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Build an AI Chatbot with LangGraph and OpenAI (Step-by-Step)

A hands-on tutorial to build a production-ready agentic AI using LangGraph, FastAPI and OpenAI.

RK
Rohit K.
AI Engineer, Botmartz · April 15, 2024 · 15 min read
◈ Open in Google Colab⌥ View Code on GitHub
Read Time
15 min
Failure Modes
5
Code Snippets
3
Runnable Notebook
1
Botmartz AI Insight
Evaluating Retrieval, Chunking, and Generation in Production
Retrieval QualityChunking StrategyHybrid SearchContinuous EvaluationGuardrailsTakeaways
# Build an AI Chatbot with LangGraph and OpenAI (Step-by-Step) In this hands-on tutorial, we go through the step-by-step process of building a stateful, multi-agent chatbot using LangGraph, FastAPI, and OpenAI. ## Why LangGraph? Traditional DAG frameworks struggle with loops and recursive flows. LangGraph addresses this by introducing a graph-based state machine, enabling complex reasoning loops and agent collaboration.

Closing Takeaways

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Measure retrieval precision and recall in isolation before touching the model.
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Chunk along document structure, not arbitrary character counts.
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Combine vector and keyword search — hybrid retrieval beats either alone.
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Treat evaluation as continuous infrastructure, not a launch-week report.
Try It Yourself
A runnable Google Colab notebook with the eval harness and hybrid search code from this post.
◈ Open in Colab⌥ GitHub Repo
#Enterprise RAG#Evaluation#Production AI#LangChain
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RK
Rohit K.
AI Engineer at Botmartz, building enterprise RAG and agent systems in production. Contributing to open-source libraries.

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