# Unrolling the Codex agent loop By Michael Bolin, Member of the Technical Staff Codex CLI is our cross-platform local software agent, designed to produce high-quality, reliable software changes while operating safely and efficiently on your machine. We’ve learned a tremendous amount about how to build a world-class software agent since we first launched the CLI in April. To unpack those insights, this is the first post in an ongoing series where we’ll explore various aspects of how Codex works, as well as hard-earned lessons. For an even more granular view on how the Codex CLI is built, check out our open source repository at [https://github.com/openai/codex](https://github.com/openai/codex). Many of the finer details of our design decisions are memorialized in GitHub issues and pull requests if you’d like to learn more. To kick off, we’ll focus on the _agent loop_, which is the core logic in Codex CLI that is responsible for orchestrating the interaction between the user, the model, and the tools the model invokes to perform meaningful software work. We hope this post gives you a good view into the role our agent (or “harness”) plays in making use of an LLM. Before we dive in, a quick note on terminology: at OpenAI, “Codex” encompasses a suite of software agent offerings, including Codex CLI, Codex Cloud, and the Codex VS Code extension. This post focuses on the Codex _harness_, which provides the core agent loop and execution logic that underlies all Codex experiences and is surfaced through the Codex CLI. For ease here, we’ll use the terms “Codex” and “Codex CLI” interchangeably. ## The agent loop At the heart of every AI agent is something called “the agent loop.” A simplified illustration of the agent loop looks like this: To start, the agent takes _input_ from the user to include in the set of textual instructions it prepares for the model known as a _prompt_. The next step is to query the model by sending it our instructions and asking it to generate a response, a process known as _inference_. During inference, the textual prompt is first translated into a sequence of input [tokens](https://platform.openai.com/docs/concepts#tokens)—integers that index into the model’s vocabulary. These tokens are then used to sample the model, producing a new sequence of output tokens. The output tokens are translated back into text, which becomes the model’s response. Because tokens are produced incrementally, this translation can happen as the model runs, which is why many LLM-based applications display streaming output. In practice, inference is usually encapsulated behind an API that operates on text, abstracting away the details of tokenization. As the result of the inference step, the model either (1) produces a final response to the user’s original input, or (2) requests a _tool call_ that the agent is expected to perform (e.g., “run `ls` and report the output”). In the case of (2), the agent executes the tool call and appends its output to the original prompt. This output is used to generate a new input that’s used to re-query the model; the agent can then take this new information into account and try again. This process repeats until the model stops emitting tool calls and instead produces a message for the user (referred to as an _assistant message_ in OpenAI models). In many cases, this message directly answers the user’s original request, but it may also be a follow-up question for the user. Because the agent can execute tool calls that modify the local environment, its “output” is not limited to the assistant message. In many cases, the primary output of a software agent is the code it writes or edits on your machine. Nevertheless, each turn always ends with an assistant message—such as “I added the `architecture.md` you asked for”—which signals a termination state in the agent loop. From the agent’s perspective, its work is complete and control returns to the user. The journey from _user input_ to _agent response_ shown in the diagram is referred to as one _turn_ of a conversation (a _thread_ in Codex). Though this _conversation turn_ can include many iterations between the **model inference** and **tool calls**. Every time you send a new message to an existing conversation, the conversation history is included as part of the prompt for the new turn, which includes the messages and tool calls from previous turns: This means that as the conversation grows, so does the length of the prompt used to sample the model. This length matters because every model has a _context window_, which is the maximum number of tokens it can use for one inference call. Note this window includes both input _and_ output tokens. As you might imagine, an agent could decide to make hundreds of tool calls in a single turn, potentially exhausting the context window. For this reason, _context window management_ is one of the agent’s many responsibilities. Now, let’s dive in to see how Codex runs the agent loop. ## Model inference The Codex CLI sends HTTP requests to the [Responses API](https://platform.openai.com/docs/api-reference/responses) to run model inference. We’ll examine how information flows through Codex, which uses the Responses API to drive the agent loop. The Responses API endpoint that the Codex CLI uses is [configurable](https://developers.openai.com/codex/config-advanced#custom-model-providers), so it can be used with any endpoint that [implements the Responses API](https://www.openresponses.org/): - When using ChatGPT login with the Codex CLI, it uses `https://chatgpt.com/backend-api/codex/responses` as the endpoint - When using API-key authentication with OpenAI hosted models, it uses `https://api.openai.com/v1/responses` as the endpoint - When running Codex CLI with `--oss` to use [gpt-oss](/content/index/introducing-gpt-oss/index.html) with [ollama 0.13.4+](https://github.com/openai/codex/pull/8798) or [LM Studio 0.3.39+](https://lmstudio.ai/blog/openresponses), it defaults to `http://localhost:11434/v1/responses` running locally on your computer - Codex CLI can be used with the Responses API hosted by a cloud provider such as Azure Let’s explore how Codex creates the prompt for the first inference call in a conversation. ### Building the initial prompt As an end user, you don’t specify the prompt used to sample the model verbatim when you query the Responses API. Instead, you specify various input types as part of your query, and the Responses API server decides how to structure this information into a prompt that the model is designed to consume. You can think of the prompt as a “list of items”; this section will explain how your query gets transformed into that list. In the initial prompt, every item in the list is associated with a role. The `role` indicates how much weight the associated content should have and is one of the following values (in decreasing order of priority): `system`, `developer`, `user`, `assistant`. The [Responses API](https://platform.openai.com/docs/api-reference/responses/create) takes a JSON payload with many parameters. We’ll focus on these three: - [`instructions`](https://platform.openai.com/docs/api-reference/responses/create#responses_create-instructions): system (or developer) message inserted into the model’s context - [`tools`](https://platform.openai.com/docs/api-reference/responses/create#responses_create-tools): a list of tools the model may call while generating a response - [`input`](https://platform.openai.com/docs/api-reference/responses/create#responses_create-input): a list of text, image, or file inputs to the model In Codex, the `instructions` field is read from the [`model_instructions_file`](https://github.com/openai/codex/blob/338f2d634b2360ef3c899cac7e61a22c6b49c94f/codex-rs/core/src/config/mod.rs#L1474-L1483) in `~/.codex/config.toml`, if specified; otherwise, the `base_instructions` associated with a model are used. Model-specific instructions live in the Codex repo and are bundled into the CLI (e.g., [`gpt-5.2-codex_prompt.md`](https://github.com/openai/codex/blob/e958d0337e98f6398771917867d7de689dab3b7a/codex-rs/core/gpt-5.2-codex_prompt.md)). The `tools` field is a list of tool definitions that conform to a schema defined by the Responses API. For Codex, this includes tools that are provided by the Codex CLI, tools that are provided by the Responses API that should be made available to Codex, as well as tools provided by the user, usually via MCP servers: #### JavaScript ```javascript 1[ 2 // Codex's default shell tool for spawning new processes locally. 3 { 4 "type": "function", 5 "name": "shell", 6 "description": "Runs a shell command and returns its output...",