1
Resolve structure, don't rank it
Codna builds a real dependency and blast-radius graph deterministically — no embeddings, no LLM, ~60ms per repo for zero tokens. Call paths are resolved, not retrieved by similarity.
Comparison
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.
The problem
How Augment Code understands your repo
Augment Code is a strong enterprise tool. Its Context Engine indexes your codebase into vector embeddings and keeps that index synced as files change, so an agent can retrieve semantically related code on demand. Retrieval is a real engineering achievement and the privacy story is serious — SOC 2 Type II, ISO 42001, no training on your code, self-host and VPC options. But embeddings are an approximation of structure: similarity is ranked, not resolved, so the right caller or the actual blast radius can sit just outside the top results. And the work is metered — Augment bills in credits per request, so understanding the repo is a recurring cost on every task.
How Codna fixes it
How Codna does it differently
Whole repo100,000+
Evidence bundle~600
2
Hand over evidence, not a query
The agent receives a ~600-token bundle: the suspect files, the exact call paths, the failing test. No per-lookup retrieval bill — the understanding cost is zero.
3
Verify before it ships
Every patch must pass your own test suite. A fix that fails tests never ships — the failing test is the oracle, not a confidence score.
codna fix . --issue "the checkout test is failing"
What you get
Codna's edge over Augment Code
Resolved structure, not ranked retrieval
A retrieval-index approach finds related code by ranking it for similarity. Codna builds the real dependency and blast-radius graph deterministically — no embeddings, no RAG, about 60ms per repo for zero LLM tokens. The result is call paths that are resolved, not approximated.
Understanding that costs zero tokens
Most agents pay to read the repo on every task. Codna maps it for zero LLM tokens and hands the agent a ~600-token evidence bundle, so the understanding step recurs at no token cost — about $0.04 per verified fix at public model rates.
Every fix gated by a passing test
Codna never ships a patch that fails your suite — the failing test is the oracle, not a confidence score. It runs as a CLI, an MCP server, and a native GitHub App, so it adds verification to the agent you already run.
The proof
Fewer tokens. Faster. Verified.
Frequently asked
Retrieval indexes are good at surfacing related code. The difference is kind, not speed: a retrieval index ranks code by similarity, while Codna resolves the actual dependency and blast-radius graph deterministically. One returns likely-related files; the other returns the real call paths — for zero LLM tokens.
It can do either, but it most often complements. Codna is a deterministic understanding layer plus a test-gated fix agent, not an IDE or a context platform, so many teams keep Augment Code and add Codna to scope and verify the work. Where you want fixes proven green before they ship, Codna's hard test gate is the part a context engine doesn't provide.
Augment Code pairs an agent with a context engine built on a retrieval index, so it surfaces semantically related code on demand. Codna skips retrieval entirely and resolves a dependency and blast-radius graph from the source itself — deterministically, in about 60ms per repo for zero LLM tokens. Then it hands the agent a ~600-token evidence bundle instead of ranked search results.
Yes. Codna ships as a CLI, an MCP server for Cursor and Claude, and a native GitHub App, so it slots in around the tool you already run. Use it as the precision and verification layer: it scopes the change to the real blast radius and proves the patch against your tests before it lands.
Codna is model-agnostic via bring-your-own-key, so you choose the model — including a managed option. The deterministic engine maps repos across languages from the source itself: 130 repos spanning 110 languages mapped in 9.2 seconds at 100% ecosystem accuracy, for zero LLM tokens.
It is, and we won't pretend otherwise. Codna matches the intent — self-host, bring-your-own-key, fail-closed egress, and no training on your code — so the wedge is determinism and verified fixes, not privacy. On cost, Codna maps the repo for zero LLM tokens and fixes from a ~600-token bundle, about $0.04 per verified fix at public model rates.
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