1
Map for zero tokens
Codna builds a dependency and blast-radius graph of the repository from its import patterns. No model call, zero tokens.
Comparison
Codna vs Augment Code
Augment Code is an AI coding platform. Codna is a deterministic understanding layer with a scoped agent: zero tokens to map, evidence instead of the whole repo, and a fix that explains itself.
The problem
Retrieval answers where. A fix needs what breaks.
Finding relevant code is one problem. Knowing what a change reaches is another. Codna's blast-radius graph answers the second, so the fix accounts for callers and dependents outside the patch.
How Codna fixes it
How Codna works
2
Hand over evidence, not the repo
The agent receives an evidence bundle scoped to the issue: the suspect files, the call paths, the failing test. Codna prints the raw-to-bundle token size on every run.
3
Report, gate, open the PR
Every fix reports root cause, confidence, blast radius and regression risk, and passes a risk gate before it is applied or a pull request opens. With --open-pr, or through the GitHub App, the pull request states the issue, the root cause, the symbols touched and a confidence score, and asks for review before merging. Codna never merges.
codna fix . --issue "the checkout test is failing"
What you get
What you get
Structure, resolved
Codna builds a dependency and blast-radius graph of the repository from its import patterns. No model call, zero tokens.
Understanding that costs zero tokens
The agent receives an evidence bundle scoped to the issue: the suspect files, the call paths, the failing test. Codna prints the raw-to-bundle token size on every run.
Every fix explained
Every fix reports root cause, confidence, blast radius and regression risk, and passes a risk gate before it is applied or a pull request opens.
The proof
Fewer tokens. Faster. Verified.
Frequently asked
The only measured head-to-head Codna publishes is against Cursor: 87 matched bug-fix cases on identical checkouts, 5× fewer tokens, 1.7× faster, 87 of 87 verified, about $0.02 per fix. Everything else on this page describes Codna's own behaviour, not Augment Code's internals.
Alongside. Codna ships as a CLI, an MCP server for Cursor and Claude, a GitHub Action (thyn-ai/codna-action@v1) and a GitHub App that reviews every pull request and opens fix PRs from a label, a comment or a red check.
Codna computes a dependency and blast-radius graph deterministically and reports what a change reaches. It does not rank search results; it scopes a fix and explains it.
Yes. Codna is a CLI, an MCP server, a GitHub Action and a GitHub App. Add it where it helps and keep the rest of your workflow.
Bring a key from Anthropic, OpenAI, Google Gemini, Groq, Mistral, OpenRouter or xAI, stored in your OS keychain with codna key set, or sign in and use the managed allowance. The repository map is built from import patterns, so it is not tied to one language. On-device recall covers Python, JavaScript, TypeScript, Go, Rust, Java, C, C++, C#, PHP and Ruby. Your own test command verifies the fix.
Understanding runs on your machine and spends no tokens. Only the evidence bundle or the diff reaches your model provider, with your key from the OS keychain. Set privacy.egress to fail-closed in codna.yaml and Codna runs your tests only under kernel-level network denial. Secret redaction is always on. On 87 matched bug-fix cases against Cursor, Codna averaged 16,159 total tokens and about $0.02 of model spend per verified fix, 5× fewer tokens and 1.7× faster. With your own key you pay your provider directly.
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