Context7
by upstash
Context7: Real-Time Code Documentation for AI Assistants
MCP server delivering live, context-aware code documentation to LLMs, enabling AI coding tools to understand your codebase instantly.
- 43,395+ GitHub stars
- Built with TypeScript
- Model Context Protocol (MCP) server implementation for standardized AI integration
- MIT License license
About This Project
Context7 bridges the gap between your evolving codebase and AI coding assistants by providing real-time, contextually relevant documentation through the Model Context Protocol (MCP). Instead of relying on outdated training data, AI tools can access current information about your project's structure, dependencies, and conventions.
The server acts as an intelligent intermediary that processes your code repository and exposes structured documentation to LLMs and AI-powered editors. This enables more accurate code suggestions, better understanding of project-specific patterns, and reduced hallucinations when AI assistants generate or review code.
Built with TypeScript and designed for seamless integration, Context7 supports modern development workflows where AI pair programming has become essential. The vibe-coding approach ensures that AI assistants maintain awareness of your project's unique context, coding standards, and architectural decisions throughout the development process.
With over 43,000 stars, Context7 has become a cornerstone tool for developers leveraging AI assistance while maintaining code quality and consistency across their projects.
Key Features
- Model Context Protocol (MCP) server implementation for standardized AI integration
- Real-time code documentation extraction and formatting for LLM consumption
- TypeScript-based architecture for easy customization and extension
- Support for vibe-coding workflows with continuous context awareness
- Seamless integration with popular AI code editors and LLM tools
How You Can Use It
Providing AI code editors with up-to-date project documentation and structure
Enabling LLMs to generate code that follows your project's specific conventions
Reducing context switching by keeping AI assistants informed about codebase changes
Improving code review accuracy through AI tools with current project knowledge
Who Is This For?
Developers using AI-powered coding assistants, teams adopting AI pair programming, and organizations building LLM-integrated development workflows