The video introduces OpenAI’s Codex, a coding agent that helps developers automate routine tasks and streamline workflows through a CLI, VS Code extension, and cloud integration. It covers installation, configuration, best practices for project context and prompting, advanced use cases, and customization options, providing practical tips and resources for effectively integrating Codex into development processes.
The video is an introductory session on getting started with OpenAI’s Codex, presented by Derek and Charlie. Codex is OpenAI’s coding agent designed to help developers automate routine and time-consuming tasks, allowing them to focus on more complex challenges like design and architecture. The presenters outline the session’s agenda, which includes an overview of Codex, installation and setup, configuration using agents.md and config.toml, prompting best practices, tips for using the CLI and IDE, advanced use cases, and additional resources. Codex can be used through a command-line interface (CLI), a VS Code-based IDE extension, and in cloud environments, enabling developers to run tasks asynchronously and even from mobile devices.
The installation process for Codex is straightforward, with recommended methods being Homebrew or npm to ensure users have the latest updates. The CLI is open source, and users can check the changelog on the developer website. The VS Code extension can be installed directly from the extensions tab, and users are encouraged to enable auto-updates. Signing in is done via a ChatGPT Enterprise account, either through the CLI or the IDE extension, with single sign-on support. Once installed, users can check their session status, model, and configuration using simple commands, and the presenters demonstrate how to clone and set up a sample repository for hands-on learning.
A key feature of Codex is the agents.md file, which acts as a lightweight, always-loaded README to provide Codex with essential project context. This file can be generated automatically or customized for global or project-specific settings. Best practices for agents.md include keeping it concise, focusing on enabling agentic loops (such as automated testing and linting), and updating it as Codex encounters new challenges or mistakes. The presenters also discuss referencing additional documentation files for task-specific guidance and using planning templates like plans.md to help Codex manage large, multi-step tasks effectively.
Customization is further enhanced through the config.toml file, where users can set default models, reasoning effort, sandbox modes, approval policies, and feature toggles like web search. Codex supports profiles for different workflows and integrates with MCP (Model-Context Protocol) servers to connect with external tools like Figma, Jira, Linear, and Datadog. The presenters demonstrate adding a simple custom MCP server and show how Codex can fetch and integrate external data into a project. They also highlight the ability to add custom commands and prompts, making Codex highly adaptable to various team workflows.
Throughout the session, practical tips are provided for effective prompting, context management, and leveraging Codex’s features in both the CLI and IDE. Examples include using image inputs for UI changes, resuming previous sessions, generating sequence diagrams, and conducting code reviews with a focus on critical issues. Advanced use cases are covered, such as programmatic usage in CI/CD pipelines, structured output for automation, and multi-agent workflows using the OpenAI Agents SDK. The video concludes with pointers to documentation, cookbooks, and admin resources, encouraging viewers to explore Codex further and integrate it into their development processes.