OpenAI Codex is a specialized AI system designed to help write and understand code. In OpenAI’s recent announcement, Codex is described as “a cloud-based software engineering agent” that can work on many coding tasks in parallel. Built on a version of OpenAI’s o3 model fine-tuned for programming (codex-1), it was trained via reinforcement learning on real-world coding tasks. Codex can write new features, answer questions about your codebase, fix bugs, and even propose pull requests. In practice, users interact with Codex through the ChatGPT interface: a Code button lets you submit coding prompts, and an Ask button lets you query the codebase (each task runs in an isolated sandbox). After Codex completes a task, it provides logs and test results, commits changes, and can even open a PR or merge code into your repo.

Screenshot of the ChatGPT interface using Codex: a prompt box for coding tasks and task list (from OpenAI’s announcement).

As shown above, Codex is integrated into ChatGPT’s sidebar – you type a natural-language instruction and click Code. This unified interface makes it easy to issue code-related requests (“Find a bug in the last 5 commits and fix it,” etc.), monitor progress in real time, and review all changes with citations from terminal logs and test outputs. Because Codex can run linters and tests in its sandbox, it ensures that outputs meet your project’s standards before suggesting them back to you.

Key Features and Capabilities of Codex

Codex’s core strength is translating natural language into code and understanding existing code. According to OpenAI, it “translates natural language into code” (much like a powerful autocomplete). It supports over a dozen programming languages, with especially strong performance in Python, but also handling JavaScript, Go, Ruby, PHP, TypeScript, and more. In fact, OpenAI notes that Codex was trained on ~159 GB of code from 54 million GitHub repositories, so it has seen many coding patterns. In practice, this means you can prompt Codex in plain English and get back working code.

1. Cloud-Based Software Engineering Agent: Imagine having an extra pair of hands (or rather, a sophisticated AI brain) that can work on your coding tasks in parallel. Because it's cloud-based, you can access it through ChatGPT and it can handle multiple tasks simultaneously, potentially speeding up your development workflow significantly.
2. Powered by codex-1: Think of codex-1 as the specialized engine driving Codex. It's a specifically trained version of OpenAI's language models, fine-tuned on a vast amount of code and real-world coding scenarios. This specialized training allows it to generate code that not only works but also tends to align with human coding styles and preferences.
3. Task Automation: This is where Codex shines. Instead of manually writing a new feature, you can describe it to Codex, and it can generate the code for you. Need to understand a specific part of your codebase? You can ask Codex questions about it. Found a bug? Codex can attempt to fix it and even propose the changes as a pull request, ready for review.
4. Isolated Environments: Safety and organization are crucial. Each task you assign to Codex runs in its own isolated "sandbox" in the cloud. This means that any changes or operations performed by Codex for one task won't interfere with other tasks or your main repository until you explicitly choose to integrate them. The environment comes pre-loaded with your repository, so Codex has the necessary context.
5. Evidence of Actions: Trust is important, especially when an AI is making changes to your code. Codex provides transparency by giving you logs and test outputs that show exactly what it did during a task. This allows you to verify its work step-by-step before you decide to merge the changes.
6. Guided by AGENTS.md files: Think of these files as instruction manuals specifically for Codex. You can create these text files within your project to give Codex more context about your project's structure, how to run tests, and any specific coding standards you follow. This helps Codex understand your project better and work more effectively within its conventions.

Essentially, Codex aims to be a collaborative AI partner that can take on various coding-related tasks, freeing up developers to focus on more complex problem-solving and high-level design. It emphasizes transparency and control, allowing users to review and validate its work before integrating it into their projects.

Ways to Access OpenAI Codex

Developers have several ways to use Codex:

1. ChatGPT with Codex: As of 2025, OpenAI has rolled Codex into the ChatGPT product. If you’re a ChatGPT Pro/Team/Enterprise subscriber, you’ll see a Code button in the sidebar. Typing a prompt and clicking Code spins up Codex on your codebase. (ChatGPT “Plus” and “Edu” users will get access soon.) This integration means you don’t need any coding – just talk to ChatGPT.

2. OpenAI Codex CLI: OpenAI has also released a terminal-based Codex CLI tool. This runs an optimized model (o4-mini) that lets you work with your local files. You can run commands like openai codex complete or openai codex run, using your code as context. OpenAI has published pricing for this API (for example, ~$1.50 per million input tokens).

1. Codex CLI Overview and Model Update

• Codex CLI is a lightweight, open-source coding agent that runs in the terminal.
• It integrates models like o3 and o4-mini to assist developers in real time with coding tasks.

New Model: codex-mini-latest

• A smaller, faster version of codex-1 based on o4-mini.
• Designed specifically for Codex CLI with:
  • Low-latency performance.
  • Optimized for code Q&A and editing.
  • Maintains strong instruction-following capabilities and stylistic coherence.
• Available:
  • As the default model in Codex CLI.
  • Via API under the name codex-mini-latest.
• The model snapshot will be regularly updated as improvements continue.

2. Improved Developer Onboarding

• Developers can now sign in to Codex CLI using their ChatGPT account.
• Benefits:
  • No need to manually generate or configure API tokens.
  • Automatic selection of API organization.
  • Streamlined key generation and configuration.
• Bonus Credits:
  • Plus users: $5 in free API credits.
  • Pro users: $50 in free API credits.
  • Credits valid for the next 30 days after sign-in.

3. Availability, Pricing, and Limitations

Availability

• Now available to:
  • ChatGPT Pro, Enterprise, and Team users globally.
• Coming soon:
  • Support for Plus and Edu users.

Pricing (codex-mini-latest via API)

• Input tokens: $1.50 per 1M
• Output tokens: $6 per 1M
• Prompt caching: 75% discount on cached prompts

Limitations (Research Preview Phase)

• No image input capabilities yet (e.g., for frontend tasks).
• No mid-task correction or live interaction during agent execution.
• Remote agent delegation is slower than manual interactive editing.
• Interaction model still evolving—currently best suited to asynchronous collaboration.

4. Vision: Future of AI-Driven Development

Asynchronous Agent Collaboration

• Codex aims to support both real-time pairing and task delegation:
  • Real-time: Ask questions, get quick suggestions, code alongside Codex.
  • Asynchronous: Assign longer tasks, get updates later.

Planned Features

• Ability to provide mid-task guidance.
• Collaborative planning on implementation strategies.
• Proactive progress updates from agents.
• Deeper integrations with tools like:
  • GitHub
  • ChatGPT Desktop
  • Issue trackers
  • CI/CD systems

5. Broader Impact and Next Steps

• Software engineering is leading AI-powered productivity gains.
• OpenAI is collaborating with partners to understand the impact of agents on:
  • Developer workflows.
  • Skill development across different experience levels and regions.
• Long-term vision:
  • Unified developer workflow.
  • Codex agents working seamlessly across tools.
  • Empowering individuals and small teams to build more, faster.

In short, whether through API calls, Copilot in your editor, or directly in the ChatGPT web interface, you have flexible options to leverage Codex technology.

How to Access OpenAI Codex?

Here’s the OpenAI Codex access and usage steps:

How to Use OpenAI Codex (Beta) in ChatGPT

1. Access Codex in ChatGPT:

   Open ChatGPT and look at the left sidebar. You’ll see a new icon labeled “Codex (beta)” under the navigation rail. Click it to launch the Codex Agent Dashboard.

2. Set Up Multi-Factor Authentication (MFA)

   To secure your access:

   • Click “Set up MFA to continue”
   • Scan the QR code with an app like Google Authenticator or Authy
   • Enter the generated code to verify

   Done, MFA is now enabled.

3. Connect Your GitHub Account (First-time only)

   Authorize Codex with a single click:

   • You’ll be prompted to approve GitHub access via OAuth
   • You can limit access to specific repos or orgs

   Codex only reads/writes where you give it permission.

4. Select a Repository & Branch

   Choose the repo you want Codex to work on, and pick the branch.

   Codex will clone it into a secure sandbox environment.

5. Configure the Dev Environment (Optional)

   Need custom setup?

   • Add environment variables, secrets, or setup scripts
   • Linters, formatters, and common runtimes come preinstalled

   Override any tool versions if needed, just like in CI.

6. Choose a Task Template

   Pick a starting point from built-in task types, or type your own:

   • Ask: “Explain the data flow from frontend to backend.”
   • Fix: “Debug the 403 error in login route handler.”
   • Suggest: Let Codex scan your repo and propose improvements or cleanup tasks.
   • Custom Prompt: Type anything - “Generate integration tests for the payments module.”
7. Run & Multitask

   Click “Launch” to start the job.

   Codex runs it inside an isolated micro-VM, you can queue multiple tasks and keep chatting elsewhere in ChatGPT.

8. Review the Results

   Look for green check marks for successful runs.

   Click any task card to view:

   • The diff
   • Codex’s explanation
   • The step-by-step worklog
9. Merge or Refine

   Happy with the changes?

   • Click “Open PR” to send it to GitHub

   Need adjustments?

   • Just reply to the task with new instructions, Codex will update the code accordingly.

How to Access CODEX CLI?

1. Install Codex CLI globally

   npm install -g @openai/codex

2. Set your OpenAI API key

   export OPENAI_API_KEY="your-api-key-here"

   Note: This sets the key only for the current terminal session. To make it persistent, add the line to your shell config file (e.g., ~/.zshrc, ~/.bashrc).

   Alternatively, you can store the key in a .env file at the root of your project:

   OPENAI_API_KEY=your-api-key-here

   The CLI automatically loads environment variables from .env using dotenv/config.

3. Run Codex

   To use interactively:

   codex

   Or to run with a single prompt:

   codex "explain this codebase to me"

   Enable Full Auto mode (no manual approvals):

   codex --approval-mode full-auto "create the fanciest todo-list app"

   Codex will scaffold the required files, set up a sandboxed environment, install dependencies, and execute the code. Once complete, you'll be prompted to approve and apply the changes to your working directory.

4. Use different models

   To specify a different model, use the --provider flag:

   codex --provider openai/gpt-4

   You're now ready to build, refactor, and explore codebases with Codex CLI.

Example Prompts and Usage of Codex

To see Codex in action, here are some illustrative examples:

Problem statements:

from astropy.modeling import models as m
from astropy.modeling.separable import separability_matrix
cm = m.Linear1D(10) & m.Linear1D(5)

>>> separability_matrix(cm)
array([[ True, False],
       [False,  True]])

>>> separability_matrix(m.Pix2Sky_TAN() & m.Linear1D(10) & m.Linear1D(5))
array([[ True,  True, False, False],
       [ True,  True, False, False],
       [False, False,  True, False],
       [False, False, False,  True]])

>>> separability_matrix(m.Pix2Sky_TAN() & cm)
array([[ True,  True, False, False],
       [ True,  True, False, False],
       [False, False,  True,  True],
       [False, False,  True,  True]])

Natural Language to Code (Python and JavaScript)

Codex shines at translating specifications into working code. For instance, given the prompt:

Write a Python function is_prime(n) that returns True if n is a prime number and False otherwise.

Codex might output a solution like:

def is_prime(n):
    if n < 2:
        return False
    for i in range(2, int(n**0.5) + 1):
        if n % i == 0:
            return False
    return True

Likewise, for JavaScript:

Create a JavaScript function sumArray(arr) that returns the sum of elements in an array.

Codex could generate:

function sumArray(arr) {
    let total = 0;
    for (const num of arr) {
        total += num;
    }
    return total;
}

These examples show how Codex maps plain English tasks into complete code routines. The model takes care of syntax and logic, letting you focus on higher-level design.

Generating Boilerplate and Templates

You can also use Codex to bootstrap projects. For example:

Generate a basic Python Flask web server with one endpoint /hello that returns "Hello World".

Codex might respond with a boilerplate app like:

from flask import Flask

app = Flask(__name__)

@app.route('/hello')
def hello():
    return "Hello World"

if __name__ == '__main__':
    app.run(debug=True)

This shows how Codex can set up a framework or template. Similar prompts could create React components, SQL table schemas, Dockerfiles, or any repetitive structure. By asking natural-language instructions (e.g. “Create a React component called UserList that displays a list of users”), Codex delivers code ready to plug into your project.

Explaining Existing Code

Codex can help make sense of unfamiliar code. For instance, given a snippet:

def factorial(n):
    return 1 if n == 0 else n * factorial(n-1)

You could prompt: “Explain what this function does.” Codex might reply: “This function computes the factorial of n recursively. It returns 1 when n is 0; otherwise it multiplies n by the factorial of n-1.” It can highlight base cases, recursion, and so on. More complexly, if you point Codex at an entire codebase and ask “What are some key modules in this project and what do they do?”, it can scan files, group related files, and summarize their roles (as shown in OpenAI’s examples). This makes onboarding or documentation easier.

Debugging and Fixing Code

Codex is also adept at finding and fixing bugs. Suppose you have a function with a bug:

def normalize_data(values):
    total = sum(values)
    return [v / total for v in values]

If one of the values in values is 0, and you accidentally pass an empty list, this will cause a division-by-zero error. You might ask Codex: “There’s a bug in normalize_data: handle the case when sum is zero.” Codex could rewrite it as:

def normalize_data(values):
    total = sum(values)
    if total == 0:
        return [0 for v in values]  # avoid division by zero
    return [v / total for v in values]

In a real workflow (like the one on ChatGPT Codex), the agent could even run tests, notice a failure, and propose a fix interactively. The DataCamp examples show Codex scanning an entire repo, detecting a bug, and suggesting a patch diff, all with an explanatory summary.

These examples illustrate the breadth of Codex’s utility: from writing fresh code to making sense of and improving existing code.

AI-Powered Code Review

Beyond code generation, AI is revolutionizing the code review process itself. Traditional manual reviews can be slow and error-prone, but AI-driven code review tools automate many routine checks. They can rapidly scan for bugs, security flaws, code smells, or style violations – issues that human reviewers might miss or take longer to find. For example, AI models can detect known and unknown vulnerabilities across multiple languages far quicker than a human reader. In practice, teams using AI code review report dramatic efficiency gains: one industry study found AI-assisted review can shorten review cycles by around 40% while reducing production defects. By handling the low-level analysis, AI reviews let developers focus on higher-level architecture and complex logic instead.

AI code review systems typically integrate with your existing workflow – for example, tying into version control and CI/CD pipelines. They act as complementary reviewers: providing real-time feedback in pull requests, annotating code with suggestions, or generating summaries of changes. The end result is faster merges and more reliable software with less manual effort.

Entelligence.ai: AI-Driven Code Review Platform

We at Entelligence.ai bills itself as an “AI-powered engineering intelligence” platform that unifies several capabilities, with a strong emphasis on automated code review. In Entelligence, an AI agent continuously analyzes your entire codebase and incoming pull requests to surface issues and insights in context.

• Deep Contextual Review: Entelligence’s Deep Review agent examines changes across files with full context of the whole repository. It can flag subtle cross-file errors (e.g. mismatched function names, missing imports in related modules, etc.) that might elude simpler linters. This deep analysis catches complex issues early.
• Line-by-Line Feedback: The platform provides “line-by-line” code review comments. In a pull request, Entelligence can comment on individual lines of code with suggestions or warnings, much like a human reviewer would. These comments are context-aware, meaning the AI understands the code around each line to give meaningful feedback.
• PR Summaries and Chat: Rather than reading raw diffs, Entelligence generates concise summaries of pull requests. It can also engage in a conversational “chat” about the PR, answering questions or clarifying intent. For example, it might summarize “This PR adds user authentication and updates the database schema; tests were added for login flow.” These summaries help reviewers quickly grasp large changes.
• Automated Documentation/Diagrams: Entelligence goes beyond code review to also auto-generate documentation and architectural diagrams from your code. For instance, it can turn your data models into flowcharts or API endpoints into design docs with a click. While peripheral to pure code review, these features further reduce manual work in understanding code.
• Team Insights: The platform includes analytics dashboards that track metrics like code quality, review times, and individual or team performance. Engineering managers get visibility into bottlenecks or areas for improvement (e.g. which reviewers are slowest or which modules have the most issues).

Conclusion

OpenAI Codex represents a transformative leap in how software is developed, reviewed, and maintained. By seamlessly translating natural language into code, Codex empowers developers to work faster and smarter, whether through ChatGPT’s intuitive web interface or the lightweight Codex CLI tool. It excels at generating new features, explaining complex logic, fixing bugs, automating pull requests, and performing code reviews—functioning as an intelligent coding assistant capable of parallel task execution in secure, isolated environments.

The deep integration with GitHub, sandboxing capabilities, and evidence-backed changes offer developers transparency and control, while guided instructions via AGENTS.md files help Codex operate effectively within real-world codebases. With continuous updates, specialized models like codex-mini-latest, and upcoming enhancements such as mid-task interaction and deeper tool integration, Codex is not just a productivity tool—it is the foundation of a more automated, collaborative, and intelligent development future.

AI-powered tools like Codex and other AI review platforms signal a shift toward asynchronous, AI-enhanced workflows that improve speed, reliability, and scalability. Whether you’re a solo developer or part of an enterprise team, Codex is designed to streamline your development lifecycle, reduce manual overhead, and bring more ideas to life with fewer lines manually written. As AI continues to reshape software engineering, OpenAI Codex stands at the forefront of this evolution—bridging the gap between human creativity and machine efficiency.