The best part about this blog post is that none of it is a surprise – Codex CLI is open source. It's nice to be able to go through the internals without having to reverse engineer it.
Their communication is exceptional, too. Eric Traut (of Pyright fame) is all over the issues and PRs.
This came as a big surprise to me last year. I remember they announced that Codex CLI is opensource, and the codex-rs [0] from TypeScript to Rust, with the entire CLI now open source. This is a big deal and very useful for anyone wanting to learn how coding agents work, especially coming from a major lab like OpenAI. I've also contributed some improvements to their CLI a while ago and have been following their releases and PRs to broaden my knowledge.
I frankly don't understand why they keep CC proprietary. Feels to me that the key part is the model, not the harness, and they should make the harness public so the public can contribute.
At this point I just assume Claude Code isn't OSS out of embarrassment for how poor the code actually is. I've got a $200/mo claude subscription I'm about to cancel out of frustration with just how consistently broken, slow, and annoying to use the claude CLI is.
Very probably. Apparently, it's literally implemented with a React->Text pipeline and it was so badly implemented that they were having problems with the garbage collector executing too frequently.
Is it just a frontend CLI calling remote external logic for the bulk of operations, or does it come with everything needed to run lovely offline? Does it provide weights under FLOW license? Does it document the whole build process and how to redo and go further on your own?
I appreciate the sentiment but I’m giving OpenAI 0 credit for anything open source, given their founding charter and how readily it was abandoned when it became clear the work could be financially exploited.
> when it became clear the work could be financially exploited
That is not the obvious reason for the change. Training models got a lot more expensive than anyone thought it would.
You can of course always cast shade on people's true motivations and intentions, but there is a plain truth here that is simply silly to ignore.
Training "frontier" open LLMs seems to be exactly possible when a) you are Meta, have substantial revenue from other sources and simply are okay with burning your cash reserves to try to make something happen and b) you copy and distill from the existing models.
I agree that openAI should be held with a certain degree of contempt, but refusing to acknowledge anything positive they do is an interesting perspective. Why insist on a one dimensional view? It’s like a fraudster giving to charity, they can be praiseworthy in some respect while being overall contemptible, no?
Interesting that compaction is done using an encrypted message that "preserves the model's latent understanding of the original conversation":
> Since then, the Responses API has evolved to support a special /responses/compact endpoint (opens in a new window) that performs compaction more efficiently. It returns a list of items (opens in a new window) that can be used in place of the previous input to continue the conversation while freeing up the context window. This list includes a special type=compaction item with an opaque encrypted_content item that preserves the model’s latent understanding of the original conversation. Now, Codex automatically uses this endpoint to compact the conversation when the auto_compact_limit (opens in a new window) is exceeded.
Help me understand, how is a compaction endpoint not just a Prompt + json_dump of the message history? I would understand if the prompt was the secret sauce, but you make it sound like there is more to a compaction system than just a clever prompt?
Is it possible to use the compactor endpoint independently? I have my own agent loop i maintain for my domain specific use case. We built a compaction system, but I imagine this is better performance.
One thing that surprised me when diving into the Codex internals was that the reasoning tokens persist during the agent tool call loop, but are discarded after every user turn.
This helps preserve context over many turns, but it can also mean some context is lost between two related user turns.
A strategy that's helped me here, is having the model write progress updates (along with general plans/specs/debug/etc.) to markdown files, acting as a sort of "snapshot" that works across many context windows.
It depends on the API path. Chat completions does what you describe, however isn't it legacy?
I've only used codex with the responses v1 API and there it's the complete opposite. Already generated reasoning tokens even persist when you send another message (without rolling back) after cancelling turns before they have finished the thought process
Also with responses v1 xhigh mode eats through the context window multiples faster than the other modes, which does check out with this.
I think it might be a good decision though, as it might keep the context aligned with what the user sees.
If the reasoning tokens where persisted, I imagine it would be possible to build up more and more context that's invisible to the user and in the worst case, the model's and the user's "understanding" of the chat might diverge.
E.g. image a chat where the user just wants to make some small changes. The model asks whether it should also add test cases. The user declines and tells the model to not ask about it again.
The user asks for some more changes - however, invisibly to the user, the model keeps "thinking" about test cases, but never telling outside of reasoning blocks.
So suddenly, from the model's perspective, a lot of the context is about test cases, while from the user's POV, it was only one irrelevant question at the beginning.
This is effective and it's convenient to have all that stuff co-located with the code, but I've found it causes problems in team environments or really anywhere where you want to be able to work on multiple branches concurrently. I haven't come up with a good answer yet but I think my next experiment is to offload that stuff to a daemon with external storage, and then have a CLI client that the agent (or a human) can drive to talk to it.
I’ve been using agent-shell in emacs a lot and it stores transcripts of the entire interaction. It’s helped me out lot of times because I can say ‘look at the last transcript here’.
It’s not the responsibility of the agent to write this transcript, it’s emacs, so I don’t have to worry about the agent forgetting to log something. It’s just writing the buffer to disk.
that's the main gripe I have with codex; I want better observability into what the AI is doing to stop it if I see it going down the wrong path. in CC I can see it easily and stop and steer the model. in codex, the model spends 20m only for it to do something I didn't agree on. it burns OpenAI tokens too; they could save money by supporting this feature!
Sonnet has the same behavior: drops thinking on user message. Curiously in the latest Opus they have removed this behavior and all thinking tokens are preserved.
Same here! I think it would be good if this could be made by default by the tooling. I've seen others using SQL for the same and even the proposal for a succinct way of representing this handoff data in the most compact way.
I'm pretty sure that Codex uses reasoning.encrypted_content=true and store=false with the responses API.
reasoning.encrypted_content=true - The server will return all the reasoning tokens in an encrypted blob you can pass along in the next call. Only OpenaAI can decrypt them.
store=false - The server will not persist anything about the conversation on the server. Any subsequent calls must provide all context.
Combined the two above options turns the responses API into a stateless one. Without these options it will still persist reasoning tokens in a agentic loop, but it will be done statefully without the client passing the reasoning along each time.
Maybe it's changed, but this is certainly how it was back in November.
I would see my context window jump in size, after each user turn (i.e. from 70 to 85% remaining).
Built a tool to analyze the requests, and sure enough the reasoning tokens were removed from past responses (but only between user turns). Here are the two relevant PRs [0][1].
When trying to get to the bottom of it, someone from OAI reached out and said this was expected and a limitation of the Responses API (interesting sidenote: Codex uses the Responses API, but passes the full context with every request).
This is the relevant part of the docs[2]:
> In turn 2, any reasoning items from turn 1 are ignored and removed, since the model does not reuse reasoning items from previous turns.
That could explain the "churn" when it gets stuck. Do you think it needs to maintain an internal state over time to keep track of longer threads, or are written notes enough to bridge the gap?
but that's why I like Codex CLI, it's so bare bone and lightweight that I can build lots tools on top of it. persistent thinking tokens? let me have that using a separate file the AI writes to. the reasoning tokens we see aren't the actual tokens anyway; the model does a lot more behind the scenes but the API keeps them hidden (all providers do that).
Codex is wicked efficient with context windows, with the tradeoff of time spent. It hurts the flow state, but overall I've found that it's the best at having long conversations/coding sessions.
where do you save the progress updates in? and do you delete them afterwards or do you have like 100+ progress updates each time you have claude or codex implement a feature or change?
What I really want from Codex is checkpoints ala Copilot. There are a couple of issues [0][1] opened about on GitHub, but it doesn't seem a priority for the team.
They routinely mention in GitHub that they heavily prioritize based on "upvotes" (emoji reacts) in GitHub issues, and they close issues that don't receive many. So if you want this, please "upvote" those issues.
Regarding the user instruction aggregation process in the agent loop, I'm curious how you manage context retention in multi-turn interactions. Have you explored any techniques for dynamically adjusting the context based on the evolving user requirements?
I like it but wonder why it seems so slow compared to the chatgpt web interface. I still find myself more productive copying and pasting from chat much of the time. You get virtually instant feedback, and it feels far more natural when you're tossing around ideas, seeing what different approaches look like, trying to understand the details, etc. Going back to codex feels like you're waiting a lot longer for it to do the wrong thing anyway, so the feedback cycle is way slower and more frustrating. Specifically I hate when I ask a question, and it goes and starts editing code, which is pretty frequent. That said, it's great when it works. I just hope that someday it'll be as easy and snappy to chat with as the web interface, but still able to perform local tasks.
I guess nothing super surprising or new but still valuable read. I wish it was easier/native to reflect on the loop and/or histories while using agentic coding CLIs. I've found some success with an MCP that let's me query my chat histories, but I have to be very explicit about it's use. Also, like many things, continuous learning would probably solve this.
These can also be observed through OTEL telemetries.
I use headless codex exec a lot, but struggles with its built-in telemetry support, which is insufficient for debugging and optimization.
Thus I made codex-plus (https://github.com/aperoc/codex-plus) for myself which provides a CLI entry point that mirrors the codex exec interface but is implemented on top of the TypeScript SDK (@openai/codex-sdk).
It exports the full session log to a remote OpenTelemetry collector after each run which can then be debugged and optimized through codex-plus-log-viewer.
Has anyone seriously used codex cli? I was using LLMs for code gen usually through the vscode codex extension, Gemini cli and Claude Code cli. The performance of all 3 of them is utter dog shit, Gemini cli just randomly breaks and starts spamming content trying to reorient itself after a while.
However, I decided to try codex cli after hearing they rebuilt it from the ground up and used rust(instead of JS, not implying Rust==better). It's performance is quite literally insane, its UX is completely seamless. They even added small nice to haves like ctrl+left/right to skip your cursor to word boundaries.
If you haven't I genuinely think you should give it a try you'll be very surprised. Saw Theo(yc ping labs) talk about how open ai shouldn't have wasted their time optimizing the cli and made a better model or something. I highly disagree after using it.
I found codex cli to be significantly better than claude code. It follows instructions and executes the exact change I want without going off on an "adventure" like Claude code. Also the 20 dollars per month sub tier gives very generous limits of the most powerful model option (5.2 codex high).
codex the model (not the cli) is the big thing here. I've used it in CC and w/ my claude setup, it can handle things Opus could never. it's really a secret weapon not a lot of people talk about. I'm not even using xhigh most of the time.
OpenCode also has an extremely fast and reliable UI compared to the other CLIs. I’ve been using Codex more lately since I’m cancelling my Claude Pro plan and it’s solid but haven’t spent nearly as much time compared to Claude Code or Gemini CLI yet.
But tbh OpenAI openly supporting OpenCode is the bigger draw for me on the plan but do want to spend more time with native Codex as a base of comparison against OpenCode when using the same model.
I’m just happy to have so many competitive options, for now at least.
- better UI to show me what changes are going to be made.
the second one makes a huge diff and it's the main reason I stopped using opencode (lots of other reasons too). in CC, I am shown a nice diff that I can approve/reject. in codex, the AI makes lots of changes but doesn't pin point what changes it's doing or going to make.
It's pretty good, yeah. I get coherent results >95% of the time (on well-known problems).
However, it seems to really only be good at coding tasks. Anything even slightly out of the ordinary, like planning dialogue and plot lines it almost immediately starts producing garbage.
I did get it stuck in a loop the other day. I half-assed a git rebase and asked codex to fix it. It did eventually resolve all debased commits, but it just kept going. I don't really know what it was doing, I think it made up some directive after the rebase completed and it just kept chugging until I pulled the plug.
The only other tool I've tried is Aider, which I have found to be nearly worthless garbage
I strongly agree. The memory and cpu usage
of codex-cli is also extremely good. That codex-cli is open source is also valuable because you can easily get definitive answers to any questions about its behavior.
The problem with codex right now is it doesn't have hook support. It's hard to understate how big of a deal hooks are, the Ralph loop that the newer folks are losing their shit over is like the level 0, most rudimentary use of hooks.
I have a tool that reduces agent token consumption by 30%, and it's only viable because I can hook the harness and catch agents being stupid, then prompt them to be smarter on the fly. More at https://sibylline.dev/articles/2026-01-22-scribe-swebench-be...
I use 2 cli - Codex and Amp. Almost every time I need a quick change, Amp finishes the task in the time it takes Codex to build context. I think it’s got a lot to do with the system prompt and a the “read loop” as well, amp would read multiple files in one go and get to the task, but codex would crawl the files almost one by one. Anyone noticed this?
I do the same thing with both. Nothing specific to Amp. But I have read it’s great for brainstorming and planning if I “ask oracle” - oracle being their tool that enables deep thinking. So I tend to use that when I think I have multiple solutions to something or the problem is big enough and I need to plan and break it down into smaller ones
The "Listen to article" media player at the top of the post -- was super quick to load on mobile but took two attempts and a page refresh to load on desktop.
If I want to listen as well as read the article ... the media player scrolls out of view along with the article title as we scroll down ..leaving us with no way to control (pause/play) the audio if needed.
There are no playback controls other than pause and speed selector. So we cannot seek or skip forward/backward if we miss a sentence. the time display on the media player is also minimal. Wish these were a more accessible standardized feature set available on demand and not limited by what the web designer of each site decides.
I asked "Claude on Chrome" extension to fix the media player to the top. It took 2 attempts to get it right. (It was using Haiku by default -- may be a larger model was needed for this task). I think there is scope to create a standard library for such client side tweaks to web pages -- sort of like greasemonkey user scripts but at a slightly higher level of abstraction with natural language prompts.
I completely agree. I use the Codex for complex, hard-to-handle problems and use OpenCode alongside other models for development tasks. The Codex handles things quite well, including how it handles hooks, memory, etc.
The best part about this is how the program acts like a human who is learning by doing. It is not trying to be perfect on the first try, it is just trying to make progress by looking at the results. I think this method is going to make computers much more helpful because they can now handle the messy parts of solving a problem.
Codex is extremely bad to the point it is almost useless.
Claude Code is very effective. Opus is a solid model and claude very reliably solves problems and is generally efficient and doesn't get stuck in weird loops or go off in insane tangents too often. You can be very very efficient with claude code.
Gemini-cli is quite good. If you set `--model gemini-3-pro-preview` it is quite usable, but the flash model is absolute trash. Overall gemini-3-pro-preview is 'smarter' than opus, but the tooling here is not as good as claude code so it tends to get stuck in loops, or think for 5 minutes, or do weird extreme stuff. When Gemini is on point it is very very good, but it is inconsistent and likely to mess up so much that it's not as productive to use as claude.
Codex is trash. It is slow, tends to fail to solve problems, gets stuck in weird places, and sometimes has to puzzle on something simple for 15 minutes. The codex models are poor, and forcing the 5.2 model is expensive, and even then the tooling is incredibly bad and tends to just fail a lot. I check in to see if codex is any good from time to time and every time it is laughably bad compared to the other two.
I have the complete opposite experience. Claude Code is for building small demo apps. Like a 10 line Javascript example. Codex is for building GPU pipelines and emulators.
I asked Claude to summarize the article and it was blocked haha. Fortunately, I have the Claude plugin in chrome installed and it used the plugin to read the contents of the page.
The best part about this blog post is that none of it is a surprise – Codex CLI is open source. It's nice to be able to go through the internals without having to reverse engineer it.
Their communication is exceptional, too. Eric Traut (of Pyright fame) is all over the issues and PRs.
https://github.com/openai/codex
This came as a big surprise to me last year. I remember they announced that Codex CLI is opensource, and the codex-rs [0] from TypeScript to Rust, with the entire CLI now open source. This is a big deal and very useful for anyone wanting to learn how coding agents work, especially coming from a major lab like OpenAI. I've also contributed some improvements to their CLI a while ago and have been following their releases and PRs to broaden my knowledge.
[0] https://github.com/openai/codex/tree/main/codex-rs
I know very little about typescript and even less about rust. Am I getting the rust version of codex when I do `npm i -g @openai/codex`?
A stand alone rust binary would be nicer than installing node.
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For some reason a lot of people are unaware that Claude Code is proprietary.
Probably because it doesn’t matter most of the time?
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Can't really fault them when this exists:
https://github.com/anthropics/claude-code
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I frankly don't understand why they keep CC proprietary. Feels to me that the key part is the model, not the harness, and they should make the harness public so the public can contribute.
Yeah this has always seemed very silly. It is trivial to use claude code to reverse engineer itself.
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At this point I just assume Claude Code isn't OSS out of embarrassment for how poor the code actually is. I've got a $200/mo claude subscription I'm about to cancel out of frustration with just how consistently broken, slow, and annoying to use the claude CLI is.
> how poor the code actually is.
Very probably. Apparently, it's literally implemented with a React->Text pipeline and it was so badly implemented that they were having problems with the garbage collector executing too frequently.
[1] https://news.ycombinator.com/item?id=46699072#46701013
OpenCode is amazing, though.
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Interesting. Have you tested other LLMs or CLIs as a comparison? Curious which one you’re finding more reliable than Opus 4.5 through Claude Code.
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Yeah same with Claude Code pretty much and most people don’t realize some people use Windows.
I'm almost certain their code is a dumpster fire.
As for your 200$/mo sub. Dont buy it. If you read the fine print, their 20x usage is _per 5h session_, not overall usage.
Take 2x 100$ if you're hitting the limit.
Anthropic/Claude's entire UX is the worst among the bunch
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I thought Eric Traut was famous for his pioneering work in virtualization, TIL he has Pyright fame too !
Is it just a frontend CLI calling remote external logic for the bulk of operations, or does it come with everything needed to run lovely offline? Does it provide weights under FLOW license? Does it document the whole build process and how to redo and go further on your own?
I appreciate the sentiment but I’m giving OpenAI 0 credit for anything open source, given their founding charter and how readily it was abandoned when it became clear the work could be financially exploited.
> when it became clear the work could be financially exploited
That is not the obvious reason for the change. Training models got a lot more expensive than anyone thought it would.
You can of course always cast shade on people's true motivations and intentions, but there is a plain truth here that is simply silly to ignore.
Training "frontier" open LLMs seems to be exactly possible when a) you are Meta, have substantial revenue from other sources and simply are okay with burning your cash reserves to try to make something happen and b) you copy and distill from the existing models.
I agree that openAI should be held with a certain degree of contempt, but refusing to acknowledge anything positive they do is an interesting perspective. Why insist on a one dimensional view? It’s like a fraudster giving to charity, they can be praiseworthy in some respect while being overall contemptible, no?
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By this measure, they shouldn’t even try to do good things in small pockets and probably should just optimize for profits!
Fortunately, many other people can deal with nuance.
Interesting that compaction is done using an encrypted message that "preserves the model's latent understanding of the original conversation":
> Since then, the Responses API has evolved to support a special /responses/compact endpoint (opens in a new window) that performs compaction more efficiently. It returns a list of items (opens in a new window) that can be used in place of the previous input to continue the conversation while freeing up the context window. This list includes a special type=compaction item with an opaque encrypted_content item that preserves the model’s latent understanding of the original conversation. Now, Codex automatically uses this endpoint to compact the conversation when the auto_compact_limit (opens in a new window) is exceeded.
Their compaction endpoint is far and away the best in the industry. Claude's has to be dead last.
Help me understand, how is a compaction endpoint not just a Prompt + json_dump of the message history? I would understand if the prompt was the secret sauce, but you make it sound like there is more to a compaction system than just a clever prompt?
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Yes, agree completely.
Is it possible to use the compactor endpoint independently? I have my own agent loop i maintain for my domain specific use case. We built a compaction system, but I imagine this is better performance.
I would guess you can if you're using their Responses api for inference within your agent.
Yes you can and I really like it as a feature. But it ties you to OpenAI…
How does this work for other models that aren’t OpenAI models
It wouldn’t work for other models if it’s encoded in a latent representation of their own models.
One thing that surprised me when diving into the Codex internals was that the reasoning tokens persist during the agent tool call loop, but are discarded after every user turn.
This helps preserve context over many turns, but it can also mean some context is lost between two related user turns.
A strategy that's helped me here, is having the model write progress updates (along with general plans/specs/debug/etc.) to markdown files, acting as a sort of "snapshot" that works across many context windows.
It depends on the API path. Chat completions does what you describe, however isn't it legacy?
I've only used codex with the responses v1 API and there it's the complete opposite. Already generated reasoning tokens even persist when you send another message (without rolling back) after cancelling turns before they have finished the thought process
Also with responses v1 xhigh mode eats through the context window multiples faster than the other modes, which does check out with this.
How do you know/toggle which API path you are using?
I think it might be a good decision though, as it might keep the context aligned with what the user sees.
If the reasoning tokens where persisted, I imagine it would be possible to build up more and more context that's invisible to the user and in the worst case, the model's and the user's "understanding" of the chat might diverge.
E.g. image a chat where the user just wants to make some small changes. The model asks whether it should also add test cases. The user declines and tells the model to not ask about it again.
The user asks for some more changes - however, invisibly to the user, the model keeps "thinking" about test cases, but never telling outside of reasoning blocks.
So suddenly, from the model's perspective, a lot of the context is about test cases, while from the user's POV, it was only one irrelevant question at the beginning.
I made a skill that reflects on past conversations via parallel headless codex sessions. Its great for context building. Repo: https://github.com/olliepro/Codex-Reflect-Skill
This is effective and it's convenient to have all that stuff co-located with the code, but I've found it causes problems in team environments or really anywhere where you want to be able to work on multiple branches concurrently. I haven't come up with a good answer yet but I think my next experiment is to offload that stuff to a daemon with external storage, and then have a CLI client that the agent (or a human) can drive to talk to it.
git worktrees are the canonical solution
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I’ve been using agent-shell in emacs a lot and it stores transcripts of the entire interaction. It’s helped me out lot of times because I can say ‘look at the last transcript here’.
It’s not the responsibility of the agent to write this transcript, it’s emacs, so I don’t have to worry about the agent forgetting to log something. It’s just writing the buffer to disk.
I think this explains why I'm not getting the most out of codex, I like to interrupt and respond to things i see in reasoning tokens.
that's the main gripe I have with codex; I want better observability into what the AI is doing to stop it if I see it going down the wrong path. in CC I can see it easily and stop and steer the model. in codex, the model spends 20m only for it to do something I didn't agree on. it burns OpenAI tokens too; they could save money by supporting this feature!
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Sonnet has the same behavior: drops thinking on user message. Curiously in the latest Opus they have removed this behavior and all thinking tokens are preserved.
Same here! I think it would be good if this could be made by default by the tooling. I've seen others using SQL for the same and even the proposal for a succinct way of representing this handoff data in the most compact way.
I don't think this is true.
I'm pretty sure that Codex uses reasoning.encrypted_content=true and store=false with the responses API.
reasoning.encrypted_content=true - The server will return all the reasoning tokens in an encrypted blob you can pass along in the next call. Only OpenaAI can decrypt them.
store=false - The server will not persist anything about the conversation on the server. Any subsequent calls must provide all context.
Combined the two above options turns the responses API into a stateless one. Without these options it will still persist reasoning tokens in a agentic loop, but it will be done statefully without the client passing the reasoning along each time.
Maybe it's changed, but this is certainly how it was back in November.
I would see my context window jump in size, after each user turn (i.e. from 70 to 85% remaining).
Built a tool to analyze the requests, and sure enough the reasoning tokens were removed from past responses (but only between user turns). Here are the two relevant PRs [0][1].
When trying to get to the bottom of it, someone from OAI reached out and said this was expected and a limitation of the Responses API (interesting sidenote: Codex uses the Responses API, but passes the full context with every request).
This is the relevant part of the docs[2]:
> In turn 2, any reasoning items from turn 1 are ignored and removed, since the model does not reuse reasoning items from previous turns.
[0]https://github.com/openai/codex/pull/5857
[1]https://github.com/openai/codex/pull/5986
[2]https://cookbook.openai.com/examples/responses_api/reasoning...
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That could explain the "churn" when it gets stuck. Do you think it needs to maintain an internal state over time to keep track of longer threads, or are written notes enough to bridge the gap?
but that's why I like Codex CLI, it's so bare bone and lightweight that I can build lots tools on top of it. persistent thinking tokens? let me have that using a separate file the AI writes to. the reasoning tokens we see aren't the actual tokens anyway; the model does a lot more behind the scenes but the API keeps them hidden (all providers do that).
Codex is wicked efficient with context windows, with the tradeoff of time spent. It hurts the flow state, but overall I've found that it's the best at having long conversations/coding sessions.
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where do you save the progress updates in? and do you delete them afterwards or do you have like 100+ progress updates each time you have claude or codex implement a feature or change?
What I really want from Codex is checkpoints ala Copilot. There are a couple of issues [0][1] opened about on GitHub, but it doesn't seem a priority for the team.
[0] https://github.com/openai/codex/issues/2788
[1] https://github.com/openai/codex/issues/3585
I’ve never understood checkpoints / forks. When do you use them?
Gemini CLI has this
They routinely mention in GitHub that they heavily prioritize based on "upvotes" (emoji reacts) in GitHub issues, and they close issues that don't receive many. So if you want this, please "upvote" those issues.
Regarding the user instruction aggregation process in the agent loop, I'm curious how you manage context retention in multi-turn interactions. Have you explored any techniques for dynamically adjusting the context based on the evolving user requirements?
I like it but wonder why it seems so slow compared to the chatgpt web interface. I still find myself more productive copying and pasting from chat much of the time. You get virtually instant feedback, and it feels far more natural when you're tossing around ideas, seeing what different approaches look like, trying to understand the details, etc. Going back to codex feels like you're waiting a lot longer for it to do the wrong thing anyway, so the feedback cycle is way slower and more frustrating. Specifically I hate when I ask a question, and it goes and starts editing code, which is pretty frequent. That said, it's great when it works. I just hope that someday it'll be as easy and snappy to chat with as the web interface, but still able to perform local tasks.
xhigh reasoning effort for 5.2 Thinking is not available for ChatGPT Plus subscribers in the web interface.
I guess nothing super surprising or new but still valuable read. I wish it was easier/native to reflect on the loop and/or histories while using agentic coding CLIs. I've found some success with an MCP that let's me query my chat histories, but I have to be very explicit about it's use. Also, like many things, continuous learning would probably solve this.
These can also be observed through OTEL telemetries.
I use headless codex exec a lot, but struggles with its built-in telemetry support, which is insufficient for debugging and optimization.
Thus I made codex-plus (https://github.com/aperoc/codex-plus) for myself which provides a CLI entry point that mirrors the codex exec interface but is implemented on top of the TypeScript SDK (@openai/codex-sdk).
It exports the full session log to a remote OpenTelemetry collector after each run which can then be debugged and optimized through codex-plus-log-viewer.
Has anyone seriously used codex cli? I was using LLMs for code gen usually through the vscode codex extension, Gemini cli and Claude Code cli. The performance of all 3 of them is utter dog shit, Gemini cli just randomly breaks and starts spamming content trying to reorient itself after a while.
However, I decided to try codex cli after hearing they rebuilt it from the ground up and used rust(instead of JS, not implying Rust==better). It's performance is quite literally insane, its UX is completely seamless. They even added small nice to haves like ctrl+left/right to skip your cursor to word boundaries.
If you haven't I genuinely think you should give it a try you'll be very surprised. Saw Theo(yc ping labs) talk about how open ai shouldn't have wasted their time optimizing the cli and made a better model or something. I highly disagree after using it.
I found codex cli to be significantly better than claude code. It follows instructions and executes the exact change I want without going off on an "adventure" like Claude code. Also the 20 dollars per month sub tier gives very generous limits of the most powerful model option (5.2 codex high).
I work on SSL bio acoustic models as context.
codex the model (not the cli) is the big thing here. I've used it in CC and w/ my claude setup, it can handle things Opus could never. it's really a secret weapon not a lot of people talk about. I'm not even using xhigh most of the time.
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OpenCode also has an extremely fast and reliable UI compared to the other CLIs. I’ve been using Codex more lately since I’m cancelling my Claude Pro plan and it’s solid but haven’t spent nearly as much time compared to Claude Code or Gemini CLI yet.
But tbh OpenAI openly supporting OpenCode is the bigger draw for me on the plan but do want to spend more time with native Codex as a base of comparison against OpenCode when using the same model.
I’m just happy to have so many competitive options, for now at least.
Seconded. I find codex lacks only two things:
- hooks (this is a big one)
- better UI to show me what changes are going to be made.
the second one makes a huge diff and it's the main reason I stopped using opencode (lots of other reasons too). in CC, I am shown a nice diff that I can approve/reject. in codex, the AI makes lots of changes but doesn't pin point what changes it's doing or going to make.
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It's pretty good, yeah. I get coherent results >95% of the time (on well-known problems).
However, it seems to really only be good at coding tasks. Anything even slightly out of the ordinary, like planning dialogue and plot lines it almost immediately starts producing garbage.
I did get it stuck in a loop the other day. I half-assed a git rebase and asked codex to fix it. It did eventually resolve all debased commits, but it just kept going. I don't really know what it was doing, I think it made up some directive after the rebase completed and it just kept chugging until I pulled the plug.
The only other tool I've tried is Aider, which I have found to be nearly worthless garbage
I strongly agree. The memory and cpu usage of codex-cli is also extremely good. That codex-cli is open source is also valuable because you can easily get definitive answers to any questions about its behavior.
I also was annoyed by Theo saying that.
The problem with codex right now is it doesn't have hook support. It's hard to understate how big of a deal hooks are, the Ralph loop that the newer folks are losing their shit over is like the level 0, most rudimentary use of hooks.
I have a tool that reduces agent token consumption by 30%, and it's only viable because I can hook the harness and catch agents being stupid, then prompt them to be smarter on the fly. More at https://sibylline.dev/articles/2026-01-22-scribe-swebench-be...
Same goes for Claude Code. Literally has vim bindings for editing prompts if you want them.
Codex has Ctrl-G to start an $EDITOR of your choice, FWIW.
CC is the clunkiest PoS software I've ever used in terminal; feels like it was vibe coded and anthroshit doesn't give a shit
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Codex’s only caveat is too slow.
This is the biggest UX killer，unfortunately
Wow, this part where they describe skills sounds quite odd https://github.com/openai/codex/blob/99f47d6e9a3546c14c43af9...
Why wouldnt they just expose the files directly? Having the model ask for them as regular files sounds a bit odd
That's the whole point of skills - they help reduce context window usage by letting the model open only the ones that are relevant.
I use 2 cli - Codex and Amp. Almost every time I need a quick change, Amp finishes the task in the time it takes Codex to build context. I think it’s got a lot to do with the system prompt and a the “read loop” as well, amp would read multiple files in one go and get to the task, but codex would crawl the files almost one by one. Anyone noticed this?
Which Gpt model and reasoning level did you use in Codex and Amp?
Generally I have noticed Gpt 5.2 codex is slower compared to Sonnet 4.5 in Claude Code.
Amp doesn't have a conventional model selector - you choose fast vs smart (I think that's what it is called).
In smart mode it explores with Gemini Flash and writes with Opus.
Opus is roughly the same speed as Codex, depending on thinking settings.
Amp uses Gemini 3 Flash to explore code first. That's model is a great speed/intelligence trade-off especially for that use case.
What is your general flow with amp? I plan to try it out myself and have been on the fences for a while.
I do the same thing with both. Nothing specific to Amp. But I have read it’s great for brainstorming and planning if I “ask oracle” - oracle being their tool that enables deep thinking. So I tend to use that when I think I have multiple solutions to something or the problem is big enough and I need to plan and break it down into smaller ones
Codex agent loop:
https://i.ytimg.com/vi/74U04h9hQ_s/maxresdefault.jpg
I think this should be called the Homer Simpson loop, it seems more apt
They sadly renamed the Ralph Wiggum loop due to copyright concerns so little hope for Homer :(
https://github.com/anthropics/claude-plugins-official/commit...
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Offtopic but --
The "Listen to article" media player at the top of the post -- was super quick to load on mobile but took two attempts and a page refresh to load on desktop.
If I want to listen as well as read the article ... the media player scrolls out of view along with the article title as we scroll down ..leaving us with no way to control (pause/play) the audio if needed.
There are no playback controls other than pause and speed selector. So we cannot seek or skip forward/backward if we miss a sentence. the time display on the media player is also minimal. Wish these were a more accessible standardized feature set available on demand and not limited by what the web designer of each site decides.
I asked "Claude on Chrome" extension to fix the media player to the top. It took 2 attempts to get it right. (It was using Haiku by default -- may be a larger model was needed for this task). I think there is scope to create a standard library for such client side tweaks to web pages -- sort of like greasemonkey user scripts but at a slightly higher level of abstraction with natural language prompts.
I completely agree. I use the Codex for complex, hard-to-handle problems and use OpenCode alongside other models for development tasks. The Codex handles things quite well, including how it handles hooks, memory, etc.
If anyone cares to use Codex in a nice Docker container: https://github.com/DeepBlueDynamics/codex-container
The best part about this is how the program acts like a human who is learning by doing. It is not trying to be perfect on the first try, it is just trying to make progress by looking at the results. I think this method is going to make computers much more helpful because they can now handle the messy parts of solving a problem.
Tool call during thinking is something similar to this I am guessing. Deepseek has a paper on this.
Or am I not understanding this right?
Pity it doesn't support other llms.
It does, it's just a bit annoying.
I have this set up as a shell script (or you could make it an alias):
with ~/.codex/config.toml containing:
https://developers.openai.com/codex/config-advanced#custom-m...
Codex is extremely bad to the point it is almost useless.
Claude Code is very effective. Opus is a solid model and claude very reliably solves problems and is generally efficient and doesn't get stuck in weird loops or go off in insane tangents too often. You can be very very efficient with claude code.
Gemini-cli is quite good. If you set `--model gemini-3-pro-preview` it is quite usable, but the flash model is absolute trash. Overall gemini-3-pro-preview is 'smarter' than opus, but the tooling here is not as good as claude code so it tends to get stuck in loops, or think for 5 minutes, or do weird extreme stuff. When Gemini is on point it is very very good, but it is inconsistent and likely to mess up so much that it's not as productive to use as claude.
Codex is trash. It is slow, tends to fail to solve problems, gets stuck in weird places, and sometimes has to puzzle on something simple for 15 minutes. The codex models are poor, and forcing the 5.2 model is expensive, and even then the tooling is incredibly bad and tends to just fail a lot. I check in to see if codex is any good from time to time and every time it is laughably bad compared to the other two.
I have the complete opposite experience. Claude Code is for building small demo apps. Like a 10 line Javascript example. Codex is for building GPU pipelines and emulators.
thx :)
I asked Claude to summarize the article and it was blocked haha. Fortunately, I have the Claude plugin in chrome installed and it used the plugin to read the contents of the page.
Great achievement. What did you learn?
Nothing particularly insightful other than avoiding messing with previous messages so as not to mess with the cache.
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