What is Codex in ChatGPT and how does it work?

TL;DR

Codex in ChatGPT enables remote code agents that run in parallel across repositories and compute. It uses Codex One, a specialized coding model, to explain code, fix bugs, and generate proactive tasks, all within a configured environment. The system demonstrates multi agent orchestration and end to end reinforcement learning to improve maintainability and speed.

Transcript

hello everyone and welcome Software engineering is changing and by the end of 2025 it's going to look fundamentally different In 2021 we announced our first model that we called codeex This was the first time that we've really demonstrated what we would now call Vibe coding Just a couple weeks ago we released Codex CLI which is a local agent that r... Read More

Key Insights

• Codex is a remote software agent system that runs many tasks in parallel across a repository environment.
• Codex One is the optimized coding model designed for real world code changes, not just benchmarks.
• Tasks run in isolated microVM sandboxes with dedicated CPU, memory, and network policies for safety and reproducibility.
• The system supports explain tasks, bug fixing tasks, and proactive task suggestion tasks to continuously improve the codebase.
• Agents can reproduce issues by writing test scripts and running them to verify behavior.
• Agent MD files provide guidelines on repository structure, style, and commit messages to steer behavior.
• Codeex demonstrates end to end reinforcement learning to ensure agents complete cycles from coding to testing.
• The demo includes a CLI and integration with GitHub, supporting local and remote agent interactions.
• More videos from this channel:
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  • How to train a robotic hand policy using simulation
  • How to amplify internal expertise with AI
• More from this channel: OpenAI

Questions & Answers

Q: What is Codex in ChatGPT and what is its purpose?

Codex in ChatGPT is a remote software agent system that runs multiple tasks in parallel on a user’s repository. Its purpose is to explain code to newcomers, find and fix bugs, and generate proactive task suggestions, all while operating within an OpenAI compute environment. The system uses a specialized Codex One model optimized for real world coding needs, including comments, extraneous changes, and coding style, to accelerate development.

Q: How does Codex One differ from previous models?

Codex One is described as the best coding model to date, optimized beyond benchmarks to handle the kind of code developers actually want to merge. It focuses on understanding code structure, maintaining style, and producing useful changes rather than just raw performance metrics. This makes it better suited for practical tasks like fixing bugs and maintaining readability within a repository.

Q: What kinds of tasks can Codex agents perform?

Codex agents can perform explain tasks to describe the codebase, code tasks to locate and fix bugs, and proactive tasks where the agent suggests improvements or new tasks. In the demo, agents also fix typos, adjust timeouts, and create tests to reproduce issues, illustrating end to end automation from diagnosis to verification.

Q: How are tasks executed and isolated in Codex?

Tasks run in their own microVM sandbox, each with its own file system, CPU, memory, and network policy. This isolation ensures that agents can operate independently without interfering with one another. The environment can be configured with secrets, setup scripts, and dependencies to mimic a developer’s local setup while maintaining safety.

Q: What role do agent MD files play in the Codex workflow?

Agent MD files provide explicit guidelines and instructions for how the agent should behave, including repository layout, commit messages, and PR messages. During a task, the agent consults the MD file to align its actions with the project’s expectations, enabling more predictable and controllable automation.

Q: How does the system verify that an agent’s work is correct?

The agents perform verification by running tests and linters, and by generating and executing small scripts to reproduce issues. This end to end reinforcement learning approach ensures the agent completes the entire cycle from coding to testing, and adheres to style and correctness checks before proposing commits.

Q: What does the demo reveal about scaling and collaboration?

The demo shows multiple agents working concurrently on different repos, demonstrating scalable parallel processing. It emphasizes collaboration by allowing developers to interact with the system through a CLI and GitHub integration, and by presenting tasks that can be delegated or run automatically across projects.

Q: When is Codex in ChatGPT available to users?

The Codex system is being rolled out to ChatGPT for Pro, Enterprise, and Teams users starting today, with follow up plans for Plus and EDU. This rollout indicates a phased approach to adoption, enabling enterprises to harness parallel task execution and automated code improvements within their existing tools and workflows.

Summary & Key Takeaways

• Codex in ChatGPT introduces a remote software agent that runs tasks in parallel across repos, powered by Codex One. The demo highlights explainability, debugging, and proactive task generation within a configurable environment. The approach emphasizes scaling software engineering with agent collaboration.

• The talk showcases how agents operate in a sandboxed OpenAI compute environment, manage multiple tasks concurrently, and adjust code bases through linting, formatting, and testing to maintain code quality.

• The speakers describe end to end reinforcement learning used to train agents, demonstrate real time task execution, and illustrate how this framework can accelerate development while preserving code style and maintainability.