Kodit: A Code Indexing MCP Server

Kodit connects your AI coding assistant to external codebases to provide accurate and up-to-date snippets of code.

[![Documentation](https://img.shields.io/badge/Documentation-6B46C1?style=for-the-badge&logo=readthedocs&logoColor=white)](https://docs.helix.ml/kodit/) [![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg?style=for-the-badge)](./LICENSE) [![Discussions](https://img.shields.io/badge/Discussions-181717?style=for-the-badge&logo=github&logoColor=white)](https://github.com/helixml/kodit/discussions)

:star: _Help us reach more developers and grow the Helix community. Star this repo!_ **Helix Kodit** is an **MCP server** that connects your AI coding assistant to external codebases. It can: - Improve your AI-assisted code by providing canonical examples direct from the source - Index local and public codebases - Integrates with any AI coding assistant via MCP - Search using keyword and semantic search - Integrate with any OpenAI-compatible or custom API/model If you're an engineer working with AI-powered coding assistants, Kodit helps by providing relevant and up-to-date examples of your task so that LLMs make fewer mistakes and produce fewer hallucinations. ## Features ### Codebase Indexing Kodit connects to a variety of local and remote codebases to build an index of your code. This index is used to build a snippet library, ready for ingestion into an LLM. - Index local directories and public Git repositories - Build comprehensive snippet libraries for LLM ingestion - Support for 20+ programming languages including Python, JavaScript/TypeScript, Java, Go, Rust, C/C++, C#, HTML/CSS, and more - Advanced code analysis with dependency tracking and call graph generation - Intelligent snippet extraction with context-aware dependencies - Efficient indexing with selective reindexing (only processes modified files) - Privacy first: respects .gitignore and .noindex files - Auto-indexing configuration for shared server deployments - Enhanced Git provider support including Azure DevOps - Index private repositories via a PAT - Improved progress monitoring and reporting during indexing - Advanced code slicing infrastructure with Tree-sitter parsing - Automatic periodic sync to keep indexes up-to-date ### MCP Server Relevant snippets are exposed to an AI coding assistant via an MCP server. This allows the assistant to request relevant snippets by providing keywords, code, and semantic intent. Kodit has been tested to work well with: - Seamless integration with popular AI coding assistants - Tested and verified with: - [Cursor](https://docs.helix.ml/kodit/getting-started/integration/#integration-with-cursor) - [Cline](https://docs.helix.ml/kodit/getting-started/integration/#integration-with-cline) - [Claude Code](https://docs.helix.ml/kodit/reference/mcp/) - Please contribute more instructions! ... any other assistant is likely to work ... - Advanced search filters by source, language, author, date range, and file path - Hybrid search combining BM25 keyword search with semantic search - Enhanced MCP tools with rich context parameters and metadata ### Enterprise Ready Out of the box, Kodit works with a local SQLite database and very small, local models. But enterprises can scale out with performant databases and dedicated models. Everything can even run securely, privately, with on-premise LLM platforms like [Helix](https://helix.ml). Supported databases: - SQLite - [Vectorchord](https://github.com/tensorchord/VectorChord) Supported providers: - Local (which uses tiny CPU-only open-source models) - OpenAI - Secure, private LLM enclave with [Helix](https://helix.ml). - Any other OpenAI compatible API Enhanced deployment options: - Docker Compose configurations with VectorChord - Kubernetes manifests for production deployments ## Quick Start 1. [Install Kodit](https://docs.helix.ml/kodit/getting-started/installation/) 2. [Index codebases](https://docs.helix.ml/kodit/getting-started/quick-start/) 3. [Integrate with your coding assistant](https://docs.helix.ml/kodit/getting-started/integration/) ### Documentation - [Getting Started Guide](https://docs.helix.ml/kodit/getting-started/) - [Reference Guide](https://docs.helix.ml/kodit/reference/) - [Contribution Guidelines](.github/CONTRIBUTING.md) ## Roadmap The roadmap is currently maintained as a [Github Project](https://github.com/orgs/helixml/projects/4). ## 💬 Support For commercial support, please contact [Helix.ML](founders@helix.ml). To ask a question, please [open a discussion](https://github.com/helixml/kodit/discussions). ## License [Apache 2.0 © 2026 HelixML, Inc.](./LICENSE)