I’ve been testing OpenAI Codex macOS app since it dropped earlier today. And honestly, it’s changing how I think about coding with AI. Instead of just getting suggestions for my next line of code. I’m now managing multiple AI agents that actually complete entire features while I work on other stuff.
The launch comes at an interesting time. OpenAI says usage has doubled since they rolled out the GPT-5.2-Codex model back in late 2025. Last month alone, over 1 million developers gave it a shot. What caught my attention? They’re offering temporary free access for ChatGPT Free and Go users. And if you’re already paying for a plan, your rate limits just doubled.
What separates Codex from tools like GitHub Copilot is the scope of what it handles. You’re not getting autocomplete here. You’re delegating actual work—building features, squashing bugs, reviewing pull requests. Everything runs in isolated cloud sandboxes. Which means you can experiment without worrying about breaking your local setup. This fits into the broader trend we’re seeing in 2026 where AI tools are becoming more autonomous and less hand-holdy.
What is OpenAI Codex and How Does It Work?
After spending a few hours with Codex. I can break down what it actually does versus the marketing speak. It runs on GPT-5.2-Codex which handles those long, tedious coding tasks that used to eat up entire afternoons. The difference between this and earlier models is pretty noticeable when you’re working on something that requires maintaining context across hundreds of lines of code.
When I’m coding in my terminal or IDE, Codex can navigate my entire repository. It edits files, runs tests and does it all in secure cloud environments that mirror my codebase. The multi-agent feature is where things get interesting. I can have one agent refactoring my backend while another updates the frontend components. They work in parallel, which cuts down project time significantly.
The code review functionality surprised me. I expected basic syntax checking but it actually understands what my code is trying to accomplish. You can set reviews to happen automatically or request them when you need a fresh perspective on something tricky. Integration with Slack, Linear and GitHub works smoothly. I get notifications when agents finish tasks or hit roadblocks.
Let me give you a practical example from this morning. I told Codex to add user authentication to a React project I’m working on. It mapped out the work, assigned different agents to the frontend login component and backend JWT handling, wrote everything, ran the test suite and created a pull request. I reviewed it over coffee, requested a couple tweaks and merged it. The whole process took maybe 45 minutes versus the half-day I’d normally spend on it.
The New macOS App Features and First Impressions
Before today, working with Codex meant switching between the command line, various IDE extensions and the web interface. The new macOS app centralizes everything. I’ve got a dashboard showing all active agents, can jump between projects without losing context and monitor long-running tasks that might take a couple hours to complete.
The multi-agent workflow genuinely speeds things up. A feature that would normally take me two or three days wrapped up in about six hours because I had multiple agents handling different components simultaneously. The sandbox security gives me peace of mind agents have limited write access and restricted network calls. So there’s minimal risk of accidentally pushing something catastrophic to production.
My main gripe? It’s Mac-only at launch. I split time between my MacBook and a Windows desktop. So I’m stuck using the web interface on half my setups. OpenAI confirmed a Windows version is in development, but no timeline yet. The interface feels a bit unpolished in spots nothing dealbreaking, just rough edges you’d expect from a day-one release. The doubled rate limits for paid plans and free trial access make this a low-risk time to experiment.
OpenAI Codex vs Claude Code vs Cursor
I’ve been using Claude Code and Cursor for the past few months. so naturally I wanted to see how Codex compares based on actual use cases.
Claude Code still has an edge on complex reasoning tasks. When I’m debugging something that requires understanding multiple interconnected systems. Claude tends to provide deeper analysis. The plugin ecosystem is also more mature. But Codex’s parallel agent execution is something Claude doesn’t really match. If I need multiple things happening simultaneously, Codex wins. Plus, since I already use ChatGPT and other OpenAI tools everything syncs up nicely.
Cursor is a completely different experience. It lives inside your IDE and gives you real-time feedback as you type. I can see diffs immediately and accept or reject changes on the fly. It’s perfect for that hands-on, I want to see every change as it happens workflow. Codex is better when I want to delegate an entire chunk of work and check back later. I’m not watching it code I’m assigning tasks and reviewing completed work.
My workflow now involves all three, honestly. I use Cursor for active coding sessions where I want constant feedback. Codex handles bigger features I can delegate. Claude Code comes in when I need to debug something particularly gnarly. The free Codex trial makes testing this combination easy without committing financially.
How to Get Started with OpenAI Codex
Setting up Codex took me less than five minutes. I went to openai.com/codex, downloaded the macOS app and logged in with my existing ChatGPT account. ChatGPT Plus runs $20 monthly. Though the temporary free access lets you test everything before deciding if it’s worth the subscription.
Start with something manageable for your first task. I began with Refactor this Python script for better performance just to see how it approached optimization. Once you understand its workflow, you can tackle bigger projects. My second task was adding dark mode to a landing page moderately complex but not mission-critical if something went wrong.
A few lessons I learned the hard way: always review the agent’s plan before it starts executing. I skipped this once and the agent took an approach I wouldn’t have chosen. Also, those pull requests Codex creates? Read through them carefully. The code is usually solid, but I’ve caught a few edge cases the AI missed. Keep using sandboxes for anything touching production. I made this a hard rule after reading about someone who didn’t and regretted it.
Don’t try replacing your entire development workflow immediately. I’m still using my local tools for most things. Codex handles specific tasks where parallel execution or cloud delegation makes sense. For solo developers and solopreneurs, this is like having a junior developer on your team. You’re still architecting and making the important decisions, but repetitive implementation work gets offloaded.
What’s Next for OpenAI Codex in 2026
After spending most of today with the Codex macOS app, I think we’re seeing a genuine shift in developer tools. This isn’t just better autocomplete. It’s AI that can take ownership of complete features. The combination of the new app and temporary free access means 2026 might be when agentic AI coding moves from experimental to standard practice.
I’m expecting OpenAI to release the Windows version within a few months based on demand I’m seeing in developer communities. More automation features are probably coming. I wouldn’t be surprised if they offer local deployment options for enterprise teams with security requirements. They’ve been responsive to feedback so far which suggests rapid iteration ahead.
If you’ve been curious about AI coding tools but haven’t taken the plunge, now’s a good time. Download Codex and test it on a side project before committing to your main work. What would you delegate first if you had an AI teammate handling the implementation? I’d love to hear what other developers are planning to build with this drop your thoughts in the comments.
