Getting Started with Codex: Installation and Usage Guide for OpenAI's AI Coding Agent

What is Codex: From Code Completion to Autonomous Programmer

To truly understand the value of Codex, you first need to grasp one thing—it has gone through two distinctly different stages of development.

The early Codex was an AI model launched by OpenAI, and also the core engine behind GitHub Copilot. GitHub Copilot was released in 2021 as the first large-scale, commercially deployed AI coding assistant tool, powered at its foundation by the OpenAI Codex model—a model based on the GPT-3 architecture and fine-tuned on billions of lines of public code (primarily from GitHub), enabling it to understand code context and predict the next line or block of code.

It's worth noting that GPT-3 (Generative Pre-trained Transformer 3), released by OpenAI in 2020, is a large language model with 175 billion parameters that uses the self-attention mechanism of the Transformer architecture to process sequential data. The Transformer architecture was introduced by Google's research team in the 2017 paper Attention Is All You Need, fundamentally reshaping the technological landscape of natural language processing. Its core innovation—the self-attention mechanism—allows the model to simultaneously "see" information from all other positions in a sequence when processing a token at any given position, and to dynamically determine the importance of each position through learned weights. This is fundamentally different from the previously dominant RNN (Recurrent Neural Network) approach, which required processing sequences step by step in order, and it greatly improves parallel computation efficiency and the ability to model long-range dependencies. In code understanding scenarios, the self-attention mechanism enables the model to precisely capture cross-line associations between a "function definition" and a "function call," scope relationships between variable declarations and references, and the mapping between comment semantics and corresponding code logic—these are precisely the core capabilities required for code completion tasks.

In the coding domain, OpenAI took the technical route of "general language model + domain fine-tuning" to teach the model the syntactic structures of programming languages, function-call conventions, and common algorithmic patterns. The core of this approach lies in combining Transfer Learning with Supervised Fine-Tuning (SFT): during pre-training, the model learns the statistical regularities of language and world knowledge from vast amounts of general text; during fine-tuning, it continues training on code-specific datasets at a smaller learning rate, shifting its parameter distribution toward the programming domain while retaining its general capabilities. Code repositories on GitHub naturally contain strongly correlated structures between function signatures, documentation comments, unit tests, and implementations, providing abundant supervised signals for the model to learn the "intent → implementation" mapping. This technical paradigm was later adopted by virtually all AI coding tools, including competitors such as Amazon CodeWhisperer and Google's Gemini Code Assist. Copilot's success validated the commercial viability of "AI-assisted programming" and spawned a large number of subsequent competitors, thereby pioneering the entire AI coding assistant category. The early Codex worked more like a "co-pilot": you would write a natural-language description, such as "generate a sorting function," and it would automatically complete the corresponding code. Its historical significance lies in enabling machines, for the first time, to directly translate human language into executable code—a genuine icebreaker in the field of AI programming.

The new generation of Codex is a brand-new AI coding agent launched by OpenAI after reviving this brand. An AI agent refers to an AI system that can autonomously perceive its environment, formulate plans, and execute multi-step tasks—distinct from traditional models with single input-output cycles. Its core is the ReAct (Reasoning + Acting) loop: a framework proposed by Google researchers in 2022 that combines the "chain-of-thought reasoning" of large language models with "external tool interaction" into an alternating, cyclical process. Specifically, each ReAct step contains three phases: Thought (the model describes its current reasoning process in natural language, e.g., "I need to first check whether the file exists"), Action (invoking a specific tool, such as executing the shell command ls -la), and Observation (incorporating the tool's returned results into the context, e.g., "the file doesn't exist, I need to create it"), then proceeding to the next round of Thought. This "think-act-observe" closed loop enables the AI to handle complex tasks that require multi-step verification, rather than generating potentially erroneous output all at once. The model not only generates text but can also invoke external tools (such as terminal commands, file systems, and network requests) and feed the results back into the next reasoning step. In a programming context, this means Codex can form a complete loop of "write code → run code → read errors → fix code"—when code execution returns a TypeError, the model can incorporate the error message as a new observation into its reasoning and generate a targeted fix, rather than relying on static knowledge from training to guess, and rather than simply outputting a string of code it cannot self-verify.

This architectural shift also has a key technical pillar: the substantial expansion of the context window. The GPT-4o series of models on which the new-generation Codex is based expanded the context window to 128K tokens (about 100,000 words), and combined with Retrieval-Augmented Generation (RAG) technology, can build a vector index of the codebase and dynamically retrieve the most relevant code snippets by relevance to inject into the context—this enables the agent to focus on local modifications while understanding the overall architecture, and is one of the key technical breakthroughs in the leap from "code completion" to "independently taking on tasks." The complete architectural foundation also includes Function Calling capabilities, a long context window, and a Tool Use framework; together, these three support the agent's autonomous execution capabilities. Therefore, the new generation of Codex is no longer an assistant role, but an "AI programmer" capable of independently taking on complete tasks—you simply submit a requirement, and it autonomously analyzes, writes, runs, and tests code in a cloud sandbox environment, ultimately delivering a usable solution.

The cloud sandbox here is technically implemented, in most cases, using containerization—most commonly Linux containers (such as Docker) or more lightweight micro virtual machines (such as AWS Firecracker). The security isolation of Linux containers relies on two core kernel mechanisms: Namespaces handle resource-view isolation, ensuring that processes inside a container "cannot see" other processes, network interfaces, and file systems on the host; cgroups (control groups) handle resource quota management, limiting the maximum CPU time slices, memory usage, and disk I/O bandwidth. Compared with traditional virtual machines (VMs), which must run a complete guest operating system kernel, containers share the host kernel, reducing startup time to the millisecond level, allowing Codex to quickly create a brand-new, clean execution environment for each new task. AWS Firecracker strikes a balance between security and speed—it runs an independent microVM kernel to provide stronger security isolation while keeping startup time under 125 milliseconds, and is widely used by serverless services such as Lambda. Each sandbox is essentially an independent operating-system process namespace with its own file system, network stack, and process tree; even if the running code has errors or the system crashes, it will not affect the user's local computer. By executing tasks in a cloud sandbox, Codex achieves three major advantages: security (isolating risk), compute elasticity (invoking cloud resources on demand), and parallelism (multiple sandbox instances running different tasks simultaneously). The entire process requires no human intervention and is closer to the working mode of a junior development engineer.

The Four Forms of Codex

Codex is not a single product, but comes in four forms covering different use scenarios, and developers can flexibly choose according to their own habits.

Command-Line Tool (CLI)

The most fully-featured and flexible form, operated through a terminal (such as the Windows CMD window), aimed at experienced developers accustomed to command-line environments. It's flexible and powerful, but has a relatively higher learning curve.

IDE Extension Plugin

Can be seamlessly embedded into mainstream code editors such as VS Code and Cursor, serving as a smart assistant during daily coding. For developers already accustomed to working in an IDE, it requires almost no change to their workflow.

Web Version

Accessed directly through a browser with no local installation needed, suitable for quick experimentation or handling lightweight tasks.

Desktop App (Codex App)

A standalone desktop program positioned as the "command center for AI programming." Its core highlight is support for multiple AI agents working in parallel, allowing tasks to be delegated to cloud sandboxes for parallel execution—very well suited for handling complex, long-cycle development projects. Since this form currently offers the most complete experience and the widest usage, this tutorial series will use the desktop app as the primary subject of explanation.

Codex vs Claude Code: Which to Choose

Many developers who have used Claude Code (CC) are concerned about the differences between the two tools. Their positioning is similar, but they each emphasize different key dimensions.

On pricing, Codex is more competitive. To complete a project of equivalent scale, Codex costs around $2.50, while Claude Code may cost more than $10—a gap of nearly four times, which represents considerable cost savings for high-frequency users.

The fundamental reason for this gap lies in the billing mechanism: both tools use a pay-per-token model (a token is the basic unit the model uses to process text; typically 100 English words equal about 75 tokens). It should be specifically noted that the actual token consumption of AI coding tools far exceeds users' intuitive expectations—the system prompt (the hidden instructions that define the agent's behavior) typically occupies 500–2,000 tokens; the input and output of each round of tool calls (executing commands, reading files) can consume hundreds to thousands of tokens; and the full history of a multi-round conversation is repeatedly appended to the context of each request, meaning that for a 10-round conversation task, the actual billed token count could be the cumulative value of "average tokens per round" multiplied by 10. The price difference between Codex and Claude Code mainly stems from two dimensions: the different per-token pricing of the underlying models (GPT-4o series vs Claude 3 series), and the difference in efficiency between the two systems in context-management strategies and tool-call frequency when completing equivalent tasks. Therefore, effective cost-control strategies include: refining requirement descriptions to reduce clarification rounds, breaking large tasks into independent subtasks (resetting the context each time), and choosing model versions with higher token efficiency for specific scenarios.

On stability, Codex performs more consistently in terms of rate limiting, account bans, and "dumbing down" issues, which indirectly indicates that Claude Code has encountered similar problems in actual use.

On capability focus, the two tools present a clear division of labor, which is closely related to the emphasis of their respective training data and the optimization direction of their underlying models:

• Claude Code leans more toward logical correctness and code structure, and performs prominently in back-end framework development scenarios such as Django and Flask. Back-end framework code has high logical density and complex business rules, requiring more rigorous reasoning, and the Claude series models have advantages in the length and consistency of reasoning chains.
• Codex focuses more on UI details, color schemes, animation effects, and interaction cues, making it more favored in front-end development scenarios such as HTML, CSS, and JavaScript. Front-end code heavily depends on understanding "visual aesthetic intent"—appropriate color schemes, smooth animation curves, reasonable spacing systems—which happens to be a capability direction OpenAI's models accumulated through multimodal training.

In one sentence: Choose Claude Code for rigorous back-end logic; prioritize Codex for polished front-end experiences or if you're cost-sensitive.

Pre-Installation Preparation: Three Essential Software

Before officially installing Codex, you need to configure three foundational environments. It's recommended to use the "Huorong App Store" for one-stop installation, saving you the trouble of downloading each from the official website individually, and without ad interference.

Git

Git is currently the most widely used distributed version control system in the world, created in 2005 by Linus Torvalds, the founder of the Linux kernel. Its core function is to track the history of file changes, allowing developers to switch between different versions and merge collaborative code modifications from multiple people. Git's distributed architecture manages commit history based on a Directed Acyclic Graph (DAG) data structure, where each commit node stores a complete snapshot of the file system (rather than a delta patch), and data integrity is guaranteed through SHA-1 hash values.

In AI programming scenarios, the value of Git has undergone an important functional transformation—it has been elevated from a "multi-person collaboration tool" to a "human audit mechanism for AI behavior." When an AI agent executes multi-step tasks, it may modify dozens of files, delete old functions, and introduce new dependencies within a single session—if these operations are not recorded by Git, they would be difficult to trace and roll back. The git diff HEAD command can precisely display all file-level changes the AI has made from the start of the task to the present moment; git bisect can perform a binary search to find the specific commit that introduced a bug; and git stash allows temporarily saving the AI's work progress so you can switch to another urgent task and restore it later. The recommended best practice is: before Codex begins work, create a new branch (git checkout -b feature/ai-task) so that all of the AI's modifications occur on that branch; after the task is complete, use git diff main to comprehensively review all of the AI's changes; and merge to the main branch only after you're satisfied. This workflow transforms the AI's "black box" operations into a reviewable, rollback-able transparent process, and is a key practice for incorporating AI coding tools into professional development standards.

Search for "Git" in the Huorong App Store and install it. Once complete, press Win + R to open CMD, and enter the following command to verify the installation:

git --version

If a version number is output (such as 2.49.0), the installation was successful.

Node.js

Node.js is a server-side runtime environment built on the Chrome V8 JavaScript engine, released in 2009, enabling JavaScript to run on the server side outside the browser. It has npm (Node Package Manager) built in, which currently hosts more than 2 million open-source packages and is the core infrastructure of the modern front-end toolchain.

Node.js's non-blocking I/O model is based on the Event Loop mechanism: all I/O operations (file reads/writes, network requests, child process calls) are executed asynchronously; the main thread does not wait for operations to complete, but instead registers callback functions and continues processing other events. This naturally aligns with the working mode of AI coding agents that "execute multiple subtasks in parallel"—when the Codex CLI runs locally, its main job is to coordinate multiple concurrent I/O operations: sending requests to the OpenAI API (network I/O), reading and writing local code files (disk I/O), and invoking child processes to execute shell commands (process I/O). A single Node.js process can simultaneously manage multiple parallel API requests and file operations without needing to spawn a new thread for each concurrent task, significantly reducing memory overhead.

npm's package.json file records the precise versions of all direct and indirect dependencies of a project, and combined with the package-lock.json lock file, ensures complete consistency of dependency installation across different development environments—this is especially important for AI coding tools: when Codex generates a project in the cloud sandbox and it is reproduced on the user's local machine, dependency version consistency is the fundamental guarantee that the "AI environment" and "user environment" behave consistently. The npm scripts mechanism also allows standardized commands (such as npm run build and npm run test) to be defined in package.json, and Codex can invoke these standard commands to drive the project's build, test, and deployment processes without needing to understand the project's specific build details. Almost all front-end build tools (Webpack for module bundling, Vite for a blazing-fast development server, Babel for JavaScript syntax transpilation) are distributed and run as Node.js packages, and the Codex CLI itself is a Node.js application published and installed via npm.

Search for "Node.js" in the Huorong Store and install it. After installation, enter the following in the command line:

node --version

If a version number is output (such as v24.14.0), you're set. Node.js is both a JavaScript runtime environment and an important tool for subsequently installing various development dependencies.

VS Code

Search for "VS Code" in the Huorong Store and install it. This code editor produced by Microsoft has a clean interface: the main editing area is in the center, the file explorer is on the left, and the right side can connect to the agent architecture for AI interaction. It is the most commonly used IDE in the Codex ecosystem.

Installing and Configuring Codex

Once the three prerequisite software are ready, proceed to installing Codex itself. Note: Codex cannot be found in the Huorong App Store; you must download it from the official website.

Open the Codex official website and choose to download the Windows version. The downloaded file is named codex install, which is an installation bootstrapper rather than the main software body—after double-clicking to run it, it will automatically pull and install the Codex main program. Once installation is complete, a Codex icon will appear on the desktop.

First-launch configuration process:

1. The interface prompts "Set start with Codex," requiring you to enter an OpenAI API Key starting with sk. An API Key is the identity credential for accessing OpenAI services, using the Bearer Token authentication scheme; each HTTP request must carry the key in the Authorization header (in the format Authorization: Bearer sk-...). It is essentially a high-entropy random string (typically 51 characters) directly bound to the user account—each time Codex initiates an AI inference request, it carries this key, and the OpenAI server uses it to identify the user and bill by actual usage.

   Security Tip: Once an API Key is leaked, the holder can invoke the API as that user and incur charges. It's recommended to adopt a three-layer protection strategy: the first layer is the "principle of least privilege"—create a separate key for Codex in the OpenAI console to facilitate independent revocation; the second layer is "spending limits"—set a monthly Hard Limit and a Soft Limit, where the former automatically stops all API calls once reached and the latter triggers an email notification; the third layer is "environment variable isolation"—store the key using operating-system environment variables or a .env file combined with .gitignore, and never hardcode it in plaintext in code files and push it to GitHub. OpenAI's Usage Dashboard provides token consumption details broken down by model and by time, serving as the first-hand data source for investigating abnormal Codex billing.

2. After entering the key, click Continue to enter the identity selection interface (Student, Data Analyst, Designer, Product Manager, etc.).

3. After selecting the corresponding identity (such as Student), click Continue again to enter the main interface.

The main interface defaults to English, with a toolbar on the left (New Chat, Search, project list, etc.) and the core input box on the right, with the prompt text "What should we get to..." At this point, configuration is complete.

Creating a Workspace and Starting a Conversation

Since Codex supports multiple parallel tasks, it's recommended to create separate folders as workspaces for different projects.

Recommended directory structure: Create a main directory on a local drive (such as the D drive), for example codex demo, and then create subdirectories inside such as codex list and codex shop, with names freely chosen per project.

Two ways to import a workspace:

1. Menu import: Via File → Open Folder, select the target folder.
2. Drag-and-drop import: Directly drag the folder into the Project area of the interface.

After importing multiple folders, you can switch the currently operated project at the bottom of the dialog box, enabling parallel management of multiple projects.

After selecting a project, enter your requirement in the input box and press Enter, and a "spinning" animation will immediately appear on the interface—this indicates the task has entered the In Progress state. Codex tasks have three states in total: In Progress, Completed, and Awaiting Input; the spinning icon means the agent is processing your request in the cloud sandbox.

Summary

As the first lesson in the Codex series, this article focused on clarifying three core questions:

1. Conceptual understanding: Codex is OpenAI's new-generation AI coding agent, evolving from the early code-completion model that powered GitHub Copilot (fine-tuned on GPT-3's Transformer architecture and self-attention mechanism, along with the technical route of transfer learning and supervised fine-tuning) into an "AI programmer" that can independently take on tasks in a cloud sandbox. The key to this underlying technical transformation lies in the ReAct loop (the think-act-observe closed loop), the 128K-token long context window, the introduction of the function-calling and tool-use frameworks, and the secure sandbox execution environment based on Linux container technology.
2. Forms and selection: It offers four usage forms, with the desktop app being the most complete; compared with Claude Code, Codex has lower pricing (a gap of about four times), stronger stability, and greater advantages in front-end UI development. The fundamental reasons lie in the differences in the training direction of the two underlying models and in token consumption efficiency; effective cost-control strategies are refining requirement descriptions and splitting independent subtasks to reset the context.
3. Installation and configuration: Install the three prerequisite software in order—Git (a version control tool based on the DAG data structure, elevated in AI programming to an audit mechanism for humans to audit AI behavior), Node.js (a JavaScript runtime and npm package manager based on the event loop, naturally suited to the concurrent I/O workloads of AI tools), and VS Code (a code editor)—then download Codex from the official website and configure the OpenAI API Key (be sure to adopt the three-layer security protection strategy to set spending limits), and finally create a folder workspace to begin using it.

Having mastered "what it is" and "how to install it," the next step will delve into Codex's hands-on development techniques. Stay tuned.

Key Takeaways