LLMs-txt Overview

Overview

Below you can find a list of 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
LangChain Python	https://python.langchain.com/llms.txt	N/A
LangChain JS	https://js.langchain.com/llms.txt	N/A

Review the output

Even with access to up-to-date documentation, current state-of-the-art models may not always generate correct code. Treat the generated code as a starting point, and always review it before shipping code to production.

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 still use llms.txt effectively through an MCP server.

🚀 Use the mcpdoc Server

We provide an MCP server that was designed to serve documentation for LLMs and IDEs:

👉 langchain-ai/mcpdoc GitHub Repository

This MCP server allows integrating llms.txt into tools like Cursor, Windsurf, Claude, and Claude Code.

📘 Setup instructions and usage examples 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.