Building an Agentic RAG System with LangGraph

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prerequisites
intermediate Python • basics of state machines • completion of Projects 1–3 (or equivalent reusable `retrieve(query, k)` and grounded-answer prompt) • basic familiarity with LangChain/LangGraph installation
skills learned
LangGraph state machines and conditional edges • query analysis and routing • tool-calling agents • self-evaluation and iterative refinement loops • retry/fallback patterns • FastAPI deployment of an agentic workflow
Matteus Tanha
1 week · 4-6 hours per week · INTERMEDIATE
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In this liveProject, you'll step into the shoes of an AI engineer turning a retrieval-and-generation pipeline into an autonomous agent. Building on the retrieval, hybrid search, and grounded-answer components from the earlier projects, you'll design a LangGraph state machine that analyzes each incoming question, routes it, and decides when to retrieve. You'll wrap your hybrid retriever as a tool the agent can call, add a self-evaluation node that scores its own answers, and build an iterative refinement loop with retry and fallback strategies for when the first attempt falls short. Finally, you'll deploy the whole agentic workflow behind a FastAPI endpoint with basic logging and monitoring. By the end, you'll have a modular, agentic RAG system you can adapt to any large document corpus.

This project is a part of the series Building an Agentic RAG Application.
This project is designed for learning purposes and is not a complete, production-ready application or solution.

project author

Matteus Tanha
Dr. Matteus Tanha is an AI engineer and architect with over a decade of experience building production machine learning and agentic AI systems. He is co-founder of Alpha Quants, a boutique AI consultancy serving finance and enterprise clients, and has led AI initiatives at organizations including the Financial Times and Zurich Insurance. At the Financial Times, he architected AskFT, a retrieval-augmented research assistant combining semantic search and LLM orchestration to serve over a million monthly users. His work spans hybrid retrieval systems, knowledge graphs, and multi-agent orchestration, with deep expertise in RAG architectures and vector and graph databases. Matteus holds a Ph.D. in Computational Chemistry from Carnegie Mellon University, where his research applied machine learning methods to quantum chemical computation.

prerequisites

This liveProject is for developers who want to build an agentic RAG system. This project builds on Projects 1-3; you'll need their reusable retrieve(query, k) and grounded-answer components (or equivalents).


TOOLS
  • Intermediate Python
  • Basics of Jupyter Notebooks
  • Basics of the command line
  • LangGraph
TECHNIQUES
  • Basics of state machines
  • LLMs and prompting
  • RAG at a high level

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