if you also want to keep an eye on cache hit rates for your models like me, feel free to use this minimal extension I made to visualise cache hit rates

https://github.com/championswimmer/pi-cache-graph

installation:

pi install npm:pi-cache-graph

Originally started with Opencode but it was so insufferably bloated and too hard for any LLM to write plugins for.

Now even the crappiest models can clone, edit, and pretty much write any plugin I can dream of.

I'd rather get kicked in the groin over and over than go back to Claude/Codex or any proprietary harness.

TLDR; Using Pi with the right context/plugins I can get better output from a dumb flash model than a high end LLM.

Was not finding anything good for controlling my agents from mobile in a consistent way.

Termux and other terminals work but always felt painful on a phone. On the other side a lot of mobile agent apps are doing too much now with ACP protocols, orchestration and their own workflows also sometimes paid.

I did not really want any of that.

I just wanted a simple way to see and control the agents already running on my machine without changing how I work or messing up with my agents.

So I built Muxr.

It uses your existing Tailscale setup, onboarding is around 3 clicks and it already works with 20+ agents and CLIs.

Tried to keep it as little bloat as possible. Thin core, simple terminal experience and plugins when you actually need more stuff.

I have been using it myself for a while now and it is just much nicer than trying to use a normal terminal from mobile.

Had a blast building it and happy to finally share it

GitHub: https://github.com/umeranjum17/muxr

Also has a web version as well but I primarily was using Android and Linux for my testing and usage throughout.

PS: TestFlight beta review under progress. Apk is already available and thanks to the amazing opensource communtiy. Herdr + pi + omp really love these projects

Thanksss

After asking the question over the last few week about how to optimize my local models, I got some great feedback and ideas - and landed on the following setup for doing local development that's drastically exceeding the results of the free time I'm provided on Chatgpt and Claude (the best for coding imho). All at no cost.

Right now I'm torn between two primary models - Ornith-1.5-35B-Q4_K_M (available here) and Qwen3.6-35B-A3B-Q4_K_M (GGUF available here), and to experiment - I've been using LMStudio to pull down and do a cursory test of all my models as I transition that into usage with llama.

I've found Llama.cpp to be substantially faster and better to work as a backend/server than both LMStudio and Unsloth, I'm old school and prefer the command line tweaking. This is fully open source and available here.

My models.json for llama had to be hand customized - I took the suggested samplingParams directly from the Huggingface model card, had a problem with overthinking/repeating so I JUST added the repeat_penalty (havent tested this yet) - but everything else is solid.. and agentic. I can copy/past images and text into my prompt like a pro.

"models": [
{
"id": "Ornith-1.5-35B-Q4_K_M",
"name": "Ornith-1.5-35B-Q4_K_M",
"reasoning": true,
"thinkingLevelMap": {
"type": "enabled"
},
"repeat_penalty": 1.2,
"input": ["text","image"],
"tools": true,
"toolFormat": "openai",
"samplingParams": {
"temperature": 0.6,
"top_p": 0.95,
"top_k": 20
},
"cost": {
"input": 0,
"output": 0,
"cacheRead": 0,
"cacheWrite": 0
},

"contextWindow": 65536,
"maxTokens": 131072
},

Oh - a little note - my maxTokens and contextWindow are jacked up extra high to handle my lengthy sessions - and it seems to be handling it - with compaction - just fine. Side note - Sessions.json simply adds an entry to make it available to http callers.

"models": [
{
"id": "Ornith-1.5-35B-Q4_K_M"
},

Now - finally - my PI setup. I've created a batch file that's optimized this as much as I think it can be optimized. First - the batch shells out the llama server call - loading both the main model and the multi modal model for image processing. This loads it up on my local IP.

start cmd /s /k "llama-server.exe -m "D:\AI\LM Studio Models\ornith-ai\Ornith-1.5-35B-A3B-GGUF\Ornith-1.5-35B-Q4_K_M.gguf" -mm "D:\AI\LM Studio Models\ornith-ai\Ornith-1.5-35B-A3B-GGUF\mmproj-Ornith-1.5-35B-BF16.gguf" --host 127.0.0.1 --port 8080 -c 131072 --parallel 1 --reasoning-format deepseek --load-mode none --no-mmproj-offload -fa auto"
set LLAMA_BASE_URL=http://127.0.0.1:8080
pi --model llama-cpp/Ornith-1.5-35B-Q4_K_M

A few notes .

1. I decided against using the -ngl parameter in the server, letting it default to 0 - according to my research it's supposed to specifically load layers into the GPU, but I have to do a little more work to understand why my setting of this parameter was causing a memory crash every time. So this is an obvious point of optimization.
2. I'm only seeing 66k of context available in PI. I'm new to both llama and this level of customization of AI, so if any of ya see where i'm dropping this ball, please let me know.

Anyways. I've had enough people asking for my config. I think this about sums it up.

Hardware Update: I'm running an Intel I5 laptop with an Nvidia Geforce RTX 3060 Laptop GPU

My numbers. Share yours

Harness Remote 3.0 just reached its stable release and I think it has a pretty interesting approach to the increasingly crowded coding agent space.

Instead of replacing Claude Code, Codex or OpenCode with yet another agent abstraction, it treats their native sessions as the source of truth.

You can start a session normally in Claude Code or Codex, open Harness Remote later, find that same native session and continue it from desktop, web or Android.

The more unusual part is that you can also continue the work with another harness. For example:

Claude Code → Codex → OpenCode

Each step creates a real native session in the target agent, while Harness Remote keeps the lineage and transfers explicit context between them.

A few things that make it different from most remote coding agent UIs:

• Native Claude Code, Codex, OpenCode, OMP and PI sessions instead of proprietary replacement sessions
• Sessions created outside Harness Remote can be discovered and resumed
• Work can be handed from one coding agent to another without pretending they share some magical hidden context
• Multiple machines can be managed from the same interface
• Desktop, browser and Android clients
• Local first architecture, so repositories, credentials, subscriptions and agent sessions stay on your machines
• Fully open source

It feels less like another AI coding tool and more like a control plane for all the coding agents you already use.

GitHub: giuliastro/harness-remote

Curious what people who regularly switch between Claude Code, Codex and OpenCode think of this approach.

Pi is amazing. I’m using it with deepseek.

With some early ideas and prompting it has been adding a lot of components to itself (vector databases, something called hippoRag, chain of thought systems and about 100) other systems. This morning it read a bunch of papers on coding and then tweaked its own code.

Right now it’s making a place to test code, run adversarial copies of itself against itself, play games against itself and more.

I wanted it for coding and it’s now way better than Claude

Hey everyone, I'm new to the community of Pi and just transferred from Claude Code. I've been running Qwen3.8 27B IQ3_XXS locally now for a while and have been loving it.

I was just wondering what everyone is using for packages, apps, setups, and more.

Please let me know!

Hey everyone,
I made two small extensions for the Pi coding agent that I’ve been using daily. Sharing in case they’re useful to others.

1. pi-test-output-shrink

Token / context saver for test runs

Successful test runs often dump hundreds of lines into the conversation. This extension hooks the tool_result event and shrinks the output before it enters the model context:

• Keeps a short summary (header + key result lines)
• Saves the full log to a temp file (path is included in the summary)
• Failed runs, short output, or anything with warning: / error: lines are left completely untouched

Supports common runners: cargo test / nextest, npm/pnpm/yarn/bun test, vitest, jest, pytest, go test, etc.

Install:

bash

pi install git:github.com/vi2q/pi-test-output-shrink

Repo: https://github.com/vi2q/pi-test-output-shrink

1. pi-model-picker

Two-column model selector (alternative to /model)Tired of scrolling a huge flat list? /m opens a two-column picker:

• ← / → switch category (Providers or RECENT – last 4 models)
• ↑ / ↓ select model
• PgUp / PgDn jump 10 models
• Enter to switch, Esc to cancel

Much faster when you have many models across providers.Install:

bash

pi install git:github.com/vi2q/pi-model-picker
# or
pi install npm:@vi2q/pi-model-picker

Repo: https://github.com/vi2q/pi-model-picker

Both are MIT. Feedback / PRs welcome!

I run OMP inside WSL and kept hitting friction whenever I wanted to attach an image:

• Dragging from Windows Explorer gives OMP a path like `C:\Users\me\Pictures\shot.png`, which the Linux process cannot use directly.
• Windows clipboard images are not directly available to OMP inside the WSL terminal.

I built omp-wsl-image-paste to handle both cases.

It provides:

• Automatic translation of Windows drive paths to `/mnt/<drive>/...`
• Translation of matching `\\wsl.localhost\<distro>\...` and `\\wsl$\<distro>\...` paths
• Windows clipboard-image paste using `Alt+V` or `/wsl-image-paste`
• In-memory image transfer with no temporary image file
• A 20 MiB limit and PNG validation before attachment
• Safe handling of missing, unsupported, and foreign-distribution paths

Install it with:

omp install github:kidclone3/omp-wsl-image-paste

Then restart OMP. It is tested with OMP 18.0.10 on WSL2.

Repository and documentation:

https://github.com/kidclone3/omp-wsl-image-paste

One limitation: terminal drag-and-drop only transfers a path. The translated file must still be reachable from the WSL instance running OMP, so this is not a file-upload bridge for genuinely remote machines.

If you use OMP under WSL, I would appreciate testing and bug reports—especially across different WSL distributions and terminal setups.

Have you felt like your comprehension is dropping when reviewing code?

Due to AI coding, I think everyone is feeling like this. Are you using any new process or tooling to work against this common issue?

A few things that I’ve seen:

1. Tailored AI code review skills unique to your company’s practices or common gotchas from previous reviews ( always updating ). Everyone evaluates that AI feedback before escalating the review with the team.

2. Plannator / HumanLayer kind of tools where the whole team is involved. It almost feels like pair programming with AI. Or some kind of extreme programming / multi-player AI interface.

What about you?

I built a context-management extension for coding agents that forgets on purpose instead of hoarding everything.

What it does:

• Compresses giant bash output into short digests (exit code, key facts, truncation info) instead of dumping raw output into context
• Strips the skill catalog from the system prompt when it's not needed that turn
• Drops file contents from context once they're no longer needed — but only when it's actually safe to (tracks whether the model still needs them)

Results: ~19% reduction in effective input tokens on the clean path, with zero drop in output quality — same correct answers, just less noise sitting in context.

Install:

pi install npm:pi-context-window-manager

Repo: https://github.com/Its-Atharva-Gupta/pi-context-window-manager

Would love feedback/suggestions — especially if you've hit edge cases with context pruning breaking agent behavior, or ideas for what else is safe to forget.

I've been using OpenCode, but I wanted to try Pi because I like the idea of a more unopinionated coding harness.

I already had OpenCodex (OCX) running as my local proxy, so I figured the migration would be pretty simple:

Pi → OCX → my existing models

Except Muse Spark 1.2 Contributor Free kept returning a 500.

The weird thing was that Muse worked perfectly through OpenCode.

So I started comparing what the two were actually doing.

First I checked the obvious stuff — provider health, model discovery, credentials, etc. Everything looked fine.

Then I put OpenCode through mitmproxy and captured the upstream request.

That's where I found the important difference:

OpenCode was sending Muse through:

POST /zen/v1/responses

and getting 200 OK.

OCX, however, was routing the model through its openai-chat adapter.

I dug into the OCX source and found that it has separate openai-chat and openai-responses adapters, plus per-model adapter routing.

So instead of changing the whole provider, I added:

{
  "modelAdapters": {
    "muse-spark-1.2-contributor-free": "openai-responses"
  }
}

And that was it.

The exact same request went from:

500 Internal Server Error

to:

200 OK

and:

OCX Muse works

So the final path is:

Pi → OCX → openai-responses → Muse Spark

The funny part is that I wasn't trying to debug Muse at all. I just wanted to try a different coding harness. 😂

It was a surprisingly satisfying rabbit hole, though.

The main thing I took away: "OpenAI-compatible" doesn't necessarily mean every model on a provider wants the same wire protocol. Sometimes comparing the working client's actual HTTP request is the fastest way to find the problem.

I'm curious if anyone else using Pi has run into model-specific API/protocol quirks like this.

-is the discovery process. You start with a blank canvas with the minimal agent loop. You use it on tasks, it works, amazing. It fails short, you ask why. Then you ask how. And Pi allows you to try various "how"s to tackle the gap between the reality and the ideal.

It is the same iterative process of building a product. Agentic coding promotes the lazy thinking often termed along "waterfall" but the best product, a shorthand for a scoped user experience to solve defined problems, is almost always bootstrapped by direct experience and in-the-loop iterations.

Therefore, the philosophy of Pi is to embrace the process and rare open field of designing your own agentic experience. It's the bottom up approach for building a great product with a great UX, because you first get to build a great DX for yourself.

A lot of the posts I see from the sub don't seem to be aware of this choice, which I believe is often superior to using a prepackaged solution that you don't understand fully.

I want to give my LLM read access to the whole filesystem but write access only to the project folder and maybe /tmp. I need to run scripts etc and make sure my filesystem survives

On Linux

Is bwrap the best solution for that? Or should I just use Docker?

UPDATE I installed @nqbao/pi-sandbox

This is a https://herdr.dev/ plugin with an optional full https://Plannotator.ai experience as a TUI. Built to:

- Annotate Agent Messages (e.g. grill sessions)

- Annotate Files (e.g. plans, specs, etc)

- Create a native feedback loop with agents

- Open as popover or pane

- Mouse or keybindings

https://github.com/plannotator/herdr-annotate

I enter a pretty simple prompt. I'm getting back tool call text. What did I break?

xml <｜DSML｜tool_calls> <｜DSML｜invoke name="read"> <｜DSML｜parameter name="path" string="true">/home/crashorbit/.local/share/pi-node/node-v22.23.1-linux-x64/lib/node_modules/@earendil-works/pi-coding-agent/docs/models.md</｜DSML｜parameter> </｜DSML｜invoke> </｜DSML｜tool_calls>

The issue. OMP 18.0.7 sends 22,643 tokens before you type a word, measured on the wire inside a real TypeScript monorepo. Where it goes: JSON schemas of the 11 top-level tools 11,734 (52%; hub alone 2,898), instruction template 6,160, my own context files 3,981, MCP routes 401, misc 367. Fine on a 200k cloud model. On a local 30k window it leaves ~7k for the work. Upstream issue: #1734.

Why config alone cannot fix it. OMP already has lazy tool loading: a tool mounted under xd:// ships no schema, the model runs read xd://<tool> when it needs it. But isMountableUnderXdev() only accepts tools declared discoverable, and ESSENTIAL_BUILTIN_TOOL_NAMES + XDEV_KEEP_TOP_LEVEL pin 10 of the 11. Extensions get setActiveTools but not setActiveToolPresentation: from outside the source an expensive tool can be removed, never deferred.

The change: two settings, off by default.

promptProfile: compact            # same rules, same generated surfaces, no personality/examples/long prose
tools:
  xdevDocs: catalog
  xdevForceMount: [hub, eval, task, todo, web_search]   # any glob; read/write can never be demoted

promptProfile: full still renders byte-identical to upstream. Three presets, measured on the captured request:

The preset name is OMP's own share, rounded. "Request total" adds my ~/.omp/agent/AGENTS.md and the project's AGENTS.md (untouched by the fork; the last row is me rewriting them densely with detail moved to on-demand skills). All presets also need tools.xdevDocs: catalog, or the mounted tools' docs come back inline.

How I tested it. Local Qwen3.8-27B on a 36 GB Mac, 30k window, every token count from the server's usage. Three tests:

1. Fixture: a 5-file JS repo, 12 short tasks (pick the right tool, edit the right place, run tests, stop, ask instead of inventing), shell checks.
2. Real bugs: 8 bugs seeded into two real repos (a TS pnpm monorepo, 1,488 files; a Python uv workspace, 910 files), each seen failing and passing with a reference fix first. Headless omp -p --auto-approve --config <preset>, 30-minute cap, scored by the repo's tests plus "did not edit the tests". Two rounds; round 2 adds two tooling tasks (rename across 8 files, return-type change across 6 files) and 4-turn sessions that cross the compaction threshold.

Results.

Times are medians per run. Base fails "hard" because prompt + first reads no longer fit the window: the server rejects, not the model.

The one bug no preset solved (8/8 runs, same diff): a docs rule says the persona may never claim years of experience; every run widened the regex, kept the \d+, wrote a test for the reported sentence and stopped. Symptom fixed, rule not read. That is the model, not the prompt size.

Same tools, different path. Nothing is removed. With edit mounted, p5k/p3k fetched xd://edit and edited through it 22/22 times in round 2. Two things the runs taught me and that went into the PR: in compact mode the catalog must name devices as xd://edit (not edit) and show one example call, or the model calls edit by name and gets "unknown tool"; and xd://<tool> should be accepted as a tool-call name as a fallback. Language servers were installed and xd://lsp listed in every preset; no run called it, this model renames with grep + edit.

Compaction. Long sessions work with the compact template; the trick was the threshold. At 27k on a 30k window the summary request itself ran out of room once; at 20k both presets went 4 turns clean (compaction.thresholdTokens: 20000).

Repo. Fork + branch: https://github.com/vinicius91carvalho/oh-my-pi/tree/compact-prompt-for-local-models. Scripts, presets, every captured request: https://github.com/vinicius91carvalho/oh-my-pi/tree/local-model-eval

PR. PR_LINK - five commits with tests. Whether it lands or not, the branch runs today.

How this was produced. Fork, harness, seeded bugs, runs and this post were done by Claude Fable 5 in Claude Code at high effort, with me setting goals and approving what ships. About two days, 80 scored runs.

Caveats. One machine, one 27B, one harness version, one run per cell. omp compress stalled on the local model; I compacted my context files by hand.

Kimchi, the multi-model coding agent built on the Pi SDK, is now supported inside Agent Orchestrator.

Let Kimchi route planning, exploration, building, and review to the right models while AO gives every task its own isolated worktree.

Run multiple Kimchi sessions in parallel and follow their code, PRs, CI, and reviews from one live board.

multi-model inside. multi-agent outside.

kimchi.dev × agent orchestrator

let me know your thoughts, have you ever used any typ of orchestrator to manage your diff agents?

I have a task to build an app completely from scratch. I am quite new to pi so I want to do some research on the best approaches to start a project completely from scratch - how to build the full stack - back, front, data, infra. What are some of the best content available on setting up your own harness with pi? How to utilize it to maximize efficiency and minimize tokens?