mirror of
https://gitee.com/wanwujie/deer-flow
synced 2026-04-03 06:12:14 +08:00
ca4ada5aa7
* fix: Missing Required Fields in Plan Validation * fix: the exception of plan validation * Fixed the test errors * Addressed the comments of the PR reviews * fix: multiple web_search ToolMessages only showing last result
1211 lines
50 KiB
Python
1211 lines
50 KiB
Python
# Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
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# SPDX-License-Identifier: MIT
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import json
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import logging
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import os
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from functools import partial
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from typing import Any, Annotated, Literal
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from langchain_core.messages import AIMessage, HumanMessage, ToolMessage
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from langchain_core.runnables import RunnableConfig
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from langchain_core.tools import tool
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from langchain_mcp_adapters.client import MultiServerMCPClient
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from langgraph.types import Command, interrupt
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from src.agents import create_agent
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from src.config.agents import AGENT_LLM_MAP
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from src.config.configuration import Configuration
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from src.llms.llm import get_llm_by_type, get_llm_token_limit_by_type
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from src.prompts.planner_model import Plan
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from src.prompts.template import apply_prompt_template
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from src.tools import (
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crawl_tool,
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get_retriever_tool,
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get_web_search_tool,
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python_repl_tool,
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)
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from src.tools.search import LoggedTavilySearch
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from src.utils.context_manager import ContextManager, validate_message_content
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from src.utils.json_utils import repair_json_output, sanitize_tool_response
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from ..config import SELECTED_SEARCH_ENGINE, SearchEngine
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from .types import State
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from .utils import (
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build_clarified_topic_from_history,
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get_message_content,
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is_user_message,
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reconstruct_clarification_history,
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)
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logger = logging.getLogger(__name__)
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@tool
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def handoff_to_planner(
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research_topic: Annotated[str, "The topic of the research task to be handed off."],
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locale: Annotated[str, "The user's detected language locale (e.g., en-US, zh-CN)."],
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):
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"""Handoff to planner agent to do plan."""
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# This tool is not returning anything: we're just using it
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# as a way for LLM to signal that it needs to hand off to planner agent
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return
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@tool
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def handoff_after_clarification(
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locale: Annotated[str, "The user's detected language locale (e.g., en-US, zh-CN)."],
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research_topic: Annotated[
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str, "The clarified research topic based on all clarification rounds."
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],
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):
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"""Handoff to planner after clarification rounds are complete. Pass all clarification history to planner for analysis."""
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return
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def needs_clarification(state: dict) -> bool:
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"""
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Check if clarification is needed based on current state.
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Centralized logic for determining when to continue clarification.
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"""
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if not state.get("enable_clarification", False):
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return False
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clarification_rounds = state.get("clarification_rounds", 0)
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is_clarification_complete = state.get("is_clarification_complete", False)
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max_clarification_rounds = state.get("max_clarification_rounds", 3)
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# Need clarification if: enabled + has rounds + not complete + not exceeded max
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# Use <= because after asking the Nth question, we still need to wait for the Nth answer
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return (
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clarification_rounds > 0
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and not is_clarification_complete
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and clarification_rounds <= max_clarification_rounds
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)
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def preserve_state_meta_fields(state: State) -> dict:
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"""
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Extract meta/config fields that should be preserved across state transitions.
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These fields are critical for workflow continuity and should be explicitly
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included in all Command.update dicts to prevent them from reverting to defaults.
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Args:
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state: Current state object
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Returns:
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Dict of meta fields to preserve
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"""
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return {
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"locale": state.get("locale", "en-US"),
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"research_topic": state.get("research_topic", ""),
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"clarified_research_topic": state.get("clarified_research_topic", ""),
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"clarification_history": state.get("clarification_history", []),
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"enable_clarification": state.get("enable_clarification", False),
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"max_clarification_rounds": state.get("max_clarification_rounds", 3),
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"clarification_rounds": state.get("clarification_rounds", 0),
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"resources": state.get("resources", []),
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}
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def validate_and_fix_plan(plan: dict, enforce_web_search: bool = False) -> dict:
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"""
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Validate and fix a plan to ensure it meets requirements.
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Args:
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plan: The plan dict to validate
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enforce_web_search: If True, ensure at least one step has need_search=true
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Returns:
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The validated/fixed plan dict
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"""
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if not isinstance(plan, dict):
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return plan
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steps = plan.get("steps", [])
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# ============================================================
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# SECTION 1: Repair missing step_type fields (Issue #650 fix)
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# ============================================================
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for idx, step in enumerate(steps):
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if not isinstance(step, dict):
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continue
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# Check if step_type is missing or empty
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if "step_type" not in step or not step.get("step_type"):
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# Infer step_type based on need_search value
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inferred_type = "research" if step.get("need_search", False) else "processing"
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step["step_type"] = inferred_type
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logger.info(
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f"Repaired missing step_type for step {idx} ({step.get('title', 'Untitled')}): "
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f"inferred as '{inferred_type}' based on need_search={step.get('need_search', False)}"
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)
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# ============================================================
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# SECTION 2: Enforce web search requirements
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# ============================================================
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if enforce_web_search:
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# Check if any step has need_search=true (only check dict steps)
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has_search_step = any(
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step.get("need_search", False)
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for step in steps
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if isinstance(step, dict)
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)
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if not has_search_step and steps:
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# Ensure first research step has web search enabled
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for idx, step in enumerate(steps):
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if isinstance(step, dict) and step.get("step_type") == "research":
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step["need_search"] = True
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logger.info(f"Enforced web search on research step at index {idx}")
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break
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else:
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# Fallback: If no research step exists, convert the first step to a research step with web search enabled.
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# This ensures that at least one step will perform a web search as required.
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if isinstance(steps[0], dict):
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steps[0]["step_type"] = "research"
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steps[0]["need_search"] = True
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logger.info(
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"Converted first step to research with web search enforcement"
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)
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elif not has_search_step and not steps:
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# Add a default research step if no steps exist
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logger.warning("Plan has no steps. Adding default research step.")
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plan["steps"] = [
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{
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"need_search": True,
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"title": "Initial Research",
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"description": "Gather information about the topic",
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"step_type": "research",
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}
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]
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# ============================================================
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# SECTION 3: Ensure required Plan fields are present (Issue #710 fix)
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# ============================================================
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# Set locale from state if not present
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if "locale" not in plan or not plan.get("locale"):
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plan["locale"] = "en-US" # Default locale
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logger.info("Added missing locale field with default value 'en-US'")
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# Ensure has_enough_context is present
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if "has_enough_context" not in plan:
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plan["has_enough_context"] = False # Default value
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logger.info("Added missing has_enough_context field with default value 'False'")
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# Ensure title is present
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if "title" not in plan or not plan.get("title"):
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# Try to infer title from steps or use a default
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if steps and isinstance(steps[0], dict) and "title" in steps[0]:
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plan["title"] = steps[0]["title"]
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logger.info(f"Inferred missing title from first step: {plan['title']}")
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else:
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plan["title"] = "Research Plan" # Default title
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logger.info("Added missing title field with default value 'Research Plan'")
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return plan
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def background_investigation_node(state: State, config: RunnableConfig):
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logger.info("background investigation node is running.")
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configurable = Configuration.from_runnable_config(config)
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query = state.get("clarified_research_topic") or state.get("research_topic")
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background_investigation_results = []
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if SELECTED_SEARCH_ENGINE == SearchEngine.TAVILY.value:
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searched_content = LoggedTavilySearch(
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max_results=configurable.max_search_results
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).invoke(query)
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# check if the searched_content is a tuple, then we need to unpack it
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if isinstance(searched_content, tuple):
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searched_content = searched_content[0]
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# Handle string JSON response (new format from fixed Tavily tool)
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if isinstance(searched_content, str):
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try:
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parsed = json.loads(searched_content)
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if isinstance(parsed, dict) and "error" in parsed:
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logger.error(f"Tavily search error: {parsed['error']}")
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background_investigation_results = []
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elif isinstance(parsed, list):
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background_investigation_results = [
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f"## {elem.get('title', 'Untitled')}\n\n{elem.get('content', 'No content')}"
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for elem in parsed
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]
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else:
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logger.error(f"Unexpected Tavily response format: {searched_content}")
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background_investigation_results = []
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except json.JSONDecodeError:
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logger.error(f"Failed to parse Tavily response as JSON: {searched_content}")
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background_investigation_results = []
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# Handle legacy list format
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elif isinstance(searched_content, list):
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background_investigation_results = [
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f"## {elem['title']}\n\n{elem['content']}" for elem in searched_content
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]
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return {
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"background_investigation_results": "\n\n".join(
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background_investigation_results
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)
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}
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else:
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logger.error(
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f"Tavily search returned malformed response: {searched_content}"
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)
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background_investigation_results = []
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else:
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background_investigation_results = get_web_search_tool(
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configurable.max_search_results
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).invoke(query)
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return {
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"background_investigation_results": json.dumps(
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background_investigation_results, ensure_ascii=False
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)
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}
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def planner_node(
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state: State, config: RunnableConfig
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) -> Command[Literal["human_feedback", "reporter"]]:
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"""Planner node that generate the full plan."""
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logger.info("Planner generating full plan with locale: %s", state.get("locale", "en-US"))
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configurable = Configuration.from_runnable_config(config)
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plan_iterations = state["plan_iterations"] if state.get("plan_iterations", 0) else 0
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# For clarification feature: use the clarified research topic (complete history)
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if state.get("enable_clarification", False) and state.get(
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"clarified_research_topic"
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):
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# Modify state to use clarified research topic instead of full conversation
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modified_state = state.copy()
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modified_state["messages"] = [
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{"role": "user", "content": state["clarified_research_topic"]}
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]
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modified_state["research_topic"] = state["clarified_research_topic"]
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messages = apply_prompt_template("planner", modified_state, configurable, state.get("locale", "en-US"))
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logger.info(
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f"Clarification mode: Using clarified research topic: {state['clarified_research_topic']}"
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)
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else:
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# Normal mode: use full conversation history
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messages = apply_prompt_template("planner", state, configurable, state.get("locale", "en-US"))
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if state.get("enable_background_investigation") and state.get(
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"background_investigation_results"
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):
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messages += [
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{
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"role": "user",
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"content": (
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"background investigation results of user query:\n"
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+ state["background_investigation_results"]
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+ "\n"
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),
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}
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]
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if configurable.enable_deep_thinking:
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llm = get_llm_by_type("reasoning")
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elif AGENT_LLM_MAP["planner"] == "basic":
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llm = get_llm_by_type("basic")
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else:
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llm = get_llm_by_type(AGENT_LLM_MAP["planner"])
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# if the plan iterations is greater than the max plan iterations, return the reporter node
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if plan_iterations >= configurable.max_plan_iterations:
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return Command(
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update=preserve_state_meta_fields(state),
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goto="reporter"
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)
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full_response = ""
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if AGENT_LLM_MAP["planner"] == "basic" and not configurable.enable_deep_thinking:
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response = llm.invoke(messages)
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if hasattr(response, "model_dump_json"):
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full_response = response.model_dump_json(indent=4, exclude_none=True)
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else:
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full_response = get_message_content(response) or ""
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else:
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response = llm.stream(messages)
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for chunk in response:
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full_response += chunk.content
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logger.debug(f"Current state messages: {state['messages']}")
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logger.info(f"Planner response: {full_response}")
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# Validate explicitly that response content is valid JSON before proceeding to parse it
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if not full_response.strip().startswith('{') and not full_response.strip().startswith('['):
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logger.warning("Planner response does not appear to be valid JSON")
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if plan_iterations > 0:
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return Command(
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update=preserve_state_meta_fields(state),
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goto="reporter"
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)
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else:
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return Command(
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update=preserve_state_meta_fields(state),
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goto="__end__"
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)
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try:
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curr_plan = json.loads(repair_json_output(full_response))
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except json.JSONDecodeError:
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logger.warning("Planner response is not a valid JSON")
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if plan_iterations > 0:
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return Command(
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update=preserve_state_meta_fields(state),
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goto="reporter"
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)
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else:
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return Command(
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update=preserve_state_meta_fields(state),
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goto="__end__"
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)
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# Validate and fix plan to ensure web search requirements are met
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if isinstance(curr_plan, dict):
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curr_plan = validate_and_fix_plan(curr_plan, configurable.enforce_web_search)
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if isinstance(curr_plan, dict) and curr_plan.get("has_enough_context"):
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logger.info("Planner response has enough context.")
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new_plan = Plan.model_validate(curr_plan)
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return Command(
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update={
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"messages": [AIMessage(content=full_response, name="planner")],
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"current_plan": new_plan,
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**preserve_state_meta_fields(state),
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},
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goto="reporter",
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)
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return Command(
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update={
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"messages": [AIMessage(content=full_response, name="planner")],
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"current_plan": full_response,
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**preserve_state_meta_fields(state),
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},
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goto="human_feedback",
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)
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def extract_plan_content(plan_data: str | dict | Any) -> str:
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"""
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Safely extract plan content from different types of plan data.
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Args:
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plan_data: The plan data which can be a string, AIMessage, or dict
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Returns:
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str: The plan content as a string (JSON string for dict inputs, or
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extracted/original string for other types)
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"""
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if isinstance(plan_data, str):
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# If it's already a string, return as is
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return plan_data
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elif hasattr(plan_data, 'content') and isinstance(plan_data.content, str):
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# If it's an AIMessage or similar object with a content attribute
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logger.debug(f"Extracting plan content from message object of type {type(plan_data).__name__}")
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return plan_data.content
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elif isinstance(plan_data, dict):
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# If it's already a dictionary, convert to JSON string
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# Need to check if it's dict with content field (AIMessage-like)
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if "content" in plan_data:
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if isinstance(plan_data["content"], str):
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logger.debug("Extracting plan content from dict with content field")
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return plan_data["content"]
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if isinstance(plan_data["content"], dict):
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logger.debug("Converting content field dict to JSON string")
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return json.dumps(plan_data["content"], ensure_ascii=False)
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else:
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logger.warning(f"Unexpected type for 'content' field in plan_data dict: {type(plan_data['content']).__name__}, converting to string")
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return str(plan_data["content"])
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else:
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logger.debug("Converting plan dictionary to JSON string")
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return json.dumps(plan_data)
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else:
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# For any other type, try to convert to string
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logger.warning(f"Unexpected plan data type {type(plan_data).__name__}, attempting to convert to string")
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return str(plan_data)
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def human_feedback_node(
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state: State, config: RunnableConfig
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) -> Command[Literal["planner", "research_team", "reporter", "__end__"]]:
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current_plan = state.get("current_plan", "")
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# check if the plan is auto accepted
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auto_accepted_plan = state.get("auto_accepted_plan", False)
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if not auto_accepted_plan:
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feedback = interrupt("Please Review the Plan.")
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# Handle None or empty feedback
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if not feedback:
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logger.warning(f"Received empty or None feedback: {feedback}. Returning to planner for new plan.")
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return Command(
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update=preserve_state_meta_fields(state),
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goto="planner"
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)
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# Normalize feedback string
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feedback_normalized = str(feedback).strip().upper()
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# if the feedback is not accepted, return the planner node
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if feedback_normalized.startswith("[EDIT_PLAN]"):
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logger.info(f"Plan edit requested by user: {feedback}")
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return Command(
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update={
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"messages": [
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HumanMessage(content=feedback, name="feedback"),
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],
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**preserve_state_meta_fields(state),
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},
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goto="planner",
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)
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elif feedback_normalized.startswith("[ACCEPTED]"):
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logger.info("Plan is accepted by user.")
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else:
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logger.warning(f"Unsupported feedback format: {feedback}. Please use '[ACCEPTED]' to accept or '[EDIT_PLAN]' to edit.")
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return Command(
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update=preserve_state_meta_fields(state),
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goto="planner"
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)
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# if the plan is accepted, run the following node
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plan_iterations = state["plan_iterations"] if state.get("plan_iterations", 0) else 0
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goto = "research_team"
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try:
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# Safely extract plan content from different types (string, AIMessage, dict)
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original_plan = current_plan
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current_plan_content = extract_plan_content(current_plan)
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logger.debug(f"Extracted plan content type: {type(current_plan_content).__name__}")
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# Repair the JSON output
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current_plan = repair_json_output(current_plan_content)
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# 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, AttributeError) as e:
|
|
logger.warning(f"Failed to parse plan: {str(e)}. Plan data type: {type(current_plan).__name__}")
|
|
if isinstance(current_plan, dict) and "content" in original_plan:
|
|
logger.warning(f"Plan appears to be an AIMessage object with content field")
|
|
if plan_iterations > 1: # the plan_iterations is increased before this check
|
|
return Command(
|
|
update=preserve_state_meta_fields(state),
|
|
goto="reporter"
|
|
)
|
|
else:
|
|
return Command(
|
|
update=preserve_state_meta_fields(state),
|
|
goto="__end__"
|
|
)
|
|
|
|
# Build update dict with safe locale handling
|
|
update_dict = {
|
|
"current_plan": Plan.model_validate(new_plan),
|
|
"plan_iterations": plan_iterations,
|
|
**preserve_state_meta_fields(state),
|
|
}
|
|
|
|
# Only override locale if new_plan provides a valid value, otherwise use preserved locale
|
|
if new_plan.get("locale"):
|
|
update_dict["locale"] = new_plan["locale"]
|
|
|
|
return Command(
|
|
update=update_dict,
|
|
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")
|
|
logger.info(f"Coordinator locale: {locale}")
|
|
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 research_topic if provided
|
|
if tool_args.get("research_topic"):
|
|
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.",
|
|
}
|
|
)
|
|
|
|
current_response = latest_user_content or "No response"
|
|
logger.info(
|
|
"Clarification round %s/%s | topic: %s | current user response: %s",
|
|
clarification_rounds,
|
|
max_clarification_rounds,
|
|
clarified_topic or initial_topic,
|
|
current_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 - let LLM choose the appropriate one
|
|
tools = [handoff_to_planner, handoff_after_clarification]
|
|
|
|
# Check if we've already reached max rounds
|
|
if clarification_rounds >= max_clarification_rounds:
|
|
# Max rounds reached - force handoff by adding system instruction
|
|
logger.warning(
|
|
f"Max clarification rounds ({max_clarification_rounds}) reached. Forcing handoff to planner. Using prepared clarified topic: {clarified_topic}"
|
|
)
|
|
# Add system instruction to force handoff - let LLM choose the right tool
|
|
messages.append(
|
|
{
|
|
"role": "system",
|
|
"content": f"MAX ROUNDS REACHED. You MUST call handoff_after_clarification (not handoff_to_planner) with the appropriate locale based on the user's language and research_topic='{clarified_topic}'. Do not ask any more questions.",
|
|
}
|
|
)
|
|
|
|
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:
|
|
# Check if we've reached max rounds - if so, force handoff to planner
|
|
if clarification_rounds >= max_clarification_rounds:
|
|
logger.warning(
|
|
f"Max clarification rounds ({max_clarification_rounds}) reached. "
|
|
"LLM didn't call handoff tool, forcing handoff to planner."
|
|
)
|
|
goto = "planner"
|
|
# Continue to final section instead of early return
|
|
else:
|
|
# 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,
|
|
"goto": goto,
|
|
"__interrupt__": [("coordinator", response.content)],
|
|
},
|
|
goto=goto,
|
|
)
|
|
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"
|
|
|
|
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
|
|
|
|
# clarified_research_topic: Complete clarified topic with all clarification rounds
|
|
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",
|
|
"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.")
|
|
logger.debug("Entering research_team_node - coordinating research and coder agents")
|
|
pass
|
|
|
|
|
|
def validate_web_search_usage(messages: list, agent_name: str = "agent") -> bool:
|
|
"""
|
|
Validate if the agent has used the web search tool during execution.
|
|
|
|
Args:
|
|
messages: List of messages from the agent execution
|
|
agent_name: Name of the agent (for logging purposes)
|
|
|
|
Returns:
|
|
bool: True if web search tool was used, False otherwise
|
|
"""
|
|
web_search_used = False
|
|
|
|
for message in messages:
|
|
# Check for ToolMessage instances indicating web search was used
|
|
if isinstance(message, ToolMessage) and message.name == "web_search":
|
|
web_search_used = True
|
|
logger.info(f"[VALIDATION] {agent_name} received ToolMessage from web_search tool")
|
|
break
|
|
|
|
# Check for AIMessage content that mentions tool calls
|
|
if hasattr(message, 'tool_calls') and message.tool_calls:
|
|
for tool_call in message.tool_calls:
|
|
if tool_call.get('name') == "web_search":
|
|
web_search_used = True
|
|
logger.info(f"[VALIDATION] {agent_name} called web_search tool")
|
|
break
|
|
# break outer loop if web search was used
|
|
if web_search_used:
|
|
break
|
|
|
|
# Check for message name attribute
|
|
if hasattr(message, 'name') and message.name == "web_search":
|
|
web_search_used = True
|
|
logger.info(f"[VALIDATION] {agent_name} used web_search tool")
|
|
break
|
|
|
|
if not web_search_used:
|
|
logger.warning(f"[VALIDATION] {agent_name} did not use web_search tool")
|
|
|
|
return web_search_used
|
|
|
|
|
|
async def _execute_agent_step(
|
|
state: State, agent, agent_name: str, config: RunnableConfig = None
|
|
) -> Command[Literal["research_team"]]:
|
|
"""Helper function to execute a step using the specified agent."""
|
|
logger.debug(f"[_execute_agent_step] Starting execution for agent: {agent_name}")
|
|
|
|
current_plan = state.get("current_plan")
|
|
plan_title = current_plan.title
|
|
observations = state.get("observations", [])
|
|
logger.debug(f"[_execute_agent_step] Plan title: {plan_title}, observations count: {len(observations)}")
|
|
|
|
# Find the first unexecuted step
|
|
current_step = None
|
|
completed_steps = []
|
|
for idx, step in enumerate(current_plan.steps):
|
|
if not step.execution_res:
|
|
current_step = step
|
|
logger.debug(f"[_execute_agent_step] Found unexecuted step at index {idx}: {step.title}")
|
|
break
|
|
else:
|
|
completed_steps.append(step)
|
|
|
|
if not current_step:
|
|
logger.warning(f"[_execute_agent_step] No unexecuted step found in {len(current_plan.steps)} total steps")
|
|
return Command(
|
|
update=preserve_state_meta_fields(state),
|
|
goto="research_team"
|
|
)
|
|
|
|
logger.info(f"[_execute_agent_step] Executing step: {current_step.title}, agent: {agent_name}")
|
|
logger.debug(f"[_execute_agent_step] Completed steps so far: {len(completed_steps)}")
|
|
|
|
# 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"<finding>\n{step.execution_res}\n</finding>\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],
|
|
**preserve_state_meta_fields(state),
|
|
},
|
|
goto="research_team",
|
|
)
|
|
|
|
# Process the result
|
|
response_content = result["messages"][-1].content
|
|
|
|
# Sanitize response to remove extra tokens and truncate if needed
|
|
response_content = sanitize_tool_response(str(response_content))
|
|
|
|
logger.debug(f"{agent_name.capitalize()} full response: {response_content}")
|
|
|
|
# Validate web search usage for researcher agent if enforcement is enabled
|
|
web_search_validated = True
|
|
should_validate = agent_name == "researcher"
|
|
validation_info = ""
|
|
|
|
if should_validate:
|
|
# Check if enforcement is enabled in configuration
|
|
configurable = Configuration.from_runnable_config(config) if config else Configuration()
|
|
if configurable.enforce_researcher_search:
|
|
web_search_validated = validate_web_search_usage(result["messages"], agent_name)
|
|
|
|
# If web search was not used, add a warning to the response
|
|
if not web_search_validated:
|
|
logger.warning(f"[VALIDATION] Researcher did not use web_search tool. Adding reminder to response.")
|
|
# Add validation information to observations
|
|
validation_info = (
|
|
"\n\n[WARNING] This research was completed without using the web_search tool. "
|
|
"Please verify that the information provided is accurate and up-to-date."
|
|
"\n\n[VALIDATION WARNING] Researcher did not use the web_search tool as recommended."
|
|
)
|
|
|
|
# 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}")
|
|
|
|
# Include all messages from agent result to preserve intermediate tool calls/results
|
|
# This ensures multiple web_search calls all appear in the stream, not just the final result
|
|
agent_messages = result.get("messages", [])
|
|
logger.debug(
|
|
f"{agent_name.capitalize()} returned {len(agent_messages)} messages. "
|
|
f"Message types: {[type(msg).__name__ for msg in agent_messages]}"
|
|
)
|
|
|
|
# Count tool messages for logging
|
|
tool_message_count = sum(1 for msg in agent_messages if isinstance(msg, ToolMessage))
|
|
if tool_message_count > 0:
|
|
logger.info(
|
|
f"{agent_name.capitalize()} agent made {tool_message_count} tool calls. "
|
|
f"All tool results will be preserved and streamed to frontend."
|
|
)
|
|
|
|
return Command(
|
|
update={
|
|
"messages": agent_messages,
|
|
"observations": observations + [response_content + validation_info],
|
|
**preserve_state_meta_fields(state),
|
|
},
|
|
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,
|
|
interrupt_before_tools=configurable.interrupt_before_tools,
|
|
)
|
|
return await _execute_agent_step(state, agent, agent_type, config)
|
|
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,
|
|
interrupt_before_tools=configurable.interrupt_before_tools,
|
|
)
|
|
return await _execute_agent_step(state, agent, agent_type, config)
|
|
|
|
|
|
async def researcher_node(
|
|
state: State, config: RunnableConfig
|
|
) -> Command[Literal["research_team"]]:
|
|
"""Researcher node that do research"""
|
|
logger.info("Researcher node is researching.")
|
|
logger.debug(f"[researcher_node] Starting researcher agent")
|
|
|
|
configurable = Configuration.from_runnable_config(config)
|
|
logger.debug(f"[researcher_node] Max search results: {configurable.max_search_results}")
|
|
|
|
tools = [get_web_search_tool(configurable.max_search_results), crawl_tool]
|
|
retriever_tool = get_retriever_tool(state.get("resources", []))
|
|
if retriever_tool:
|
|
logger.debug(f"[researcher_node] Adding retriever tool to tools list")
|
|
tools.insert(0, retriever_tool)
|
|
|
|
logger.info(f"[researcher_node] Researcher tools count: {len(tools)}")
|
|
logger.debug(f"[researcher_node] Researcher tools: {[tool.name if hasattr(tool, 'name') else str(tool) for tool in tools]}")
|
|
logger.info(f"[researcher_node] enforce_researcher_search is set to: {configurable.enforce_researcher_search}")
|
|
|
|
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.")
|
|
logger.debug(f"[coder_node] Starting coder agent with python_repl_tool")
|
|
|
|
return await _setup_and_execute_agent_step(
|
|
state,
|
|
config,
|
|
"coder",
|
|
[python_repl_tool],
|
|
)
|