Cursor
Adicione Codna como servidor MCP para que o Cursor possa consultar compreensão determinística de repositório.
Integrações
Codna funciona onde os agentes funcionam.
Use Codna através do CLI, servidor MCP, GitHub App nativa e a sua própria chave de modelo ou LLM gerido.
Integrações
Claude
Exponha ferramentas Codna ao Claude através de MCP para correções orientadas por evidências.
GitHub
App nativa para triage de issues, PRs de correção e evidências de revisão.
CI
Execute Codna em pipelines de build para compreender falhas e gerar patches.
Fornecedores de modelo
O seu agente. O seu fornecedor. A sua chave.
Codna trata da camada de compreensão determinística. O agente pode usar o fornecedor que escolher através de BYOK ou da opção de LLM gerido.
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.