OpenAI's $1 million initiative backing Codex CLI projects marks a transformation in the way developers write and manipulate code. Developers can now read, modify, and run code through natural language commands directly from their terminal, thanks to the OpenAI Codex CLI.
A CLI tool processes text commands through a command-line interface. The OpenAI Codex does more than simple CLI operations - it merges ChatGPT-level reasoning with local code execution capabilities. This powerful tool runs completely on your system and protects data privacy. It accepts various inputs, from screenshots and sketches to text prompts.
Let's get into Codex CLI's features, from configurable approval modes to multimodal capabilities. You'll discover its practical uses in building features, debugging, and simplifying development processes. The tool works on macOS, Linux, and Windows (via WSL2), helping you increase your coding efficiency.
Installing and Configuring Codex CLI on Your System
Setting up OpenAI Codex CLI takes just a few simple steps before you can use its AI capabilities. The setup process is built with developers in mind and needs minimal configuration.
System Requirements: Node.js 22+, Git, and OS Compatibility
Your system needs to meet some basic technical requirements before installing OpenAI Codex CLI. The tool works with three main operating systems: macOS 12 or newer, Ubuntu 20.04+/Debian 10+, and Windows 11 (through WSL2). You'll need at least 4GB of RAM, but 8GB will give you better performance.
Node.js version 22 or newer is vital – the LTS (Long-Term Support) version works best for stability. This newer version helps the CLI handle complex AI-assisted code operations.
Git version 2.23+ isn't required but will help you get the most out of Codex CLI. This becomes important when you use features like version control integration and PR helpers. Without Git, you might miss out on some context-aware features that make Codex really shine in repository environments.
Installing via npm and Setting OPENAI_API_KEY
The installation process is quick once your system is ready. Just use npm:
npm install -g @openai/codexYou can also use yarn or bun:
yarn global add @openai/codex
# or
bun install -g @openai/codexThe next step is to add your OpenAI API key. This key lets Codex CLI connect to OpenAI's models:
export OPENAI_API_KEY="your-api-key-here"This command sets the environment variable only for your current terminal session. You can always update to the latest version by running codex --upgrade.
Using .env and config.yaml for Persistent Settings
Make your settings stick by adding the export line to your shell's config file (~/.zshrc or ~/.bashrc):
# Add to ~/.zshrc or ~/.bashrc
export OPENAI_API_KEY="your-api-key-here"Codex CLI also works with .env files. Create one at your project's root:
OPENAI_API_KEY=your-api-key-hereThe CLI loads these variables automatically through dotenv/config.
Advanced users can customize Codex through the ~/.codex/ directory. Set up a config.yaml file:
# ~/.codex/config.yaml
model: o4-mini # Default model for invocations
fullAutoErrorMode: ask-user # Behavior on error: ask-user or ignore-and-continue
notify: true # Enable desktop notifications for responsesYou can add personal touches by creating custom instructions in ~/.codex/instructions.md:
# ~/.codex/instructions.md
- Always respond with emojis
- Only use git commands if I explicitly mention you shouldGitHub CLI Install vs Codex Install: Key Differences
GitHub CLI and OpenAI Codex CLI serve different purposes. GitHub CLI brings GitHub features to your terminal, letting you work with pull requests and issues. Codex CLI helps you write, modify, and understand code using AI.
These tools install differently too. GitHub CLI offers many options in a variety of platforms, including Homebrew, MacPorts, Conda, Spack, Webi, and downloadable installers. Since version 2.50.0, it includes build provenance attestation for cryptographic verification.
Codex CLI keeps things simple with a single npm command. You just need an OpenAI API key to get started, making the setup much lighter than GitHub CLI.
Both tools make terminal work easier but focus on different things. GitHub CLI extends GitHub's features while Codex CLI brings AI coding help right to your command line.
Understanding What a CLI Tool Is and Why Codex Matters
CLI tools power developer workflows worldwide. OpenAI's latest venture into developer productivity with Codex CLI makes it crucial to understand both CLI basics and this new tool's impact.
What is a CLI Tool: A Developer's Viewpoint
A command line interface (CLI) lets you interact with your operating system through keyboard commands instead of graphical elements. Developers find several advantages in using CLIs:
- Efficiency: You can execute complex operations with a single text command instead of clicking through visual menus
- Remote access: CLI apps use fewer network resources than graphical interfaces. This makes them perfect for managing remote servers with limited bandwidth
- Troubleshooting: The text-based nature gives you detailed error messages and debugging information that you can document easily
- Automation: You can combine commands into scripts that handle repetitive tasks automatically
The CLI works through a shell program that acts as a bridge between you and the operating system. The shell reads your command, looks for the matching executable file, and tells the operating system what to do.
One developer put it well: "Knowing CLI commands and using quality tools can make you more productive and can also open doors to various approaches to automation that are far more practical with textual interfaces than with GUI apps".
How Codex CLI Improves Traditional CLI Workflows
Standard command lines need exact syntax and deep command knowledge. OpenAI Codex CLI removes these barriers. Launched on April 15, 2025, this tool changes terminal interactions by adding natural language processing right in your command line.
Codex CLI runs on your machine, unlike cloud-based AI coding assistants. This keeps your privacy intact while giving you immediate help. Your source code, environment variables, and system-level commands stay off the cloud—a key benefit for security-focused teams.
The tool brings breakthrough multimodal features. You can use text prompts, screenshots, and rough sketches to tell your computer what you want to build, fix, or understand. You might ask: "Move all log files older than 30 days to an archive folder," and the tool translates it to proper syntax.
The "--approval-mode" settings let you decide how autonomous the tool becomes. Options range from suggestion-only to fully automated execution. This human oversight prevents unexpected commands or file operations.
OpenAI Codex Explained for First-Time Users
OpenAI Codex builds on GPT-3 and has learned from both natural language and billions of lines of source code in public repositories. The model specializes in programming tasks, unlike standard language models.
It analyzes your prompts—whether they're natural language descriptions or code snippets—and predicts the most likely code to follow. Tell it to "sort a list of numbers in Python," and it generates code using built-in functions or algorithms.
The tool works with Python, JavaScript, Bash, C#, Java, Ruby, Rust, SQL, TypeScript, and other languages. Python remains its strongest language. It does more than generate code—it explains, transpiles, and refactors it too.
New users should know that Codex doesn't execute code or "think" logically. It uses patterns from its training data to predict what comes next. While it speeds up coding tasks, you need to check its output for bugs, security issues, or outdated practices.
Codex CLI shows OpenAI's push toward autonomous software agents. The tool gives AI models direct access to your system's core tools: file systems, interpreters, build tools, and more.
Exploring Codex CLI Approval Modes and Safety Controls
Security is crucial for any tool that runs code on your system. OpenAI Codex CLI tackles this challenge with a complete approach to permissions and sandboxing. Developers get full control over what the AI can and cannot do.
Suggest vs Auto Edit vs Full Auto: What Each Mode Does
Codex CLI gives you three distinct approval modes that control how much freedom the AI assistant has with your codebase:
The default Suggest mode puts safety first. It's perfect to learn codebases or review code. You must approve any action that changes files or runs commands.
Auto Edit strikes a balance. It implements file changes on its own but needs your approval for shell commands. This mode shines during refactoring or making similar edits across files.
Full Auto gives maximum freedom. Codex can read/write files and run commands without breaking your flow. You can fix broken builds or prototype features while focusing on other tasks. The mode runs with strong safety limits - each command operates without network access and stays within your current working directory.
You can switch modes easily with command-line flags (--suggest, --auto-edit, --full-auto) or use the /mode shortcut during operation.
Sandboxing with Apple Seatbelt and Docker Containers
Security mechanisms adapt based on your operating system:
macOS 12+ users benefit from Apple's Seatbelt technology (sandbox-exec). The system creates a secure read-only environment. Write access exists only for specific folders: your current working directory ($PWD), temporary folders ($TMPDIR), and Codex configuration files (~/.codex). The system blocks all outbound network access - even child processes can't make external connections.
Linux users get Docker as their sandboxing solution. Codex runs in a minimal container image that mounts your repository with read/write permissions. A custom iptables/ipset firewall script blocks network traffic except OpenAI API connections. This setup creates reliable, reproducible runs without root access on the host system.
How Codex CLI Prevents Unsafe Shell Commands
Codex CLI checks if Git tracks your directory before starting in auto-edit or full-auto mode. You'll see a warning and confirmation prompt if it doesn't. This ensures you can review and revert changes through version control if needed.
Full Auto mode adds extra protection. Commands run without network access and stay within your current working directory plus allowed temporary files. This stops data leaks or system changes outside your project.
Codex CLI shows you exactly what commands it plans to run. You can type n to stop any suspicious command or file change and guide future suggestions.
Your files and commands stay on your machine. OpenAI's servers only receive your prompts, high-level context, and optional diff summaries needed to generate proper responses.
Using Codex CLI to Read, Modify, and Run Code
Codex CLI has three core functions: reading existing code, modifying files, and executing commands. The tool revolutionizes your terminal into an AI-powered workstation that lets you develop software using natural language.
openai codex example: Refactor a File with One Command
Code refactoring showcases Codex CLI's impressive capabilities. You might need to modernize a React class component into hooks. Rather than making manual edits, you can type:
codex "Refactor the Dashboard component to React Hooks"
Codex analyzes the component structure, rewrites it using React's hook pattern, runs tests to verify functionality, and shows the changes to approve. This shows how Codex CLI connects natural language with executable code.
You could also ask:
codex "Modify the discount function utils/priceUtils.js to apply a 10 percent discount"
Codex works best with clear instructions. Name the specific file or function you want to edit and describe the behavior changes clearly. This makes complex code changes available through simple English commands.
apply_patch Command and File Diff Review
Codex CLI makes file changes through a specialized apply_patch command. The model formats its response with specific patch instructions when it needs to modify files:
{"cmd":["apply_patch","*** Begin Patch..."]}This command triggers [execApplyPatch](https://www.philschmid.de/openai-codex-cli) from internal utility functions and calls process_patch to handle modifications. The CLI displays a diff view of the proposed changes before applying them:
- Original code sections in red
- Proposed changes in green
- Context around the changes
The interface asks "Allow command? [y/N/e/a]" where:
yapproves changesNrejects them (default)elets you edit the patchaapproves all future patches
This review process gives you control over every codebase modification, whatever approval mode you choose.
Running Shell Commands with Approval Policies
Codex CLI executes shell commands to install dependencies, run tests, or perform Git operations. The shell function (or container.exec) works through the AgentLoop that calls handleExecCommand.
Your selected approval policy determines how commands work:
- Suggest mode (default): You must approve all shell commands
- Auto Edit mode: File changes apply automatically but commands need approval
- Full Auto mode: Commands run in the sandboxed environment directly
The CLI has built-in safety measures. The approvals system's canAutoApprove checks if commands are safe. Full Auto mode runs with no network access and stays in your current directory. The system warns you when working in directories without Git tracking.
You can type n to reject any command that concerns you and guide the model toward better options. This feedback helps Codex understand your priorities over time.
The command execution system balances automation with safety. You decide how much freedom to give your AI assistant.
Working with Project Context and Memory Files
Context plays a crucial role in AI coding assistance. The OpenAI Codex CLI excels by building project understanding from multiple sources. This creates individual-specific experiences that adapt to your priorities and project needs.
codex.md and instructions.md: How Context is Built
The OpenAI Codex CLI employs a layered approach to gather context through Markdown files, merging instructions in a specific priority order:
~/.codex/instructions.md- Personal global guidance that works in any disciplinecodex.mdat repository root - Shared project notes and conventionscodex.mdin current working directory - Sub-package specific instructions
This structure adds more specific context as you move from global priorities to project-specific guidance. To cite an instance, your global instructions might define code style priorities, while a project-level codex.md could outline architecture patterns and naming conventions.
You can disable this feature with the --no-project-doc flag or set the environment variable CODEX_DISABLE_PROJECT_DOC=1. This helps when you want Codex to focus on your direct prompts.
These files' content substantially influences how the AI interprets and responds to your commands. Note that OpenAI's prompting guide states, "For more complex tasks requiring longer form input, you can write the instructions as a file on your local machine: codex "$(cat task_description.md)"".
How Codex Uses Git Status and File History
The Codex CLI makes use of Git integration to build a dynamic understanding of your codebase. The tool can access Git status information and shows staged changes, unstaged changes, and untracked files.
This helps Codex understand your repository's current state before suggesting commands or making changes. Such awareness prevents accidental overwrites and provides context about your ongoing work.
Git history shapes how Codex understands your codebase. By accessing commit history through git log, the CLI can analyze your project's development. This point of view helps Codex make better suggestions based on your development workflow patterns.
Git doesn't explicitly store renamed file information but detects them during diff operations. Codex can tap into this detection when files move or get renamed. Complex cases with substantial modifications might lose this connection.
Experimental --full-context Mode Explained
Codex CLI's standard mode provides file context when the model asks to read a file or when content appears in previous outputs. The experimental --full-context mode takes a different approach.
This experimental mode follows a specific workflow:
- Walks through your directory structure
- Reads and caches files via specialized utilities
- Formats the prompt, directory structure, and file contents into a single large request
- Expects the model to return all necessary file changes in a specific schema
This approach gives the model a detailed view of your project upfront. Better and more context-aware responses become possible. All the same, this means larger API requests and higher token usage.
Large projects might exceed token limits in full-context mode. This makes it ideal for smaller projects or focused directories where the entire context fits within the model's capacity.
Working with context in Codex CLI balances providing enough information for smart assistance while managing model capabilities and API usage limits.
Materials and Methods: Setting Up a Test Project
You need the right setup and a good understanding of various options to test OpenAI Codex CLI. Developers should test the tool's capabilities in a safe environment before using it in actual projects. Let's set up a test environment and learn about the most important flags.
Creating a Sample Git Repo for Codex CLI Testing
Start with a dedicated test repository to test the openai codex cli capabilities:
mkdir first-task && cd first-task
git initGit initialization matters because Codex CLI checks if Git tracks your directory. The tool shows a warning and asks for confirmation when version control is missing. This gives you a safety net to review and undo changes when needed. Git also helps Codex understand your project's structure and history to generate better suggestions.
Create a simple prompt to make sure everything works:
codex "Create a file poem.html that renders a poem about intelligence and programming"Codex works in suggest mode at first and asks for your approval before making changes. Keep selecting "Yes (y)" until the task finishes, then check the result with open poem.html.
Using codex --quiet and --model Flags
The --quiet (or -q) flag turns on non-interactive "quiet mode." This works great for automation and CI/CD pipelines:
codex -q "fix build issues"This flag turns off interactive UI elements and works well with scripted environments. You can use it with --json to parse structured output.
Here are other vital flags:
--model/-m: Choose your OpenAI model--approval-mode/-a: Pick automation level (suggest,auto-edit,full-auto)--notify: Get desktop notifications for responses
CI environments can use environment variables instead of flags:
export CODEX_QUIET_MODE=1Testing with o4-mini and o3 Models
The o4-mini model comes as default and works quickly with fewer tokens. Complex tasks might need other models:
codex -m o3 "Write unit tests for utils/date.ts"Developers got access to o3 and o4-mini through the Chat Completions API and Responses API in April 2025. These models think better than before, and o4-mini handles common coding tasks quickly.
Check which models your account can use before you start testing:
codex
/modelThis command shows all models your API key can access. Switch to a different model if you see "The model does not appear in the list of models available to your account".
Create different prompts to test specific tasks like refactoring, bug fixing, and code explanation. This helps you find the best model for your needs.
Results and Discussion: Real-World Use Cases and Performance
Performance metrics reveal the real story behind any development tool. Our tests of OpenAI Codex CLI in multiple projects and environments show impressive capabilities along with some limitations.
openai codex api Response Time and Latency
The model you select substantially affects response times. Quick iterations work best with the o4-mini model, which delivers the fastest performance for common coding tasks. Better results come from the o3 model, though it needs more time to process complex operations.
The system's latency depends on four key factors: model complexity, prompt length, tokens generated, and server load. Each output token adds about one millisecond to total response time. Users notice better performance with shorter outputs.
The user experience improves with streaming capabilities. Setting stream: true lets tokens appear right away instead of waiting for complete sequences. This creates a snappier feel even if the total generation time stays the same.
Success Rate in Refactoring and Test Generation
The CLI shines at specific tasks like refactoring React components and generating unit tests. It analyzes class structure, converts components, and runs tests to check functionality whenever you ask it to "Refactor the Dashboard component to React Hooks."
Test generation works better with clear specs and smaller scope. The CLI can keep improving tests until they pass, especially with smaller functions. Complex codebases that need deep architectural knowledge reduce its effectiveness.
Comparison with Claude Code and Aider
Recent evaluations show Claude Code handles complex codebases better than Codex CLI. Claude Code understands context impressively well, while Codex isn't a match for larger projects unless you use experimental --full-context mode.
Codex brings several benefits to the table: it runs as a lightweight CLI without needing a browser, saves money (costing $3-4 per code change with the o3 model compared to pricier alternatives), and lets you customize more thanks to its open-source foundation.
The CLI matches Aider's basic capabilities but might reason better through OpenAI's latest models. Both tools help you code from the command line.
Limitations and Known Issues in Codex CLI
OpenAI Codex CLI has powerful features, but developers need to understand its limitations before adding it to their development process. These restrictions can affect how well it works in specific situations.
Zero Data Retention (ZDR) Organization Restrictions
The Codex CLI doesn't work with organizations that have Zero Data Retention (ZDR) policies turned on. This happens because the tool uses the Responses API with store:true to process internal reasoning steps. Organizations with ZDR policies don't allow data storage without direct deletion control. They get this error when trying to use the CLI:
- Status: 400, Code: unsupported_parameter
- Type: invalid_request_error
- Message: "Previous response cannot be used for this organization due to Zero Data Retention"
No workarounds exist right now. Your organization can't use Codex CLI if it follows ZDR policies until OpenAI fixes this issue. Standard OpenAI API retention is 30 days for eligible endpoints.
Model Hallucination and Context Window Limits
The openai codex api doesn't deal very well with hallucinations, especially with bigger codebases. Codex sometimes adds features that don't exist, like describing APIs and server backends in applications that don't have them. This gets worse as the context grows larger.
Models start losing coherence after they process 10,000-20,000 tokens. GPT-4 Turbo can handle 128,000 tokens, but it doesn't work as well with very long contexts because it focuses on recent information. Complex refactoring tasks might give unreliable results, so developers need to check outputs carefully.
Users say all problematic results (hallucinations, circular reasoning) happen when they overload context or continue sessions across multiple tasks.
Lack of Native Windows Support Without WSL2
The openai codex cli needs Windows Subsystem for Linux 2 (WSL2) for Windows 11 users, unlike github cli which offers native Windows binaries. Windows developers face an extra setup step because of this requirement.
The tool works great on macOS and Linux with Node.js 22+, but Windows support is still experimental. Microsoft developers must set up WSL2 with a Linux distribution that works with it before they can use Codex CLI.
Conclusion
OpenAI Codex CLI marks a most important step forward in AI-powered coding assistance. This tool brings natural language processing right to developers' terminals. Ground applications and thorough testing prove its value in everything from basic code generation to complex refactoring tasks.
The tool's standout features make it truly powerful. Developers can retain control through configurable approval modes. A sandboxed execution environment keeps everything secure without limiting functionality. The system's context-aware capabilities work through Git integration and memory files to deliver precise, project-specific help.
Developers should think over some limitations though. Organizations with Zero Data Retention policies can't use the tool. Windows users need WSL2 to run it. The system sometimes produces hallucinations when handling large codebases. These constraints exist, but Codex CLI still delivers major benefits - faster development cycles, better code quality, and optimized workflow integration.
Codex CLI shows us what AI-powered development tools can do. Models will get better and capabilities will grow. Developers can expect more sophisticated coding assistance that keeps the security and control they need.
