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 1, + "max_tokens": 256, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "presencePenaltyEnabled": false, + "presence_penalty": 0.4, + "prompts": [ + { + "content": "User's query:\n{sys.query}\n\nRefined question:\n{Agent:ThreePathsDecide@content}\n\nWeb search result:\n{Agent:WildGoatsRule@content}\n\nRetrieval result:\n{Agent:WildGoatsRule@content}", + "role": "user" + } + ], + "sys_prompt": "Role: You are an Answer Organizer.\nTask: Generate the answer based on the provided content from: User's query, Refined question, Web search result, Retrieval result.\n\nRequirements:\n - Answer should be in markdown format.\n - Answer should include all \n - Do not make thing up when there's no relevant information to user's question. \n", + "temperature": 0.1, + "temperatureEnabled": true, + "tools": [], + "topPEnabled": false, + "top_p": 0.3, + "user_prompt": "", + "visual_files_var": "" + }, + "label": "Agent", + "name": "Answer Organizer" + }, + "dragging": false, + "id": "Agent:SmartSchoolsCross", + "measured": { + "height": 84, + "width": 200 + }, + "position": { + "x": 1134.5321493898284, + "y": 221.46972754101765 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "agentNode" + }, + { + "data": { + "form": { + "content": [ + "{Agent:SmartSchoolsCross@content}" + ] + }, + "label": "Message", + "name": "Answer" + }, + "dragging": false, + "id": "Message:ShaggyRingsCrash", + "measured": { + "height": 56, + "width": 200 + }, + "position": { + "x": 1437.758553651028, + "y": 235.45081267288185 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "messageNode" + }, + { + "data": { + "form": { + "text": "This Agent rewrites your question for better search & retrieval results." + }, + "label": "Note", + "name": "Note: Refine Question" + }, + "dragHandle": ".note-drag-handle", + "id": "Note:BetterCupsBow", + "measured": { + "height": 136, + "width": 244 + }, + "position": { + "x": 270, + "y": 390 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "noteNode" + }, + { + "data": { + "form": { + "text": "This Agent answers questions using web search results." + }, + "label": "Note", + "name": "Note: Search Agent" + }, + "dragHandle": ".note-drag-handle", + "dragging": false, + "id": "Note:OddGoatsBeg", + "measured": { + "height": 136, + "width": 244 + }, + "position": { + "x": 689.3401860180043, + "y": -204.46057070562227 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "noteNode" + }, + { + "data": { + "form": { + "text": "This Agents generates the answer based on the provided content from: User's query, Refined question, Web search result, Retrieval result." + }, + "label": "Note", + "name": "Note: Answer Organizer" + }, + "dragHandle": ".note-drag-handle", + "dragging": false, + "height": 188, + "id": "Note:SlowBottlesHope", + "measured": { + "height": 188, + "width": 251 + }, + "position": { + "x": 1152.1929528629184, + "y": 375.08305219772546 + }, + "resizing": false, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "noteNode", + "width": 251 + }, + { + "data": { + "form": { + "description": "This is an agent for a specific task.", + "user_prompt": "This is the order you need to send to the agent." + }, + "label": "Tool", + "name": "flow.tool_0" + }, + "dragging": false, + "id": "Tool:TrueCrewsTake", + "measured": { + "height": 228, + "width": 200 + }, + "position": { + "x": 642.9703031510875, + "y": 144.80253344921545 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "toolNode" + }, + { + "data": { + "form": { + "text": "This is a chat assistant template that integrates information extracted from a knowledge base and web searches to respond to queries. Let's start by setting up your knowledge base in 'Retrieval'!" + }, + "label": "Note", + "name": "Workflow Overall Description" + }, + "dragHandle": ".note-drag-handle", + "dragging": false, + "height": 163, + "id": "Note:BumpySteaksPump", + "measured": { + "height": 163, + "width": 389 + }, + "position": { + "x": -36.59148337976953, + "y": 1.488564577528809 + }, + "resizing": false, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "noteNode", + "width": 389 + } + ] + }, + "history": [ + [ + "user", + "你好" + ] + ], + "memory": [], + "messages": [], + "path": [ + "begin", + "Agent:ThreePathsDecide" + ], + "retrieval": [ + { + "chunks": [], + "doc_aggs": [] + }, + { + "chunks": {}, + "doc_aggs": {} + } + ], + "task_id": "183442fc6dd811f091b1047c16ec874f" + }, + "avatar": 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" +} + + diff --git a/agent/tools/base.py b/agent/tools/base.py index 0c1823c59..5dbe26da3 100644 --- a/agent/tools/base.py +++ b/agent/tools/base.py @@ -49,11 +49,12 @@ class LLMToolPluginCallSession(ToolCallSession): def tool_call(self, name: str, arguments: dict[str, Any]) -> Any: assert name in self.tools_map, f"LLM tool {name} does not exist" - self.callback(name, arguments, " running ...") if isinstance(self.tools_map[name], MCPToolCallSession): resp = self.tools_map[name].tool_call(name, arguments, 60) else: resp = self.tools_map[name].invoke(**arguments) + + self.callback(name, arguments, resp) return resp def get_tool_obj(self, name): diff --git a/api/apps/sdk/session.py b/api/apps/sdk/session.py index 4a8b556d7..b7e9f109c 100644 --- a/api/apps/sdk/session.py +++ b/api/apps/sdk/session.py @@ -848,6 +848,10 @@ def begin_inputs(agent_id): return get_error_data_result(f"Can't find agent by ID: {agent_id}") canvas = Canvas(json.dumps(cvs.dsl), objs[0].tenant_id) - return get_result(data=canvas.get_component_input_form("begin")) + return get_result(data={ + "title": cvs.title, + "avatar": cvs.avatar, + "inputs": canvas.get_component_input_form("begin") + }) diff --git a/api/db/services/canvas_service.py b/api/db/services/canvas_service.py index 832ffb4f6..d4286b895 100644 --- a/api/db/services/canvas_service.py +++ b/api/db/services/canvas_service.py @@ -134,7 +134,9 @@ def completion(tenant_id, agent_id, session_id=None, **kwargs): assert e, "Session not found!" if not conv.message: conv.message = [] - canvas = Canvas(json.dumps(conv.dsl), tenant_id, session_id) + if not isinstance(conv.dsl, str): + conv.dsl = json.dumps(conv.dsl, ensure_ascii=False) + canvas = Canvas(conv.dsl, tenant_id, agent_id) else: e, cvs = UserCanvasService.get_by_id(agent_id) assert e, "Agent not found." @@ -142,7 +144,8 @@ def completion(tenant_id, agent_id, session_id=None, **kwargs): if not isinstance(cvs.dsl, str): cvs.dsl = json.dumps(cvs.dsl, ensure_ascii=False) session_id=get_uuid() - canvas = Canvas(cvs.dsl, tenant_id, session_id) + canvas = Canvas(cvs.dsl, tenant_id, agent_id) + canvas.reset() conv = { "id": session_id, "dialog_id": cvs.id, diff --git a/deepdoc/parser/html_parser.py b/deepdoc/parser/html_parser.py index ee1c9b2d6..81183cf71 100644 --- a/deepdoc/parser/html_parser.py +++ b/deepdoc/parser/html_parser.py @@ -29,7 +29,6 @@ def get_encoding(file): class RAGFlowHtmlParser: def __call__(self, fnm, binary=None): - txt = "" if binary: encoding = find_codec(binary) txt = binary.decode(encoding, errors="ignore") diff --git a/rag/res/synonym.json b/rag/res/synonym.json index d5f1336ca..ea61b9e1c 100644 --- a/rag/res/synonym.json +++ b/rag/res/synonym.json @@ -10535,5 +10535,12 @@ "q2": "二季度", "q3": "三季度", "q4": "四季度", +"周一": ["礼拜一", "星期一"], +"周二": ["礼拜二", "星期二"], +"周三": ["礼拜三", "星期三"], +"周四": ["礼拜四", "星期四"], +"周五": ["礼拜五", "星期五"], +"周六": ["礼拜六", "星期六"], +"周日": ["礼拜日", "星期日", "星期天", "礼拜天"], "上班": "办公" }