صرف-ایجنٹ ٹولز پڑھتے ہیں۔ Codna سمجھتا ہے۔
زیادہ تر کوڈنگ ایجنٹس ریپو تلاش کرنے کے لیے ماڈل استعمال کرتے ہیں۔ Codna پہلے تعینشدہ نقشہ استعمال کرتا ہے، پھر ایجنٹ کو درستگی سے ٹھیک کرنے دیتا ہے۔
| Capability | Codna | Agent-only tools (Cursor, Copilot, Devin) | Static code search |
|---|---|---|---|
| Repo understanding | Deterministic graph · ~60ms | LLM reads files | Keyword / AST |
| Tokens before fixing | 0 | ~200k | 0 |
| Bug localization | Graph + evidence | Prompt exploration | Manual |
| Fix verified by your tests | Every fix | Varies | No |
| Patch generation | Agent · tight context | Agent · broad context | No |
| Cost per verified fix | ~$0.02 | ~$1+ | — |
| GitHub fix PRs | Native app | Varies | No |
| Privacy mode | Self-host + BYOK | Varies | Local |
Head-to-head
Compare Codna directly.
Codna vs Codex CLI →
Both can fix a bug. The difference is what they read first. The Codex CLI explores your repo inside the model; Codna maps it deterministically for zero tokens, then fixes from a tiny evidence bundle.
Codna vs Gemini CLI →
The Gemini CLI reads your repository through the model to find a bug. Codna already knows where the bug lives — it mapped the repo deterministically before the first prompt.
Codna vs Cursor →
Cursor is a full AI IDE, and a good one. Codna is not trying to replace it. Codna is the precision layer underneath: a deterministic repo graph and a hard test gate that make the agent you already drive in Cursor surgical.
Codna vs Cline →
Cline is a capable open-source coding agent that drives the model through your codebase. Codna takes the other path: a deterministic repo graph and a hard test gate, so every fix is verified. In an 87-task head-to-head, Codna verified 100% of fixes to Cline's 73.6% — using 4× fewer tokens and running 5.4× faster.
Codna vs Claude Code →
Both can fix a bug. The difference is what they read first. Claude Code is a capable agent that explores your repo inside the model; Codna maps it deterministically for zero tokens, then hands the agent a tiny evidence bundle. Keep Claude Code. Make it surgical.
Codna vs Devin →
Devin runs the whole job for you, end to end. Codna does one part with certainty: it maps the repo deterministically for zero tokens, then ships only fixes your own tests pass. Use it to make whatever agent you run surgical.
Codna vs Windsurf →
Both help you understand a codebase before you change it. The difference is how that understanding is built. Windsurf generates a Codemap with a model. Codna builds the same graph deterministically — no LLM, no tokens — then gates every fix on your tests.
Codna vs Augment Code →
Both find the right code before they fix it. Augment's Context Engine retrieves it with embeddings, billed per lookup. Codna maps the repo deterministically for zero tokens, then fixes from a tiny evidence bundle — and ships only what passes your tests.
Codna vs Sourcegraph Amp / Cody →
Sourcegraph is built to find context across every repo you own. Codna is built to make the fix provably correct. One answers "where does this live?"; the other answers "is this patch actually right?"
Codna vs Greptile →
Greptile reads your pull request and tells you where the bug is. Codna takes it from there — it maps the repo deterministically, writes the fix, and proves it against your tests. One reviews. The other repairs and verifies.
Codna vs CodeRabbit →
CodeRabbit reads a pull request and tells you what is wrong. Codna takes the next step: it fixes it. One reviews the diff; the other maps the repo deterministically and ships a patch your tests already passed.
Codna vs Aider →
Both fix bugs from the command line. The difference is what they read first, and what they prove after. Aider ranks files into a token budget and reads them through the model. Codna maps the whole repo deterministically for zero tokens, then ships only fixes that pass your tests.
Frequently asked
Most tools send large chunks of your codebase to an LLM and hope for the best. Codna maps your repo deterministically in ~60ms for zero tokens, then hands the AI a focused ~600-token evidence bundle — 162x less context than reading the repo. Every fix is verified by your own tests before it ships.
Autonomous agents explore your codebase with the LLM itself, burning tokens on context they mostly don't need. Codna separates understanding from fixing: a deterministic engine builds a dependency and blast-radius graph first, then a small AI agent fixes from evidence alone. Against Cursor that meant 5× fewer tokens and 1.7× faster — roughly $0.02 per verified fix.
Every fix Codna proposes is run against your existing test suite before it's surfaced. If your tests don't pass, the fix doesn't ship. When using the GitHub App, this means only verified fixes open pull requests.
You can self-host the engine, bring your own API key, and egress is fail-closed — meaning nothing leaves if the connection isn't explicitly allowed. Codna does not train on your code.
Codna supports 250+ languages. The deterministic engine mapped 130 repos in 9.2 seconds for zero LLM tokens, and support is broad by design — the graph-based approach is not tied to any particular ecosystem.
Because the agent fixes from a tight evidence bundle rather than the full repository, LLM costs come out to roughly $0.02 per fix. Head-to-head against Cursor, Codna used 5× fewer tokens and ran 1.7× faster.