Cursor
将 Codna 作为 MCP 服务器添加，让 Cursor 能查询确定性代码库理解。
集成
Claude
通过 MCP 向 Claude 暴露 Codna 工具，实现证据驱动的修复。
GitHub
原生应用，支持问题分类、修复 PR 和审查证据。
CI
在构建流水线中运行 Codna，理解失败原因并生成补丁。
模型提供商
你的智能体。你的提供商。你的密钥。
Codna 负责确定性理解层。智能体可通过 BYOK 或托管 LLM 选项使用你选择的提供商。
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.