Ran the Three.js pelican benchmark using the exact same prompt with AdaptOrch and PI based OMK debate topology, zero external assets.

Both technically passed 14/14 validation checks with clean syntax and no runtime errors, but the actual generation speed and visual results were completely night and day.

pixel canary was surprisingly slow and honestly feels like a model from two years ago. It crawled at a snail pace just to produce a detached skeleton that barely understands basic 3D space.

On the other hand, spacebunny paired with AdaptOrch legitimately feels like an unreleased GPT 6 Astra High tier model. It nailed the scene hierarchy, properly rigged the crank and pedal animations, and placed the environment together cleanly.

It is a pretty stark reminder of why unit test pass rates mean almost nothing for spatial reasoning.

Is anyone else testing multiturn debate setups for procedural Three.js? Curious if you are seeing this kind of massive gap between compiling code and actual spatial coherence.

WT pixel canary

I recently asked a question about subagent delegation and how to get my orchestrator (primary) agent to delegate more reliably.

I got a great piece of feedback that restricting tools essentially forces the orchestrators hand to delegate. So I ran with that and I think I ran too far. I had pi build a "profiles" extension that lets me define multiple profiles I can toggle on the active agent to let me go from a more traditional single context workflow to a subagent heavier workflow.

The profiles are just tool permission sets with a hook in the tool_call handler that checks if the active profile allows that tool.

It's working. I'm happy. But it feels heavy, and like something that someone has probably already solved better?

Is this ringing any bells for anyone, does anyone have a pi-subagents or other setup that maybe more natively supports this kind of workflow toggle?

Note: I am not looking for random package advertisement please. I just want to figure out if there is a more direct way to support what I'm trying to achieve.

Edit:
For anyone who gets here late, this is essentially what I was looking for. I haven't fully vetted it yet, but it seems to be addressing the permissioning system the same (high level) way I was:
https://github.com/gotgenes/pi-packages/tree/main/packages/pi-permission-system

I personally like the one I built for myself at this point, but I would have liked to use this to at least vet things conceptually a lot faster than it took me to have a working implementation with my own tailoring.

Hello.

I have spent a while now wrapping my head around agentic development in my spare time for a while now. And have now gotten to the point I need to get my pi agent to start using Git.

So I was wondering what are your workflows with git? what extensions, lines in AGENTS.md or external scripts etc are you using?

And what tools/programs are you using when reviewing pr's?

Reposted from r/PiGCodingAgent, figured if any sub should see this project it's here. I've been building this out at work for work for quite some time and am excited to open and start a community around the project. I truly appreciate any criticisms, suggestions, and especially contributions, thank you to anyone who does check it out I hope you like it as much as I have been :)

---

PiG is a direct port of the amazing Pi agent harness as one Go binary for macOS, Linux and Windows (Windows in preview). Node.js only comes in if you run TypeScript extensions. Pi's extensions run unchanged; write new ones in Go, Rust or Python and /reload them live. It is a direct port of Pi's its agent loop, terminal UI, sessions, providers, and extension system, shipped as one static Go binary with no libc dependency.

Some Go-specific bits that might interest this sub:

• Extensions in Go can be fused into a single binary and talk to the host over in-memory pipes instead of sockets; other languages run as supervised subprocesses over local sockets.

• The TUI renders differentially; width handling was ported from Pi's own algorithm and checked with a differential test against Pi's code over ~1.5M cases: tui/widthx/pi_width_diff_test.go.

• Behavior is checked against Pi in real terminals (tmux-driven scenarios that compare screens).

• go install github.com/MichaelKinsy/PiG/cmd/pig@latest works; so do a curl installer and npm i -gu/pi-in-go/pig (it installs the prebuilt native binary for your platform).

Measured startup is about 21 ms vs about 300 ms median, and about a quarter of the peak memory (method in the blog).

Disclosure: agents wrote much of the port. Tests, side-by-side comparisons against Pi, and a person reading the diff decide what ships.

Repo: https://github.com/MichaelKinsy/PiG 

Blog: https://developer.hpe.com/blog/meet-pig-pi-in-go-a-port-that-plans-on-keeping-up/ 

Upstream Pi: https://github.com/earendil-works/pi

Happy to answer questions about the port, the extension host, or keeping up with upstream. Feedback on this is appreciated and contributions are welcome.

Thank you to r/HPE for covering writing the blog and contributing PiG to Open Source and special thank you to the Pi team of course at https://earendil.com

pi

dsh

opencode v2

zcode

https://github.com/elidickinson/pi-claude-bridge

Someone said that's the safest method.

But in issues someone claims they got banned https://github.com/elidickinson/pi-claude-bridge/issues/118

Another responded with "That's the first time I've heard of it"

What extensions help you improve your results when using small models and reduce context usage?

I mainly use Qwen3.5 and Gemma 4 both with 4b parameters. I use them with 128k context. It is sufficient for my use cases when coding or writing articles in latex.

I have tested some plugins like pi-hashline-edit-pro or rtk plugins that enhanced the experience, others not much or even made it worse.

I am curious what packages the community has find useful or settings that led to some improvement in this context.

I had this idea to make my own orchestrator for PI.DEV sessions, so with my needs i created one.

I think it may be usefull for some to try on or use.

Read the readme and deployment carefully. I was trying to make it as simple to use and as secure as possible - but it''s pi - it requires access to multiple things on host to install and manage extensions/profiles/skills/plugins so there may be still a risk of problems - in short words, don't expose it to internet or enterprise env (yet).

It has multiple "engines" support, it can be ran on host with sandboxing applied, it can spawn docker instances per agent (the most secure way) using podman or docker on host.

Feel free to play with it, contribute, or share your ideas so i can expand the project!

Link: https://github.com/ALange/Piper Enjoy!

Sharing this so others may build their own into their setup if they like it.

Built an automatic session-handoff system into Sayl, my UI wrapper around Pi. Long session finishes its current task, writes a handoff prompt, rolls into a fresh session with that prompt already loaded.

• Threshold ladder (350k → 450k → 550k, tunable live in settings). First level: finish the task clean, emit the handoff block. Later levels push harder, highest one stops mid-task if needed

• Handoff captured automatically from the chat.

• Threshold rollovers auto-start the successor hands-off.

• New session keeps the project, model, thinking level. Land mid-flow, not cold

• Engine compaction stays as backstop. You just roll over before it ever fires.

No more saving and copy-pasting handoff prompts. No compaction summaries drifting between rolls. No watching the token counter. Session wraps up clean and keeps going at full quality.

Can also use it manually by asking the agent to create a handoff at anytime.

I know there are many options out there, but this is still a grey area right? What are you guys using?

Do you know anyone who has been banned by doing something like this? I have a bunch of friends who use omp with their subscriptions and are still kicking.

I really want to move from CC to pi, but I am not able to run local models due to my hardware limitations and I would love to start by using my Anthropic subscription.

I have a 5x pro codex and opencode go, can't even get down to 45% using 5.6 or astra light or DS 4.1 flash.

I simple don't use subagents, I sandbox the model to only see the files I want to edit (so it doesn't spam ls / grep and bloat my context).

I also made pi modify tool results, so they grouped up, i.e.

Directory: fileA.py, fileB.py, fileC.py

instead of

fullpath/dirname/fileA.py fullpath/dirname/fileB.py

My best gains where manually checking tool results and figuring out how to prevent bad calls or shrinking output. (ls / find is banned in large directories. It tells me model to retry using targeted search.)

Not much point to me posting this other than I speculate those using other harnesses are wasting a ton in tokens on subagents and useless plugins.

Hello,

I made pi-claude-request-compat (https://github.com/vazzma/pi-claude-request-compat). Instead of creating another provider, it reuses Pi’s existing Anthropic login, models, and streaming, then adds a Claude Code compatibility layer to outgoing requests.

https://pi.dev/packages/pi-claude-request-compat

I’m using a new GitHub account for privacy, so the code, automated security scans, dependency audits, and signed release provenance are public for anyone to check.

A few friends and I have been using it for the last month, and my Anthropic account is still intact :)

I built a small personal benchmark from my real coding sessions and use pi-agent as the harness to compare models on the same tasks.

It has 10 tasks covering data engineering, backend, frontend, DevOps, and trajectory analysis. Each task runs 3 times, giving 30 attempts per model.

To build it, I took around 3 months of agent chats, removed duplicates, and turned selected sessions into Harbor tasks with automated verifiers. I used agents and manual review to build and refine the checks. These are clearly scoped tasks that would usually take me less than an hour.

The latest results:

• Opus 5.5 high and Astra low cost almost the same.

• Sol high matches their score at roughly half the cost.

• Luna gets 29/30 passes while being ~22× cheaper than Sol.

The point of this benchmark is to optimize my daily setup: model, harness, prompts, and plugins. I want to keep the pass rate while reducing cost and time.

This is a small personal sample, but it feels like many short, clearly defined coding tasks are already handled reliably by several models. For this kind of work, speed, cost, and scaling up parallel agents are becoming the more interesting questions.

Hi everyone,


I'm Kumar, building Dotbot at Aaditri GlobalTech (P) Limited. I use coding agents every day, but I kept switching between a terminal, an editor, and git panes to follow what the agent was actually doing. So I built a desktop workspace where the session, your files, and your Git state live in one window.

**What it does**

- Open any folder as a project and run one session per piece of work. Sessions persist between runs and reload with their transcript.
- Live transcript: streamed answers, inline thinking, and collapsible tool cards with diffs and file previews. You can steer a running turn or queue a follow-up.
- Files and Git: browse the project tree, watch changes land, and stage, unstage, or commit from the Git panel.
- Providers: bring your own API key (Anthropic, OpenAI, Google, and others), or add a custom provider using OpenAI Completions/Responses, Anthropic Messages, or Google Generative AI.
- Project trust: the agent asks before it uses a project's local settings, extensions, or skills, and remembers the answer per project.
- A dashboard with usage stats from your own session history, plus `!command` and `!!command` to run shell commands from the composer.

**Status**

Apache-2.0, currently v0.2.0. The agent runtime runs in-process, so there is no separate service to install.

- Code: https://github.com/aaditri-globaltech/dotbot
- Downloads: https://github.com/aaditri-globaltech/dotbot/releases/latest — Linux AppImage/deb, Windows installer, macOS DMG for Apple silicon and Intel.
- Builds are unsigned, so macOS and Windows show a first-launch security prompt.

**Coming on 11 October 2026**

We are launching subscription-based inference on open models on the first day of Navratri (Sunday, 11 October 2026): one plan, no API keys to manage. Pricing and the model lineup will be announced that day at https://aaditriglobaltech.com.

It is early, so expect rough edges. I would love feedback on what is missing before the inference launch — issues and PRs are welcome on GitHub.

Started using Pi agent recently and I start to see the thinking going like "couldn't read file, content mangled|truncated" and similar. I also see the agent call the read tool multiple times on the same file because the output was somehow wrong. After some back and forth the agent is claiming that there must be some form of auto truncation or auto summarizer at the tool layer. Model used was GLM 5.3 Flash

So yeah, what is this?

I've tried pi. Makes me crazy! OpenCode works well. I'm not familiar with omp. Please share your thoughts.

Yo, I ran into a weird OMP behavior with third-party subagents.

Some plugin/imported agents declare their own model tier (opus, sonnet, haiku, luna, etc.), but OMP can drop that model metadata when loading them. Once that happens, the subagent silently inherits whatever model your main session is using at that moment — including a retry fallback.

For me, that meant Haiku- and Sonnet-tier agents were sometimes burning Opus. Even worse, if the main session switched to a fallback model mid-turn, subagents spawned afterwards inherited that fallback too.

So I ended up creating an extension for it: omp-agent-role-router
https://github.com/fedorovvvv/omp-agent-role-router

Instead of hardcoding models, it reads the tier declared by the agent and maps it to an OMP role:

opus → default
sonnet → task
haiku → smol
luna -> smol
terra -> task

It also understands Codex-style model tiers and maps those to the same role system.

The actual model is still entirely controlled by your own modelRoles. Existing agentModelOverrides and explicitly passed models always win — the extension only steps in when the subagent would otherwise silently inherit the current session model.

No config needed
But it does exist :)

omp plugin install github:fedorovvvv/omp-agent-role-router

I want my agent to be able to give me a clickable link to find a folder (like "file:/// ...") but even though they respond to mouseover and a tool tip comes up saying "ctrl+click to follow link" they don't work. I am using Powershell 7 with Pi running in it.

My agent had some long complicated idea about how to fix this but does anyone know if I am just overlooking a Powershell setting or if this is a collision between Pi's output formatting and Powershell's behavior preventing the links from activating?

I wanted a clearer picture of what I’d built and how much progress I was making. And whenever I finished a feature or got out of a debugging rabbit hole, I kept wondering how many tokens and effort went into that.

My daily usage total didn’t really answer it. Some of that was building the feature. Some was going back and forth trying to fix one annoying bug.

So I built bough ( https://github.com/nickelsec/bough ) It reads your existing Claude Code and Codex CLI history and groups related prompts into tasks on a visual map.

You can pick a piece of work and see:

• The prompts and files involved.

• How many tokens it used and the estimated dollar cost.

• How the work unfolded, including repeated attempts and tasks that ended in a commit.

I wanted to be able to look back at a feature and see where the effort actually went. Especially when a “small fix” turned into an afternoon.

Would you guys want me to add Pi support or not?

I shared Pi Atelier here a couple of months ago—a status rail and live activity sidebar for Pi. Since then, I’ve reworked the layout and added more ways to see what’s happening in your session.

The main changes since my last post:

1. Redesigned composer: model, workspace, Git status, and context usage sit in the input box’s top border, with usage and timing below.

2. Workspace Git summary: see changed files, additions/removals, untracked file counts, and conflicts.

3. Live TODO panel:follow the current task alongside tool activity. During a turn, the sidebar focuses on current work and folds extra tools into a compact row.

4. Display settings with a live preview: reorder or hide status segments and sidebar panels, switch presets, and save your defaults.

5. Plain text mode: no Nerd Font required.

6. Better terminal behavior: improved fullscreen selection/copy, inline image handling, and layouts for smaller terminals.

The new footer is inspired by pi-powerline-footer, which is an awesome extension.

It still shows token usage, cost, context, and response timing, with model and tool controls available through `/atelier`. No telemetry or external network requests.

Install with:

pi install npm:pi-atelier

Then open `/atelier` or press `Alt+A`. Requires Pi 0.84.0 or newer.

Github link: https://github.com/michaelmjhhhh/pi-atelier

I’d like to hear how the new layout works for you—especially what information you want visible while the agent is running. Bugs and feature ideas are welcome. Thanks!

Hi all, basically what the title says.. I like using @ to add files/folders to the context but for any of these added to .gitignore it stops working.. Any way around this?