Co-marketing ↗

Original Documentation

Documentation Index#
Fetch the complete documentation index at: https://docs.langchain.com/llms.txt Use this file to discover all available pages before exploring further.

With over 60 million monthly downloads, LangChain has a large audience of developers building LLM applications. Beyond just listing integrations, we aim to highlight high-quality, educational examples that inspire developers and advance the ecosystem.

While we occasionally share integrations, we prioritize content that provides meaningful insights and best practices. Our main social channels are Twitter and LinkedIn, where we highlight the best examples.

Content we’re excited to promote#

Blogs, YouTube videos and other media showcasing educational content. Note that we prefer content that is NOT framed as "here's how to use integration XYZ", but rather "here's how to do ABC", as we find that is more educational and helpful for developers. End-to-end applications are great resources for developers looking to build. We prefer to highlight applications that are more complex/agentic in nature, and that use [LangGraph](https://github.com/langchain-ai/langgraphjs) as the orchestration framework. We get particularly excited about anything involving:

Long-term memory systems
Human-in-the-loop interaction patterns
Multi-agent architectures

We love highlighting novel research! Whether it is research built on top of LangChain or that integrates with it.

Edit this page on GitHub or file an issue.

Connect these docs to Claude, VSCode, and more via MCP for real-time answers.

Link last verified June 7, 2026. View original ↗

Source: LangChain Docs

Link last verified: 2026-03-04