Complete Guide to OpenAI Codex: From Installation & Configuration to Automation in Practice

OpenAI Codex isn't just a chatbot—it can work directly within real projects, modifying code across files, running automated tests, investigating bugs, and even helping you turn an idea from concept into a usable product. But faced with such a powerful tool, many people open the interface and freeze: "How exactly am I supposed to use this?" Based on a detailed tutorial video by Bilibili creator Ellie, this article systematically covers the complete workflow of Codex, from installation and configuration to advanced project management.

OpenAI Codex: Positioning and Technical Background

OpenAI Codex was originally a code generation system based on the GPT series of large language models. When first released in 2021, it primarily served as the underlying engine for GitHub Copilot. The new version of Codex released in 2025 has evolved from a simple code completion tool into an AI Agent with full project comprehension capabilities. It can execute code in sandbox environments, access file systems, and run terminal commands—essentially combining the reasoning capabilities of large language models with operating system-level execution abilities. This transformation from "advisor" to "executor" represents the industry trend of AI tools evolving from Copilot (co-pilot) to Autopilot (self-driving).

Installation and Basic Configuration

Download and Login

Installing Codex is straightforward. Visit openai.com/codex to download the installer for your system, complete the installation, and open it. On first launch, Codex will ask you to log in, offering two methods:

• ChatGPT Account Login: This is the most recommended approach for getting started. Note that Codex usage is tied to your subscription plan—Free, Go, Plus, and Pro tiers each provide different amounts of computing resources. Free accounts have very limited quotas, so if you plan to use it for real projects, at least a Plus plan is recommended.
• OpenAI API Key Login: Pay-per-use billing through the API, suitable for developers or users with specific needs.

Codex relies on cloud computing power to run. Each time Codex executes a task, it actually launches an isolated computing environment on OpenAI's cloud servers (similar to containerized deployment), where it clones your project code, installs dependencies, and performs operations. This means complex tasks consume significant computing resources. The differences between subscription tiers are mainly reflected in: available model versions (e.g., o3 vs o4-mini), number of concurrent tasks, maximum runtime per task, and total monthly compute quota. The Pro plan typically provides the highest priority compute allocation and longest task execution times.

Permission Settings: Three Levels of Control

Codex's permission management is a key concept that determines how much freedom Codex has on your computer:

1. Ask for Approval: Codex can read files and recommend changes, but cannot edit or execute commands on its own.
2. Approve for Me: Codex can automatically read and edit files—this is the recommended mode for daily use.
3. Full Control: Give Codex a task, and it will independently complete all steps.

This three-tier permission design embodies the core principle of "Human-in-the-Loop" in AI safety. In scenarios where AI agents can perform real operations, permission control is the key mechanism for preventing accidental damage. "Ask for Approval" mode is similar to the sudo confirmation mechanism in Linux systems, where every sensitive operation requires manual authorization; "Approve for Me" is like granting regular permissions to a trusted user; "Full Control" is equivalent to root access. This progressive authorization design lets users flexibly adjust their level of control based on the risk level of the task and their trust in AI output. When working with production environment code or projects containing sensitive data, it's advisable to always use lower permission levels.

Project Folder Setup

Before using Codex, you need to specify a working folder. Click "Work in a project," select an existing folder or create a new one, and Codex will place all project files there.

Practical Demo: Using Codex to Organize Video Assets

The video showcases a highly practical use case—using Codex to organize a large collection of unboxing video footage shot years ago. The volume of footage was so large that the creator couldn't even remember what was in each clip.

After sending instructions to Codex, it began examining all files in the folder, not only sorting them by recording time but also attempting to understand the role each clip plays in the video. Ultimately, Codex generated an Excel spreadsheet detailing the content description of each video, and automatically created a storyboard and README file.

When Codex processes video assets, it doesn't "watch" the video content like a human would. It primarily obtains information by reading file metadata, including filename, creation time, modification time, file size, video duration, resolution, encoding format, and more. For deeper content understanding, Codex may invoke multimodal capabilities to analyze video keyframes, or infer content categories through file naming patterns and directory structures. This also explains why good file naming habits significantly improve AI tool processing effectiveness—the richer the metadata, the more accurate the AI's inferences.

There's an important cognitive distinction here: Codex isn't telling you what to do—it's directly executing work within your project. But this doesn't mean you can blindly trust the results; review is still necessary.

Common Issues and Pitfalls to Avoid

• Wrong project folder selected: Always confirm the current working folder is correct before sending a task.
• Permission mismatch: Ensure permission settings match your expectations.
• Instructions too vague: "Clean up these assets" is too broad. A better approach: "Examine the footage in the folder, do not delete, move, or modify original files, create a suggested sequence, descriptive filenames, and a separate asset inventory."
• Real-time course correction: If you notice things going off track during processing, you can input new instructions at any time and click "Steer" to adjust direction.

Advanced Project Management Techniques

Real projects don't end after a single prompt. You'll change your mind, add tasks, test new ideas, fix problems—this requires systematic project management capabilities.

Conversation Management Strategy

A single Project can contain multiple Conversations, each corresponding to a specific task. For example: one conversation for asset organization, another for script writing, and a third for missing footage review.

While you could cram everything into one conversation, it becomes extremely chaotic as work progresses. Separating different tasks into different conversations is key to maintaining project manageability.

Special emphasis: It's not recommended to have Codex complete an entire project in one go, especially during the learning phase. Reviewing 5 small tasks is far easier than reviewing one large task with 50 modifications.

Fork Feature

When you want to test a different direction while preserving the current approach, the Fork feature is invaluable. For example, if the original video is arranged chronologically but you want to try a "show the finished product first, then trace back to the unboxing" structure—Fork creates a new conversation branch from the current node. Both directions remain independent, making comparison easy.

Version Control

Version control is like creating save points for your project. You can have Codex create checkpoints of the current state, and every subsequent modification becomes traceable. If you're unhappy with changes, you can roll back to a previous version.

Codex's version control feature is most likely built on Git under the hood—the most widely used distributed version control system in the industry. Git builds a complete project history by recording every file change (commit), allowing users to jump between any points in time. Codex wraps this technical concept in a more user-friendly "checkpoint" interface, lowering the barrier for non-developer users. For developers, Codex's version control can seamlessly integrate with existing Git workflows—every AI-executed modification generates a corresponding commit record, ensuring all changes are traceable and reversible. This is particularly important in team collaboration—other members can clearly see which modifications were AI-generated.

The difference between Fork and Version Control: Fork creates branches in conversation direction; version control tracks the history of actual project files. Using both together lets you explore different approaches while ensuring file safety.

Skills, Plugins, and Automation Settings

Skills: Say Goodbye to Repetitive Instructions

If you find yourself repeating the same instructions every time you start a new project—"don't delete original files," "create an asset inventory," "use consistent naming conventions"—then it's time to use Skills.

Skills are essentially reusable Standard Operating Procedures (SOPs). You can package your preferred workflow into a skill, and next time you only need to invoke the skill name. For example, create a "Video Project Organizer" skill that includes all steps: protecting original assets, checking categories, suggesting names, creating inventories, and flagging uncertain clips.

In a new project, a single sentence invoking this skill will have Codex execute the preset workflow without writing instructions from scratch.

Plugins: Extending Capability Boundaries

Plugins are more powerful than skills—they can package larger reusable workflows, including skills, external application connections, and MCP server configurations. Codex's plugin directory covers data analysis, product design, Chrome browser control, and can even let Codex operate any application on your computer.

The MCP (Model Context Protocol) mentioned here is an open standard proposed by Anthropic and widely adopted by the industry. It defines how AI models communicate with external tools and data sources. In Codex's plugin system, MCP servers act as bridges between AI and external applications—they "translate" external service API capabilities into standardized interfaces that AI models can understand and invoke. For example, Canva's MCP server converts natural language instructions like "create a thumbnail" into specific Canva API call parameters. This architecture allows Codex's capabilities to expand infinitely—any service that provides an API can theoretically be integrated into the Codex ecosystem through the MCP protocol.

The video demonstrates the Canva plugin—having Codex create video thumbnails, then jumping directly to the Canva website for further editing after completion, achieving seamless integration between AI and design tools.

Automation Task Configuration

For repetitive work, you can set up automated tasks. For example: automatically checking for new assets every Friday, generating weekly reports of incomplete tasks, or reviewing missing items in project folders. But note: Automation should eliminate repetitive labor, not eliminate your awareness of project status.

Mobile Remote Control

Codex supports remote control via your phone. After scanning a QR code in Codex settings to connect your phone, you can check progress, review issues, and approve or reject requests even when away from your computer. The prerequisite is that your computer must remain powered on and running.

Summary: Three Levels of Mastery for Codex

The core value of Codex isn't being a "smarter search box"—it's the ability to participate in work as a genuine project collaborator. But to unlock its potential, you need to master three levels of capability:

1. Foundation Level: Proper installation, permission configuration, and writing clear instructions
2. Management Level: Leveraging conversation separation, Fork branching, and version control to organize complex projects
3. Efficiency Level: Eliminating repetitive labor through skills, plugins, and automation

The AI Agent working model that Codex represents is redefining human-machine collaboration. Traditional AI assistance tools (like code completion and grammar checking) follow the "tool paradigm"—humans lead, AI assists. Codex represents the "delegation paradigm"—humans define goals and constraints, AI autonomously plans and executes. This shift requires users to develop new capabilities: from "how to write code" to "how to define requirements, set boundaries, and review output." This closely resembles delegation skills in management—good managers don't do everything themselves; they excel at defining task boundaries, establishing review mechanisms, and intervening at critical points. Mastering this "AI management ability" may become one of the core competitive advantages in the future workplace.

The most important point: Codex is not a magic button. Give it clear tasks, control its access permissions, review its output, and correct it when needed—that's the right approach to using it.