AI Engineer's Guide to Langchain
The Langchain library is a powerful tool for AI engineering, acting as the foundation of the broader LangChain-ecosystem (that is, LangGraph, LangSmith, LangServe, etc). In this course, you'll learn the fundamentals of building with LLMs and the essentials of LangChain — allowing you to build modern agentic systems and potentially move onto other components in the ecosystem such as LangGraph.
by Joshua & James Briggs
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When to Use LangChain
Guidelines for when we should use LangChain and what problems the framework does and does not solve.
Chapter 2Getting Started with LangChain
LangChain is one of the most popular open source libraries for AI Engineers. Here we will introduce the library.
Chapter 3AI Observability with LangSmith
An introduction to LangSmith, an observability service for the LangChain-ecosystem.
Chapter 4Prompt Templating and Techniques in LangChain
Prompting is a critical part of building AI software. Here we'll learn general prompting techniques and specific LangChain tooling for prompting.
Chapter 5Conversational Memory in LangChain
Exploring the various types of conversational memory and best practices for implementing them in LangChain v0.3 and beyond.
Chapter 6Introduction to LangChain Agents
An introduction to LangChain's agents in v0.3 and up using both traditional and LCEL syntax.
Chapter 7LangChain Agent Executor Deep Dive
A deep dive into LangChain's Agent Executor, exploring how to build your custom agent execution loop in LangChain v0.3.
Chapter 8LangChain Expression Language (LCEL)
An introduction to LangChain's Expression Language (LCEL), the recommended syntax for building agents and chains.
Chapter 9LangChain Streaming
All you need to know about streaming, allowing us to receive, parse, and send LLM-generated data in real-time.
Capstone Project: AI Agent App
Finish the course by building an AI agent app using LangChain. Note: the capstone project is found at the end of the video.