The agent engineering platform for building LLM-powered applications in Python
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Guided Walkthroughs
Building a RAG Pipeline8 steps
Follow data from a PDF document through ingestion, embedding, and indexing, then through retrieval and generation when a user asks a question.
An Agent Reasons and Calls a Tool6 steps
Follow a user message from input through agent reasoning, tool invocation, result processing, and final response generation.
How LCEL Composes a Chain6 steps
The journey of a user input through an LCEL pipeline — from prompt template formatting through model invocation to structured output parsing.
Swapping LLM Providers Without Rewriting5 steps
How LangChain's layered architecture lets you swap from OpenAI to Anthropic to a local model with minimal code changes.
