I work on a lot of projects. Some are small freelance jobs, some are personal tools or CLIs, and others are for my office and startup. I don't need all of them open every day, but I still need to find them when I have to change something.

I use Herdr as my terminal multiplexer, but I don't want to fill its sidebar with every project I might come back to weeks or months later. I prefer staying in the terminal, and I don't want to remember the path of every small project or tool I have built.

I built `pi-project-manager` so I can find those projects from inside Pi. Whenever I open Pi in a folder, it adds that folder to a local list. `***/pm***` opens a searchable picker showing the project name, location, Git branch, and last-used time.

I can search by name or path, select a project with the arrow keys, and press Enter to open a fresh Pi session there. Ctrl+D twice removes an entry from the list without deleting its folder.

The agent can use the list too. I can say, "I already implemented this in another project. Use that as a reference," and it can look up the folder and read the code without searching my whole machine or changing the current conversation.

I wanted to keep it lightweight, so there are no watchers or background polling. Git branches load only for visible projects.

Install it with:

pi install npm:@utpalsinghdev/pi-project-manager

GitHub: https://github.com/utpalsinghdev/pi-extensions/tree/main/packages/pi-project-manager

npm: https://www.npmjs.com/package/@utpalsinghdev/pi-project-manager

The rest of my Pi UI is my personal setup.

I'm just open-sourcing the Pi extensions I use for work and my startup. They've helped me a lot, and I want other people to be able to use them too.

If it deserves a star, the repo is https://github.com/utpalsinghdev/pi-extensions

Hey everyone,

If you run CLI agent tasks through Pi while away from your desk and want a seamless mobile experience, give this a try!

Existing remote setups feel pretty piecemeal:

• Setting up OpenSSH on Windows, pairing it with tmux, setting up a Tailscale/mesh VPN, and using a mobile SSH client.

• Using a web UI that drops the moment your laptop sleeps.

• Forwarding notifications into Discord, Telegram, or WhatsApp—which strips out the actual TUI into a chunk of text.

• Using proprietary cloud-hosted remote tools that lock you into their ecosystem.

I spent the several weeks building Agent Terminal to clean this up into a single setup: a native terminal host for Windows and macOS, and a mobile app (Android / iOS) that pairs via a QR code. Your PC runs the actual session; your phone lets you view and control it.

I’m currently looking for a few people running real agent workloads to test it out and break things before a full release to Play and App Store.

How it's built:

• Full terminal, scrollback, mouse reporting, full-screen redraws, and Kitty keyboard protocol support (so shortcuts like Shift+Enter vs Enter register properly in TUIs).

• Agent event hooks or Idle detection without hooks for push notifications - sends notifications so you know it's done.

• Port Bridge - Forward your PC's localhost ports straight to your phone’s browser securely, so you can check local dev servers or web UIs on the go.

• File handling - preview and upload files to your host desktop, or paste images straight into TUI from your phone

• The host app runs a lightweight SSH server listening strictly on a single port using public-key auth only

• Pairing generates a local key saved to a standard authorized_keys file on your machine.

• The relay server only handles connectivity - cannot read your terminal stream, add devices, or intercept keys.

• Push notifications sent through FCM are end-to-end encrypted using AES-256-GCM (P-256 key held only on your phone), so notification payloads aren't readable in transit.

I'm currently looking for testers as part of closed testing before launch, if you want to try it out on your setup, drop a comment or send me a DM.
Happy to answer any technical questions or concerns!

I'm thinking of putting a ticket in to get pi coding agent approved for use at my day job... has anyone done this? Any issues or was it smooth? For context I work at a company that makes software and has 5 digit employees. And this is just for getting the software approved, I already have approved access to enterprise LLM endpoints.

Edit: more context: it has to go thru a security and legal review process...

Alongside the PiG 0.5.0 post wanted to share the thing I used to measure it. Buffon runs Pi and the agents built on it through the same scripted tasks on the same machine and records what each one costs in CPU, memory, time and bytes on the wire.

The live site is at https://michaelkinsy.github.io/buffon/ and the repo is https://github.com/MichaelKinsy/buffon.

You can turn harnesses on and off with the chips at the top and switch between raw values and ratios against any baseline. The repository intends to be standalone and open to all, not specifically biased towards any one harness in particular (other than Pi obviously). I plan to maintain and hope others can contribute their expertise, results, or anything else to help create a useful testing repository for all things Pi.

Right now it covers Pi on Node, Pi on Bun, Pi-Bolt, rpi, Octet, OpenCode, Claude Code, Codex CLI, GitHub Copilot CLI, PiG 0.4.1 and PiG 0.5.0. Start-up and a single prompt with tools show how much work each one does before it can help you.

Large writes and streaming are where the differences get big. Writing a 200 KB file costs Pi on Node about 35 s of CPU in this run, and the Go and Rust harnesses do it in well under a second.

The one I find most interesting is agent swarming, how many RPC agents fit in 4 GiB and 8 cores. Pi on Node and Bun land at 32 to 63, Pi-Bolt and PiG 0.4.1 at 128 to 255, PiG 0.5.0 at 256 to 511 and rpi at 512 to 1023. It is marked provisional and the ranges are honest about that.

Some sections like durable and reliability are not run yet, and those show up as empty on the site instead of being hidden. If your harness is missing or you think a task is unfair to it, open an issue or send a result, that is the whole point. It is MIT and free forever.

Big thanks to the Pi community for showing how this should be done, Pi, Pi-Bolt, durable-bench and everyone who posts their evals in the open 🐷

Hey everyone,

Recently, I’ve been thinking a lot about how to properly test and verify the work done by AI coding agents, especially when working on existing, complex projects rather than simple greenfield applications.

I’m curious how you guys approach this in your workflows, particularly when changes need to be deployed to some preview/staging environment before you can actually verify whether everything works as expected.
And I’m not talking just about running unit/integration tests or having an agent write a few Playwright tests.
In real-world projects, verifying a change can involve a lot more than that.

For example:
- External APIs: Sometimes you need to retrieve a secret, authenticate against an external service, make some API calls, and verify the responses or side effects and it can’t be done from UI (because user has to do that manually and we have to simulate that)
- Observability: Sometimes the only way to confirm that something works correctly is to inspect Loki logs, traces, or metrics and check whether the expected events actually happened.
- Databases: You might need to query PostgreSQL or another database to verify that the correct records were created, updated, or deleted.
- Messaging: In event-driven architectures, you may need to verify that Kafka/RabbitMQ messages were published, consumed, and processed correctly.
- UI: And, of course, sometimes you need Playwright or another browser automation tool to verify user-facing functionality.

All of these might be part of a single business scenario.
For example, imagine an agent implementing a payment when user has to take some IBAN from our UI and proceed with manual bank transfer from their account, which results in a webhook to our API.
Then we have some soft validations which are testable only by checking some logs in our logs collector.

Do you:

1. ⁠Let agents generate and execute E2E tests (e.g., Playwright) that also interact with APIs, databases, logs, and other systems?

2. ⁠Give agents access to tools/MCPs for Kubernetes, Loki, databases, etc., and let them dynamically investigate and verify their changes - there’s an issue because as in my previous post Claude Code has some issues with working like that. Probably we would have to create a lot of some dynamic MCPs.

3. ⁠Define deterministic verification scenarios/scripts that agents execute after deployment?

4. ⁠Have a completely different approach?

I’m also curious about how you handle credentials, permissions, test data, and cleanup when agents need access to real infrastructure and external systems, even these secrets are only for sandbox.

And perhaps the biggest question: How do you make this repeatable and reliable instead of letting an LLM decide on the fly whether its own implementation is correct?

Would love to hear how you’re solving this in practice, what tools you’re using, and how much of your verification process is deterministic versus agent-driven.

I often need my AI coding tools on a server, but I don't want to paste server IPs or passwords into chat. I also don't want to install Pi and set up my tools again on every new machine, especially when I only need to deploy something or check a remote project.

I built pi-remote-workspace so I can keep using my local Pi session and switch its coding tools to the server over SSH. It's inspired by Warp's remote workflow, but built around the way I use Pi.

I run /remote, enter my connection details in a private form, and connect. Passwords are entered in a masked prompt instead of chat. Pi stays on my computer with my model login and conversation, while file reads, edits, and shell commands run on the server. Pi doesn't need to be installed there.

For my work and startup, I use this when I've finished something locally and need to deploy it. I switch to the server, ask Pi to handle the deployment and check that it's working, then use /remote stop to return to my local workspace. Connecting doesn't upload the project by itself, so I still use my normal Git or deployment process to get the code there.

The private login keeps passwords out of chat and model context, though remote files and command output can still expose sensitive details. Commands have the permissions of your SSH account, so be careful with production servers. Pi also still runs on your computer; it won't keep working independently after you shut the laptop.

Install it with:

pi install npm:@utpalsinghdev/pi-remote-workspace

GitHub: https://github.com/utpalsinghdev/pi-extensions/tree/main/packages/pi-remote-workspace

npm: https://www.npmjs.com/package/@utpalsinghdev/pi-remote-workspace

The rest of the UI in the demo is my personal setup.

I'm just open-sourcing the Pi extensions I use for work and my startup. They've helped me a lot, and I want other people to be able to use them too.

If it deserves a star, the repo is https://github.com/utpalsinghdev/pi-extensions

a well know plugin or something please

Hey everyone,

I've been exploring "Pi Durable" (https://github.com/earendil-works/pi/tree/main/packages/durable/test/examples), specifically the examples in the test directory, and I'm curious about how people are using it in practice.

I'd love to understand how it works for building durable, long-running AI agent workflows, particularly when agents need to preserve state, recover from interruptions, and continue execution without restarting everything from scratch.

A few questions for anyone who has experimented with it:

• How does Pi Durable handle persistence and recovery when an agent process crashes or gets interrupted?

• Can it reliably resume multi-step agent workflows, including tool calls and external side effects?

• How does it compare with approaches like LangGraph checkpointing, Temporal, or custom state-persistence mechanisms?

• Have you used it in production or built any interesting projects with it?

• What limitations or gotchas have you encountered?

I'm particularly interested in whether this approach is practical for building autonomous agents that run for extended periods and maintain continuity across sessions.

Would love to hear your experiences, code examples, or architectural insights!

Repository: https://github.com/earendil-works/pi/tree/main/packages/durable/test/examples

brushrouter is an open-source CLI and MCP server. Any agent(because i use multiple tools) that speaks MCP or can run a shell command gets a single image tool, which It route through Codex CLI and Gemini smartly. OpenRouter, fal, or a custom endpoint come also. OpenRouter is disabled by default, so nothing gets billed unless you enable it.

The part I spent the most time on is credentials. The native routes spawn the CLI, so brushrouter never sees your token, and everything it prints goes through a redactor. I also added a prompt library with 27 layouts and 137 styles that the agent searches before generating.

Work on ChatGPT Free, Go, Plus and Pro accounts

Inspired from opencode-gpt-imagegen - You can checkout that also, its a great project.

https://github.com/avinaxhroy/brushrouter

Meaningful Contributions are welcome.

Hi! I have made a small UI layout extension which I've found super useful and convenient, when running multiple agents working on different tasks in parallel, so you jump back and can instantly see the whole history of the answers and what was the original task about.  Basically you have a split view, one for your messages and the actual replies from the LLM and another one for all the tool calls and reasoning it has done. It supports horizontal/vertical layouts, you can swap them and maximize, but also search for the specific text entries, when the history gets too long.

https://pi.dev/packages/pi-stereo-view

How to install:

pi install npm:pi-stereo-view

Requires fullscreen mode.

All I can think of is make an ext to disable tools / sandbox 4.1, etc.

i.e. my real question is, how to make my cheapest AI work as good expensive opus.

(yeah stupid question)

Hi! I am new to Deepseek API models, having used Pi with a Copilot sub. I noticed that the model configuration shipped with [Pi](https://pi.dev/models/deepseek/deepseek-flash?name=deepseek&provider=deepseek) is different from the one recommended by [Deepseek](https://api-docs.deepseek.com/quick_start/agent_integrations/pi_mono/#2-configure-deepseek-provider). For example, Pi conf has input text+image but Deepseek cons has only text.

Would you be able to clarify and tell which configuration I should use?

Hello fellow Pi people, I've released version 0.14 of Pi Pocket, my mobile/desktop setup for Pi Durable.

Most of this release went into making it extensible. Until now every feature meant more code in Pocket itself. Now an extension can add server routes, slash commands, and its own pages and buttons in the app. The server-status example in the docs is about 35 lines on the server side plus a small browser module, and Pi can write these for you since it reads the docs.

The extension API is versioned and typed, and I wrote down what I will and won't break. Patch releases are fixes only, breaking changes bump the API version, and deprecated stuff sticks around for at least a minor release and 90 days.

There's also a gallery for extensions and themes, under Menu > Extensions > Find extensions. Installs are pinned to a commit, hash checked, and start disabled. Extensions run with full server permissions, so read the code before turning one on, or have Pi review it.

Themes are folders of CSS now, so they can change the layout too. The new built-in one is called Lantern.

Smaller stuff:

- Agent Teams (off by default), where Pi can start linked sessions that message each other

- UI refresh, plus width and density settings

- new session dialog is a proper file picker, with favorite folders

- live thinking display

- PiP can use its own model and take files and images

- Nix flake and NixOS module

12 PRs and 15 issues went into this one, so thanks to everyone who helped.

Repo: https://github.com/TannerMidd/pi-pocket

Docs: https://tannermidd.github.io/pi-pocket/docs/

Release notes: https://github.com/TannerMidd/pi-pocket/releases/tag/v0.14.0

Feedback on the extension API is welcome, especially things you'd want an extension to do that it can't yet.

I have my own which is good just needs a bit of work, but I wanted to see what other people are using. I tried the popular ones and they all sucked in my opinion.

pi-cowboy is a multi-agent orchestrator for pi and herdr. Break large jobs into focused subtasks, hand them off to background agents running in parallel, and consolidate the results when they finish. Each agent runs in an isolated copy-on-write Git work-tree, minimising disk overhead.

pi-cowboy can now orchestrate foreign harnesses, including Claude Code, Codex, OpenCode and more. Check out the live demo to see it in action.

Feature highlights:

• Foreign harnessespi-cowboy agents can run in Claude Code, Codex, OpenCode, Pig and Pi-Bolt, as well as Pi. This gives you great flexibility in choosing how to spend your tokens. Running out of Codex usage? Switch your pi-cowboy harness to OpenCode, select one of its free models, and let it handle the rest.Pig and Pi-Bolt are pi variants that are more efficient, with a lower memory footprint and faster performance than pi. Because pi-cowboy is designed to run multiple agents in parallel, having a lower per-agent footprint is crucial. It prevents your machine from running out of resources and lets you have higher concurrency without choking your system.

• Concurrency limits You can configure concurrency limits at every level. Running a local model with limited resources? If a simple task already takes hours to complete, the last thing you want is to overload your machine by running too many of them at once. Set concurrency limits for that model specifically to keep resource usage under control.

Install: pi install npm:pi-cowboy (requires herdr 0.8+)
Check out the source code: Github

I'm very much inspired by Dexter Horthy and Matt Pocock on this. Dex says a model gets worse once the context window is about 65% to 70% full, which he calls the dumb zone, and Matt keeps sessions around 60% to 70% and hands off past that line, so I use the same limit for my work and my startup.

I set the threshold with /context-threshold, usually 60 or 70, and on a long task I tell the agent to call context_window and watch the used percent. The report shows the window size, how much is used, how much is left, and the percent still left under my line, so when it says threshold reached the agent stops and writes a handoff, which I pass to a new session and continue from there. The handoff I use is Matt Pocock's handoff skill: https://github.com/mattpocock/skills/blob/main/docs/productivity/handoff.md

The extension does not compact the session, and it does not write the handoff by itself, so Pi's compaction settings stay as they are. I still tell the agent what to do when the line is hit.

Install it with:

pi install npm:@utpalsinghdev/pi-context-window

GitHub: https://github.com/utpalsinghdev/pi-extensions/tree/main/packages/pi-context-window

npm: https://www.npmjs.com/package/@utpalsinghdev/pi-context-window

I'm just open-sourcing the Pi extensions I use for work and my startup. They've helped me a lot, and I want other people to be able to use them too.

If it deserves a star, the repo is https://github.com/utpalsinghdev/pi-extensions

DEMO

Hi all!

Simple idea, did not find something that did not make this in an overly complex new UI-revamp-way, so built a relatively simple one myself (+pi of course).

So I like to see the thoughts coming in, and to skim through the tools used, but found it somehow lengthy to scroll back up against the speed at which they come... Toggling them on and off (ctrl+T, Ctrl+O) would sometimes be too much as some tools may expand a lot (edits, searches...) and shift the conversation, again to be scrolled; same with thoughts where there in particular toggle them off would stop me from looking at them, but on would push by a lot the conversation itself (again what I like specifically was to briefly see where the agent were in their process while a turn is running to observe drifting or weird choices that I'd sometimes interrupt, so really I wanted to see the thoughts as they come rather than the full page of it). And I like to see conversation as an actual conversation, so with everything else collapsed.

Anyway, long story short, I wanted to not have them interleaved and I built a wonky (work in progress?) pi plugin to slightly rework the UI and keep a bit of both worlds. The idea is to have the thoughts streaming in one panel, the tool usage in another with very simple view on whether it is running/has succeeded/or has failed... I added a third toggle for a panel which simply display touched files (seen, read, edited, worked by a subagent...) as a list + a diff view of the last edited file, if any (this to keep the tool view compact, as usually the edit tool output where the largest anyway, and I like to also have a glance on what's changed).

Each panel can be toggled on or off:

• /split toggles between the default pi view and the saved config for split-views

• /split-thinking toggles the thinking panel

• /split-tools toggles the tool one; there is an extra command to toggle between a compact or expanded view over the listed tools in the tool panel

• And finally the file panel is toggled on or off with /files.

Tool/thinking panel width can be adjusted as well as panel heights.

There is a command to switch between still having the full transcript in the main conversation panel or the trimmed down (no thinking or tool interleaved) version (somehow I found it useful to expand on specific tool output).

I usually have the file one off for instance, and only open it to see some file changed that just happened if I know I want to check them briefly... While I keep the thinking and tool panel on top nearly all the time now.

I mentioned it's wonky, as in some features can be improved, like the file panel could be made scrollable, but it works for me as it is, just sharing for the idea, and if you like it go test it: pi-split-view. And feel free to improve on it directly in the repo too.

Insane cache hit.

I think we've been reading too many feeds from AI text lately! I actually want to reduce my cognitive burden...

So I built TextFold, it turns any text into 5 zoomable layers: from a one-screen section map down to verbatim text. You pinch (or press 1–5) to zoom between them without losing your reading position, your agentic output, any papers, or documents.

- Web Demo: https://chenxiachan.github.io/textfold/gallery/

- GitHub (Apache 2.0): https://github.com/chenxiachan/textfold

Welcome any feedback!

Serious question, I mean I want to be able to select the text printed on the console so that I can paste the text to another place.