180 lines
7.6 KiB
Python
180 lines
7.6 KiB
Python
"""Deep Agent - 基于create_deep_agent的深度分析智能体"""
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from deepagents.middleware.filesystem import FilesystemMiddleware
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from deepagents.middleware.patch_tool_calls import PatchToolCallsMiddleware
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from deepagents.middleware.subagents import SubAgentMiddleware
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from langchain.agents import create_agent
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from langchain.agents.middleware import (
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TodoListMiddleware,
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ToolCallLimitMiddleware,
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)
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from yuxi.agents.common import BaseAgent, load_chat_model
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from yuxi.agents.common.backends import create_agent_composite_backend
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from yuxi.agents.common.middlewares import RuntimeConfigMiddleware, SummaryOffloadMiddleware, save_attachments_to_fs
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from yuxi.agents.common.middlewares.knowledge_base_middleware import KnowledgeBaseMiddleware
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from yuxi.agents.common.middlewares.skills_middleware import SkillsMiddleware
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from yuxi.agents.common.toolkits.buildin.tools import _create_tavily_search
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from yuxi.services.mcp_service import get_tools_from_all_servers
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from yuxi.utils import logger
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from .context import DeepContext
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def _create_fs_backend(rt):
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"""创建文件存储后端"""
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return create_agent_composite_backend(rt)
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def _get_research_sub_agent(search_tools: list) -> dict:
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"""Get research sub-agent config with search tools."""
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return {
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"name": "research-agent",
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"description": ("利用搜索工具，用于研究更深入的问题。将调研结果写入到主题研究文件中。"),
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"system_prompt": (
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"你是一位专注的研究员。你的工作是根据用户的问题进行研究。"
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"进行彻底的研究，然后用详细的答案回复用户的问题，只有你的最终答案会被传递给用户。"
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"除了你的最终信息，他们不会知道任何其他事情，所以你的最终报告应该就是你的最终信息！"
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"将调研结果保存到主题研究文件中 /sub_research/xxx.md 中。"
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),
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"tools": search_tools,
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}
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critique_sub_agent = {
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"name": "critique-agent",
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"description": "用于评论最终报告。给这个代理一些关于你希望它如何评论报告的信息。",
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"system_prompt": (
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"你是一位专注的编辑。你的任务是评论一份报告。\n\n"
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"你可以在 `final_report.md` 找到这份报告。\n\n"
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"你可以在 `question.txt` 找到这份报告的问题/主题。\n\n"
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"用户可能会要求评论报告的特定方面。请用详细的评论回复用户，指出报告中可以改进的地方。\n\n"
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"如果有助于你评论报告，你可以使用搜索工具来搜索信息\n\n"
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"不要自己写入 `final_report.md`。\n\n"
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"需要检查的事项：\n"
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"- 检查每个部分的标题是否恰当\n"
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"- 检查报告的写法是否像论文或教科书——它应该是以文本为主，不要只是一个项目符号列表！\n"
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"- 检查报告是否全面。如果任何段落或部分过短，或缺少重要细节，请指出来。\n"
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"- 检查文章是否涵盖了行业的关键领域，确保了整体理解，并且没有遗漏重要部分。\n"
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"- 检查文章是否深入分析了原因、影响和趋势，提供了有价值的见解\n"
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"- 检查文章是否紧扣研究主题并直接回答问题\n"
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"- 检查文章是否结构清晰、语言流畅、易于理解。"
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),
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}
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class DeepAgent(BaseAgent):
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name = "深度分析智能体"
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description = "具备规划、深度分析和子智能体协作能力的智能体，可以处理复杂的多步骤任务"
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context_schema = DeepContext
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capabilities = [
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"file_upload",
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"todo",
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"files",
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]
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def __init__(self, **kwargs):
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super().__init__(**kwargs)
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self.graph = None
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self.checkpointer = None
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async def get_tools(self):
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"""返回 Deep Agent 的专用工具"""
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from yuxi import config
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tools = []
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if config.enable_web_search:
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tavily = _create_tavily_search()
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if tavily:
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tools.append(tavily)
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if not tools:
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logger.warning("No search tools configured, DeepAgent will work without web search")
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return tools
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async def get_graph(self, **kwargs):
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"""构建 Deep Agent 的图"""
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# 获取上下文配置
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context = self.context_schema.from_file(module_name=self.module_name)
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model = load_chat_model(context.model)
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sub_model = load_chat_model(context.subagents_model)
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search_tools = await self.get_tools()
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all_mcp_tools = await get_tools_from_all_servers()
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# 合并搜索工具和 MCP 工具
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# Build subagents with search tools
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research_sub_agent = _get_research_sub_agent(search_tools)
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# 主 Agent 上下文优化：90k tokens 触发压缩（128k context window 的 70%）
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summary_middleware = SummaryOffloadMiddleware(
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model=model,
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trigger=("tokens", 90000),
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trim_tokens_to_summarize=4000,
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summary_offload_threshold=500,
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max_retention_ratio=0.5,
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)
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# 子 Agent 独立的上下文优化：更激进的压缩策略
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sub_summary_middleware = SummaryOffloadMiddleware(
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model=sub_model,
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trigger=("tokens", 50000),
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trim_tokens_to_summarize=2000,
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summary_offload_threshold=300,
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max_retention_ratio=0.4,
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)
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subagents_middleware = SubAgentMiddleware(
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default_model=sub_model,
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default_tools=search_tools,
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subagents=[critique_sub_agent, research_sub_agent],
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default_middleware=[
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RuntimeConfigMiddleware(
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model_context_name="subagents_model",
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enable_model_override=True,
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enable_system_prompt_override=False,
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enable_tools_override=False,
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),
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PatchToolCallsMiddleware(),
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sub_summary_middleware,
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# 子 Agent 搜索工具限制：tavily_search 最多 8 次
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ToolCallLimitMiddleware(
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tool_name="tavily_search",
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run_limit=8,
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exit_behavior="continue",
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),
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],
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general_purpose_agent=True,
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)
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# 使用 create_deep_agent 创建深度智能体
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graph = create_agent(
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model=model,
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system_prompt=context.system_prompt,
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middleware=[
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FilesystemMiddleware(backend=_create_fs_backend), # 文件系统后端
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RuntimeConfigMiddleware(extra_tools=all_mcp_tools),
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SkillsMiddleware(), # Skills 中间件（提示词注入、依赖展开、动态激活）
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save_attachments_to_fs, # 附件注入提示词
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TodoListMiddleware(),
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PatchToolCallsMiddleware(),
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KnowledgeBaseMiddleware(), # 知识库工具
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subagents_middleware,
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summary_middleware,
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# 工具调用限制：tavily_search 总调用最多 20 次
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ToolCallLimitMiddleware(
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tool_name="tavily_search",
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thread_limit=20,
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exit_behavior="continue",
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),
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# 总工具调用轮次限制：防止单次运行无限循环
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ToolCallLimitMiddleware(
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run_limit=50,
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exit_behavior="end",
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),
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],
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checkpointer=await self._get_checkpointer(),
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)
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return graph
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