Axon

Building the knowledge graph for AI code agents.

Indexes any codebase into a structural knowledge graph — every dependency, call chain, cluster, and execution flow — then exposes it through smart MCP tools so AI agents never miss code.

$ axon analyze .

Walking files...               142 files found
Parsing code...                142/142
Tracing calls...               847 calls resolved
Analyzing types...             234 type relationships
Detecting communities...       8 clusters found
Detecting execution flows...   34 processes found
Finding dead code...           12 unreachable symbols
Analyzing git history...       18 coupled file pairs
Generating embeddings...       623 vectors stored

Done in 4.2s — 623 symbols, 1,847 edges, 8 clusters, 34 flows

The Problem

Your AI agent edits UserService.validate(). It doesn't know that 47 functions depend on that return type, 3 execution flows pass through it, and payment_handler.py changes alongside it 80% of the time.

Breaking changes ship.

This happens because AI agents work with flat text. They grep for callers, miss indirect ones, and have no understanding of how code is connected. Context windows are finite. LSPs don't expose call graphs. Grepping gives you strings, not structure.

The agent needs a knowledge graph — not more text.

How Axon Solves It

Most code intelligence tools give the agent raw files and hope it reads enough. Axon takes a different approach: precompute structure at index time so every tool call returns complete, actionable context.

A 12-phase pipeline runs once over your repo. After that:

• axon_impact("validate") returns all 47 affected symbols, grouped by depth (will break / may break / review), with confidence scores — in a single call