The Complete LangChain, LangGraph, & LangSmith Course (2026)

Modularize with LangChain, Build Agents with LangGraph, and Monitor with LangSmith

What you’ll learn
Master LCEL Orchestration: Build highly efficient, composable AI pipelines using the LangChain Expression Language and Runnable interfaces.
Develop Autonomous Agents: Use create_agent to build intelligent assistants that can reason and interact with external tools.
Architect Stateful Graphs: Design non-linear AI workflows using LangGraph to handle complex branching and looping logic.
Implement Robust Persistence: Use Checkpointers to save graph state, enabling agents to resume tasks and maintain memory.
Enable Production Monitoring: Set up LangSmith to trace every step of your application’s execution for rapid debugging and optimization.
Evaluate AI Performance: Leverage LangSmith datasets and functions to monitor latency, cost, and output quality in real-world scenarios.

Requirements
Basic Python Knowledge: Familiarity with Python syntax, functions, and list comprehensions is required.
API Basics: A general understanding of how to use API keys (e.g., OpenAI or Anthropic) and make basic web requests.
Problem-Solving Mindset: No prior AI experience is needed, but a desire to build and iterate on complex systems is essential.

Description
Welcome to the most complete, up-to-date guide on the modern AI stack: LangChain, LangGraph, and LangSmith. In 2026, building a simple wrapper around an LLM is no longer enough. To create applications that are truly useful, they must be reliable, stateful, and observable. This course is designed to take you from a beginner to a professional AI Engineer by mastering the three pillars of the LangChain ecosystem.We begin with LangChain, where you will learn to move beyond basic API calls using the LangChain Expression Language (LCEL). You’ll master the art of orchestration, building “Runnable Chains” that are efficient, asynchronous, and production-ready. From there, we transition into LangGraph, the industry standard for building agentic workflows. You will learn to move away from linear chains and into cyclic state machines, implementing persistence with Checkpointers so your agents never “forget” a conversation or lose their place during a crash.Finally, we bridge the gap between development and production with LangSmith. You will learn how to monitor your applications in real-time, debug complex reasoning paths with detailed traces, and use evaluation suites to ensure your AI behaves exactly as intended. This isn’t just a coding course; it’s a blueprint for building enterprise-grade AI systems that are as robust as they are intelligent.

Who this course is for
Aspiring AI Engineers looking for a single, comprehensive path to master the entire LangChain ecosystem from scratch.
Software Developers who need to move beyond simple chatbots and build complex, stateful agentic workflows.
Data Scientists and Product Managers wanting to understand how to monitor, trace, and evaluate LLM applications in production.

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