Codex Subagents Guide

This guide describes the Codex-native inference setup.

Codex can run locally from the terminal, inspect and edit files, and run commands in the selected directory. Keep Codex sessions rooted in inference/ so project-local configuration, scripts, and result paths resolve correctly.

Checked-In Codex Assets

• .codex/config.toml Project-scoped Codex agent registry.
• .codex/agents/inference-lab.toml Parent coordinator.
• .codex/agents/optimizer.toml Read-only speed optimizer.
• .codex/agents/benchmarker.toml Local endpoint benchmarker.

Recommended Start

Start Codex from inference/ and let the main session use the checked-in subagents:

codex

For a longer prompt skeleton:

python3 scripts/print_codex_kickoff.py

Workflow

1. Read AGENTS.md, README.md, and this guide.
2. Spawn optimizer for one-variable speed candidates.
3. Spawn benchmarker for reproducible endpoint measurements.
4. Use scripts/resolve_hf_gguf.py for exact GGUF command construction.
5. Append completed measurements to research/results.tsv.

Docs MCP

For OpenAI product or Codex documentation questions, use the OpenAI developer documentation MCP server when available. A user-level install can be added with:

codex mcp add openaiDeveloperDocs --url https://developers.openai.com/mcp

This inference project does not require OpenAI docs access for llama.cpp benchmarking itself; the MCP note is for Codex-specific usage questions.

References

• https://developers.openai.com/codex/cli
• https://developers.openai.com/learn/docs-mcp