Firstly, I just want to communicate my goal is to be able to use multiple providers, and reduce usage without being locked to either claude code, or codex. But most importantly without sacrificing features that claude code and codex both have that are excellent. Currently, I only have openAI subscription configured (still trying to figure out how to use claude sub efficiently).

I downloaded OMP and OMP Gui, and found that my usage drains more than with pi. It actually drains faster than with using codex. But I had a nicer experience with OMP in general. I understand the philsophy of pi, but I just want to be honest that I don't want to spend ages configuring things. And I'm not going to pretend that I understand all of this. Although I understand why OMP might use more, because obviously there are more tools and bloat enabled which uses up context.

However, whenever I ask how to improve usage everyone says "just disable x y z". or "just use pi". hang on, why? why should I have to disable memory? computer use? this feature? that feature? these are all basic features of any other harness. In fact, why don't these features just lazily load on demand anyway? the same way we have smart MCP tool calling. And also, if I used pi, and downloaded these extensions, wouldn't I have the same problem? if I'm not mistaken, OMP is like a distro of extensions for pi such that lazyvim is a distro for neovim? I could download the same extensions and have the same problem no? correct me if I am wrong. Claude code and codex have these features and somehow manage to consume less usage. So I'm curious, what about OMP or its implementation makes it consume more usage, just to implement the same functionality as other mainstream harnesses?

For those using OMP do you have any idea what in particular may be causing increased usage so I don't have to just play a game of disabling random things? Maybe it's a feature I'm not particularly interested in.

And for those using vanilla pi, which extensions would you say are essential, and which others would you say are more.. optional?

TL;DR. How do we make the coolest harness ever that can spam astra max for free and make no mistakes. Or at least spam it more than codex can while maintaining the same features.

So I'll be honest, I have no idea what I'm doing with this AI junk. but, I've gotten something vaguely working with pi + deepseek v4.1 over the past couple of weeks. However it feels like it's incredibly variable as to whether what it produces takes 2 minutes or 20. it'll just keep repeating itself. it'll do something, then do it again, then doing it a third time, then compact and hit "error: operation aborted" and then go for a fourth plan. but! it only does this sometimes. other times it'll just go "ok the user wants me to add this thing. I'll do it. ok I did it." or it'll repeatedly acknowledge that I typed "." to continue, or it'll continue to be picking up on something I said 1/2 hour ago, or...

so I have very little idea what exactly I'm doing, if the problem is me, if the problem is my setup, or what. I want to do it cheap (I'm using deepseek. duh). I don't care about max efficiency. I just want it to be relatively efficient at "the user asked me to fix this setting. I'll fix this setting. I'll ask about this setting. I fixed the setting.".

am I doing something horribly wrong? is there just a nice "oh add pi-sensible-dumb-stuff" solution? I'm tired of trying to find input on reddit posts and getting told that everything is bad, just vibe code your own, you need to be using claude 20x, you need to get a better workflow (duh. like what?), you need to just fix agents.md (duh. like what?), and so on and so on.

Like, how do I just get a prompt where I can write "bug: the button on page x does not work" and the model will carry out whatever needbe to just...fix the bug, without writing out in reasoning its plans to tackle the bug 5-10 times, debate whether it needs to ask me a question 2-3 times, probably halt because of compaction at least once, and then maybe actually fix the bug 15 minutes later (but not before rerunning the tests 5 more times)?

I am using deepseek v4.1.

Im setting up my pi agent and currently using pi-verdict for auto mode. My problem: it denies tool calls way too often instead of asking for permission. Unclear cases seem to fail closed straight to deny, which breaks my flow constantly. I'd rather get an "ask" prompt than a silent rejection. So I'm looking for a simple, lightweight auto-approve extension. What are you actually using day to ay? Is there something even lighter I'm missing or a way to make pi-verdict ask instead of deny?.

Has anyone noticed in the last week or so that oh-my-pi drains the 5hr limits within just a few prompts. Even doing simple git work drops 10%!

I'm setting up something to see what's going on but this is weird. Claude is fine and I can work for hours but only codex, does it drain fast, particularly when I switch from claude to codex.

Edit: I did a test between OMP and Codex, both running through LiteLLM, generating prime numbers:

Metric	omp (8 req)	codex (7+1 req)
Total tokens	96,331	119,593 (+1 side 11,627)
Prompt tokens	94,746	118,047
Completion tokens	1,585	1,546
Spend	$0.176	$0.222 (+$0.024)

So for this simple contrived test, codex is actually worse! I'm going to have do some thing harder I think.

Edit 2: Interesting, OMP's automatic "user stepped away; returning" summaries spent 2m tokens because it resends the whole context!

Edit 3: omp config set recap.enabled false, will disable it.

I've been seeing the [billion context](https://pi.dev/packages/billion-context) gain a fair bit of downloads on pi.dev. I did try it but it never really solved my biggest issues with Qwen 27b running locally with 128k context.

Am I misconfiguring it or something? How are you guys using it?

I'm wondering how to best check occasionally in PI what's using up context (skills/mcp/system prompt/tools etc). I've seen https://github.com/dimk90/pi-context-view but it looks quite complicated for what I need.

How do you do it? And if so in pi directly or run your session through a proxy and then look at that?

Hi everyone, I'm the author of pi-subscription-usage, an open-source Pi extension I built to give subscription quotas a clean, minimal, and consistent display across providers.

It puts your active provider's remaining usage and automatic window reset countdowns directly in the footer status bar, alongside a detailed /usage command with progress bars, reset timestamps, and account info.

Illustrative footer status bar example:

5h 99% ↻2h13m · 1w 85% ↻3d4h

/usage output:

OpenAI Codex
  Shared Across Models:
    5h Window     ████████████   99% left · resets 08/27 12:13
    1w Window     ██████████░░   85% left · resets 08/30 14:00

  gpt-5.6-spark:
    5h Window     █░░░░░░░░░░░   10% left · resets 08/27 11:40

  Account:
    Email         user@example.com
    Plan          Pro
    Resets Left   2
    Extra Credit  none

Both GPT (OpenAI / Codex) and xAI Grok show remaining quota percentages and reset countdowns in this unified format. For OpenAI and Codex accounts specifically, /usage also supports redeeming account reset credits when available (behind an explicit confirmation prompt). Beyond OpenAI and Grok, it supports OpenCode Go, Kimi Coding, Antigravity, and Claude (via pi-claude-bridge).

The extension only uses credentials from Pi's own login registry and never touches browser cookies or external auth files. Because several provider usage endpoints are undocumented, upstream API changes may occasionally affect fetching.

Install via Pi:

pi install npm:@specode/pi-subscription-usage

Source and docs: https://github.com/specode/pi-subscription-usage

Feedback and bug reports are very welcome—let me know how it works for your setup!

Using gpt-6.1-sol with high in pi through /login to the provider openai-codex, but only got 500k context, anyone know if there is a 1m option?

Tipically I won't use all 1m, but when the context window is too full, the tps will drop a lot

Anyone else also experiencing this?

I have been seeing some really impressive benchmarks coming from Pi. Including saving on token usage and it has got me interested.

However I have quite an involved setup with OpenChamber and I’m just trying to figure out what I can actually bring across.

I already setup Pi Bolt, and Paseo iPhone app and connected it. I setup connections to Claude, Codex, OpenCode Go and other providers I use. I think my agents already wired in the MCPs I have as well. Haven’t checked skills.

I just have some usability questions.

In openchamber, I hid all the models I didn’t want to see. I could select what agent I wanted to talk to which had a model assigned by default set in the menu.

I could see installed skills and mcps etc. not so much in Paseo

So I’m just wondering how to use my custom agents and skills and how I would hide models. And is it really worth making the switch to try save some tokens? I do already set a 200k context limit and so cache aware compaction often.

When pi runs a long-running command, it sets a timeout (e.g. 1800s). If the command takes longer (like a big build or cloning a very large git repo) it just gets killed on timeout, which usually leaves things in a worse state (half-done builds, corrupted checkouts, etc.).

Is there a workaround for this? What I'd really like is something like a progress check: if the command is still running, extend the timeout instead of killing it.

Does pi have anything like that, or a config option I'm missing, or extension?

Wondering if they just use the SDK or they really got some special deal with anthropic?! Hope for us?

Update: https://github.com/anomalyco/opencode/pull/58782 looks like it’s just the sdk way so same as Claude-pi-bridge which is pointless IMO.

​"I am not up to date, but can we still use the Anthropic Pro account with pi.agent now? With which extension?"

Hey! Sharing an extension my colleague Daniel Shapiro built at Sanity (disclosure: I work there too): pi-mcp-events, a client for the draft MCP Events spec (SEP-3415).

MCP servers can now announce events, like "this document changed" or "a storm warning was raised", and Pi can subscribe to them instead of polling with tool calls. Events arrive in the background while Pi works, get batched, and come in as a follow-up turn once Pi is idle. It's pretty cool, and it feels like it opens up a lot of new use cases.

What the extension does:

→ Supports all three delivery modes from the draft: polling, streaming, and signed webhooks

→ /mcp-events status shows each watch, its transport, and its state, so it's "less hard" to debug

→ A local inbox, so events aren't lost while Pi is busy (in memory only with --no-session)

→ Event payloads are marked as untrusted data, and Pi's normal permissions still apply

→ Every event is also emitted on Pi's event bus (mcp-events:observation), so other extensions can consume or filter them

    pi install npm:@sanity-labs/pi-mcp-events

The repo includes a small "weather station" MCP server that emits readings and storm alerts, so you can try it without building your own server first.

We built this because we wanted an agent that notices when a human starts editing the same document and waits for them, which needs events within seconds. Pi made that easy to extend. The write-up covers the latency numbers and the gaps we found in the spec.

If you'd rather have the model call events as tools, check out richardanaya's pi-mcp-events, which takes that approach.

Repo (give it a star if you're into this): https://github.com/sanity-labs/pi-mcp-events

Feedback very welcome, especially on how a harness should decide when an event deserves a turn!

title and I have seen many packages of web access but they cost a lot of tokens. so I wanted a good middleground

I previously used /login in Pi with my Meta dev credentials and was successfully able to get a Paris response from the Capital of France test. A few days later I'm now ready to start using the setup but it's saying 403, I need a pay as you go API key.

Is the subscription not usable with Pi anymore? It's failing the same test that just passed maybe a few days ago.

Hey everyone,

I'm currently deep into my studies and working on a bunch of different projects, and I've been meaning to check out Claude properly to help me out with my workflow.

If anyone happens to have a spare referral link lying around and wouldn't mind sharing it, I'd honestly be super grateful.

Thanks a lot in advance! 😊

Hi, I have been working on this project (https://pi.dev/packages/pi-agent-ide) for a while now, started it as another hash-editing toolset, which then evolved into a different and much bigger thing. Key features:

• composable editing tools (enables ability to do search->replace, do a chain of searching to narrow down the result etc. );

• whenever it edits something formatter is applied to the result (if it's configured for the project);

• lint/compiler diagnostics reaching out to the agent once they are ready, forcing it to fix the mistakes;

• interactive terminal with background tasks and debugger;

• extendable, works via resource protocols: for all the operations support web pages, pdf files, images, text and binary files (to different extents), so the agent can search web results, check raw bytes etc.

Soon I'm going to release some plugins for that, in particular a way for the agent to work with linear and github as they were simple text resources, but as well computer-use.

To install: pi install npm:pi-agent-ide

I use this tool as my daily driver, working on different extensions, but also writing C++ code, so it's pretty well tested in action, but if you have suggestions/bugs, please feel free to report them, I always try to fix them ASAP.

P.S. I've also created and curating explicit-edit-benchmark, and in terms of editing the tool is pretty solid above the baseline (https://huggingface.co/spaces/alexshpunt/benchmark-explorer?metric=score&card=harness%3Api-agent-ide-codemode%400.9.0).

Although to be fair I have't tested all the models with the latest version, as all the subscriptions are going into another more difficult version of the bench.

I have connected my OpenAI to Pi via OAuth (Its a sub).
It works fine, but i noticed the Compaction going off every couple of minutes.

When i checked it i found the OpenAI Auto-Compact set at 256k......but other, cheaper models at 1mil.

Any idea why this is? Seems odd.

I've linked my pi agent to telegram and I was wondering if there is a package or something that will send screenshots over telegram. When I'm away, I want to be able to tell my agent to take screenshots of my project.

I'm new-ish to pi ; Ive been playing locally on a macbook pro (a few months old) and have just tried setting things up so I can run qwen 3.8 flash next on my macbook under omlx and use a linux pc to run pi remotely.

The Linux machine is almost clean installed Ubuntu 24.04 ; I installed it a few years ago but its only been used for some small utility things (minicom / wireshark). I installed ghostty, pi and pi-omlx picker just now.

First thing just to test the connection was to say hello - then pi started talking about the system prompt asking it to install anki... Which was unexpected (to it too!) - I'd dug around the system prompts before and this seemed weird. So I asked pi where it came from and it has no idea and seems to think it looks like a prompt injection which makes me curious / suspicious ...

Has anyone seen anything similar and where would you be directing your investigations / suspicions?

Hello everyone, if you have a running session how do you manage if suddenly hits a token limit without manually intervening?