Cursor
Codna を MCP サーバーとして追加し、Cursor が決定論的なリポジトリ理解をクエリできるようにする。
インテグレーション
Claude
MCP を通じて Claude に Codna ツールを公開し、エビデンス駆動の修正を実現。
GitHub
Issue トリアージ・修正 PR・レビューエビデンスのためのネイティブアプリ。
CI
ビルドパイプラインで Codna を実行し、障害を理解してパッチを生成。
モデルプロバイダー
あなたのエージェント。あなたのプロバイダー。あなたのキー。
Codna が決定論的理解レイヤーを担います。エージェントは BYOK またはマネージド LLM オプションを通じて選択したプロバイダーを使用できます。
CLIRun codna fix in any repo, CI job, or container.
MCP serverGive Cursor and Claude codebase understanding as a local tool.
GitHub AppTriage issues and open verified fix pull requests.
Bring-your-own-key works today with an Anthropic (Claude) API key. Support for more model providers is on the way.
Frequently asked
Codna ships as an MCP server. Add it once and it becomes a native code agent inside Cursor and Claude — the same deterministic graph engine and AI fix pipeline, available right where you write code.
Yes. The GitHub App detects a bug, runs the engine, verifies the fix against your tests, and opens a pull request. Every PR it creates has passed your test suite before you see it.
Yes. The CLI integrates into any CI pipeline. It maps the affected graph, generates a fix, and verifies it — all within the same job, with no external state required.
No. Codna is bring-your-own-key. You supply the model API key, and the fix runs against your chosen model. Codna does not call a hosted LLM on your behalf or retain your code.
You run the engine on your own infrastructure. Egress is fail-closed, meaning no data leaves unless you explicitly configure it. Codna does not train on your code.
Very lightweight. The engine maps a repository in roughly 60ms using zero LLM tokens. The AI agent then works from a ~600-token evidence bundle, measured at 162x less context than reading the full repo.