Unusually detailed post explains how OpenAI handles the Codex agent loop. The article dives into the technical aspects of OpenAI's Codex CLI coding agent, including the agent loop, prompt construction, caching, and context window management.
The article details how their Codex CLI coding agent functions. OpenAI engineer Michael Bolin explains the "agent loop" – the process by which the AI receives user input, generates code, runs tests, and iterates with human supervision.
* **Agent Loop Mechanics:** The agent builds prompts with prioritized components (system, developer, user, assistant) and sends them to OpenAI’s Responses API.
* **Prompt Management:** The system handles growing prompt lengths (quadratic growth) through caching, compaction, and a stateless API design (allowing for "Zero Data Retention"). Cache misses can significantly impact performance.
* **Context Window:** Codex automatically compacts conversations to stay within the AI model's context window.
* **Open Source Focus:** OpenAI open-sources the CLI client for Codex, unlike ChatGPT, suggesting a different approach to development and transparency for coding tools.
* **Challenges Acknowledged:** The article doesn't shy away from the engineering challenges, like performance issues and bugs encountered during development.
* **Future Coverage:** Bolin plans to release further posts detailing the CLI’s architecture, tool implementation, and sandboxing model.
The article details how their Codex CLI coding agent functions. OpenAI engineer Michael Bolin explains the "agent loop" – the process by which the AI receives user input, generates code, runs tests, and iterates with human supervision.
* **Agent Loop Mechanics:** The agent builds prompts with prioritized components (system, developer, user, assistant) and sends them to OpenAI’s Responses API.
* **Prompt Management:** The system handles growing prompt lengths (quadratic growth) through caching, compaction, and a stateless API design (allowing for "Zero Data Retention"). Cache misses can significantly impact performance.
* **Context Window:** Codex automatically compacts conversations to stay within the AI model's context window.
* **Open Source Focus:** OpenAI open-sources the CLI client for Codex, unlike ChatGPT, suggesting a different approach to development and transparency for coding tools.
* **Challenges Acknowledged:** The article doesn't shy away from the engineering challenges, like performance issues and bugs encountered during development.
* **Future Coverage:** Bolin plans to release further posts detailing the CLI’s architecture, tool implementation, and sandboxing model.
One source of truth for all your AI CLI skills. Install once, sync everywhere — Claude, Cursor, Codex, Gemini, and 10+ more.
An extensible Model Context Protocol (MCP) server that provides intelligent semantic code search for AI assistants. Built with local AI models using Matryoshka Representation Learning (MRL) for flexible embedding dimensions.
This article details how the author successfully ran OpenAI's Codex CLI against a gpt-oss:120b model hosted on an NVIDIA DGX Spark, accessed through a Tailscale network. It covers the setup of Tailscale, Ollama configuration, and the process of running the Codex CLI with the remote model, including building a Space Invaders game.
Lightweight coding agent that runs in your terminal, allowing chat-driven development with the power to execute code and manipulate files.
The Book of Kells, a revered illuminated manuscript from medieval Europe, has been digitized and made available online by Trinity College Library. This high-resolution digital collection offers unprecedented access to the intricate and detailed artwork of this Irish cultural treasure, enhancing its global reach beyond physical visits to Dublin. The article also mentions a free online course on the Book of Kells, offered by Trinity College professors Rachel Moss and Fáinche Ryan, which explores the manuscript’s history and its significance to various communities, especially the Irish people.
