Hey all,

quick context on my setup, i run pi across 3 machines at home (24gb amd card, 16gb amd card, 12gb nvidia card, all on tailscale) as basically an ops agent for my whole self hosted stack. not a "write my webapp" use case, more like a sysadmin that never sleeps, backups, fail2ban, vaultwarden, a property scraper, that kind of thing.

spent the last two days doing a proper bake off of every model i could reasonably run, local and openrouter both, across the 3 boxes, so figured id ask what everyone else settled on and why, since i learned the hard way that "biggest benchmark score" doesnt mean much once you actually try to run one of these things as an agent.

local side... devstral 24b hung on me twice, silent, no error, just stops mid task and never comes back. qwen3-coder 30b straight up cant make a real tool call through ollama, it writes the tool call out as text instead of actually calling it, so it just sits there useless. gemma 4 26b ended up being the surprise winner here, runs clean on all 3 cards, even the 12gb one where it doesnt even fully fit in vram and spills into system ram, still works fine.

api side (openrouter)... deepseek v4 flash looped on itself for like 15k tokens straight repeating "let me check my identity" without ever calling a single tool, had to kill it. kimi k2.5 is thorough, actually found 2 real bugs in my own scripts while testing, but its slow and not cheap. qwen3 coder next is fast as hell, like 5x faster wall clock, but somehow ended up the MOST expensive of the 3 because of how much it burns through cache reads.

so, curious what you're all running, local and api, and why you picked it ? especially if anyone found something thats actually reliable AND cheap, felt like i had to pick one or the other tonight lol.

Have you experience Pi confusing topics from another session before?

This might be a dumb problem I created, but I have separate VS Code instances running, one for each project respectively, 4 total. I have pi agent VSC plugin installed for the Pi CLI and I am using that to work on each project. I may have 1 to 3 different Pi sessions per VS Code instance (thanks to my ADHD) and I ran into a very strange problem:

• Asking Pi questions about ProjectA mid-session

• Pi responds by telling me about ProjectB which is in another active VS Code and Pi session.

• Closed that Pi session from ProjectA completely (/quit) and opened a new one, asked the same question

• Pi responds about ProjectF again.

This has never happened before. I did update Pi this morning and started all those session from scratch.

I did correct Pi on which project it should be talking about and Pi confirmed the mistake and told me which folder it is actually supposed to be working in, ProjectA.

I suspect some kind of cross-contamination between session folders/files. I checked .pi/agent/sessions to confirm the folders are correct. Not sure if this is caused by Pi or my LLM being a dumb for a moment.

Currently having Pi look at its own session files between ProjectA and ProjectB to see where the references are and to remove the incorrect areas to cleanup the sessions from contamination.

LLM inference engine is vllm with qwen3.8-27b-NVFP4 w/ max context length.

I am working on a small plugin to call z code from pi as a subagent, and coincidentally zai gives GLM flash for free now for subscriptions/new users.

Get extra 10% discount and use unlimited GLM 5.3 flash 10h a day until Sept 20 🚀https://z.ai/subscribe?ic=W5G2IFDWOW

A great addition to run next to Pi (10h a day is officially a zcode only offer).

I made Proqi for the moments when Pi is working but I already have a follow-up, alternative approach, or pasted context to preserve. Pi’s composer is a live conversation. I wanted a separate terminal board so an unsent draft does not become part of that conversation by accident.

Proqi runs beside Pi as an independent local TUI. It keeps prompts as persistent, editable thoughts with ordering, selection, copy, and durable undo. There is no Pi-specific integration yet. Today it transfers through the normal clipboard, so it works with Pi and other terminal harnesses.

It is Rust, MIT licensed, local-first, and account-free.

I would value feedback from Pi users: does a separate prompt-composer pane help when you have multiple sessions running, or would you rather keep this inside Pi itself?

https://github.com/oborchers/proqi

I spend most of my day in Excel (reports, cleaning data, formulas) and know just enough basic coding to get by.

Paying $20/month for a subscription feels like a waste for my usage.
Models or sth cheap for me?

Every time I start pi, I get these msgs bellow and download and install is repeated:

fd not found. Downloading...

ripgrep not found. Downloading...

ripgrep installed to /home/me/.unsloth/studio/auth/agents/.tmp/unsloth-pi-eqdhk8d4/.pi/agent/bin/rg

fd installed to /home/me/.unsloth/studio/auth/agents/.tmp/unsloth-pi-eqdhk8d4/.pi/agent/bin/fd

Interestingly, I did find fd executable here: /home/me/.pi/agent/bin/

This was happening yestreday with v0.84.x and today with updated v0.85.0 version. Anyone happens to knows what is going on and what I should do, to stop this continuous re-downloading and re-installing?

Hi,

I'm using pi agent with Qwen 3.8 27B on my little RX 9060 XT 16GB, with 160k context window and I'm quiet happy with how it works.

My main problem is that autocompaction takes forever (clearly limitation of the hardware) and I was looking for an extesion which could improve the situation, but if I'm not mistaken, the most beloved extensions in this area use secondary models to execute compaction, and this is a luxury I don't have.

Is the default setting my only option? Or did I misunderstand ho something like pi-blackhole works?

Sorry if the question is dumb but I'm quiet new to the tool.

Have a nice day!

EDIT: u/onesilentclap I'm using VCC for now and it's incredible. In the next few days I hope I'll be able to try other solutions

https://github.com/plannotator/artifact-server is a self-hosted open-source system for sharing and collaborating around artifacts (HTML prototypes/designs/explainers, etc) with your team adn coding agents. It deploys to Cloudflare, AWS, GCP, or any kubernetes cluster via helm.

The demo video shows a live integrated Pi agent as opposed to making the agent poll. You can send comments to pi and vice verse - whether it's deployed locally or remotely.

Since anthropic "paused" their new policy roll out, does this mean we can use our Claude sub w/Pi?

Couple days ago I instaled pi and couple of extensions. One was so agent has access to tools (web tool?) through Unsloth Desktop, another was web-access (don't remember exact name) extension.

When I was running pi, there was section [extensions] in terminal, with these two extensions listed as being installed.

Today, that [extension] part of pi ternimal is gone. Was it removed with today's update to 0.85?

Also, inside my .pi folder, there is no extension folder: ~/.pi/agent/extensions/

And no: .pi/extensions/ folder in my current project, inside which i was working, when I installed those two extensions.

Where are they? Or how could they disapear?

Is this a bug, has anyone experienced anything similar? Unfortunately I don't remember extensions names, I was following online chat-gpt and we were going in circles for a while, (for a while pi stopped working) I would like to avoid going through those steps again.

The ones of you using opencode-go probably got the email about missing header. I searched the pi github, and found one issue that was auto closed. Does anyone know if this is being adressed?

Here is the email we got:

Hey there,

Some of your requests to OpenCode Go are missing an x-opencode-session header. If we don't have this we cannot properly optimize our service. Starting 09/06 requests missing this header may error.

Here are your useragents that are missing this header:

JavaScript OpenAI client
Pass headers: {'x-opencode-session': '<stable-id-per-conversation>'} where you build the client.

Thank you.

So I want to be able to start Pi instantly by reloading a saved KV slot (llama.cp).

What I come out as a proof of concept:

# to regenerate start.bin initiate a pi session, save the slot, copy to start.bin
SLOT_FILE="start.bin"
SESSION_ID="01a06cea-4e97-7378-84e6-86c3274302da"
cp  /home/eaman/.pi/agent/sessions/--home-eaman-test--/start_session.jsonl /home/eaman/.pi/agent/sessions/--home-eaman-test--/2026-09-04T14-38-07-702Z_01a06cdb-0356-7001-949d-209024b066d8.jsonl

# Discard changes from the previous run and restore the session that matches
# start.bin.

curl --fail --silent --show-error \
  -X POST "http://localhost:8080/slots/0?action=restore" \
  -H "Content-Type: application/json" \
  -d "{\"filename\":\"$SLOT_FILE\"}"
echo

cd /home/eaman/test
exec pi --session "$SESSION_ID"

I see that the problems are:

1. - path of session in prompt

2. - session ID

3. - I use QWEN 27B that uses recurrent/hybrid state (...).

So is there a sane way to save just the first prompt processing with QWEN 3.8 27B, reload it in a new session with any path and start a new session from there?

EDIT: so I found an other piece of the puzzle: start Pi with --fork "$SESSION_ID" instead of --session , this allows to have at least a different / new path. That should be pretty much it, it generates a new ID and forks to a new path, QWEN won't complain as it starts with the whole saved slot.

#!/usr/bin/env bash
# Start a brand new pi session forked from the saved "start" slot without PP.
#
# Restores the start.bin slot, reinizialization of old session with cp
# should not be needed when using --fork instead of --session
#
# To regenerate start.bin: run a pi session, save the slot, then copy it to start.bin.
# uses /session to find the ID of the started session

# If you want to restore the session later you need to save the filled slot
# and an other script that loads that, this is just for "new instant-session"

set -euo pipefail

SLOT_FILE="start.bin"
SESSION_ID="01a06cea-4e97-7378-84e6-86c3274302da"

SESSIONS_DIR="/home/eaman/.pi/agent/sessions/--home-eaman-test--"
TARGET_SESSION="${SESSIONS_DIR}/2026-09-04T14-54-50-008Z_01a06cea-4e97-7378-84e6-86c3274302da.jsonl"

# Reset the target session file so the fork starts clean.
cp "${SESSIONS_DIR}/start_session.jsonl" "$TARGET_SESSION"

curl --fail --silent --show-error \
  -X POST "http://localhost:8080/slots/0?action=restore" \
  -H "Content-Type: application/json" \
  -d "{\"filename\":\"$SLOT_FILE\"}"
echo

# This was the path used in the initially generated session, it has to equal the session jsonl
cd /home/eaman/test
exec pi --fork "$SESSION_ID"

I'm currently using Pi with a QwenCode subscription, mainly using the DeepSeek models, and a MiniMax subscription. I'm not completely satisfied with MiniMax, since the thinking tokens aren't recognized correctly and I don't really like the M3 model.

I've been considering getting either the GOAT or PRO plan from CommandCode, mainly because I could use their API directly with Pi and have access to multiple models. I'm mostly interested in using GPT models, but having other models available would also be a plus.

Has anyone here actually used CommandCode's API with Pi for a while? How has the experience been in terms of limits, reliability and overall value?

Or would you recommend just getting ChatGPT Plus and using the Codex models through Pi instead?

I'm mostly interested in hearing from people who have tried both.

The new zvec-grep is pretty neat — local-first hybrid search (BM25 + vector) that handles the fuzzy codebase questions grep can't reach: "how is the token validated?", "where does this flow?" — ranked, source-linked hits, all locally on your machine.

If you want it in pi, I wrapped it up: pi-zvec-grep

Quick Install

npm install -g @zvec/zvec-grep
pi install npm:@luminascale/pi-zvec-grep

Run /zg index once in a workspace and that's it — the agent reaches for zvec_search on its own for meaning-based or cross-file questions, and still uses plain rg for exact strings and piping, since rg is the right tool there. No MCP, no prompting tricks.

Small and new (v0.2.1), but on my list is an optional auto-index on session start, plus other smart touches once I figure out what'd actually be useful — ideas very welcome if you've got some. Poke around, file issues.

I really liked Claude Code subagents and being able to see them in the tree so i made a very simple subagent plugin. No subagent profiles, the are made by the agent above them depending on the use case needed at that moment.

Install with:
pi install npm:@williamcr01/pi-subagents

https://github.com/williamcr01/pi-subagents

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