EtienneChollet/ontomics

ontomics gives any coding agent **instant knowledge** of your codebase. One tool call instead of 19. ~20x fewer tokens.
https://github.com/user-attachments/assets/01afa8a0-1bc2-4686-94d7-965fef7610c3
Visualization for the voxelmorph project -- a library for unsupervised learning in image registration
Benchmark
Tested with Claude Sonnet — same question, with and without ontomics.
"What does 'transform' mean in this codebase?" on voxelmorph (full transcript):
| | With ontomics | Without |
|----------------|---------------|------------|
| Tool calls | 1 | 19 |
| Tokens | ~3.7k | ~76k |
| Time | 5s | 1m 15s |
| Answer quality | Complete | Complete |
"What are the main domain concepts in this codebase?" on ScribblePrompt (full transcript):
| | With ontomics | Without |
|----------------|---------------|------------|
| Tool calls | 1 | 26 |
| Tokens | ~3.7k | ~61.6k |
| Time | ~5s | 56s |
| Answer quality | Complete | Complete |
Both conditions produced complete, correct answers. ontomics got there in one call.
What it does that search can't
Search tells you where a string appears. An LSP tells you where a symbol is defined and referenced. Neither answers: what are the domain concepts in this codebase? How do they relate? What naming conventions emerged? What changed in the domain vocabulary since last release? Which functions behave similarly, regardless of what they're named?
ontomics builds a semantic index of your project's domain — clustering related symbols into concepts, detecting naming conventions from usage frequency, resolving abbreviations, grouping functions by behavioral similarity, and tracking how the vocabulary evolves over time. That index can be exported as a portable artifact to bootstrap conventions in other repos.
Behavioral similarity
Beyond naming and concepts, ontomics embeds raw function bodies using CodeRankEmbed (768-dim, contrastive code retrieval) and clusters them by behavioral similarity. This surfaces relationships that neither naming nor call graphs expose:
The result also reveals that random_transform appears at two locations with different similarity scores — a sign of implementation duplication that concept-level search would miss entirely.
Install
Install once, available in every project. No configuration needed — ontomics auto-detects the repo and indexes it on first run.
ontomics requires a git repository (.git/ directory). It will refuse to index home, root, or temp directories. To index a non-git directory, pass --force.
1. Install the binary
**npm (macOS/Linux):**
**macOS (Homebrew):**
**Shell installer (macOS/Linux):**
**From source:**
2. Register with your harness
**Claude Code:**
**Codex:**
**OpenClaw:**
**pi-coding-agent:**
**Share with your team** — drop an .mcp.json in your repo root:
Supported languages
Python, TypeScript, JavaScript, Rust. Auto-detected from file extensions.
Tools
Concepts and vocabulary
| Tool | What it does |
|------|--------------|
| query_concept | Find all variants, related concepts, and occurrences of a term |
| locate_concept | Find the key signatures, classes, and files for a concept |
| describe_symbol | Get the signature, docstring, and relationships for a function or class |