# Copyright (c) 2025 Bytedance Ltd. and/or its affiliates # SPDX-License-Identifier: MIT import json import logging import os from functools import partial from typing import Annotated, Literal from langchain_core.messages import AIMessage, HumanMessage, ToolMessage from langchain_core.runnables import RunnableConfig from langchain_core.tools import tool from langchain_mcp_adapters.client import MultiServerMCPClient from langgraph.types import Command, interrupt from src.agents import create_agent from src.config.agents import AGENT_LLM_MAP from src.config.configuration import Configuration from src.llms.llm import get_llm_by_type, get_llm_token_limit_by_type from src.prompts.planner_model import Plan from src.prompts.template import apply_prompt_template from src.tools import ( crawl_tool, get_retriever_tool, get_web_search_tool, python_repl_tool, ) from src.tools.search import LoggedTavilySearch from src.utils.context_manager import ContextManager, validate_message_content from src.utils.json_utils import repair_json_output from ..config import SELECTED_SEARCH_ENGINE, SearchEngine from .types import State from .utils import ( build_clarified_topic_from_history, get_message_content, is_user_message, reconstruct_clarification_history, ) logger = logging.getLogger(__name__) @tool def handoff_to_planner( research_topic: Annotated[str, "The topic of the research task to be handed off."], locale: Annotated[str, "The user's detected language locale (e.g., en-US, zh-CN)."], ): """Handoff to planner agent to do plan.""" # This tool is not returning anything: we're just using it # as a way for LLM to signal that it needs to hand off to planner agent return @tool def handoff_after_clarification( locale: Annotated[str, "The user's detected language locale (e.g., en-US, zh-CN)."], research_topic: Annotated[ str, "The clarified research topic based on all clarification rounds." ], ): """Handoff to planner after clarification rounds are complete. Pass all clarification history to planner for analysis.""" return def needs_clarification(state: dict) -> bool: """ Check if clarification is needed based on current state. Centralized logic for determining when to continue clarification. """ if not state.get("enable_clarification", False): return False clarification_rounds = state.get("clarification_rounds", 0) is_clarification_complete = state.get("is_clarification_complete", False) max_clarification_rounds = state.get("max_clarification_rounds", 3) # Need clarification if: enabled + has rounds + not complete + not exceeded max # Use <= because after asking the Nth question, we still need to wait for the Nth answer return ( clarification_rounds > 0 and not is_clarification_complete and clarification_rounds <= max_clarification_rounds ) def validate_and_fix_plan(plan: dict, enforce_web_search: bool = False) -> dict: """ Validate and fix a plan to ensure it meets requirements. Args: plan: The plan dict to validate enforce_web_search: If True, ensure at least one step has need_search=true Returns: The validated/fixed plan dict """ if not isinstance(plan, dict): return plan steps = plan.get("steps", []) if enforce_web_search: # Check if any step has need_search=true has_search_step = any(step.get("need_search", False) for step in steps) if not has_search_step and steps: # Ensure first research step has web search enabled for idx, step in enumerate(steps): if step.get("step_type") == "research": step["need_search"] = True logger.info(f"Enforced web search on research step at index {idx}") break else: # Fallback: If no research step exists, convert the first step to a research step with web search enabled. # This ensures that at least one step will perform a web search as required. steps[0]["step_type"] = "research" steps[0]["need_search"] = True logger.info( "Converted first step to research with web search enforcement" ) elif not has_search_step and not steps: # Add a default research step if no steps exist logger.warning("Plan has no steps. Adding default research step.") plan["steps"] = [ { "need_search": True, "title": "Initial Research", "description": "Gather information about the topic", "step_type": "research", } ] return plan def background_investigation_node(state: State, config: RunnableConfig): logger.info("background investigation node is running.") configurable = Configuration.from_runnable_config(config) query = state.get("clarified_research_topic") or state.get("research_topic") background_investigation_results = [] if SELECTED_SEARCH_ENGINE == SearchEngine.TAVILY.value: searched_content = LoggedTavilySearch( max_results=configurable.max_search_results ).invoke(query) # check if the searched_content is a tuple, then we need to unpack it if isinstance(searched_content, tuple): searched_content = searched_content[0] # Handle string JSON response (new format from fixed Tavily tool) if isinstance(searched_content, str): try: parsed = json.loads(searched_content) if isinstance(parsed, dict) and "error" in parsed: logger.error(f"Tavily search error: {parsed['error']}") background_investigation_results = [] elif isinstance(parsed, list): background_investigation_results = [ f"## {elem.get('title', 'Untitled')}\n\n{elem.get('content', 'No content')}" for elem in parsed ] else: logger.error(f"Unexpected Tavily response format: {searched_content}") background_investigation_results = [] except json.JSONDecodeError: logger.error(f"Failed to parse Tavily response as JSON: {searched_content}") background_investigation_results = [] # Handle legacy list format elif isinstance(searched_content, list): background_investigation_results = [ f"## {elem['title']}\n\n{elem['content']}" for elem in searched_content ] return { "background_investigation_results": "\n\n".join( background_investigation_results ) } else: logger.error( f"Tavily search returned malformed response: {searched_content}" ) background_investigation_results = [] else: background_investigation_results = get_web_search_tool( configurable.max_search_results ).invoke(query) return { "background_investigation_results": json.dumps( background_investigation_results, ensure_ascii=False ) } def planner_node( state: State, config: RunnableConfig ) -> Command[Literal["human_feedback", "reporter"]]: """Planner node that generate the full plan.""" logger.info("Planner generating full plan") configurable = Configuration.from_runnable_config(config) plan_iterations = state["plan_iterations"] if state.get("plan_iterations", 0) else 0 # For clarification feature: only send the final clarified question to planner if state.get("enable_clarification", False) and state.get("clarified_question"): # Create a clean state with only the clarified question clean_state = { "messages": [{"role": "user", "content": state["clarified_question"]}], "locale": state.get("locale", "en-US"), "research_topic": state["clarified_question"], } messages = apply_prompt_template("planner", clean_state, configurable, state.get("locale", "en-US")) logger.info( f"Clarification mode: Using clarified question: {state['clarified_question']}" ) else: # Normal mode: use full conversation history messages = apply_prompt_template("planner", state, configurable, state.get("locale", "en-US")) if state.get("enable_background_investigation") and state.get( "background_investigation_results" ): messages += [ { "role": "user", "content": ( "background investigation results of user query:\n" + state["background_investigation_results"] + "\n" ), } ] if configurable.enable_deep_thinking: llm = get_llm_by_type("reasoning") elif AGENT_LLM_MAP["planner"] == "basic": llm = get_llm_by_type("basic").with_structured_output( Plan, method="json_mode", ) else: llm = get_llm_by_type(AGENT_LLM_MAP["planner"]) # if the plan iterations is greater than the max plan iterations, return the reporter node if plan_iterations >= configurable.max_plan_iterations: return Command(goto="reporter") full_response = "" if AGENT_LLM_MAP["planner"] == "basic" and not configurable.enable_deep_thinking: response = llm.invoke(messages) full_response = response.model_dump_json(indent=4, exclude_none=True) else: response = llm.stream(messages) for chunk in response: full_response += chunk.content logger.debug(f"Current state messages: {state['messages']}") logger.info(f"Planner response: {full_response}") try: curr_plan = json.loads(repair_json_output(full_response)) except json.JSONDecodeError: logger.warning("Planner response is not a valid JSON") if plan_iterations > 0: return Command(goto="reporter") else: return Command(goto="__end__") # Validate and fix plan to ensure web search requirements are met if isinstance(curr_plan, dict): curr_plan = validate_and_fix_plan(curr_plan, configurable.enforce_web_search) if isinstance(curr_plan, dict) and curr_plan.get("has_enough_context"): logger.info("Planner response has enough context.") new_plan = Plan.model_validate(curr_plan) return Command( update={ "messages": [AIMessage(content=full_response, name="planner")], "current_plan": new_plan, }, goto="reporter", ) return Command( update={ "messages": [AIMessage(content=full_response, name="planner")], "current_plan": full_response, }, goto="human_feedback", ) def human_feedback_node( state: State, config: RunnableConfig ) -> Command[Literal["planner", "research_team", "reporter", "__end__"]]: current_plan = state.get("current_plan", "") # check if the plan is auto accepted auto_accepted_plan = state.get("auto_accepted_plan", False) if not auto_accepted_plan: feedback = interrupt("Please Review the Plan.") # if the feedback is not accepted, return the planner node if feedback and str(feedback).upper().startswith("[EDIT_PLAN]"): return Command( update={ "messages": [ HumanMessage(content=feedback, name="feedback"), ], }, goto="planner", ) elif feedback and str(feedback).upper().startswith("[ACCEPTED]"): logger.info("Plan is accepted by user.") else: raise TypeError(f"Interrupt value of {feedback} is not supported.") # if the plan is accepted, run the following node plan_iterations = state["plan_iterations"] if state.get("plan_iterations", 0) else 0 goto = "research_team" try: current_plan = repair_json_output(current_plan) # increment the plan iterations plan_iterations += 1 # parse the plan new_plan = json.loads(current_plan) # Validate and fix plan to ensure web search requirements are met configurable = Configuration.from_runnable_config(config) new_plan = validate_and_fix_plan(new_plan, configurable.enforce_web_search) except json.JSONDecodeError: logger.warning("Planner response is not a valid JSON") if plan_iterations > 1: # the plan_iterations is increased before this check return Command(goto="reporter") else: return Command(goto="__end__") return Command( update={ "current_plan": Plan.model_validate(new_plan), "plan_iterations": plan_iterations, "locale": new_plan["locale"], }, goto=goto, ) def coordinator_node( state: State, config: RunnableConfig ) -> Command[Literal["planner", "background_investigator", "coordinator", "__end__"]]: """Coordinator node that communicate with customers and handle clarification.""" logger.info("Coordinator talking.") configurable = Configuration.from_runnable_config(config) # Check if clarification is enabled enable_clarification = state.get("enable_clarification", False) initial_topic = state.get("research_topic", "") clarified_topic = initial_topic # ============================================================ # BRANCH 1: Clarification DISABLED (Legacy Mode) # ============================================================ if not enable_clarification: # Use normal prompt with explicit instruction to skip clarification messages = apply_prompt_template("coordinator", state, locale=state.get("locale", "en-US")) messages.append( { "role": "system", "content": "CRITICAL: Clarification is DISABLED. You MUST immediately call handoff_to_planner tool with the user's query as-is. Do NOT ask questions or mention needing more information.", } ) # Only bind handoff_to_planner tool tools = [handoff_to_planner] response = ( get_llm_by_type(AGENT_LLM_MAP["coordinator"]) .bind_tools(tools) .invoke(messages) ) goto = "__end__" locale = state.get("locale", "en-US") research_topic = state.get("research_topic", "") # Process tool calls for legacy mode if response.tool_calls: try: for tool_call in response.tool_calls: tool_name = tool_call.get("name", "") tool_args = tool_call.get("args", {}) if tool_name == "handoff_to_planner": logger.info("Handing off to planner") goto = "planner" # Extract locale and research_topic if provided if tool_args.get("locale") and tool_args.get("research_topic"): locale = tool_args.get("locale") research_topic = tool_args.get("research_topic") break except Exception as e: logger.error(f"Error processing tool calls: {e}") goto = "planner" # ============================================================ # BRANCH 2: Clarification ENABLED (New Feature) # ============================================================ else: # Load clarification state clarification_rounds = state.get("clarification_rounds", 0) clarification_history = list(state.get("clarification_history", []) or []) clarification_history = [item for item in clarification_history if item] max_clarification_rounds = state.get("max_clarification_rounds", 3) # Prepare the messages for the coordinator state_messages = list(state.get("messages", [])) messages = apply_prompt_template("coordinator", state, locale=state.get("locale", "en-US")) clarification_history = reconstruct_clarification_history( state_messages, clarification_history, initial_topic ) clarified_topic, clarification_history = build_clarified_topic_from_history( clarification_history ) logger.debug("Clarification history rebuilt: %s", clarification_history) if clarification_history: initial_topic = clarification_history[0] latest_user_content = clarification_history[-1] else: latest_user_content = "" # Add clarification status for first round if clarification_rounds == 0: messages.append( { "role": "system", "content": "Clarification mode is ENABLED. Follow the 'Clarification Process' guidelines in your instructions.", } ) logger.info( "Clarification round %s/%s | topic: %s | latest user content: %s", clarification_rounds, max_clarification_rounds, clarified_topic or initial_topic, latest_user_content or "N/A", ) current_response = latest_user_content or "No response" clarification_context = f"""Continuing clarification (round {clarification_rounds}/{max_clarification_rounds}): User's latest response: {current_response} Ask for remaining missing dimensions. Do NOT repeat questions or start new topics.""" messages.append({"role": "system", "content": clarification_context}) # Bind both clarification tools tools = [handoff_to_planner, handoff_after_clarification] response = ( get_llm_by_type(AGENT_LLM_MAP["coordinator"]) .bind_tools(tools) .invoke(messages) ) logger.debug(f"Current state messages: {state['messages']}") # Initialize response processing variables goto = "__end__" locale = state.get("locale", "en-US") research_topic = ( clarification_history[0] if clarification_history else state.get("research_topic", "") ) if not clarified_topic: clarified_topic = research_topic # --- Process LLM response --- # No tool calls - LLM is asking a clarifying question if not response.tool_calls and response.content: if clarification_rounds < max_clarification_rounds: # Continue clarification process clarification_rounds += 1 # Do NOT add LLM response to clarification_history - only user responses logger.info( f"Clarification response: {clarification_rounds}/{max_clarification_rounds}: {response.content}" ) # Append coordinator's question to messages updated_messages = list(state_messages) if response.content: updated_messages.append( HumanMessage(content=response.content, name="coordinator") ) return Command( update={ "messages": updated_messages, "locale": locale, "research_topic": research_topic, "resources": configurable.resources, "clarification_rounds": clarification_rounds, "clarification_history": clarification_history, "clarified_research_topic": clarified_topic, "is_clarification_complete": False, "clarified_question": "", "goto": goto, "__interrupt__": [("coordinator", response.content)], }, goto=goto, ) else: # Max rounds reached - no more questions allowed logger.warning( f"Max clarification rounds ({max_clarification_rounds}) reached. Handing off to planner. Using prepared clarified topic: {clarified_topic}" ) goto = "planner" if state.get("enable_background_investigation"): goto = "background_investigator" else: # LLM called a tool (handoff) or has no content - clarification complete if response.tool_calls: logger.info( f"Clarification completed after {clarification_rounds} rounds. LLM called handoff tool." ) else: logger.warning("LLM response has no content and no tool calls.") # goto will be set in the final section based on tool calls # ============================================================ # Final: Build and return Command # ============================================================ messages = list(state.get("messages", []) or []) if response.content: messages.append(HumanMessage(content=response.content, name="coordinator")) # Process tool calls for BOTH branches (legacy and clarification) if response.tool_calls: try: for tool_call in response.tool_calls: tool_name = tool_call.get("name", "") tool_args = tool_call.get("args", {}) if tool_name in ["handoff_to_planner", "handoff_after_clarification"]: logger.info("Handing off to planner") goto = "planner" # Extract locale if provided locale = tool_args.get("locale", locale) if not enable_clarification and tool_args.get("research_topic"): research_topic = tool_args["research_topic"] if enable_clarification: logger.info( "Using prepared clarified topic: %s", clarified_topic or research_topic, ) else: logger.info( "Using research topic for handoff: %s", research_topic ) break except Exception as e: logger.error(f"Error processing tool calls: {e}") goto = "planner" else: # No tool calls detected - fallback to planner instead of ending logger.warning( "LLM didn't call any tools. This may indicate tool calling issues with the model. " "Falling back to planner to ensure research proceeds." ) # Log full response for debugging logger.debug(f"Coordinator response content: {response.content}") logger.debug(f"Coordinator response object: {response}") # Fallback to planner to ensure workflow continues goto = "planner" # Apply background_investigation routing if enabled (unified logic) if goto == "planner" and state.get("enable_background_investigation"): goto = "background_investigator" # Set default values for state variables (in case they're not defined in legacy mode) if not enable_clarification: clarification_rounds = 0 clarification_history = [] clarified_research_topic_value = clarified_topic or research_topic if enable_clarification: handoff_topic = clarified_topic or research_topic else: handoff_topic = research_topic return Command( update={ "messages": messages, "locale": locale, "research_topic": research_topic, "clarified_research_topic": clarified_research_topic_value, "resources": configurable.resources, "clarification_rounds": clarification_rounds, "clarification_history": clarification_history, "is_clarification_complete": goto != "coordinator", "clarified_question": handoff_topic if goto != "coordinator" else "", "goto": goto, }, goto=goto, ) def reporter_node(state: State, config: RunnableConfig): """Reporter node that write a final report.""" logger.info("Reporter write final report") configurable = Configuration.from_runnable_config(config) current_plan = state.get("current_plan") input_ = { "messages": [ HumanMessage( f"# Research Requirements\n\n## Task\n\n{current_plan.title}\n\n## Description\n\n{current_plan.thought}" ) ], "locale": state.get("locale", "en-US"), } invoke_messages = apply_prompt_template("reporter", input_, configurable, input_.get("locale", "en-US")) observations = state.get("observations", []) # Add a reminder about the new report format, citation style, and table usage invoke_messages.append( HumanMessage( content="IMPORTANT: Structure your report according to the format in the prompt. Remember to include:\n\n1. Key Points - A bulleted list of the most important findings\n2. Overview - A brief introduction to the topic\n3. Detailed Analysis - Organized into logical sections\n4. Survey Note (optional) - For more comprehensive reports\n5. Key Citations - List all references at the end\n\nFor citations, DO NOT include inline citations in the text. Instead, place all citations in the 'Key Citations' section at the end using the format: `- [Source Title](URL)`. Include an empty line between each citation for better readability.\n\nPRIORITIZE USING MARKDOWN TABLES for data presentation and comparison. Use tables whenever presenting comparative data, statistics, features, or options. Structure tables with clear headers and aligned columns. Example table format:\n\n| Feature | Description | Pros | Cons |\n|---------|-------------|------|------|\n| Feature 1 | Description 1 | Pros 1 | Cons 1 |\n| Feature 2 | Description 2 | Pros 2 | Cons 2 |", name="system", ) ) observation_messages = [] for observation in observations: observation_messages.append( HumanMessage( content=f"Below are some observations for the research task:\n\n{observation}", name="observation", ) ) # Context compression llm_token_limit = get_llm_token_limit_by_type(AGENT_LLM_MAP["reporter"]) compressed_state = ContextManager(llm_token_limit).compress_messages( {"messages": observation_messages} ) invoke_messages += compressed_state.get("messages", []) logger.debug(f"Current invoke messages: {invoke_messages}") response = get_llm_by_type(AGENT_LLM_MAP["reporter"]).invoke(invoke_messages) response_content = response.content logger.info(f"reporter response: {response_content}") return {"final_report": response_content} def research_team_node(state: State): """Research team node that collaborates on tasks.""" logger.info("Research team is collaborating on tasks.") pass async def _execute_agent_step( state: State, agent, agent_name: str ) -> Command[Literal["research_team"]]: """Helper function to execute a step using the specified agent.""" current_plan = state.get("current_plan") plan_title = current_plan.title observations = state.get("observations", []) # Find the first unexecuted step current_step = None completed_steps = [] for step in current_plan.steps: if not step.execution_res: current_step = step break else: completed_steps.append(step) if not current_step: logger.warning("No unexecuted step found") return Command(goto="research_team") logger.info(f"Executing step: {current_step.title}, agent: {agent_name}") # Format completed steps information completed_steps_info = "" if completed_steps: completed_steps_info = "# Completed Research Steps\n\n" for i, step in enumerate(completed_steps): completed_steps_info += f"## Completed Step {i + 1}: {step.title}\n\n" completed_steps_info += f"\n{step.execution_res}\n\n\n" # Prepare the input for the agent with completed steps info agent_input = { "messages": [ HumanMessage( content=f"# Research Topic\n\n{plan_title}\n\n{completed_steps_info}# Current Step\n\n## Title\n\n{current_step.title}\n\n## Description\n\n{current_step.description}\n\n## Locale\n\n{state.get('locale', 'en-US')}" ) ] } # Add citation reminder for researcher agent if agent_name == "researcher": if state.get("resources"): resources_info = "**The user mentioned the following resource files:**\n\n" for resource in state.get("resources"): resources_info += f"- {resource.title} ({resource.description})\n" agent_input["messages"].append( HumanMessage( content=resources_info + "\n\n" + "You MUST use the **local_search_tool** to retrieve the information from the resource files.", ) ) agent_input["messages"].append( HumanMessage( content="IMPORTANT: DO NOT include inline citations in the text. Instead, track all sources and include a References section at the end using link reference format. Include an empty line between each citation for better readability. Use this format for each reference:\n- [Source Title](URL)\n\n- [Another Source](URL)", name="system", ) ) # Invoke the agent default_recursion_limit = 25 try: env_value_str = os.getenv("AGENT_RECURSION_LIMIT", str(default_recursion_limit)) parsed_limit = int(env_value_str) if parsed_limit > 0: recursion_limit = parsed_limit logger.info(f"Recursion limit set to: {recursion_limit}") else: logger.warning( f"AGENT_RECURSION_LIMIT value '{env_value_str}' (parsed as {parsed_limit}) is not positive. " f"Using default value {default_recursion_limit}." ) recursion_limit = default_recursion_limit except ValueError: raw_env_value = os.getenv("AGENT_RECURSION_LIMIT") logger.warning( f"Invalid AGENT_RECURSION_LIMIT value: '{raw_env_value}'. " f"Using default value {default_recursion_limit}." ) recursion_limit = default_recursion_limit logger.info(f"Agent input: {agent_input}") # Validate message content before invoking agent try: validated_messages = validate_message_content(agent_input["messages"]) agent_input["messages"] = validated_messages except Exception as validation_error: logger.error(f"Error validating agent input messages: {validation_error}") try: result = await agent.ainvoke( input=agent_input, config={"recursion_limit": recursion_limit} ) except Exception as e: import traceback error_traceback = traceback.format_exc() error_message = f"Error executing {agent_name} agent for step '{current_step.title}': {str(e)}" logger.exception(error_message) logger.error(f"Full traceback:\n{error_traceback}") # Enhanced error diagnostics for content-related errors if "Field required" in str(e) and "content" in str(e): logger.error(f"Message content validation error detected") for i, msg in enumerate(agent_input.get('messages', [])): logger.error(f"Message {i}: type={type(msg).__name__}, " f"has_content={hasattr(msg, 'content')}, " f"content_type={type(msg.content).__name__ if hasattr(msg, 'content') else 'N/A'}, " f"content_len={len(str(msg.content)) if hasattr(msg, 'content') and msg.content else 0}") detailed_error = f"[ERROR] {agent_name.capitalize()} Agent Error\n\nStep: {current_step.title}\n\nError Details:\n{str(e)}\n\nPlease check the logs for more information." current_step.execution_res = detailed_error return Command( update={ "messages": [ HumanMessage( content=detailed_error, name=agent_name, ) ], "observations": observations + [detailed_error], }, goto="research_team", ) # Process the result response_content = result["messages"][-1].content logger.debug(f"{agent_name.capitalize()} full response: {response_content}") # Update the step with the execution result current_step.execution_res = response_content logger.info(f"Step '{current_step.title}' execution completed by {agent_name}") return Command( update={ "messages": [ HumanMessage( content=response_content, name=agent_name, ) ], "observations": observations + [response_content], }, goto="research_team", ) async def _setup_and_execute_agent_step( state: State, config: RunnableConfig, agent_type: str, default_tools: list, ) -> Command[Literal["research_team"]]: """Helper function to set up an agent with appropriate tools and execute a step. This function handles the common logic for both researcher_node and coder_node: 1. Configures MCP servers and tools based on agent type 2. Creates an agent with the appropriate tools or uses the default agent 3. Executes the agent on the current step Args: state: The current state config: The runnable config agent_type: The type of agent ("researcher" or "coder") default_tools: The default tools to add to the agent Returns: Command to update state and go to research_team """ configurable = Configuration.from_runnable_config(config) mcp_servers = {} enabled_tools = {} # Extract MCP server configuration for this agent type if configurable.mcp_settings: for server_name, server_config in configurable.mcp_settings["servers"].items(): if ( server_config["enabled_tools"] and agent_type in server_config["add_to_agents"] ): mcp_servers[server_name] = { k: v for k, v in server_config.items() if k in ("transport", "command", "args", "url", "env", "headers") } for tool_name in server_config["enabled_tools"]: enabled_tools[tool_name] = server_name # Create and execute agent with MCP tools if available if mcp_servers: client = MultiServerMCPClient(mcp_servers) loaded_tools = default_tools[:] all_tools = await client.get_tools() for tool in all_tools: if tool.name in enabled_tools: tool.description = ( f"Powered by '{enabled_tools[tool.name]}'.\n{tool.description}" ) loaded_tools.append(tool) llm_token_limit = get_llm_token_limit_by_type(AGENT_LLM_MAP[agent_type]) pre_model_hook = partial(ContextManager(llm_token_limit, 3).compress_messages) agent = create_agent( agent_type, agent_type, loaded_tools, agent_type, pre_model_hook ) return await _execute_agent_step(state, agent, agent_type) else: # Use default tools if no MCP servers are configured llm_token_limit = get_llm_token_limit_by_type(AGENT_LLM_MAP[agent_type]) pre_model_hook = partial(ContextManager(llm_token_limit, 3).compress_messages) agent = create_agent( agent_type, agent_type, default_tools, agent_type, pre_model_hook ) return await _execute_agent_step(state, agent, agent_type) async def researcher_node( state: State, config: RunnableConfig ) -> Command[Literal["research_team"]]: """Researcher node that do research""" logger.info("Researcher node is researching.") configurable = Configuration.from_runnable_config(config) tools = [get_web_search_tool(configurable.max_search_results), crawl_tool] retriever_tool = get_retriever_tool(state.get("resources", [])) if retriever_tool: tools.insert(0, retriever_tool) logger.info(f"Researcher tools: {tools}") return await _setup_and_execute_agent_step( state, config, "researcher", tools, ) async def coder_node( state: State, config: RunnableConfig ) -> Command[Literal["research_team"]]: """Coder node that do code analysis.""" logger.info("Coder node is coding.") return await _setup_and_execute_agent_step( state, config, "coder", [python_repl_tool], )