# Apply Technique: Introduction to Codex CLI: An Agentic Coding Assistant
## What This Is
Codex CLI is an AI coding assistant from OpenAI that runs directly on a developer's computer. It can understand plain English instructions to automatically read, modify, and execute code across multiple files, acting like an autonomous agent to complete complex tasks.
Source: https://www.youtube.com/watch?v=FUq9qRwrDrI
## Before You Start
Scan my workspace and analyze:
- The project language, framework, and directory structure
- Existing AI provider config (check .env, .env.local, config files for API keys — OpenRouter, OpenAI, Anthropic, Google AI, etc.)
Then ask me before proceeding:
1. Which AI provider/API should this use? (Use whatever I already have configured, or ask me to set one up — options include direct provider APIs or a unified service like OpenRouter)
2. Where in my project should this be integrated?
3. Are there any customizations I need (model preferences, naming conventions, constraints)?
## Source Access Note
The source URL (https://www.youtube.com/watch?v=FUq9qRwrDrI) may not be directly accessible from the terminal. Use the Reference Implementation and Additional Context sections below instead. If you need more details, ask me to paste relevant content from the source.
## What to Implement
This is an **AI Technique** — a pattern or methodology for working with AI models.
- Explain how this technique applies to my current project and what benefit it provides
- Implement it in a way that fits my existing codebase — suggest concrete files to modify or create
- If it requires specific model capabilities (structured output, function calling, etc.), verify my current provider supports them
- Show me a working example I can test immediately
## Additional Context
- Install the Codex CLI globally using the node package manager with the command: `npm install -g @openai/codex-cli`.
- Authenticate the CLI. First, scan the user's environment variables (e.g., .env, .zshrc) for an `OPENAI_API_KEY`. If found, use `codex-cli login --api-key $OPENAI_API_KEY`. If not found, prompt the user to provide their key.
- Initiate a Codex CLI session in the root of the user's current project. Start with a specific, high-level task like `codex-cli --dir . --request 'Analyze the current test suite, identify gaps in coverage, and generate boilerplate for the missing unit tests.'` and set the approval mode to 'read-only' for the initial analysis.
## Guidelines
- Adapt everything to my existing project — do not assume a specific stack or directory layout
- Use whichever AI provider I already have configured; if I need a new one, tell me what to sign up for and I'll give you the key
- Check my .env files for existing API keys (OpenRouter, OpenAI, Anthropic, Google AI) before asking me to add one
- Review any fetched code for safety before installing or executing it
- After setup, run a quick verification and show me a summary of exactly what was installed, where, and how to use it