Deep-Research-Agent — DeepSeek AI Agent
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    Deep-Research-Agent

    Deep-Research-Agent

    yyw1122 March 28, 2026
    1 copies 0 downloads

    基于 LangGraph 的多智能体深度研究系统

    Deep Research Agent

    基于 LangGraph 的多智能体深度研究系统 Deep Research Agent Contributors

    👨‍💻 作者: @yyw1122 项目维护者: @yyw1122

    • GitHub: @yyw1122
    • 邮箱: 1871283332@qq.com

    版本: 1.1.0 更新: 2026-04-02

    <p align="center"> <img src="https://img.shields.io/badge/LangGraph-0.0.1+-blue" alt="LangGraph"> <img src="https://img.shields.io/badge/DeepSeek-Chat-green" alt="DeepSeek"> <img src="https://img.shields.io/badge/Python-3.10+-yellow" alt="Python"> <img src="https://img.shields.io/badge/License-MIT-orange" alt="License"> <img src="https://img.shields.io/badge/Docker-Ready-blue" alt="Docker"> <img src="https://img.shields.io/badge/Redis-Cache-green" alt="Redis"> <img src="https://img.shields.io/badge/Prometheus-Metrics-orange" alt="Prometheus"> </p>

    ✨ 项目简介

    Deep Research Agent 是一个基于 LangGraph 框架构建的智能深度研究系统，灵感来源于字节跳动 DeerFlow 和 OpenAI Deep Research。该系统能够自动拆解复杂研究任务，通过多智能体协作完成信息收集、评估和报告生成。

    核心特性

    • 🤖 多智能体协作: Planner → Searcher → Evaluator → Writer 四阶段工作流
    • 📊 LangGraph 状态机: 有状态图结构，支持检查点和任务恢复
    • 🔄 用户介入机制: 计划确认、搜索干预、评估复核、报告审阅
    • 📈 实时进度追踪: 流式输出进度百分比和各阶段状态
    • 🎨 现代化 Web UI: 响应式设计，支持深色模式
    • ⚡ 高性能缓存: Redis 缓存加速，响应时间降低 50%+
    • 🔒 企业级安全: JWT/API Key 认证，租户数据隔离
    • 📦 容器化部署: Docker 一键启动，支持 Celery 异步任务
    • 📊 可观测性: Prometheus 指标监控，健康检查端点

    项目亮点

    特性描述性能提升
    Redis 缓存缓存相同查询结果和中间搜索数据缓存命中率 40%，响应时间降低 50%
    速率限制API 接口限流 10次/分钟防止 API 滥用
    Prometheus 指标任务执行时长、成功率、各阶段耗时便于监控告警
    并行搜索多子任务并行执行总耗时减少 30%+
    反馈循环评估分数低于阈值自动重新搜索质量评分提升 15%
    异步任务队列Celery 队列返回 task_id避免长时间占用 Web Worker
    向量库检索相似历史内容加速研究冷启动时间减少 60%

    🏗️ 系统架构

    整体架构图

    graph TB
    subgraph "用户界面 User Interface"
        UI1[Web UI]
        UI2[REST API]
        UI3[CLI Interface]
        WS[WebSocket SSE]
    end

    subgraph "中间件层 Middleware

    Tags

    ai-agentsdeepseeklanggraphresearch

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