Repo understanding without model tokens.
Codna parses symbols, imports, call paths, tests, and dependencies into a live graph the agent can query.
Codna maps your repo in ~60ms for zero tokens. Then an agent ships a fix your own tests verify — roughly pennies each.
Codna parses symbols, imports, call paths, tests, and dependencies into a live graph the agent can query.
Instead of dumping files into a context window, Codna creates a compact bundle: suspect files, call chain, failing test, and risk map.
bundle: failing_test: checkout.spec.ts suspect_files: 4 call_paths: 7 estimated_context: ~600 tokens
Core capabilities
Map any local path or git URL in ~60ms for zero LLM tokens, and see where the change belongs.
Fix from a ~600-token evidence bundle — root cause, confidence, and regression risk attached.
Review generated changes with blast radius, tests touched, and API impact.
Open pull requests via the GitHub App — verified by your own test suite, with the evidence attached.
Distribution
A deterministic engine builds a dependency and blast-radius graph in about 60ms, using zero LLM tokens. That graph produces a focused ~600-token evidence bundle — 162x less context than reading the repository — so the AI agent works only on what matters.
Every fix is verified by your own test suite before it ships. Nothing merges until your tests pass.
Codna has mapped 130 repositories in 9.2 seconds for zero tokens. If your project has tests, Codna can work with it.
In head-to-head testing across 87 tasks, Codna used 5× fewer tokens than Cursor and ran 1.7× faster, with every fix verified by the project's own tests (87/87). Both agents were measured on the same tasks.
Codna ships as a CLI, an MCP server that works inside Cursor and Claude, and a native GitHub App that opens verified fix pull requests directly in your repo.
No. You can self-host Codna, bring your own API key, and egress is fail-closed. Your code is never used for training.