Build autonomous AI agents with Python using LangChain and LangGraph. Learn tool calling, stateful workflows, RAG-powered agents, multi-agent systems, and production deployment. From the ReACT pattern to a full customer support agent capstone.
This free course teaches you how to build autonomous AI agents with Python using LangChain and LangGraph. Starting from first principles, you will learn what makes a system truly "agentic", then work through the Python tools and frameworks that make agent development practical in 2026. The course progresses from tool calling and function use through stateful agent design with LangGraph, giving you the architectural vocabulary to build agents that persist context across turns and take multi-step actions independently.
The middle modules tackle two of the hardest practical problems in agentic AI: giving agents long-term memory via retrieval-augmented generation (RAG), and coordinating multiple specialized agents that collaborate on a shared goal. You will see how multi-agent systems are structured and where they outperform single-agent approaches. The final lessons cover production deployment so your agents can run reliably outside a notebook, and the course closes with a capstone project where you build a complete customer support agent from scratch.
This course is designed for Python developers and technically curious learners who already have some coding experience and want to move beyond basic LLM calls into genuinely autonomous systems. No prior LangChain knowledge is required. Completing the course and passing the final exam earns a certificate of completion you can add to your LinkedIn profile or resume, demonstrating hands-on experience with one of the most in-demand skill sets in applied AI today.
10 modules • 10 lessons
Finish every lesson and pass the final exam to earn this free, shareable certificate.
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June 15, 2026
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The course walks through the full agent development stack: what AI agents are, how to set up the Python environment, tool calling, stateful workflows with LangGraph, memory and RAG, multi-agent systems, and production deployment. It closes with a capstone project building a customer support agent end to end.
Yes, the course is completely free. You can work through all modules at your own pace, and finishing the course plus the final exam earns a certificate of completion at no cost.
You should be comfortable writing Python functions and working with libraries. Prior experience with LangChain or LangGraph is not required, as the course introduces both frameworks from the ground up.
The course is built around LangChain and LangGraph for agent orchestration and stateful workflows. You will also work with tool calling patterns, retrieval-augmented generation techniques, and production deployment practices relevant to real Python agent projects.
Yes. Completing all lessons and passing the final exam earns a certificate of completion that you can share on LinkedIn or include on a resume to show practical experience building agentic AI systems.
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