mirror of
https://gitee.com/wanwujie/deer-flow
synced 2026-04-03 22:32:12 +08:00
103 lines
3.2 KiB
Python
103 lines
3.2 KiB
Python
# Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
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# SPDX-License-Identifier: MIT
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import asyncio
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import logging
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from src.graph import build_graph
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# Configure logging
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logging.basicConfig(
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level=logging.INFO, # Default level is INFO
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format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
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)
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def enable_debug_logging():
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"""Enable debug level logging for more detailed execution information."""
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logging.getLogger("src").setLevel(logging.DEBUG)
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logger = logging.getLogger(__name__)
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# Create the graph
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graph = build_graph()
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async def run_agent_workflow_async(
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user_input: str,
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debug: bool = False,
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max_plan_iterations: int = 1,
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max_step_num: int = 3,
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enable_background_investigation: bool = True,
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):
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"""Run the agent workflow asynchronously with the given user input.
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Args:
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user_input: The user's query or request
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debug: If True, enables debug level logging
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max_plan_iterations: Maximum number of plan iterations
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max_step_num: Maximum number of steps in a plan
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enable_background_investigation: If True, performs web search before planning to enhance context
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Returns:
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The final state after the workflow completes
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"""
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if not user_input:
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raise ValueError("Input could not be empty")
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if debug:
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enable_debug_logging()
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logger.info(f"Starting async workflow with user input: {user_input}")
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initial_state = {
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# Runtime Variables
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"messages": [{"role": "user", "content": user_input}],
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"auto_accepted_plan": True,
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"enable_background_investigation": enable_background_investigation,
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}
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config = {
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"configurable": {
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"thread_id": "default",
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"max_plan_iterations": max_plan_iterations,
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"max_step_num": max_step_num,
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"mcp_settings": {
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"servers": {
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"mcp-github-trending": {
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"transport": "stdio",
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"command": "uvx",
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"args": ["mcp-github-trending"],
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"enabled_tools": ["get_github_trending_repositories"],
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"add_to_agents": ["researcher"],
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}
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}
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},
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},
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"recursion_limit": 100,
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}
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last_message_cnt = 0
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async for s in graph.astream(
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input=initial_state, config=config, stream_mode="values"
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):
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try:
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if isinstance(s, dict) and "messages" in s:
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if len(s["messages"]) <= last_message_cnt:
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continue
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last_message_cnt = len(s["messages"])
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message = s["messages"][-1]
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if isinstance(message, tuple):
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print(message)
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else:
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message.pretty_print()
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else:
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# For any other output format
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print(f"Output: {s}")
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except Exception as e:
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logger.error(f"Error processing stream output: {e}")
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print(f"Error processing output: {str(e)}")
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logger.info("Async workflow completed successfully")
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if __name__ == "__main__":
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print(graph.get_graph(xray=True).draw_mermaid())
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