Pulls up-to-date library docs into your LLM context, no fine-tuning needed
Context7 indexes documentation for thousands of open-source libraries and serves it through MCP. The LLM client requests docs for a specific library version, Context7 returns the relevant slices, the agent gets accurate up-to-date API surface area without needing the model to be trained on the latest release. Solves the perennial LLM-hallucinates-an-API-that-does-not-exist problem for library code.
Stopped Cursor from inventing React APIs that do not exist.
Context7 is the missing piece for LLM-assisted coding against rapidly-evolving libraries. We pin to React 19, Context7 serves React 19 docs to Cursor, the suggestions stop hallucinating hooks that got renamed two majors ago.
Coverage of PyPI is surprisingly good for an early product.
I maintain a 30k-star Python library. Context7 indexed it without me doing anything. Users tell me Claude actually answers questions about my library correctly now.
Watch token usage. Doc dumps add up.
Context7 is great but each doc fetch is 500-2000 tokens depending on the topic. On a heavy day our agent uses 200k+ context just on doc retrieval. Plan for it.
Methodology
Every review on this page is verified through GitHub OAuth and weighted by reviewer credibility, use-case match, and conflict-of-interest disclosure. Aggregate scores combine with recency decay so rankings reflect current reality. Read full methodology →
