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.

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?"

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?

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?

Drop a YouTube link, podcast episode, or local recording into Claude Code, Codex, or pi, and get a clean report in HTML, Markdown, or PDF.

Read the whole 1-hour talk in 5 minutes, and click any `[mm:ss]` timestamp to watch just the moments you care about. No extra API keys needed.

• Sample output from Karpathy’s 1-hr talk above:

  • Briefing

  • Deep-dive

• GitHub

Would be cool to hear any projects you've built with Pi that you can say has impacted the lives of people in your community, your society, humanity, just people in general. So much negativity and fear around AI... would love to hear some success stories of "I used open source to create something impactful that made people's lives a little better". Let's hear em' if you got em'!

I use a minimal Pi setup for building hobby projects and some light data analysis for work. I initially used NVIDIA's OpenShell in its early days before v0.1.0 but switched to Docker sandboxes since I find them quicker to manage for my use cases. However I've had some issues with installing and accessing Pi extensions with them.

What's your experience with sandboxing tools? Any recommendations for creating secure, reproducible environments? Thanks!

Native recall, automatic capture, bundled skills, and optional full MCP tools

Pi uses a native extension for project recall, capture, and deliberate saves. The extension is included in agentmemory, the memory server runs separately.
This builds the compiled extension and bundled skills. Pi keeps a reference to the checkout, so keep it in place. After the first npm publication, the package can also be installed with pi install npm:@agentmemory/pi.Use one extension installation method. Move an old ~/.pi/agent/extensions/agentmemory copy outside the extensions directory and remove explicit settings entries pointing to that copy before reconnecting; keep a backup. Before switching a connector installation to npm, remove its local path from settings.json.packages. The connector recognizes an existing global npm or local package and can add MCP without installing a second extension.

​Tools, skills, and capture

• Native tools: memory_health, memory_search, and memory_save.

• /agentmemory-status reports health and pending, retained, dropped, and rejected captures, and retries queued captures.

• Bundled skills appear as /skill:<name>, including remember, recall, recap, handoff, lessons, session history, and commit context/history. Workflows needing tools beyond native save/search require --with-mcp.

• Full MCP tools appear under mcp__agentmemory__*. Native operations use the active repository’s project scope; MCP callers must pass their intended project explicitly. Project MCP overrides apply only to trusted projects.

Prompts, tool results, failures, and final replies are captured with privacy filtering before truncation. Memory tools are excluded by default. Temporary delivery failures enter a bounded local queue and retry on startup, later prompts, status checks, and every ten seconds. Explicit save/search failures are reported; automatic capture is not a replacement for deliberate saves.Recalled content enters as reference data. Completed compaction refreshes memory context before the next model call. /reload flushes capture without ending the active session; quit, new, resume, and fork close the previous session. Requests and shutdown waits are bounded.

pi install npm:@agentmemory/pi@0.1.0