Agent-only tools read. Codna understands.
A repo mapped for zero tokens. An evidence bundle scoped to the issue. Every fix reported with root cause, confidence and regression risk.
| Capability | Codna | Agent-only tools (Cursor, Copilot, Devin) | Static code search |
|---|---|---|---|
| Repo understanding | Deterministic graph · 0 tokens | Model reads files | Keyword / AST |
| Tokens before fixing | 0 | Model context | 0 |
| Bug localization | Graph + evidence | Prompt exploration | Manual |
| Fix reported with | Root cause · confidence · risk | Varies | No |
| Patch generation | Agent · scoped context | Agent · broad context | No |
| Model cost per verified fix | ~$0.02 avg · 87 cases | Varies | — |
| PR review and fix PRs | App · Action · CLI | Varies | No |
| Privacy mode | Your machine + your key | Varies | Local |
Head-to-head
Compare Codna directly.
Codna vs Codex CLI →
The Codex CLI is an agent you drive from the terminal. Codna understands the repository first, for zero tokens, then fixes from evidence and explains the result.
Codna vs Gemini CLI →
The Gemini CLI is an agent you drive from the terminal. Codna maps the repository first, for zero tokens, then fixes from evidence with root cause and risk attached.
Codna vs Cursor →
On 87 matched bug-fix cases, Codna used 5× fewer tokens and ran 1.7× faster than Cursor. Both verified 87 of 87. Codna also runs inside Cursor as an MCP server.
Codna vs Claude Code →
Claude Code is an agent. Codna is the understanding it works from: a deterministic map for zero tokens, an evidence bundle scoped to the issue, and a fix that explains itself. It plugs in over MCP.
Codna vs Devin →
Devin is an autonomous agent. Codna is a deterministic understanding layer with a scoped agent behind it: zero tokens to map, a fix that explains itself, and a pull request you merge.
Codna vs Windsurf →
Windsurf is an agentic editor. Codna maps the repository deterministically for zero tokens, fixes from evidence, and reports root cause, confidence and regression risk on every fix.
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.
Codna vs Sourcegraph Amp / Cody →
Amp and Cody are coding agents with code search behind them. Codna maps the repository deterministically for zero tokens, fixes from evidence, and reports root cause, confidence and regression risk.
Codna vs Greptile →
Greptile is a pull request review tool. Codna reviews every pull request with severity, confidence and a clear verdict, and opens the fix when you ask.
Codna vs CodeRabbit →
CodeRabbit is a pull request review tool. Codna reviews every pull request with a clear verdict and opens the fix when you ask, from the same deterministic map it uses to fix bugs.
Codna vs Aider →
Aider is a pair programmer in the terminal. Codna is a deterministic understanding layer with a scoped agent: zero tokens to map, a fix that explains itself, and a pull request you review.
Frequently asked
Most tools read your codebase through the model. Codna maps the repo deterministically for zero tokens, then hands the agent an evidence bundle scoped to the issue. On 87 matched cases against Cursor that meant 5× fewer tokens and 1.7× faster, with 87 of 87 fixes verified.
Autonomous agents explore the codebase with the model itself. Codna separates understanding from fixing: a deterministic engine builds the dependency and blast-radius graph first, then the agent fixes from evidence alone and reports root cause, confidence and regression risk.
In the benchmark, a case counted only when the fix verified: 87 of 87 for Codna. On your machine, codna fix --tests --apply runs your tests in a sandbox and re-fixes until they pass. Every write path runs a risk gate first.
Understanding runs on your machine. Only the evidence bundle or the diff reaches your model provider, with your key. Set privacy.egress to fail-closed in codna.yaml and Codna runs your tests only under kernel-level network denial. Your code is not used to train models unless you opt in.
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.
About $0.02 of model spend per verified fix on average over 87 measured cases. With your own key you pay your provider directly; the CLI itself is free on your machine.