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Stop stuffing every instruction into one giant prompt and hoping the model holds it together. Build a LangGraph workflow where specialized agents share typed state, run in parallel where it pays, and produce structured output you can actually trust.
Message a mentor about fit, prerequisites, or where to start. Replies come on WhatsApp, usually within a day.
Engineers are learning here from
Build a real multi-agent system that analyzes CVs against job descriptions. Learn LangGraph state management, specialized agents, Pydantic structured output, and parallel execution patterns used in production AI workflows.
Orchestrate specialized agents with LangGraph to analyze documents at production quality.
What you'll ship
What you'll learn
Curriculum
From monolith to agents
Feel the pain of a single mega-prompt, then extract your first specialist agent
LangGraph workflows
Wire agents together with StateGraph, route conditionally, and return structured output
Production optimization
Pass richer context between agents, harden against failures, and fan out independent work in parallel
Who it's for
who can call an LLM API but have never structured a real agent workflow
who want a concrete LangGraph project to reference in production code
evaluating multi-agent patterns before committing a team to one
FAQ
No. You start from a monolithic script and rebuild it as a graph step by step. By the end the entire API surface feels natural.
The repo supports OpenRouter, Gemini, Fireworks, and OpenAI behind a provider interface. You only need one API key. The graph code does not care which backend you pick.
The project is a full FastAPI service with streaming progress, document parsing, and a roster of specialized agents. The same patterns ship in real analysis pipelines.
Yes. Once you understand state, nodes, and edges in LangGraph, other frameworks read like variations on the same theme.
Pricing
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Multi-agent document analysis with LangGraph
$29 one-time