GitHub - ai-builders-group/build-production-ai-agents: Official source code for the AI Agent Foundations: Codebase Analyst course. The main branch contains the final solution, starter provides the boilerplate for practice, and lesson-X-complete tags track the code at the end of each lesson. Use the 'Issues' tab for any course-related questions.

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Build a Production-Ready AI Agent

A professional, project-based curriculum to build, harden, and deploy an AI Codebase Analyst using LangGraph, Pydantic, and Docker.

🚀 About This Project

This repository is the official "Open Source Lab" for the book Production-Ready AI Agents.

Most AI tutorials stop at a Jupyter Notebook. This project starts there, but quickly moves into production engineering. You will build The AI Codebase Analyst: an autonomous agent that ingests a local code repository, understands the file structure, and answers complex queries about the code via an interactive UI.

The "Lab" vs. The "Textbook"

  • This Repository (The How): Provides the code, the patterns, and the step-by-step implementation guide.
  • The Book (The Why): Covers the architectural theory, state machine design patterns, and the deep engineering decisions behind building scalable agents. Get the book here.

🛠️ The Tech Stack

We define "Production-Ready" as Stateful, Reliable, and Observable.

  • 🧠 Logic: LangGraph (Stateful orchestration)
  • 🛡️ Reliability: Pydantic (Structured output & validation)
  • 💾 Memory: FAISS & Vector Stores (RAG)
  • 🖥️ Interface: Chainlit (Interactive UI)
  • 🚀 Deployment: Docker & Google Cloud Run

📚 The Curriculum

The course is divided into 4 sprints. You can find the detailed instructions for each lesson in the /curriculum folder.

Module 1: The Core Engine

  • Lesson 1: Professional Workspace Setup & Observability
  • Lesson 2: The Agent as a State Machine (LangGraph)
  • Lesson 3: Binding Tools & Reasoning

Module 2: The Knowledge Base (RAG)

  • Lesson 4: Ingestion Pipeline (Load, Split, Embed, Store)
  • Lesson 5: The Retrieval Tool (Connecting the Brain to the Memory)

Module 3: Production Pillars

  • Lesson 6: Structured Output (Enforcing Pydantic Schemas)
  • Lesson 7: Security & Guardrails (Preventing Injection)
  • Lesson 8: Resilience (Retries, Fallbacks, and Error Handling)

Module 4: Shipping It

  • Lesson 9: The Interactive UI (Chainlit Integration)
  • Lesson 10: Containerization (Docker & CI/CD)

👨‍💻 How to Use This Repository

This repo uses Git Tags to manage the curriculum. You can code along from scratch, or jump to the solution for any specific lesson.

1. Start Here

Clone the repo. You will land on the starter branch. This contains the boilerplate infrastructure but no solution code.

git clone https://github.com/ai-builders-group/build-production-ai-agents.git
cd build-production-ai-agents

Open the file curriculum/START_HERE_Welcome.pdf to begin.

2. Stuck? Check the Solution

We have created immutable tags for the completion of every lesson. If you are stuck on Lesson 3, you can peek at the solution code.

To view the solution for a specific lesson: git checkout lesson-3-complete

To return to your workspace: git checkout starter

3. The Final Build

To see the finished, 100% production-ready application: git checkout main

💬 Community & Support

You are not building alone.

  • Need Help? Join the #💬-course-discussion channel in our Discord.
  • Found a bug? Open a GitHub Issue.
  • Discussion: Share your build and get feedback.

👉 Join the AI Builders HQ Discord

License

This project is open-source (MIT). Feel free to fork it, break it, and rebuild it.

Built by AI Builders Group
Architecting the future of AI Agents.