A god's-eye view of your codebase for AI agents. Stop grepping. Ensure correctness and completeness for any task your AI agent performs.
What it does · Why · Get started · Maturity · Benchmarks · CLI · Graph output · Cross-file coverage · Run locally
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Give it a repository. It builds a knowledge graph of your code using formal methods: for every function, exactly who calls it and what it calls, derived by logical rules rather than guessed. Your AI agents, and you, then understand, explore, search and edit the code from that map instead of grepping. Grep cannot see calls through an interface, a subclass or a callback, and its output gets truncated, so agents silently miss what was cut. The map prunes 99.9% of the codebase, so agents keep their context for the task, not the search.
On real bugs. On 748 held-out Defects4J bugs, scored once after the rules were frozen, the tests AxiomCode picks from source include every bug-revealing test for 94.9% of bugs (best tree-sitter builder: 61.5%), at an F1 of 72.4 against what Defects4J observes by running the suite.
Why: types make a better graph. Choosing tests means following calls several hops back from a change, and one wrong link loses every test beyond it. A tree-sitter based CST builder matches a call to a declaration by name; AxiomCode resolves it the way the compiler does, from the receiver's type. Scored against the compiler's own answer, compiled bytecode for Java and the type checker for TypeScript, over five open-source projects per language:
| share of calls linked to their exact target | AxiomCode | GitNexus | CodeGraph | Code-Review-Graph | Graphify |
|---|---|---|---|---|---|
| Java, 33,257 calls (bytecode) | 96.5% | 78.9% | 78.5% | 71.9% | 67.5% |
| TypeScript, 9,829 calls (type checker) | 88.8% | 65.1% | 65.4% | 71.6% | 49.8% |
Across files, it recovers which files call into which with an F1 of