47 posts tagged “codex”
OpenAI's coding agent tools: Codex CLI, Codex Desktop, Codex Cloud.
2026
Welcome to the Datasette blog. We have a bunch of neat Datasette announcements in the pipeline so we decided it was time the project grew an official blog.
I built this using OpenAI Codex desktop, which turns out to have the Markdown session transcript export feature I've always wanted. Here's the session that built the blog. See also issue 179.
Codex CLI 0.128.0 adds /goal
(via)
The latest version of OpenAI's Codex CLI coding agent adds their own version of the Ralph loop: you can now set a /goal and Codex will keep on looping until it evaluates that the goal has been completed... or the configured token budget has been exhausted.
It looks like the feature is mainly implemented though the goals/continuation.md and goals/budget_limit.md prompts, which are automatically injected at the end of a turn.
Never talk about goblins, gremlins, raccoons, trolls, ogres, pigeons, or other animals or creatures unless it is absolutely and unambiguously relevant to the user's query.
— OpenAI Codex base_instructions, for GPT-5.5
GPT-5.5 prompting guide. Now that GPT-5.5 is available in the API, OpenAI have released a wealth of useful tips on how best to prompt the new model.
Here's a neat trick they recommend for applications that might spend considerable time thinking before returning a user-visible response:
Before any tool calls for a multi-step task, send a short user-visible update that acknowledges the request and states the first step. Keep it to one or two sentences.
I've already noticed their Codex app doing this, and it does make longer running tasks feel less like the model has crashed.
OpenAI suggest running the following in Codex to upgrade your existing code using advice embedded in their openai-docs skill:
$openai-docs migrate this project to gpt-5.5
The upgrade guide the coding agent will follow is this one, which even includes light instructions on how to rewrite prompts to better fit the model.
Also relevant is the Using GPT-5.5 guide, which opens with this warning:
To get the most out of GPT-5.5, treat it as a new model family to tune for, not a drop-in replacement for
gpt-5.2orgpt-5.4. Begin migration with a fresh baseline instead of carrying over every instruction from an older prompt stack. Start with the smallest prompt that preserves the product contract, then tune reasoning effort, verbosity, tool descriptions, and output format against representative examples.
Interesting to see OpenAI recommend starting from scratch rather than trusting that existing prompts optimized for previous models will continue to work effectively with GPT-5.5.
A pelican for GPT-5.5 via the semi-official Codex backdoor API
GPT-5.5 is out. It’s available in OpenAI Codex and is rolling out to paid ChatGPT subscribers. I’ve had some preview access and found it to be a fast, effective and highly capable model. As is usually the case these days, it’s hard to put into words what’s good about it—I ask it to build things and it builds exactly what I ask for!
[... 884 words]Hijacks your Codex CLI credentials to make API calls with LLM, as described in my post about GPT-5.5.
Is Claude Code going to cost $100/month? Probably not—it’s all very confusing
Anthropic today quietly (as in silently, no announcement anywhere at all) updated their claude.com/pricing page (but not their Choosing a Claude plan page, which shows up first for me on Google) to add this tiny but significant detail (arrow is mine, and it’s already reverted):
[... 1,202 words]Thoughts on OpenAI acquiring Astral and uv/ruff/ty
The big news this morning: Astral to join OpenAI (on the Astral blog) and OpenAI to acquire Astral (the OpenAI announcement). Astral are the company behind uv, ruff, and ty—three increasingly load-bearing open source projects in the Python ecosystem. I have thoughts!
[... 1,378 words]Use subagents and custom agents in Codex (via) Subagents were announced in general availability today for OpenAI Codex, after several weeks of preview behind a feature flag.
They're very similar to the Claude Code implementation, with default subagents for "explorer", "worker" and "default". It's unclear to me what the difference between "worker" and "default" is but based on their CSV example I think "worker" is intended for running large numbers of small tasks in parallel.
Codex also lets you define custom agents as TOML files in ~/.codex/agents/. These can have custom instructions and be assigned to use specific models - including gpt-5.3-codex-spark if you want some raw speed. They can then be referenced by name, as demonstrated by this example prompt from the documentation:
Investigate why the settings modal fails to save. Have browser_debugger reproduce it, code_mapper trace the responsible code path, and ui_fixer implement the smallest fix once the failure mode is clear.
The subagents pattern is widely supported in coding agents now. Here's documentation across a number of different platforms:
- OpenAI Codex subagents
- Claude Code subagents
- Gemini CLI subagents (experimental)
- Mistral Vibe subagents
- OpenCode agents
- Subagents in Visual Studio Code
- Cursor Subagents
Update: I added a chapter on Subagents to my Agentic Engineering Patterns guide.
Coding agents for data analysis. Here's the handout I prepared for my NICAR 2026 workshop "Coding agents for data analysis" - a three hour session aimed at data journalists demonstrating ways that tools like Claude Code and OpenAI Codex can be used to explore, analyze and clean data.
Here's the table of contents:
I ran the workshop using GitHub Codespaces and OpenAI Codex, since it was easy (and inexpensive) to distribute a budget-restricted API key for Codex that attendees could use during the class. Participants ended up burning $23 of Codex tokens.
The exercises all used Python and SQLite and some of them used Datasette.
One highlight of the workshop was when we started running Datasette such that it served static content from a viz/ folder, then had Claude Code start vibe coding new interactive visualizations directly in that folder. Here's a heat map it created for my trees database using Leaflet and Leaflet.heat, source code here.
I designed the handout to also be useful for people who weren't able to attend the session in person. As is usually the case, material aimed at data journalists is equally applicable to anyone else with data to explore.
Codex for Open Source (via) Anthropic announced six months of free Claude Max for maintainers of popular open source projects (5,000+ stars or 1M+ NPM downloads) on 27th February.
Now OpenAI have launched their comparable offer: six months of ChatGPT Pro (same $200/month price as Claude Max) with Codex and "conditional access to Codex Security" for core maintainers.
Unlike Anthropic they don't hint at the exact metrics they care about, but the application form does ask for "information such as GitHub stars, monthly downloads, or why the project is important to the ecosystem."
How I think about Codex. Gabriel Chua (Developer Experience Engineer for APAC at OpenAI) provides his take on the confusing terminology behind the term "Codex", which can refer to a bunch of of different things within the OpenAI ecosystem:
In plain terms, Codex is OpenAI’s software engineering agent, available through multiple interfaces, and an agent is a model plus instructions and tools, wrapped in a runtime that can execute tasks on your behalf. [...]
At a high level, I see Codex as three parts working together:
Codex = Model + Harness + Surfaces [...]
- Model + Harness = the Agent
- Surfaces = how you interact with the Agent
He defines the harness as "the collection of instructions and tools", which is notably open source and lives in the openai/codex repository.
Gabriel also provides the first acknowledgment I've seen from an OpenAI insider that the Codex model family are directly trained for the Codex harness:
Codex models are trained in the presence of the harness. Tool use, execution loops, compaction, and iterative verification aren’t bolted on behaviors — they’re part of how the model learns to operate. The harness, in turn, is shaped around how the model plans, invokes tools, and recovers from failure.
Introducing GPT‑5.3‑Codex‑Spark. OpenAI announced a partnership with Cerebras on January 14th. Four weeks later they're already launching the first integration, "an ultra-fast model for real-time coding in Codex".
Despite being named GPT-5.3-Codex-Spark it's not purely an accelerated alternative to GPT-5.3-Codex - the blog post calls it "a smaller version of GPT‑5.3-Codex" and clarifies that "at launch, Codex-Spark has a 128k context window and is text-only."
I had some preview access to this model and I can confirm that it's significantly faster than their other models.
Here's what that speed looks like running in Codex CLI:
That was the "Generate an SVG of a pelican riding a bicycle" prompt - here's the rendered result:
Compare that to the speed of regular GPT-5.3 Codex medium:
Significantly slower, but the pelican is a lot better:
What's interesting about this model isn't the quality though, it's the speed. When a model responds this fast you can stay in flow state and iterate with the model much more productively.
I showed a demo of Cerebras running Llama 3.1 70 B at 2,000 tokens/second against Val Town back in October 2024. OpenAI claim 1,000 tokens/second for their new model, and I expect it will prove to be a ferociously useful partner for hands-on iterative coding sessions.
It's not yet clear what the pricing will look like for this new model.
When I want to quickly implement a one-off experiment in a part of the codebase I am unfamiliar with, I get codex to do extensive due diligence. Codex explores relevant slack channels, reads related discussions, fetches experimental branches from those discussions, and cherry picks useful changes for my experiment. All of this gets summarized in an extensive set of notes, with links back to where each piece of information was found. Using these notes, codex wires the experiment and makes a bunch of hyperparameter decisions I couldn’t possibly make without much more effort.
— Karel D'Oosterlinck, I spent $10,000 to automate my research at OpenAI with Codex
Two major new model releases today, within about 15 minutes of each other.
Anthropic released Opus 4.6. Here's its pelican:
OpenAI release GPT-5.3-Codex, albeit only via their Codex app, not yet in their API. Here's its pelican:
I've had a bit of preview access to both of these models and to be honest I'm finding it hard to find a good angle to write about them - they're both really good, but so were their predecessors Codex 5.2 and Opus 4.5. I've been having trouble finding tasks that those previous models couldn't handle but the new ones are able to ace.
The most convincing story about capabilities of the new model so far is Nicholas Carlini from Anthropic talking about Opus 4.6 and Building a C compiler with a team of parallel Claudes - Anthropic's version of Cursor's FastRender project.
Introducing the Codex app. OpenAI just released a new macOS app for their Codex coding agent. I've had a few days of preview access - it's a solid app that provides a nice UI over the capabilities of the Codex CLI agent and adds some interesting new features, most notably first-class support for Skills, and Automations for running scheduled tasks.
The app is built with Electron and Node.js. Automations track their state in a SQLite database - here's what that looks like if you explore it with uvx datasette ~/.codex/sqlite/codex-dev.db:
Here’s an interactive copy of that database in Datasette Lite.
The announcement gives us a hint at some usage numbers for Codex overall - the holiday spike is notable:
Since the launch of GPT‑5.2-Codex in mid-December, overall Codex usage has doubled, and in the past month, more than a million developers have used Codex.
Automations are currently restricted in that they can only run when your laptop is powered on. OpenAI promise that cloud-based automations are coming soon, which will resolve this limitation.
They chose Electron so they could target other operating systems in the future, with Windows “coming very soon”. OpenAI’s Alexander Embiricos noted on the Hacker News thread that:
it's taking us some time to get really solid sandboxing working on Windows, where there are fewer OS-level primitives for it.
Like Claude Code, Codex is really a general agent harness disguised as a tool for programmers. OpenAI acknowledge that here:
Codex is built on a simple premise: everything is controlled by code. The better an agent is at reasoning about and producing code, the more capable it becomes across all forms of technical and knowledge work. [...] We’ve focused on making Codex the best coding agent, which has also laid the foundation for it to become a strong agent for a broad range of knowledge work tasks that extend beyond writing code.
Claude Code had to rebrand to Cowork to better cover the general knowledge work case. OpenAI can probably get away with keeping the Codex name for both.
OpenAI have made Codex available to free and Go plans for "a limited time" (update: Sam Altman says two months) during which they are also doubling the rate limits for paying users.
One Human + One Agent = One Browser From Scratch (via) embedding-shapes was so infuriated by the hype around Cursor's FastRender browser project - thousands of parallel agents producing ~1.6 million lines of Rust - that they were inspired to take a go at building a web browser using coding agents themselves.
The result is one-agent-one-browser and it's really impressive. Over three days they drove a single Codex CLI agent to build 20,000 lines of Rust that successfully renders HTML+CSS with no Rust crate dependencies at all - though it does (reasonably) use Windows, macOS and Linux system frameworks for image and text rendering.
I installed the 1MB macOS binary release and ran it against my blog:
chmod 755 ~/Downloads/one-agent-one-browser-macOS-ARM64
~/Downloads/one-agent-one-browser-macOS-ARM64 https://simonwillison.net/
Here's the result:
It even rendered my SVG feed subscription icon! A PNG image is missing from the page, which looks like an intermittent bug (there's code to render PNGs).
The code is pretty readable too - here's the flexbox implementation.
I had thought that "build a web browser" was the ideal prompt to really stretch the capabilities of coding agents - and that it would take sophisticated multi-agent harnesses (as seen in the Cursor project) and millions of lines of code to achieve.
Turns out one agent driven by a talented engineer, three days and 20,000 lines of Rust is enough to get a very solid basic renderer working!
I'm going to upgrade my prediction for 2029: I think we're going to get a production-grade web browser built by a small team using AI assistance by then.
2025
Codex cloud is now called Codex web. It looks like OpenAI's Codex cloud (the cloud version of their Codex coding agent) was quietly rebranded to Codex web at some point in the last few days.
Here's a screenshot of the Internet Archive copy from 18th December (the capture on the 28th maintains that Codex cloud title but did not fully load CSS for me):
And here's that same page today with the updated product name:
Anthropic's equivalent product has the incredibly clumsy name Claude Code on the web, which I shorten to "Claude Code for web" but even then bugs me because I mostly interact with it via Anthropic's native mobile app.
I was hoping to see Claude Code for web rebrand to Claude Code Cloud - I did not expect OpenAI to rebrand in the opposite direction!
Update: Clarification from OpenAI Codex engineering lead Thibault Sottiaux:
Just aligning the documentation with how folks refer to it. I personally differentiate between cloud tasks and codex web. With cloud tasks running on our hosted runtime (includes code review, github, slack, linear, ...) and codex web being the web app.
I asked what they called Codex in the iPhone app and he said:
Codex iOS
Introducing GPT-5.2-Codex. The latest in OpenAI's Codex family of models (not the same thing as their Codex CLI or Codex Cloud coding agent tools).
GPT‑5.2-Codex is a version of GPT‑5.2 further optimized for agentic coding in Codex, including improvements on long-horizon work through context compaction, stronger performance on large code changes like refactors and migrations, improved performance in Windows environments, and significantly stronger cybersecurity capabilities.
As with some previous Codex models this one is available via their Codex coding agents now and will be coming to the API "in the coming weeks". Unlike previous models there's a new invite-only preview process for vetted cybersecurity professionals for "more permissive models".
I've been very impressed recently with GPT 5.2's ability to tackle multi-hour agentic coding challenges. 5.2 Codex scores 64% on the Terminal-Bench 2.0 benchmark that GPT-5.2 scored 62.2% on. I'm not sure how concrete that 1.8% improvement will be!
I didn't hack API access together this time (see previous attempts), instead opting to just ask Codex CLI to "Generate an SVG of a pelican riding a bicycle" while running the new model (effort medium). Here's the transcript in my new Codex CLI timeline viewer, and here's the pelican it drew:
Agent Skills. Anthropic have turned their skills mechanism into an "open standard", which I guess means it lives in an independent agentskills/agentskills GitHub repository now? I wouldn't be surprised to see this end up in the AAIF, recently the new home of the MCP specification.
The specification itself lives at agentskills.io/specification, published from docs/specification.mdx in the repo.
It is a deliciously tiny specification - you can read the entire thing in just a few minutes. It's also quite heavily under-specified - for example, there's a metadata field described like this:
Clients can use this to store additional properties not defined by the Agent Skills spec
We recommend making your key names reasonably unique to avoid accidental conflicts
And an allowed-skills field:
Experimental. Support for this field may vary between agent implementations
Example:
allowed-tools: Bash(git:*) Bash(jq:*) Read
The Agent Skills homepage promotes adoption by OpenCode, Cursor,Amp, Letta, goose, GitHub, and VS Code. Notably absent is OpenAI, who are quietly tinkering with skills but don't appear to have formally announced their support just yet.
Update 20th December 2025: OpenAI have added Skills to the Codex documentation and the Codex logo is now featured on the Agent Skills homepage (as of this commit.)
I ported JustHTML from Python to JavaScript with Codex CLI and GPT-5.2 in 4.5 hours
I wrote about JustHTML yesterday—Emil Stenström’s project to build a new standards compliant HTML5 parser in pure Python code using coding agents running against the comprehensive html5lib-tests testing library. Last night, purely out of curiosity, I decided to try porting JustHTML from Python to JavaScript with the least amount of effort possible, using Codex CLI and GPT-5.2. It worked beyond my expectations.
[... 1,818 words]How to use a skill (progressive disclosure):
- After deciding to use a skill, open its
SKILL.md. Read only enough to follow the workflow.- If
SKILL.mdpoints to extra folders such asreferences/, load only the specific files needed for the request; don't bulk-load everything.- If
scripts/exist, prefer running or patching them instead of retyping large code blocks.- If
assets/or templates exist, reuse them instead of recreating from scratch.Description as trigger: The YAML
descriptioninSKILL.mdis the primary trigger signal; rely on it to decide applicability. If unsure, ask a brief clarification before proceeding.
— OpenAI Codex CLI, core/src/skills/render.rs, full prompt
OpenAI are quietly adopting skills, now available in ChatGPT and Codex CLI
One of the things that most excited me about Anthropic’s new Skills mechanism back in October is how easy it looked for other platforms to implement. A skill is just a folder with a Markdown file and some optional extra resources and scripts, so any LLM tool with the ability to navigate and read from a filesystem should be capable of using them. It turns out OpenAI are doing exactly that, with skills support quietly showing up in both their Codex CLI tool and now also in ChatGPT itself.
[... 1,360 words]Building more with GPT-5.1-Codex-Max (via) Hot on the heels of yesterday's Gemini 3 Pro release comes a new model from OpenAI called GPT-5.1-Codex-Max.
(Remember when GPT-5 was meant to bring in a new era of less confusing model names? That didn't last!)
It's currently only available through their Codex CLI coding agent, where it's the new default model:
Starting today, GPT‑5.1-Codex-Max will replace GPT‑5.1-Codex as the default model in Codex surfaces. Unlike GPT‑5.1, which is a general-purpose model, we recommend using GPT‑5.1-Codex-Max and the Codex family of models only for agentic coding tasks in Codex or Codex-like environments.
It's not available via the API yet but should be shortly.
The timing of this release is interesting given that Gemini 3 Pro appears to have aced almost all of the benchmarks just yesterday. It's reminiscent of the period in 2024 when OpenAI consistently made big announcements that happened to coincide with Gemini releases.
OpenAI's self-reported SWE-Bench Verified score is particularly notable: 76.5% for thinking level "high" and 77.9% for the new "xhigh". That was the one benchmark where Gemini 3 Pro was out-performed by Claude Sonnet 4.5 - Gemini 3 Pro got 76.2% and Sonnet 4.5 got 77.2%. OpenAI now have the highest scoring model there by a full .7 of a percentage point!
They also report a score of 58.1% on Terminal Bench 2.0, beating Gemini 3 Pro's 54.2% (and Sonnet 4.5's 42.8%.)
The most intriguing part of this announcement concerns the model's approach to long context problems:
GPT‑5.1-Codex-Max is built for long-running, detailed work. It’s our first model natively trained to operate across multiple context windows through a process called compaction, coherently working over millions of tokens in a single task. [...]
Compaction enables GPT‑5.1-Codex-Max to complete tasks that would have previously failed due to context-window limits, such as complex refactors and long-running agent loops by pruning its history while preserving the most important context over long horizons. In Codex applications, GPT‑5.1-Codex-Max automatically compacts its session when it approaches its context window limit, giving it a fresh context window. It repeats this process until the task is completed.
There's a lot of confusion on Hacker News about what this actually means. Claude Code already does a version of compaction, automatically summarizing previous turns when the context runs out. Does this just mean that Codex-Max is better at that process?
I had it draw me a couple of pelicans by typing "Generate an SVG of a pelican riding a bicycle" directly into the Codex CLI tool. Here's thinking level medium:
And here's thinking level "xhigh":
I also tried xhigh on the my longer pelican test prompt, which came out like this:
Also today: GPT-5.1 Pro is rolling out today to all Pro users. According to the ChatGPT release notes:
GPT-5.1 Pro is rolling out today for all ChatGPT Pro users and is available in the model picker. GPT-5 Pro will remain available as a legacy model for 90 days before being retired.
That's a pretty fast deprecation cycle for the GPT-5 Pro model that was released just three months ago.
I've been upgrading a ton of Datasette plugins recently for compatibility with the Datasette 1.0a20 release from last week - 35 so far.
A lot of the work is very repetitive so I've been outsourcing it to Codex CLI. Here's the recipe I've landed on:
codex exec --dangerously-bypass-approvals-and-sandbox \
'Run the command tadd and look at the errors and then
read ~/dev/datasette/docs/upgrade-1.0a20.md and apply
fixes and run the tests again and get them to pass.
Also delete the .github directory entirely and replace
it by running this:
cp -r ~/dev/ecosystem/datasette-os-info/.github .
Run a git diff against that to make sure it looks OK
- if there are any notable differences e.g. switching
from Twine to the PyPI uploader or deleting code that
does a special deploy or configures something like
playwright include that in your final report.
If the project still uses setup.py then edit that new
test.yml and publish.yaml to mention setup.py not pyproject.toml
If this project has pyproject.toml make sure the license
line in that looks like this:
license = "Apache-2.0"
And remove any license thing from the classifiers= array
Update the Datasette dependency in pyproject.toml or
setup.py to "datasette>=1.0a21"
And make sure requires-python is >=3.10'I featured a simpler version of this prompt in my Datasette plugin upgrade video, but I've expanded it quite a bit since then.
At one point I had six terminal windows open running this same prompt against six different repos - probably my most extreme case of parallel agents yet.
Here are the six resulting commits from those six coding agent sessions:
Reverse engineering Codex CLI to get GPT-5-Codex-Mini to draw me a pelican
OpenAI partially released a new model yesterday called GPT-5-Codex-Mini, which they describe as "a more compact and cost-efficient version of GPT-5-Codex". It’s currently only available via their Codex CLI tool and VS Code extension, with proper API access "coming soon". I decided to use Codex to reverse engineer the Codex CLI tool and give me the ability to prompt the new model directly.
[... 1,774 words]Using Codex CLI with gpt-oss:120b on an NVIDIA DGX Spark via Tailscale. Inspired by a YouTube comment I wrote up how I run OpenAI's Codex CLI coding agent against the gpt-oss:120b model running in Ollama on my NVIDIA DGX Spark via a Tailscale network.
It takes a little bit of work to configure but the result is I can now use Codex CLI on my laptop anywhere in the world against a self-hosted model.
I used it to build this space invaders clone.
