OpenAI
OpenAI details the architecture and protocol of their new Codex App Server, a client-friendly JSON-RPC API designed to unify and power various Codex experiences across different surfaces like web apps, CLIs, and IDE extensions.
AI 生成摘要
OpenAI 闡述了其新的 Codex 應用程式伺服器（App Server）的架構與協定，這是一個便於客戶端使用的 JSON-RPC API，旨在統一並驅動網頁應用程式、命令列介面和 IDE 擴充功能等多種 Codex 體驗。
February 4, 2026
By Celia Chen, Member of the Technical Staff
OpenAI’s coding agent Codex exists across many different surfaces: the web app(opens in a new window), the CLI(opens in a new window), the IDE extension(opens in a new window), and the new Codex macOS app. Under the hood, they’re all powered by the same Codex harness—the agent loop and logic that underlies all Codex experiences. The critical link between them? The Codex App Server(opens in a new window), a client-friendly, bidirectional JSON-RPC1 API.
In this post, we’ll introduce the Codex App Server; we’ll share our learnings so far on the best ways to bring Codex’s capabilities into your product to help your users supercharge their workflows. We’ll cover the App Server’s architecture and protocol and how it integrates with different Codex surfaces, as well as tips on leveraging Codex, whether you want to turn Codex into a code reviewer, an SRE agent, or a coding assistant.
Before diving into architecture, it’s helpful to know the App Server’s backstory. Initially, the App Server was a practical way to reuse the Codex harness across products that gradually evolved into our standard protocol.
Codex CLI started as a TUI (terminal user interface), meaning Codex is accessed through the terminal. When we built the VS Code extension (a more IDE-friendly way to interact with Codex agents), we needed a way to use the same harness so as to drive the same agent loop from an IDE UI without re-implementing it. That meant supporting rich interaction patterns beyond request/response, such as exploring the workspace, streaming progress as the agent reasons, and emitting diffs. We first experimented with exposing Codex as an MCP server(opens in a new window), but maintaining MCP semantics in a way that made sense for VS Code proved difficult. Instead, we introduced a JSON-RPC protocol that mirrored the TUI loop, which became the unofficial first version(opens in a new window) of the App Server. At the time, we didn’t expect other clients to depend on the App Server, so it wasn’t designed as a stable API.
As Codex adoption grew over the next few months, internal teams and external partners wanted the ability to embed the same harness in their own products in order to accelerate their users’ software development workflows. For example, JetBrains and Xcode wanted an IDE-grade agent experience, while the Codex desktop app needed to orchestrate many Codex agents in parallel. Those demands pushed us to design a platform surface that both our products and partner integrations could safely depend on over time. It needed to be easy to integrate and backward compatible, meaning we could evolve the protocol without breaking existing clients.
Next, we’ll walk through how we designed the architecture and protocol so different clients can use the same harness.
First, let’s zoom in on what’s inside the Codex harness and how the Codex App Server exposes it to clients. In our last Codex blog, we broke down the core agent loop that orchestrates the interaction between the user, the model, and the tools. This is the core logic of the Codex harness, but there’s more to the full agent experience:
Thread lifecycle and persistence. A thread is a Codex conversation between a user and an agent. Codex creates, resumes, forks, and archives threads, and persists the event history so clients can reconnect and render a consistent timeline.
Config and auth. Codex loads configuration, manages defaults, and runs authentication flows like “Sign in with ChatGPT,” including credential state.
Tool execution and extensions. Codex executes shell/file tools in a sandbox and wires up integrations like MCP servers and skills so they can participate in the agent loop under a consistent policy model.
All the agent logic we mentioned here, including the core agent loop, lives in a part of the Codex CLI codebase called “Codex core(opens in a new window).” Codex core is both a library where all the agent code lives and a runtime that can be spun up to run the agent loop and manage the persistence of one Codex thread (conversation).
To be useful, the Codex harness needs to be accessible to clients. That’s where the App Server comes in.
The App Server is both the JSON-RPC protocol between the client and the server and a long-lived process that hosts the Codex core threads. As we can see from the diagram above, an App Server process has four main components: the stdio reader, the Codex message processor, the thread manager, and core threads. The thread manager spins up one core session for each thread, and the Codex message processor then communicates with each core session directly to submit client requests and receive updates.
One client request can result in many event updates, and these detailed events are what allow us to build a rich UI on top of the App Server. Furthermore, the stdio reader and the Codex message processor serve as the translation layer between the client and Codex core threads. They translate client JSON-RPC requests into Codex core operations, listen to Codex core’s internal event stream, and then transform those low-level events into a small set of stable, UI-ready JSON-RPC notifications.
The JSON-RPC protocol between the client and the App Server is fully bidirectional. A typical thread has a client request and many server notifications. In addition, the server can initiate requests when the agent needs input, like an approval, and then pause the turn until the client responds.
Next, we’ll break down the conversation primitives, the building blocks of the App Server protocol. Designing an API for an agent loop is tricky because the user/agent interaction is not a simple request/response. One user request can unfold into a structured sequence of actions that the client needs to represent faithfully: the user’s input, the agent’s incremental progress, artifacts produced along the way (e.g., diffs). To make that interaction stream easy to integrate and resilient across UIs, we landed on three core primitives with clear boundaries and lifecycles:
This lifecycle lets clients start rendering immediately on started, stream incremental updates on delta, and finalize on completed.
Turn: A turn is one unit of agent work initiated by user input. It begins when the client submits an input (for example, “run tests and summarize failures”) and ends when the agent finishes producing outputs for that input. A turn contains a sequence of items that represent the intermediate steps and outputs produced along the way.
Thread: A thread is the durable container for an ongoing Codex session between a user and an agent. It contains multiple turns. Threads can be created, resumed, forked, and archived. Thread history is persisted so clients can reconnect and render a consistent timeline.
Now, we’ll look at a simplified conversation between a client and an agent, where the conversation is represented by primitives:
At the beginning of the conversation, the client and the server need to establish the initialize handshake. The client must send a single initialize request before any other method, and the server acknowledges with a response. This gives the server a chance to advertise capabilities and lets both sides agree on protocol versioning, feature flags, and defaults before the real work begins. Here’s an example payload from OpenAI’s VS Code extension:
This is what the server returns:
When a client makes a new request, it will first create a thread and then a turn. The server will send back notifications for progress (thread/started and turn/started). It will also send back inputs it registers as items, like the user message here.
Tool calls are also sent back to the client as items. Additionally, the server may ask for client approval before it can run an action by sending a server request. The approval will pause the turn until the client replies with either “allow” or “deny.” This is what the approval flow looks like in the VS Code extension:
In the end, the server sends an agent message and then ends the turn with turn/completed. The agent message delta events stream pieces of the message back until the message is finalized with item/completed.
The messages in the diagram are simplified for readability. If you want to see the JSON for a full turn, you can run the test client from the Codex CLI repo:
Now, let’s look at how different client surfaces embed Codex via the App Server. We’ll cover three patterns: local apps and IDEs, Codex web runtime, and the TUI.
Across all three, the transport is JSON-RPC over stdio (JSONL). JSON-RPC makes it straightforward to build client bindings in the language of your choice. Codex surfaces and partner integrations have implemented App Server clients in languages including Go, Python, TypeScript, Swift, and Kotlin. For TypeScript, you can generate definitions directly from the Rust protocol by running:
For other languages, you can generate a JSON Schema bundle and feed it into your preferred code generator by running:
Local clients typically bundle or fetch a platform-specific App Server binary, launch it as a long-running child process, and keep a bidirectional stdio channel open for JSON-RPC. In our VS Code extension and Desktop App, for example, the shipped artifact includes the platform-specific Codex binary and is pinned to a tested version so the client always runs the exact bits we validated.
Not every integration can ship client updates frequently. Some partners like Xcode decouple release cycles by keeping the client stable and allowing it to point to a newer App Server binary when needed. That way they can adopt server-side improvements (for example, better auto-compaction in Codex core or newly supported config keys) and roll out bug fixes without waiting for a client release. The App Server’s JSON-RPC surface is designed to be backward compatible, so older clients can talk to newer servers safely.
Codex Web uses the Codex harness, but runs it in a container environment. A worker provisions a container with the checked-out workspace, launches the App Server binary inside it, and maintains a long-lived JSON-RPC over stdio2 channel. The web app (running in the user’s browser tab) talks to the Codex backend over HTTP and SSE, which streams task events produced by the worker. This keeps the browser-side UI lightweight while still giving us a consistent runtime across desktop and web.
Because web sessions are ephemeral (tabs close, networks drop), the web app cannot be the source of truth for long-running tasks. Keeping state and progress on the server means work continues even if the tab disappears. The streaming protocol and saved thread sessions make it easy for a new session to reconnect, pick up where it left off, and catch up without rebuilding state in the client.
Historically, the TUI was a “native” client that ran in the same process as the agent loop and talked directly to Rust core types rather than the app-server protocol. That made early iteration fast, but it also made the TUI a special-case surface.
Now that the App Server exists, we plan to refactor the TUI(opens in a new window) to use it so it behaves like any other client: launch an App Server child process, speak JSON-RPC over stdio, and render the same streaming events and approvals. This unlocks workflows where the TUI can connect to a Codex server running on a remote machine, keeping the agent close to compute and continuing work even if the laptop sleeps or disconnects, while still delivering live updates and controls locally.
Codex App Server will be the first-class integration method we maintain moving forward, but there are also other methods with more limited functionality. By default, we’d recommend that clients use Codex App Server to integrate with Codex, but it’s worth taking a look at different integration methods and understanding their pros and cons. Below are the most common ways to drive Codex and when each might be a good fit.
Run codex mcp-server(opens in a new window) and connect from any MCP client that supports stdio servers (e.g., OpenAI Agents SDK(opens in a new window)). This is a good fit if you already have an MCP-based workflow and want to invoke Codex as a callable tool. The downside is that you only get what MCP exposes, so Codex-specific interactions that rely on richer session semantics (e.g., diff updates) may not map cleanly through MCP endpoints.
Some ecosystems offer a portable interface that can target multiple model providers and runtimes. This can be a good fit if you want one abstraction that coordinates multiple agents. The tradeoff is that these protocols often converge on the common subset of capabilities, which can make richer interactions harder to represent, especially when provider-specific tool and session semantics matter. This space is evolving quickly, and we expect that more common standards will emerge as we figure out the best primitives to represent real-world agent workflows (skills(opens in a new window) is a good example of this).
Choose the App Server when you want the full Codex harness exposed as a stable, UI-friendly event stream. You get both the full functionality of the agent loop and other supporting features like Sign in with ChatGPT, model discovery, and configuration management. The main cost is integration work, since you need to build the client-side JSON-RPC binding in your language. In practice, however, Codex is able to do a lot of the heavy lifting if you feed it the JSON schema and documentation. Many teams we worked with were able to make to a working integration quickly using Codex.
A lightweight, scriptable CLI mode for one-off tasks and CI runs. It’s a good fit for automation and pipelines where you want a single command to run to completion non-interactively, stream structured output for logs, and exit with a clear success or failure signal.
A TypeScript library for programmatically controlling local Codex agents from within your own application. It’s best when you want a native library interface for server-side tools and workflows without building a separate JSON-RPC client. Since it shipped earlier than the App Server, it currently supports fewer languages and a smaller surface area. If there is developer interest, we may add additional SDKs that wrap the App Server protocol so teams can cover more of the harness surface without writing JSON-RPC bindings.
In this post, we shared how we approach designing a new standard for interacting with agents and how to turn the Codex harness into a stable, client-friendly protocol. We covered how the App Server exposes Codex core, lets clients drive the full agent loop, and powers a wide range of surfaces including the TUI, local IDE integrations, and the web runtime.
If this sparked ideas for integrating Codex into your own workflows, it’s worth giving App Server a try. All the source code lives in the Codex CLI open-source repo(opens in a new window). Feel free to share your feedback and feature requests. We’re excited to hear from you and to keep making agents more accessible to everyone.
Special thanks to Michael Bolin, Owen Lin, Eric Traut, and Rasmus Rygaard, who contributed to this post, and to the entire Codex team that worked on the App Server.
We use a “JSON‑RPC lite” variant: it keeps the request/response/notification shape, but omits the "jsonrpc": "2.0" header and is framed as JSONL over stdio rather than strict JSON‑RPC 2.0.
“stdio” refers to the app-server’s stdin/stdout inside the container. In hosted setups, those streams are often tunneled over a persistent network connection (e.g., WebSocket-like) to the container runtime—so it behaves like stdio even if it isn’t a literal local pipe.
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