Context7 is Upstash’s documentation platform for AI agents — more than 63,000 stars on GitHub — that feeds coding assistants up-to-date, version-specific library documentation instead of stale training data.
LLMs routinely hallucinate APIs because their training data lags behind library releases. Context7 fixes this at the source: it indexes documentation for thousands of libraries and frameworks and serves the relevant, current excerpts to your AI agent at prompt time. It runs as an MCP server, so tools such as Cursor, Claude Code and other MCP-compatible assistants can pull exact documentation for the version you are using before writing code. The open-source repository hosts the platform’s MCP server and tooling, while the hosted service at context7.com maintains the doc index. It has trended 34 times in the archive, is MIT-licensed, and is under active development with pushes in October 2026.
In practice, adding Context7 to a coding assistant feels like giving it a live documentation librarian: when you ask for code using a fast-moving library, the assistant first pulls the current docs and then writes code against real, existing APIs. Teams report the biggest wins with young, fast-changing frameworks where model training data is most outdated.
The Context7 MCP server is open source under the MIT license. The hosted platform offers a free tier for individual developers, with paid plans adding higher limits and team features — see context7.com for current pricing.
Developers who code with AI assistants daily and are tired of debugging hallucinated APIs — Context7 quietly upgrades every assistant you already use.