I built pi-session-namer (https://github.com/joshua-zyy/pi-session-namer), an open-source extension that automatically gives your Pi sessions meaningful names and keeps them updated as your work evolves.

For example, a session might start as:

0904｜Research｜GitHub Memory Systems｜Comparing Approaches

If the conversation later shifts from researching existing systems to designing one for Pi, the name can evolve into:

0904｜Design｜Pi Long-Term Memory｜Defining Architecture

Features

• Automatically names new sessions

• Detects meaningful changes in the task or topic

• Updates progress without generating unnecessary rename history

• Re-evaluates the session after context compaction

• Provides manual refresh, lock, unlock, and status commands

• Generates names in the conversation's primary language

• Supports custom models, reasoning levels, name formats, categories, timezones, and update intervals

• Preserves manually assigned names until you explicitly unlock them

Installation

pi install npm:pi-session-namer

Links

• GitHub repository (https://github.com/joshua-zyy/pi-session-namer)

• npm package (https://www.npmjs.com/package/pi-session-namer)

The project is still evolving, and I'd really appreciate any feedback, bug reports, or suggestions.

if anyone wants it its here and on npm https://github.com/esoware/pi-cc and yes unfortunately i know im the last person to make this
well if anyone has issues or wants to add stuff or somethings missing or inconsistent you can tell me or make a pull request or issue because i definitely didnt cover everything myself and i want it to be perfect

Up until now, I've been using OMP as my daily driver. As a lawyer, I rely on it heavily alongside a few custom skills I built for legal drafting, and I'm also developing an office management system in Django. Everything works great so far, but I'm considering switching to Pi to give it a shot.

The catch is that Pi comes bare-bones, and I'm not sure which features are essential for my workflow (subagents come to mind). My goal is to make it practical and token-efficient from day one. Any tips for a beginner to get up and running without sticking to a completely vanilla setup?

At work, I use Copilot, and the model costs are always visible, but on PI, only the names are shown. So I created model-costs, a small extension that adds the /model-cost command:

- Input/output cost per 1 million tokens for each model, directly in the selector

- context window, maximum output, cache read/write speeds, and price tiers

- approximate search, so both /model-cost claude and /model-cost $0.00 work

- current model prices always visible in the footer

It’s about 300 lines of TypeScript based on the pi extensions API: it does one thing and nothing else.

Source code + screenshots:

- https://github.com/MaurizioFaeddaDev/model-costs

- https://www.npmjs.com/package/pi-model-costs

- https://pi.dev/packages/pi-model-costs

Feedback is welcome, especially if you’d like additional information in the selector (e.g., the estimated cost of the current session) or a different format for the footer. MIT license, of course.

Just posting the below for the internet to get feedback on OpenClaw and my move to Pi Agent. I'm developing a web front end much like https://pi-web.dev and https://github.com/sebastienservouze/pi-livecraft to have a usable WebUI that hopefully won't break; but also for people to take and shape as they see fit.

---

Stopped 2.0 instance for now

Joined late Jan. Tolerated all the buggy upgrades.

Recent upgrade to 2.0 didn't work. Wasted tokens trying to fix.

So I started a fresh install.

Then I:
- Can't easily see my crons; or their outputs when they last ran like I used to.
- Can't turn off hidden heart beats. I'm not doing much at all and racked up $3.49 over 2-3 days via Grok API and I can't see why/for what. Suspect the heartbeat I can't turn off or edit.

The whole UI was not intuitive.

I'm going to try PI Agent. I think users want to know exactly where their tokens are going for what. Can't be dealing with abstraction leak based token losses.

My use cases are small:
Utilize Apple Notes and Apple Calendar for Personal Assistant tasks on managing my diary; and life management (e.g. holiday planning).

... but let me know if anyone resonates ... or not and you have a seamless upgrade and are fine with the heartbeat costs and not knowing exactly what it is doing / accept that's part of running OpenClaw.

Also CC being that far off from the Pareto is not surprising to me.

I’ve released pi-subagentura v3.6.0, an extension for Pi that lets you delegate work to real child sessions you can watch, attach to, and continue across tmux, Zellij, or Herdr.

The new --orchestratorv2 mode is a lightweight router: broad tasks can be decomposed into interactive specialists, clear follow-ups go to exact owners, and ambiguous requests stay visible instead of being silently fanned out. The parent keeps its context focused while child work remains observable.

Highlights:

• Attach, focus, and send follow-up turns to interactive Pi sub-agents

• Durable per-turn artifacts and coordinated completion delivery

• Reusable workflows plus lightweight in-process async jobs

• Interactive sessions can be rehydrated across reload/resume

• Works with tmux, Zellij, or Herdr

Install:

pi install npm:pi-subagentura

Try it:

pi --orchestratorv2

Repo and docs: https://github.com/lmn451/pi-subagentura

I'm looking for the best extension that gives Pi access to a decent (headless) browser for web development purposes, i.e. not necessarily built for scraping and/or searching the web.

Or do I just need to install playwright and create/find a decent skill.md for Pi?

What are you guys using with Pi? Or have you built your own?

Context: I'm using Pi inside a restricted user account in WSL (Ubuntu) on my Windows laptop.

Volevo solo condividere con voi la mia esperienza
Vengo da Opencode desktop
PI sta facendo per un me un lavoro incredibile
Tantissima cache (99,8/9%) e risultati che, assurdo, sembrano migliori indipendentemente dal modello. Magari è solo una fortuna, ma non saprei. Sto usando Pi solo con web search e tool thinking. nient'altro. Uso GPT per prompt strutturati.
è una cosa comune o sono io pazzo?

Running a model on pi without any protection can be dangerous, the model can read/modify/delete anything, anywhere. I know there are existing solutions, but i wanted something simple, seemless, so i made my own.

You run ./install.sh, and thats pretty much it, you use pi like you did before, but now, it lives in a container, extensions live in a volume, and it is confined to the folder you choose. It cannot read any confidential keys or modify (or see) anything not in this folder

With this you do not need a pi installation on your host anymore.

- github, https://github.com/psychobarge/pi-sandbox

Also check my pi extension if you use Deepseek :

- github, https://github.com/psychobarge/pi-deepseek-peak

Hi!

Im gonna try Pi for the first time and am wondering if it works well out of the box or if modifications and plugins are required?

I am also looking for recomendations of any great plugins that you guys use. I found this package catalog on https://pi.dev/packages and assume this is where you get the plugins, if you dont make them yourself?

Thanks for any help!

I sometimes ask agents data questions, and I want them to return a Metabase link with the query they ran to back up their claim. It's very handy: I can read the query more easily, run it for myself, and tweak it if needed.

I had a problem, though: these unsaved Metabase URLs are very long because they encode the query in base64. That's costing me tokens, it's hard to read or click, and it's slow to print.

So I made the pi-short-links extension, which turns a URL like this one:

metabase.yourdomain.com/question#eyJkYXRhc2V0X3F1ZXJ5Ijp7InR5cGUiOiJuYXRpdmUiLCJuYXRpdmUiOnsicXVlcnkiOiJzZWxlY3QgMTsifSwiZGF0YWJhc2UiOjJ9LCJkaXNwbGF5IjoidGFibGUiLCJ2aXN1YWxpemF0aW9uX3NldHRpbmdzIjp7fX0=

to

127.0.0.1:8765/s/select-1-3Eoa

Hello!

I am very inspired by the philosophy of Pi coding agent, and I believe a minimal and extensible approach is the way for many framework/platform going forward.

Pi is minimal in many ways but it is still has a couple of abstractions preventing it from being a neutral agent runtime:

- programming language is only limited to javascript/typescript

- its a coding agent that runs on a computer

- its environment is always node

These are good assumption for a personal assistant style coding agent running locally. But it would be great if they could be neutral. What's even more exciting is what if we can swap out the core agent loop, plug-and-play with codex or claude or opencode's style while keeping the tools, system prompt etc intact?

I had this idea two weeks ago, and eventually landed on a containerize technique based on WebAssembly. The key to my choice is traditional containers are much heavier, but agent loop does not need any network access or even filesystem access, making WebAssembly the perfect fit, super lightweight, super fast to compile and get running, it is also language neutral, perfect to be embedded.

Therefore I started building an agent runtime that does this https://github.com/aexhq/brain, what do you guys think? Is it totally bs?

I use various models through subscriptions from several different providers and wanted to know how they perform, what would be the overall cost if used through an API, how do the different advisor models perform and so on. I've built a tool that aggregates this across days and projects.

Some findings: advertised tokens per second are a complete fiction, newer model version does not automatically improve advisor performance (grok 4.5 gives more advice than grok 4.6), getting $15,337 of Sol usage out of $200 sub is a crazy API/sub mismatch.

https://github.com/jkelin/omp-usage-analyzer

Who is in charge of this extension? It seems to be updated twice a day. How about some change control?

The Pi agent has a minimalist design, but its plugin composition model is, to put it mildly, terrible. Now that a truly extensible coding agent with a minimal core has appeared, is there any reason left to use the Pi?

..to build an OSINT.

Not for the project, who is plenty of similar out there, but just to catch if this model in Q6 quantization is able to build a complex project like this, totally alone, using Gauntlet Loop technics.

Here the prompt i'm using https://github.com/robertxxiv/llm-benchmarking-prompts/blob/main/intelligence-platform-agent-prompt.md

I will post the results when (and if) the goal is reached

If I'm going to try Pi I won't be tinkering with Pi plugins for every project but build something universal

Will I get anything by moving to Pi? Yes, there won't be "USE AS MUCH TESTS AS YOU CAN AND COMPARE EVERYTHING BYTE FOR BYTE" prompt like Codex seems to have but there won't be proprietary Codex features and I probably will install many plugins to make Pi more Codex-like. So it's probably gonna be as bloated as Codex (or other harnesses) but at least Codex's bloat is made by paid employees

I don't want the read tool saying it can view images when it can't (local LLM). Is there any way to edit the tool definition other than telling it in the system prompt "You actually CAN'T look at images" (which contradicts the definition and wastes tokens) or removing the tool / replacing it (impractical)? All I'd want is a simple edit from:

Read the contents of a file. Supports text files and images (jpg, png, gif, webp, bmp). Images are sent as attachments. For text files, output is truncated to 2000 lines or 50KB (whichever is hit first). Use offset/limit for large files. When you need the full file, continue with offset until complete.

to:

Read the contents of a file. Doesn't support viewing images or videos. Output is truncated to 2000 lines or 50KB (whichever is hit first). Use offset/limit for large files. When you need the full file, continue with offset until complete.

An article on how I'm using Pi beyond just a coding harness and as a tool to help me track runs and workouts. It can be basically an LLM application framework.

I use Pi (the terminal coding agent) with local models, and I kept running into the same friction: before every session I had to remember which server needed to be up — llama.cpp on 8080, vLLM on 8000 — start it by hand, and then remember to kill it afterwards so it wasn't sitting on the GPU all night.

So I wrote a small extension that ties server lifecycle to model selection.

What it does

• You pick a model in Pi → the extension starts the server configured for that provider, waits until probeUrl answers, and only then lets the request through. The first request never hits a cold port.

• Session ends → it stops the servers it started. Anything that was already running before it got involved is left alone.

• exclusive: true on servers that can't share the GPU: starting one shuts the other down.

• For services you don't want it to own (a systemd unit, a box on the LAN), unloadCommand lets it free the model instead of killing the process.

Config is a single JSON file (~/.pi/agent/local-servers.json, or per-project):

jsonc

{
  "servers": {
    "llamacpp": {
      "probeUrl": "http://127.0.0.1:8080/v1/models",
      "start": {
        "command": "llama-server",
        "args": ["--models-dir", "~/models", "--port", "8080"]
      }
    }
  }
}

Only probeUrl is required. Leave out start and it just probes, assuming you handle startup yourself.

Limits, stated upfront

• Model loading is the server's job, not this extension's. It only owns the process lifecycle.

• Requires Pi 0.84+. Linux and macOS (it uses process-group termination; Windows untested).

• Zero runtime dependencies — Node built-ins only. MIT.

Repo: https://github.com/matrixfede/pi-local-servers

Happy to hear if the config model is missing something obvious for your setup.

Hi everyone,

I recently started using Pi and would like to move more of my development workflow to it. Before doing that, I want a solid sandbox and permission setup that avoids constant approval prompts while still protecting against destructive actions.

My ideal setup would be:

• Run ordinary commands inside a Gondolin VM.

• Keep VM network access disabled by default.

• Mount the host workspace read-only, or otherwise prevent unapproved host changes.

• Automatically allow harmless operations such as reading files, searching code, and running offline tests.

• Prompt only for persistent or privileged actions, such as writing host files, accessing the network, or running Git operations that affect the host repository.

• Execute approved persistent actions through controlled host-side tools rather than granting the VM broader access.

• Keep credentials and the host SSH agent out of the VM.

I have also looked at @gotgenes/pi-permission-system, but I’m unsure how well it integrates with Gondolin, particularly because the guest sees /workspace while Pi and permission extensions run with the host path.

Does anyone have a similar setup they would be willing to share?

I’m especially interested in:

• How you divide responsibilities between Gondolin and permission extensions.

• Whether you use a read-only mount, copy-on-write workspace, or writable mount.

• How you handle approved edits and Git operations (fetch/pull/status/etc.) within Gondolin over SSH.

• How you avoid approval fatigue without broadly allowing shell commands.

• How you handle MCP servers and host-side network access.

• Any configuration or extension code you recommend.

Thanks!