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Artificial Intelligence and Machine Learning
Building AI Agents with LangChain and LangGraph
From a single model call to an agent you can trace and evaluate
A classroom course for Python developers building applications on language models. It follows the topics of the current LangChain documentation: the standard model interface, agents built with create_agent and extended through middleware, LangGraph for orchestration, durable execution and persistence, Deep Agents, and LangSmith for tracing, debugging and evaluation. This course prepares for no exam.
8Modules
IntermediateLevel
2 monthsDuration
COURSE THUMBNAIL
What you will be able to do
Call different model providers through one interface
Build an agent and extend its behaviour with middleware
Orchestrate multi-step work with LangGraph
Make an agent's work durable across failures and restarts
Trace, debug and evaluate an agent with LangSmith
Course outline
01
Calling language models through a standard interface
02
Building a first agent with create_agent
03
Extending agents with middleware
04
Orchestration with LangGraph
05
Durable execution and persistence
06
Deep Agents
07
Tracing, debugging and evaluation with LangSmith
08
Project: an agent for a real task, traced and evaluated
Tools you will use
LangChainLangGraphLangSmithDeep AgentsPython
What you need
Comfortable programming in Python
Basic familiarity with calling web APIs
