@maxiaochao/pi-codex-edit

A Pi extension that exposes Codex-style apply_patch for GPT/Codex models and keeps Pi's native edit tool for other models.

Install

pi install npm:@maxiaochao/pi-codex-edit

The package registers apply_patch and selects the editing tool from the resolved model configuration:

• GPT/Codex model with OpenAI grammar support: apply_patch
• Other models: Pi's native edit

The default allowlist enables all gpt-5.6-* models regardless of provider. Other model families can be added after separate validation by editing the package config.json.

The package does not add a permanent AGENTS rule telling models not to use apply_patch. Only the selected editor tool is active, so the model receives one editing protocol.

What apply_patch does

The tool accepts raw Codex patch text between *** Begin Patch and *** End Patch. It supports:

• Add File
• Delete File
• Update File
• Move to
• multiple update hunks with @@ context
• EOF-oriented insertions

Before writing, it parses the complete patch, resolves paths, rejects workspace escapes and unsafe symlink/parent paths, reads all source files, and computes every update in memory. Only after preflight succeeds does it mutate files. It returns per-file diff details and reports committed paths if a later filesystem operation fails.

Configuration

The package defaults to a model-ID allowlist in config.json: "gpt-5.6-*". Provider does not affect routing. Each entry is a * glob, so this matches every gpt-5.6- variant. The route additionally requires:

model.api = openai-responses or openai-codex-responses
model.compat.supportsOpenAIGrammarTools = true

Edit config.json in the installed package only if you need to change the default allowlist. The package's current implementation keeps both tools registered for session replay and changes only the active tool set on session_start and model_select.

Documentation

• summary.md: benchmark summary and decision record.
• codex-edit-explainer.html: self-contained interactive architecture and configuration explainer.

The benchmark found equal final correctness in the fair 20-case comparison, with first editor-call success improving from 85% to 95%. Overall tool-call count and cache-inclusive token use were effectively unchanged, so this package should be rolled out first to GPT/Codex model scopes rather than treated as a universal replacement.