Use when implementing or reviewing Codex App Server integrations (initialize/thread/turn/item lifecycle, streaming events, approvals, transport behavior, and client architecture). Start with the local conceptual reference, probe the locally installed Codex App Server for installed-build truth, and use the OpenAI docs MCP (`openaiDeveloperDocs`) when you need the latest official behavior beyond the installed binary.
Instalação
Instalar com Codex ou Claude Copie este prompt, cole no Codex, Claude ou outro assistente e deixe que ele revise a página da skill e instale para você.
Use when implementing or reviewing Codex App Server integrations (initialize/thread/turn/item lifecycle, streaming events, approvals, transport behavior, and client architecture). Start with the local conceptual reference, probe the locally installed Codex App Server for installed-build truth, and use the OpenAI docs MCP (`openaiDeveloperDocs`) when you need the latest official behavior beyond the installed binary.
Codex App Server
Use this skill when building around Codex App Server primitives. Keep the client/harness thin and deterministic.
Overview
Use this file for quick orientation and durable mental models only. For exact protocol fields, approvals, config keys, transport details, and other evolving behavior, prefer the local probe workflow for installed-build truth and use official docs via MCP (openaiDeveloperDocs) for the latest documented behavior.
What App Server Is
Codex App Server is the client-friendly JSON-RPC surface Codex uses to power rich clients such as IDE extensions and partner integrations.
It hosts Codex threads as durable runtime sessions behind a bidirectional protocol.
It turns lower-level harness activity into UI-ready thread, turn, item, delta, and approval events.
It lets clients reuse the same Codex harness across different surfaces without re-implementing the agent loop.
It is the first-class integration surface when you need authentication, conversation history, approvals, streamed agent events, skills, apps/connectors, MCP status, or config/auth flows.
If the task is one-shot automation, batch work, or CI, prefer the Codex SDK instead of App Server.
Core Mental Model
Transport is JSON-RPC 2.0 with the jsonrpc header omitted on the wire.
Default transport is stdio with newline-delimited JSON messages.
WebSocket transport exists behind --listen ws://IP:PORT, but it is experimental and unsupported; use loopback or SSH-forwarded listeners unless the official docs say otherwise.
Work happens in durable threads made of turns made of items.
Items stream during a turn and complete with final state events.
Server can request approvals from the client; client must respond.
Some methods and fields require initialize.params.capabilities.experimentalApi = true; do not use them in stable clients unless the caller explicitly opts in.
Implementation Defaults
Do not build a parallel agent loop; use App Server primitives directly.
Do not use App Server just to run unattended jobs; prefer the Codex SDK for automation and CI.
Keep model/tools/runtime settings stable within a thread when possible.
Persist threadId and resume threads instead of recreating context.
Prefer deterministic ordering for any tool lists sent by the client.
Identify the client with initialize.params.clientInfo.name; enterprise integrations may need OpenAI to recognize that client name for compliance logs.
Treat generated TypeScript and JSON Schema output as version-specific to the local Codex binary that produced it.
In WebSocket mode, configure authentication before exposing anything beyond loopback; prefer token files over command-line bearer tokens.
Primary References
Local high-level architecture reference: references/unlocking-the-codex-harness-high-level.md
Local probe script: scripts/probe_app_server.py
Official docs via MCP (openaiDeveloperDocs)
Live upstream README (latest source for snapshot): https://raw.githubusercontent.com/openai/codex/refs/heads/main/codex-rs/app-server/README.md
App Server docs: https://developers.openai.com/codex/app-server/
Start with this file plus references/unlocking-the-codex-harness-high-level.md when you need the conceptual model for what App Server is and why it exists.
When the question is about the exact behavior of the locally installed Codex binary, run scripts/probe_app_server.py or probe the local app server directly before generalizing from docs.
For exact current product behavior beyond the installed binary, fetch the official docs via MCP (openaiDeveloperDocs) before answering.
If the docs are thin on architecture or motivation, use the local conceptual reference to explain the system at a high level without overriding official protocol details.
If the task is about automation, jobs, or CI, check the current Codex SDK docs before recommending App Server.
Local Probe Workflow
Use the local Codex binary when you need the installed-build-specific protocol surface, method names, request schemas, or runtime-discoverable state.
Preferred local probes:
python3 scripts/probe_app_server.py for a combined local schema and runtime summary
codex app-server generate-json-schema --out <dir> for the exact local JSON Schema bundle
codex app-server generate-ts --out <dir> for generated local TypeScript protocol bindings
a live stdio session with codex app-server for handshake and request/response probing
Useful live requests after initialize + initialized:
account/read
model/list
thread/list
thread/start
thread/resume
thread/read
turn/start
turn/steer
turn/interrupt
command/exec
config/read
mcpServerStatus/list
plugin/list
skills/list
app/list
experimentalFeature/list
config/mcpServer/reload
Use local probes to learn:
what the installed binary actually supports
the exact request and response shapes of that build
which MCP servers, skills, models, apps/connectors, plugins, accounts, config layers, and experimental features are visible at runtime
Use official docs MCP to learn:
the latest documented behavior
integration guidance and semantics
current official recommendations and compatibility guidance
Use MCP For
transport behavior and supported modes
WebSocket auth, health probes, backpressure, and retry guidance
thread, turn, and item lifecycle details
approval request payloads and available decisions
dynamic tools and experimental API behavior
auth/account, rate-limit, config, plugin, app, skills, filesystem, and command-exec methods
config keys, auth behavior, and integration surface changes
anything the user frames as latest, current, exact, or up to date
Archival Snapshot
The local README snapshot is not part of the active reference path.
