Reddit user open-sources LangGraph multi-agent research pipeline with Celery and MCP
A Reddit user on r/MachineLearning shared the architecture of an open-sourced Multi-Agent Research & Report Generator that turns a complex prompt into structured sub-topics, gathers literature and compiles a Markdown report. The agent graph runs on LangGraph as four stages — Planner, Researcher, Critic and Writer — where a Critic score below 3 sends work back to the Researcher with feedback, capped at three retries. Task ingestion is decoupled through a FastAPI gateway, Redis queue and scaled Celery workers, with LangGraph AsyncPostgresSaver checkpointing state and a Streamlit frontend showing telemetry such as iteration loops and final critic score.
It is a community example of the plumbing needed to run long, high-latency agent loops as a distributed service rather than a synchronous script, though the author reports no benchmarks.
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