<p align="center" <img src="https://raw.githubusercontent.com/sayak-sarkar/contextlake/main/docs/img/readme-banner.jpg" alt="contextlake, all your real context in one local lake. Pebble the otter surfacing from a misty lake cradling a glowing pebble of context." width="820" </p <h1 align="center"contextlake</h1 <p align="center"<strongAll your real context, in one local lake.</strong</p <p align="center" A local context layer for your AI tools: mirror your repositories, index them<br into a knowledge graph, and serve it over MCP, so agents answer from <emreal source</em instead of guessing. </p
<p align="center" <a href="https://github.com/sayak-sarkar/contextlake/actions/workflows/ci.yml"<img src="https://github.com/sayak-sarkar/contextlake/actions/workflows/ci.yml/badge.svg" alt="CI"</a <a href="https://pypi.org/project/contextlake/"<img src="https://img.shields.io/pypi/v/contextlake?color=137A8B" alt="PyPI"</a <img src="https://img.shields.io/badge/python-3.10%2B%20(3.9%2B%20core)-blue" alt="Python 3.10+ for the knowledge layer, 3.9+ for the mirror core" <img src="https://img.shields.io/badge/offline-first-2BB3A3" alt="Offline-first" <img src="https://img.shields.io/badge/license-MIT-green" alt="License: MIT" </p
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Why contextlake
Your AI assistant is only as good as what it can actually see. Point it at one file and it's sharp; ask it about the system, which service calls this API, who depends on that package, where a symbol is really defined across dozens of repos, and it starts guessing.
contextlake gives your tools the real source to read. It mirrors your repositories to your machine, indexes them into a queryable knowledge graph, and serves that graph to your editor over MCP. Everything runs locally and offline, no code leaves your machine, and it carries no credentials of its own.
How it works
contextlake is three layers you adopt one at a time. The mirror is useful on its own, and each layer above it is optional.
<p align="center" <img src="https://raw.githubusercontent.com/sayak-sarkar/contextlake/main/docs/img/architecture.png" width="860" alt="contextlake architecture. On the left, your repos: a GitLab group, plus optional Figma, Jira, and other MCP connectors. In the centre, contextlake indexes and mirrors them into a graph and embeddings, a wiki, and connectors. On the right, it serves the result over MCP to your AI tools: Claude Code, Windsurf, Kiro, Cursor, and Postman." </p
- Mirror: clone every repo you can reach in a GitLab group, GitHub org, Bitbucket
workspace, or Gitea/Codeberg/Forgejo owner into a faithful copy of its namespace tree, each on its most active branch, kept fresh with one command.
- Knowledge layer (optional): parse the mirror into a code + dependency graph across
14 languages plus Terraform infrastructure, SQL schema, and package manifests (npm / PyPI / NuGet / Maven), add semantic search, a council-verified wiki (each page reviewed and scored before publishing, low-confidence pages dropped), and connectors to Atlassian / Figma / GitLab.
- Serve: expose it all over MCP and an offline interactive graph visualizer, so
agents can answer "where is X defined?" or "who calls Y?" instead of grepping.
Each layer has its own guide: the mirror in Usage & config, the knowledge layer and serving in Knowledge layer, and the whole flow start to finish in QUICKSTART.
Install
pip install "contextlake[kb]" # the full tool: mirror + graph, search, wiki, MCP server
pip install contextlake # mirror-only core (no pip dependencies at all)