Use Headroom with Codex: measure tokens and quality

Know which component you are testing

Headroom offers a local proxy, library, MCP server and agent wrappers. Its strategies can reduce tool responses, logs, file content and conversation context. The project reports different figures for different workloads: roughly 20% for coding agents, 60–95% for JSON and 21–57% across selected README examples. Those are project claims, not a forecast for your session.

Caveman 4 can include input compression, model selection and reasoning-level selection as well as a concise-response skill. Headroom also has output-reduction options. Compare specific enabled components; a full-suite comparison cannot isolate compression if the model or reasoning setting changes.

• Source: github.com ↗
• Source: github.com ↗
• Source: github.com ↗
• Source: docs.headroomlabs.ai ↗
• Source: pypi.org ↗
• Source: docs.headroomlabs.ai ↗
• Source: docs.headroomlabs.ai ↗
• Original guide in Italian →

Create a baseline before installation

1. Choose a non-sensitive repository and three repeatable, representative tasks.
2. Run each without Headroom. Record model, settings, input and output tokens, time, rereads, build and test results.
3. Keep the same model, task instructions and evaluation criteria for the compressed run.
4. Review the resulting diff and task success, not just token counts.

Try the Codex wrapper in an isolated environment

Check the current installation guide and CLI help before running commands; package and wrapper options may change. Use pipx or a virtual environment so the trial is removable. Keep production secrets out of the test repository and check where the proxy stores original content.

python3 --version
pipx install --python python3.13 "headroom-ai[all]"
headroom --help
headroom wrap codex

# After the session, inspect the available diagnostics and help:
headroom doctor
headroom perf
headroom unwrap codex

Count total work, not only compressed payload

If an uncompressed task uses 20,000 input tokens, a 20% smaller first pass saves 4,000. If lost context then causes 6,000 tokens of extra reads, total use becomes 22,000: 10% more than baseline. Record retries and recoveries alongside original and compressed payload.

Inspect the final answer for omitted constraints, incorrect edits and extra review time. Keep the proxy only if the combined result improves for your actual tasks. Remove the wrapper and retest the original setup if quality falls.

Task | Baseline tokens | Compressed tokens | Recovery tokens | Tests | Review result
A    | ...             | ...               | ...             | ...   | ...
B    | ...             | ...               | ...             | ...   | ...
C    | ...             | ...               | ...             | ...   | ...