upstash/context7

Context7 Platform -- Up-to-date code documentation for LLMs and AI code editors

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Summary Information

Updated 2 hours ago
Added to GitGenius on January 9th, 2026
Created on March 26th, 2025
Open Issues & Pull Requests: 49 (+0)
GitHub issues: Enabled
Number of forks: 3,058
Total Stargazers: 62,818 (+2)
Total Subscribers: 163 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 13.6 hours
Mean response time: 43.3 hours
90th percentile: 3.7 days
Tracked items: 2,369

Maintainer activity

3 people did triage or write work on this repository in the last 12 months.

Counts unlabeled, assigned, unassigned, milestoned, demilestoned, locked, unlocked over the last 12 months. These are issue and pull request events that require triage or write permission. Commits and code review are not counted. labeled and renamed are excluded because GitHub issue forms record the issue author as the actor. Figures from October 7, 2026. This count is not comparable across projects: each project's automation decides which of these events a person emits.

How this project is maintained

Practically every issue opened in the past year has drawn a reply. 92% of open issues come from outside the core team, so the backlog reflects real-world use rather than internal planning. 99% of issues opened in the past year have since been closed. Three people close 89% of everything that gets resolved.

Charts & Analytics

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Issue Activity (beta)

Open issues: 21
New in 7 days: 30
Closed in 7 days: 43
Avg open age: 97 days
Stale 30+ days: 10
Stale 90+ days: 6

Recent activity

Opened in 7 days: 30
Closed in 7 days: 43
Comments in 7 days: 40
Events in 7 days: 81

Top labels

  • bug (88)
  • documentation (76)
  • enhancement (53)
  • mcp (53)
  • docs parsing (41)
  • website-parsing (17)
  • big-repo-request (12)
  • website (11)

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Detailed Description

Context7 is a platform designed to provide large language models and AI code editors with up-to-date, version-specific code documentation and examples directly within their prompts. Built in TypeScript and maintained by Upstash, the project addresses a fundamental problem in AI-assisted coding: LLMs are trained on outdated information and often generate hallucinated APIs or code examples based on old package versions. Context7 solves this by fetching current documentation and code examples from source repositories and injecting them directly into the LLM's context, eliminating the need for developers to switch between tabs or deal with inaccurate information.

The platform operates in two distinct modes to accommodate different workflows. The CLI plus Skills mode installs a skill that guides agents to fetch documentation using ctx7 CLI commands without requiring Model Context Protocol support. The MCP mode registers Context7 as a native MCP server, allowing agents to call documentation tools natively. Both approaches are designed to be seamless integrations into existing coding workflows, with support for popular AI code editors including Cursor, Claude Code, and OpenCode. Installation is streamlined through a single command that handles OAuth authentication and API key generation.

Context7 provides several core tools for developers. The CLI includes commands like ctx7 library for searching the Context7 index by library name and ctx7 docs for retrieving documentation using Context7-compatible library IDs. The MCP tools include resolve-library-id for converting general library names into Context7 IDs and query-docs for fetching relevant documentation. Users can specify exact library versions in their prompts to receive version-specific documentation, and they can use library IDs with slash syntax to skip the library-matching step entirely.

The project is distributed across multiple npm packages including the MCP server, CLI tool, TypeScript SDK, Vercel AI SDK tools, and a pi.dev extension, providing flexibility for different integration scenarios. The repository itself hosts the MCP server's source code, while the API backend, parsing engine, and crawling engine remain private infrastructure.

The platform has gained significant visibility through media coverage, with multiple YouTube creators highlighting its effectiveness for AI coding workflows. Context7 operates under an MIT license and maintains community engagement through social channels including X, Discord, and its official website at context7.com.