How to Access and Use OpenAI Codex?

“Software engineering is changing, and by the end of 2025 it’s going to look fundamentally different.” Greg Brockman’s opening line at OpenAI’s launch event set the tone for what followed. OpenAI released Codex, a cloud‑native software agent designed to work alongside human developers, shoulder the grunt work of coding, and even propose its own improvements. 

Codex is not a single product but a family of agents powered by codex‑1, OpenAI’s latest coding model. Codex CLI, arrived a few weeks ago as a lightweight companion that runs inside your terminal. Today the spotlight shifts to its bigger, remote agent that is avialble entirely on ChatGPT. You can spin up thousands of parallel “mini‑computers” on demand, and tackle multiple tasks while you’re off grabbing coffee. This article is going to be an overview of Codex on ChatGPT, and we will soon be releasing some project based articles on the topic.

From Autocomplete to Autonomous Vibe Coding

OpenAI’s journey toward agentic coding started in 2021 with the original Codex model that powered GitHub Copilot. It was, in essence, next‑generation autocomplete. Four years, and a mountain of reinforcement‑learning research, later, the team can credibly talk about “vibe coding”: telling an AI what you want done while it decides how to do it.

Built on the o3 reasoning architecture, codex‑1 is fine‑tuned on real pull‑requests, reinforced to care about linting, style guides, and passing tests, not just benchmark scores.

How to Access Codex in the ChatGPT Interface?

• Open ChatGPT and go to “Codex” sidebar in the left navigation rail you’ll see a new “Codex (beta)” icon. Click it to reveal the agent dashboard.

• Connect GitHub (first‑time only): A single OAuth click authorises Codex to read/write on your repos. You can restrict it to specific organisations or personal projects.

• Select a repository & branch: Pick the project you’d like Codex to work on (e.g., main or feature/ui‑overhaul). The agent clones this branch into its own sandbox.
• Configure the environment (optional): Add environment variables, secrets, or setup commands, just like a CI job. Linters and formatters are pre‑installed, but you can override versions.

• Choose a task template:
  • Ask – “Explain the architecture.”
  • Code – “Find and fix the flakey test in test_api.py.”
  • Suggest – Let Codex scan the repo and propose maintenance chores.
  • Or just type a custom instruction in natural language.

• Run & multitask: Press “Launch”. Each job spins up its own micro‑VM; you can queue dozens in parallel and continue chatting elsewhere in ChatGPT.
• Review results: Green check‑marks indicate passing tests. Click a task card to see the diff, the model’s explanation, and the full work‑log.
• Merge or iterate: Hit “Open PR” to push the branch back to GitHub—or reply to the task with follow‑up instructions if changes are needed.

OpenAI Codex Demo

In this section, I am sharing the different examples demostrating how this new software development agent can sort your life!

Example 1: Accelerate Development

OpenAI engineer Nacho Soto demonstrates how Codex helps him begin new tasks faster by setting up project scaffolding, such as Swift packages. Using natural language prompts, he could offload setup work and focus on building features, while Codex handles the rest in the background.

Example 2: Reiewe Workflows

Codex supports not just code generation but also review workflows. Developers review AI-generated pull requests, identify issues like formatting, and prompt Codex to make corrections. This shifts the developer’s role toward validating and refining output, letting the AI handle execution while humans focus on oversight and final approval.

Example 3: Fixing Papercuts with Codex

Engineer Max Johnson describes how Codex helps address small bugs and code quality problems—without disrupting focus. Instead of switching contexts, he delegates these tasks to Codex and reviews the output later, improving the codebase incrementally.

Example 4: Finding Error in Codebase while on a Call

Calvin, another engineer, explains how Codex assists with urgent tasks during on-call shifts. By sending stack traces to Codex, he quickly gets diagnostics or fixes. It also helps tune alerts and manage routine ops work, reducing manual overhead.

Working of Codex

Traditional LLM fine‑tuning rewards token‑level plausibility; Codex’s training loop rewards task completion. The agent writes code, runs it, observes test outcomes, and only earns a high score when everything passes. This end‑to‑end signal pushes the model toward behaviours more reminiscent of a junior developer than a language model: creating repro scripts, reading lint errors, retrying with fixes, and respecting style guides.

On benchmarks like SWE‑Bench, Codex already posts state‑of‑the‑art numbers, but OpenAI’s researchers care more about day‑to‑day mergeability, hence the focus on verifiable output and minimal diff noise.

A Peek Inside the Cloud Workshop

Every time you press ⏎ Run in the Codex sidebar, the system creates a micro‑VM sandbox: its own file‑system, CPU, RAM, and locked‑down network policy. Your repository is cloned, environment variables injected, and common developer tools (linters, formatters, test runners) pre‑installed. That isolation delivers two immediate benefits:

1. Safety & Reproducibility – Rogue scripts can’t touch your laptop or leak secrets; the whole run can be replayed later.
2. Parallelism at Scale – Need to fix typos, harmonise time‑outs, and hunt a mysterious bug? Launch three tasks and review the results side‑by‑side.

An optional AGENTS.md file acts like a README for robots: you describe the project layout, how to run tests, preferred commit style—even a request to print ASCII cats between steps. The richer the instructions, the smoother Codex behaves.

Availability, Limits & What’s Next

Codex is currently available to ChatGPT Pro, Enterprise, and Team users. Free-tier and EDU users are expected to gain access soon. During the research preview, usage is subject to generous limits, but these may evolve based on demand. Future plans include an API for Codex, integration into CI pipelines, and unification between the CLI and ChatGPT versions to allow seamless handoffs between local and cloud development.

Conclusion

“I just landed a multi‑file refactor that never touched my laptop.” – OpenAI engineer

Stories like that hint at a future where coding resembles high‑level orchestration: you provide intent, the agent grinds through the details. Codex represents a shift in how developers interact with code, moving from writing everything manually to orchestrating high-level tasks. Engineers now focus more on intent and validation, while Codex handles execution. For many, this signals the beginning of a new development workflow, where human and agent collaboration becomes the standard rather than the exception.