Codex App Server

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/
• CLI reference: https://developers.openai.com/codex/cli/reference/#codex-app-server

Reference Policy

1. 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.
2. 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.
3. For exact current product behavior beyond the installed binary, fetch the official docs via MCP (openaiDeveloperDocs) before answering.
4. 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.
5. 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.

• Archival helper only: references/openai-codex-app-server-readme-reference.md
• Keep it for offline reading or historical comparison, not for source-of-truth decisions.