Reddit 用户开源基于 LangGraph 的多智能体研究流水线，结合 Celery 与 MCP
一位 r/MachineLearning 用户分享了其开源项目 Multi-Agent Research & Report Generator 的架构：把复杂提问拆解为结构化子主题，自动搜集文献并汇编成 Markdown 报告。智能体图谱以 LangGraph 四阶段状态机运行，分别是 Planner、Researcher、Critic 与 Writer；当 Critic 评分低于 3 分时，会把反馈重新注入 Researcher 并触发重试，最多 3 次。任务接入与后台执行被解耦：FastAPI 网关接收请求后立即返回 task_id，任务进入 Redis 队列由横向扩展的 Celery worker 消费，LangGraph 的 AsyncPostgresSaver 负责持久化检查点，Streamlit 前端展示迭代次数与最终评分等遥测数据。
这是一个社区层面的工程示例，说明如何把高延迟的智能体循环封装为分布式服务而非同步脚本，不过作者并未提供任何基准测试数据。
I wanted to share the architecture behind a project I recently open-sourced: a Multi-Agent Research & Report Generator.
My goal was to build a production-grade system that takes a complex user prompt, breaks it down into structured sub-topics, autonomously gathers literature, critiques its own findings, and compiles a polished Markdown report.
Moving an agentic LLM workflow from a linear Python script into a robust, distributed web application required some heavy lifting. Here is a breakdown of the architecture and how the components in the repository fit together.
- The AI Engine & Agent Graph
At its core, the brain of the app runs on LangGraph as a four-stage state machine:
* Planner: Breaks the core question down into 3–5 specific sub-topics.
#LangGraph#Celery#Model Context Protocol#FastAPI#Redis#PostgreSQL#Streamlit#DuckDuckGo