graphify/worked/httpx/README.md

httpx Corpus Benchmark — How to Reproduce

A synthetic 6-file Python codebase modeled after httpx's architecture. Tests graphify on a realistic library codebase with clean layering: exceptions → models → auth/transport → client.

Corpus (6 files)

All input files are in raw/:

raw/
├── exceptions.py   — full HTTPError hierarchy (RequestError, TransportError, HTTPStatusError, etc.)
├── models.py       — URL, Headers, Cookies, Request, Response with raise_for_status
├── auth.py         — BasicAuth, BearerAuth, DigestAuth (challenge-response), NetRCAuth
├── utils.py        — header normalization, query param flattening, content-type parsing
├── transport.py    — ConnectionPool, HTTPTransport, AsyncHTTPTransport, MockTransport, ProxyTransport
└── client.py       — Timeout, Limits, BaseClient, Client (sync), AsyncClient

How to run

pip install graphifyy && graphify install
/graphify ./raw

Or from the CLI directly:

pip install graphifyy
graphify ./raw

What to expect

• 144 nodes, 330 edges, 6 communities
• God nodes: Client, AsyncClient, Response, Request, BaseClient, HTTPTransport
• Surprising connections: DigestAuth ↔ Response (auth.py reads Response to parse WWW-Authenticate)
• ~1x token reduction — 6 files fits in a context window, so there's no compression win here

The graph value on a small corpus is structural, not compressive: you can see the full dependency graph, identify god nodes, and understand architecture at a glance. For token reduction to matter you need 20+ files. At 52 files (Karpathy repos benchmark) graphify achieves 71.5x.

Run graphify benchmark worked/httpx/graph.json to verify the numbers yourself. Actual output is already in this folder: GRAPH_REPORT.md (human-readable) and graph.json (full graph data).