OpenAI Codex CLI

OpenAI · Beginner ·🧠 Large Language Models ·1y ago

Key Takeaways

The OpenAI Codex CLI is a lightweight coding agent that can run directly from the command line, reading and editing files, and running commands securely, with features such as multimodal reasoning and full auto mode.

Full Transcript

So what I'm thinking is maybe I can start introducing codecs from here. Fuad as always is like coding away in the background and then he can join me for the live demos. What do you think? Sounds great. Cool. Let's do it. Take one mark. Hey everyone, I'm Roma and I work on developer experience at OpenAI. What I love most is making sure developers like you feel delighted and inspired by the tools we build. Well, today we're excited to give you a quick look at something new. We've been experimenting with the Codeex CLI. Codeex is a lightweight coding agent that can run directly from your command line. It can read and edit files. It can run commands securely and you can really use it to build features or complete apps from scratch. But enough talk to see that in action. I'm gonna bring Fouad over for some live demos. Hey. Hey. Hey. How's it going? Great. Hey everyone, I'm Philad. I'm on the agents research team and I'm so excited to share codeex with you all. Why don't you uh kick us off with maybe like a project that people might have seen before. Let's say like open.fm that we use as a quick demo lab for our voice models. Yeah, let me go and pull up the website openm I think it's an open source repo, right? So I can just go ahead and clone that locally and fire up codeex. And so here we have codeex just running on my machine. The cool part is that it runs with any of our public models. And since I'm not that familiar with the codebase, I'm just going to go and start off by asking it to explain this codebase to me. And so what I'm looking at here on the screen is that you're using 03 that we just launched today. Yeah, you can actually use anything from 4.1 from a few days ago to 03 and 04 mini today. And I think one really cool thing is as it's actually calling these tools, it's running commands. You can see it run the commands directly on your machine. So now we can see it put together this whole description. I can see that it described what open IFM is. It shows me the code architecture that it's an X.js application. And finally, here's how I can actually run it. So, I'm going to go ahead and run the development server. And um did you have a use case in mind? Yeah, I mean, why don't we do something simple like dark mode for instance? Like I know developers always love dark mode and and maybe we can do something on top of that. Sure. Let me go ahead and codeex. This time I'm going to run it in full auto mode. So, what does that mean exactly? Full auto. Now, we can also edit and run commands automatically. Got it. An important point there to make sure it still stays safe and secure is that when you run it in full auto mode, it's actually running network disabled and sandbox the directory that you ran it in. So, it's perfectly safe for you to just be able to walk away. But, it's really important for us to make sure that our users stay in control when you're actually running this on your own computer. That's great. It sounds like what we're talking it's changing all the tailwind CSS uh and and making the changes we want. Yeah. So one of the nice things is that while I got this high level overview in one step, I can also had to go in and make very specific changes without actually having the context of where it's making those changes. So I think the nice part is that you know you as a developer can actually use this to both understand the code and actually make edits to it pretty seamlessly. Now that it's done, I'm going to go ahead and open up the open FM. I can open it up locally and boom, we can see it in dark mode. That's amazing. You know, that's one example where it's an existing codebase, but maybe try something new. Well, now that we've got we've gotten familiar with with codeex with this one example, I thought why don't we create something a bit more fun uh from scratch this time. A little bit like vibe coding complete app from nothing. Sure. Uh is there one that you like in particular on Mac OS for instance? Okay. Yeah. Um you know when I was a kid I used to go to the Apple store and you know play with photo booth. I don't know I don't often do this but um so yeah let's actually pull out maybe one of the filters in photo booth. Okay. Yeah. How about how about this page of filters? Yeah. So, are you able to like screenshot the app and just tell codeex that you want it like put on the web? Exactly. So, I'm going to grab a screenshot of photo booth and I'm just going to pass it in. So, this time I'm going to put it in full auto mode and it is a first reason about the image. It's going to understand what what is it even looking at. Okay, great. It understands that it's a screenshot of Mac OS. Yeah. Um, and now I'm going to tell it reimplement this in a single page HTML. And maybe I want to say um, use the web camera API and make sure it's in landscape mode. And so now it'll just go off and given that context about the screenshot of what I wanted. You know, this could be, you know, a screenshot of photo booth. It could be a screenshot of uh, Figma design. It could be a lot of different things. I've used it in different contexts where I've drawn very low fidelity mock-ups. Um and then just kind of give in codeex uh here's this context that I want and then go off and make the changes. I don't have to give it any additional context, any additional direction. It'll just go off think for a while. You can see its chain of thought reasoning as it's thinking through the problem. Both what commands it's running and also what its thoughts are. And now finally here we see the page. Now if I go ahead and open that page in my browser, I can go ahead and see that it created this photo booth. And boom, we have Oh my god, look how cool that is. It's exactly the same. Is that amazing? That's so cool. And I'm sure you could have prompted your way to get such a result, but just like one screenshot and the model understood exactly what you wanted to build. You didn't have to open a code editor this entire time. It was just all in our terminal. And sometimes I'll kick off multiple of these in parallel and just let them, you know, one explaining the codebase to me, one making some changes and it's just pretty magical to see it all come together. That's amazing. So it was just a quick look at some of the features that we have in Codeex. We've seen how it can read and edit files directly. It can run commands very securely and you have different uh knobs in order to to pick the mode that you're the most comfortable with. But my personal favorite feature is really that one like the multimodal reasoning. That's really the true magic of those reasoning models. You just feed it a piece of paper on a sketch or something and boom, you have uh code being written for you. Do you want to share anything about like uh one more thing about this? Yeah, there's always one more thing. We're really excited to actually share codeex with you. Fully open source. You can go to our GitHub, you can see the Codex repo, you can explore it, you can actually use Codex to understand more about the repo. And we're super excited to hear what you think. Yeah. And Codex works with GPT 4.1 launched on Monday and with 03 and4 mini launching today. So we really can't wait to see what you build. Thank you. Thanks. Okay, that was very good. Cool.

Original Description

Meet Codex CLI—an open-source local coding agent that turns natural language into working code. Tell Codex CLI what to build, fix, or explain, then watch it bring your ideas to life. In this video, Fouad Matin from Agents Research and Romain Huet from Developer Experience give you a first look and show how you can securely use Codex CLI locally to quickly build apps, fix bugs, and understand codebases faster. Codex CLI works with all OpenAI models, including o3, o4-mini, and GPT–4.1. Get started: https://github.com/openai/codex.
Watch on YouTube ↗ (saves to browser)
Sign in to unlock AI tutor explanation · ⚡30

Playlist

Uploads from OpenAI · OpenAI · 0 of 60

← Previous Next →
1 Robots that Learn
Robots that Learn
OpenAI
2 Emergence of Grounded Compositional Language in Multi-Agent Populations
Emergence of Grounded Compositional Language in Multi-Agent Populations
OpenAI
3 OpenAI + Dota 2
OpenAI + Dota 2
OpenAI
4 Dendi vs. OpenAI at The International 2017
Dendi vs. OpenAI at The International 2017
OpenAI
5 Competitive Self-Play
Competitive Self-Play
OpenAI
6 Learning a Hierarchy
Learning a Hierarchy
OpenAI
7 Physical Spam Detection
Physical Spam Detection
OpenAI
8 Ingredients for Robotics Research
Ingredients for Robotics Research
OpenAI
9 OpenAI Five
OpenAI Five
OpenAI
10 OpenAI Five: Dota Gameplay
OpenAI Five: Dota Gameplay
OpenAI
11 Learning Dexterity
Learning Dexterity
OpenAI
12 Learning Dexterity: Uncut
Learning Dexterity: Uncut
OpenAI
13 OpenAI Five Benchmark: Post-Game Analysis
OpenAI Five Benchmark: Post-Game Analysis
OpenAI
14 Investigating Model Based RL for Continuous Control | Alex Botev | 2018 Summer Intern Open House
Investigating Model Based RL for Continuous Control | Alex Botev | 2018 Summer Intern Open House
OpenAI
15 Generative Modelling | Sadhika Malladi | 2018 Summer Intern Open House
Generative Modelling | Sadhika Malladi | 2018 Summer Intern Open House
OpenAI
16 A pathway to more efficient generative models | Will Grathwohl | 2018 Summer Intern Open House
A pathway to more efficient generative models | Will Grathwohl | 2018 Summer Intern Open House
OpenAI
17 Learning Dexterity | Alex Ray | 2018 Summer Intern Open House
Learning Dexterity | Alex Ray | 2018 Summer Intern Open House
OpenAI
18 Robust Vision-Based State Estimation | Hsiao-Yu 'Fish' Tung | 2018 Summer Intern Open House
Robust Vision-Based State Estimation | Hsiao-Yu 'Fish' Tung | 2018 Summer Intern Open House
OpenAI
19 Using Semantic Trees In Place of Sentences | Munashe Shumba | OpenAI Scholars Demo Day 2018
Using Semantic Trees In Place of Sentences | Munashe Shumba | OpenAI Scholars Demo Day 2018
OpenAI
20 Reinforcement Learning with Prediction-Based Rewards
Reinforcement Learning with Prediction-Based Rewards
OpenAI
21 OpenAI Spinning Up in Deep RL Workshop
OpenAI Spinning Up in Deep RL Workshop
OpenAI
22 Arena Announcement and Closing | OpenAI Five Finals (6/6)
Arena Announcement and Closing | OpenAI Five Finals (6/6)
OpenAI
23 Co-Op Match | OpenAI Five Finals (5/6)
Co-Op Match | OpenAI Five Finals (5/6)
OpenAI
24 OpenAI Five vs. OG, Game 2 | OpenAI Five Finals (4/6)
OpenAI Five vs. OG, Game 2 | OpenAI Five Finals (4/6)
OpenAI
25 OpenAI Five vs. OG, Game 1 | OpenAI Five Finals (3/6)
OpenAI Five vs. OG, Game 1 | OpenAI Five Finals (3/6)
OpenAI
26 Pre-Match Panel Discussion | OpenAI Five Finals (2/6)
Pre-Match Panel Discussion | OpenAI Five Finals (2/6)
OpenAI
27 Opening Keynote | OpenAI Five Finals (1/6)
Opening Keynote | OpenAI Five Finals (1/6)
OpenAI
28 OpenAI Robotics Symposium 2019
OpenAI Robotics Symposium 2019
OpenAI
29 OpenAI Scholars Demo Day 2019
OpenAI Scholars Demo Day 2019
OpenAI
30 Multi-Agent Hide and Seek
Multi-Agent Hide and Seek
OpenAI
31 Solving Rubik’s Cube with a Robot Hand: Uncut
Solving Rubik’s Cube with a Robot Hand: Uncut
OpenAI
32 Solving Rubik’s Cube with a Robot Hand: Perturbations
Solving Rubik’s Cube with a Robot Hand: Perturbations
OpenAI
33 Solving Rubik’s Cube with a Robot Hand
Solving Rubik’s Cube with a Robot Hand
OpenAI
34 Music Generation | Christine Payne | OpenAI Scholars Demo Day 2018
Music Generation | Christine Payne | OpenAI Scholars Demo Day 2018
OpenAI
35 Deephypebot | Nadja Rhodes | OpenAI Scholars Demo Day 2018
Deephypebot | Nadja Rhodes | OpenAI Scholars Demo Day 2018
OpenAI
36 Physics Net | Ifu Aniemeka | OpenAI Scholars Demo Day 2018
Physics Net | Ifu Aniemeka | OpenAI Scholars Demo Day 2018
OpenAI
37 Art Composition Attributes + CycleGAN | Holly Grimm | OpenAI Scholars Demo Day 2018
Art Composition Attributes + CycleGAN | Holly Grimm | OpenAI Scholars Demo Day 2018
OpenAI
38 Generating Emotional Landscapes | Hannah Davis | OpenAI Scholars Demo Day 2018
Generating Emotional Landscapes | Hannah Davis | OpenAI Scholars Demo Day 2018
OpenAI
39 Looking For Grammar In All The Right Places | Alethea Power | OpenAI Scholars Demo Day 2020
Looking For Grammar In All The Right Places | Alethea Power | OpenAI Scholars Demo Day 2020
OpenAI
40 Semantic Parsing English to GraphQL | Andre Carerra | OpenAI Scholars Demo Day 2020
Semantic Parsing English to GraphQL | Andre Carerra | OpenAI Scholars Demo Day 2020
OpenAI
41 Long term credit assignment with temporal reward transp… | Cathy Yeh | OpenAI Scholars Demo Day 2020
Long term credit assignment with temporal reward transp… | Cathy Yeh | OpenAI Scholars Demo Day 2020
OpenAI
42 Social learning in independent multi-agent reinfor… | Kamal N’dousse | OpenAI Scholars Demo Day 2020
Social learning in independent multi-agent reinfor… | Kamal N’dousse | OpenAI Scholars Demo Day 2020
OpenAI
43 Quantifying Interpretability of Models Trained on Coi… | Jorge Orbay | OpenAI Scholars Demo Day 2020
Quantifying Interpretability of Models Trained on Coi… | Jorge Orbay | OpenAI Scholars Demo Day 2020
OpenAI
44 Towards Epileptic Seizure Prediction with Deep Network | Kata Slama | OpenAI Scholars Demo Day 2020
Towards Epileptic Seizure Prediction with Deep Network | Kata Slama | OpenAI Scholars Demo Day 2020
OpenAI
45 Universal Adversarial Perturbations and Language M… | Pamela Mishkin | OpenAI Scholars Demo Day 2020
Universal Adversarial Perturbations and Language M… | Pamela Mishkin | OpenAI Scholars Demo Day 2020
OpenAI
46 Introductions by Sam Altman & Greg Brockman | OpenAI Scholars Demo Day 2020
Introductions by Sam Altman & Greg Brockman | OpenAI Scholars Demo Day 2020
OpenAI
47 Introduction by Sam Altman | OpenAI Scholars Demo Day 2021
Introduction by Sam Altman | OpenAI Scholars Demo Day 2021
OpenAI
48 Breaking Contrastive Models with the SET Card Game | Legg Yeung | OpenAI Scholars Demo Day 2021
Breaking Contrastive Models with the SET Card Game | Legg Yeung | OpenAI Scholars Demo Day 2021
OpenAI
49 Large Scale Reward Modeling | Jonathan Ward | OpenAI Scholars Demo Day 2021
Large Scale Reward Modeling | Jonathan Ward | OpenAI Scholars Demo Day 2021
OpenAI
50 Words to Bytes: Exploring Language Tokenizations | Sam Gbafa | OpenAI Scholars Demo Day 2021
Words to Bytes: Exploring Language Tokenizations | Sam Gbafa | OpenAI Scholars Demo Day 2021
OpenAI
51 Learning Multiple Modes of Behavior in a Continuous… | Tyna Eloundou | OpenAI Scholars Demo Day 2021
Learning Multiple Modes of Behavior in a Continuous… | Tyna Eloundou | OpenAI Scholars Demo Day 2021
OpenAI
52 Scaling Laws for Language Transfer Learning | Christina Kim | OpenAI Scholars Demo Day 2021
Scaling Laws for Language Transfer Learning | Christina Kim | OpenAI Scholars Demo Day 2021
OpenAI
53 Contrastive Language Encoding | Ellie Kitanidis | OpenAI Scholars Demo Day 2021
Contrastive Language Encoding | Ellie Kitanidis | OpenAI Scholars Demo Day 2021
OpenAI
54 Characterizing Test Time Compute on Graph Structur… | Kudzo Ahegbebu | OpenAI Scholars Demo Day 2021
Characterizing Test Time Compute on Graph Structur… | Kudzo Ahegbebu | OpenAI Scholars Demo Day 2021
OpenAI
55 Studying Scaling Laws for Transformer Architecture … | Shola Oyedele | OpenAI Scholars Demo Day 2021
Studying Scaling Laws for Transformer Architecture … | Shola Oyedele | OpenAI Scholars Demo Day 2021
OpenAI
56 Feedback Loops in Opinion Modeling | Danielle Ensign | OpenAI Scholars Demo Day 2021
Feedback Loops in Opinion Modeling | Danielle Ensign | OpenAI Scholars Demo Day 2021
OpenAI
57 Creating a Space Game with OpenAI Codex
Creating a Space Game with OpenAI Codex
OpenAI
58 “Hello World” with OpenAI Codex
“Hello World” with OpenAI Codex
OpenAI
59 Talking to Your Computer with OpenAI Codex
Talking to Your Computer with OpenAI Codex
OpenAI
60 Data Science with OpenAI Codex
Data Science with OpenAI Codex
OpenAI

The OpenAI Codex CLI is a powerful tool for coding and development, allowing users to build projects and complete tasks with ease, using multimodal reasoning and full auto mode.

Key Takeaways
  1. Install Codex CLI
  2. Run Codex CLI in command line
  3. Use multimodal reasoning to provide input
  4. Select full auto mode for automated coding
  5. Review and test generated code
💡 The Codex CLI's multimodal reasoning feature allows users to provide input through various means, such as screenshots or sketches, making it a powerful tool for coding and development.

Related AI Lessons

Claude AI vs ChatGPT: Which One Is Actually Better in 2026?
Compare Claude AI and ChatGPT based on real-world usage and benchmarking to determine which one is better in 2026
Medium · AI
Claude AI vs ChatGPT: Which One Is Actually Better in 2026?
Compare Claude AI and ChatGPT to determine which AI model is better for your needs in 2026
Medium · Programming
IntelliBooks: Classic RAG vs Graph RAG vs Agentic RAG – Choosing the Right AI Retrieval Architecture for Enterprise AI
Learn to choose the right AI retrieval architecture for enterprise AI between Classic RAG, Graph RAG, and Agentic RAG
Dev.to AI
Fluid, natural voice translation with Gemini 3.5 Live Translate
Learn about Gemini 3.5 Live Translate, a new voice translation technology that enables fluid and natural conversations across languages
Dev.to AI
Up next
5 Levels of AI Agents - From Simple LLM Calls to Multi-Agent Systems
Dave Ebbelaar (LLM Eng)
Watch →