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LLMs-txt for LangGraph

Overview

LangGraph provides documentation files in the llms.txt format, specifically llms.txt and llms-full.txt. These files allow large language models (LLMs) and agents to access programming documentation and APIs, particularly useful within integrated development environments (IDEs).

Language Version llms.txt llms-full.txt
LangGraph Python https://langchain-ai.github.io/langgraph/llms.txt https://langchain-ai.github.io/langgraph/llms-full.txt
LangGraph JS https://langchain-ai.github.io/langgraphjs/llms.txt https://langchain-ai.github.io/langgraphjs/llms-full.txt

Differences Between llms.txt and llms-full.txt

  • llms.txt is an index file containing links with brief descriptions of the content. An LLM or agent must follow these links to access detailed information.

  • llms-full.txt includes all the detailed content directly in a single file, eliminating the need for additional navigation.

A key consideration when using llms-full.txt is its size. For extensive documentation, this file may become too large to fit into an LLM's context window.

Using llms.txt via an MCP Server

As of March 9, 2025, IDEs do not yet have robust native support for llms.txt. However, you can utilize llms.txt effectively through an MCP server.

We provide an MCP server specifically designed to serve documentation, called mcpdoc. This setup is compatible with IDEs and platforms such as Cursor, Windsurf, Claude, and Claude Code. Instructions for using mcpdoc with these tools are available in the repository.

Using llms-full.txt

The LangGraph llms-full.txt file typically contains several hundred thousand tokens, exceeding the context window limitations of most LLMs. To effectively use this file:

  1. With IDEs (e.g., Cursor, Windsurf):

    • Add the llms-full.txt as custom documentation. The IDE will automatically chunk and index the content, implementing Retrieval-Augmented Generation (RAG).

  2. Without IDE support:

    • Use a chat model with a large context window.
    • Implement a RAG strategy to manage and query the documentation efficiently.

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