mirror of
https://gitee.com/wanwujie/deer-flow
synced 2026-04-05 15:10:20 +08:00
2e010a4619
Add a new "analysis" step type to handle reasoning and synthesis tasks that don't require code execution, addressing the concern that routing all non-search tasks to the coder agent was inappropriate. Changes: - Add ANALYSIS enum value to StepType in planner_model.py - Create analyst_node for pure LLM reasoning without tools - Update graph routing to route analysis steps to analyst agent - Add analyst agent to AGENT_LLM_MAP configuration - Create analyst prompts (English and Chinese) - Update planner prompts with guidance on choosing between analysis (reasoning/synthesis) and processing (code execution) - Change default step_type inference from "processing" to "analysis" when need_search=false Co-authored-by: Willem Jiang <143703838+willem-bd@users.noreply.github.com>
92 lines
2.7 KiB
Python
92 lines
2.7 KiB
Python
# Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
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# SPDX-License-Identifier: MIT
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from langgraph.checkpoint.memory import MemorySaver
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from langgraph.graph import END, START, StateGraph
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from src.prompts.planner_model import StepType
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from .nodes import (
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analyst_node,
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background_investigation_node,
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coder_node,
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coordinator_node,
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human_feedback_node,
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planner_node,
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reporter_node,
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research_team_node,
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researcher_node,
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)
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from .types import State
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def continue_to_running_research_team(state: State):
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current_plan = state.get("current_plan")
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if not current_plan or not current_plan.steps:
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return "planner"
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if all(step.execution_res for step in current_plan.steps):
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return "planner"
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# Find first incomplete step
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incomplete_step = None
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for step in current_plan.steps:
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if not step.execution_res:
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incomplete_step = step
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break
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if not incomplete_step:
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return "planner"
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if incomplete_step.step_type == StepType.RESEARCH:
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return "researcher"
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if incomplete_step.step_type == StepType.ANALYSIS:
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return "analyst"
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if incomplete_step.step_type == StepType.PROCESSING:
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return "coder"
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return "planner"
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def _build_base_graph():
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"""Build and return the base state graph with all nodes and edges."""
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builder = StateGraph(State)
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builder.add_edge(START, "coordinator")
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builder.add_node("coordinator", coordinator_node)
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builder.add_node("background_investigator", background_investigation_node)
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builder.add_node("planner", planner_node)
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builder.add_node("reporter", reporter_node)
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builder.add_node("research_team", research_team_node)
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builder.add_node("researcher", researcher_node)
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builder.add_node("analyst", analyst_node)
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builder.add_node("coder", coder_node)
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builder.add_node("human_feedback", human_feedback_node)
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builder.add_edge("background_investigator", "planner")
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builder.add_conditional_edges(
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"research_team",
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continue_to_running_research_team,
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["planner", "researcher", "analyst", "coder"],
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)
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builder.add_edge("reporter", END)
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return builder
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def build_graph_with_memory():
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"""Build and return the agent workflow graph with memory."""
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# use persistent memory to save conversation history
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# TODO: be compatible with SQLite / PostgreSQL
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memory = MemorySaver()
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# build state graph
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builder = _build_base_graph()
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return builder.compile(checkpointer=memory)
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def build_graph():
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"""Build and return the agent workflow graph without memory."""
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# build state graph
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builder = _build_base_graph()
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return builder.compile()
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graph = build_graph()
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