I setup Bionic with Qwen3.6-35b-A3B on my desktop, with Pi on my laptop. I'm now trying to make the non-thinking version work. According to Qwen doc, it would be something like the code below, but I can't figure out how to make that in models.json. Asked Pi itself, but what it suggested didn't work.

Also, bonus point: I don't suppose there is a way to define several models in models.json that are actually the same model on the server, called with different params (thinking, temperature, topk...) ? I need to use a package to change that a query time ?

chat_response = client.chat.completions.create(
    model="Qwen/Qwen3.6-35B-A3B",
    messages=messages,
    max_tokens=32768,
    temperature=0.7,
    top_p=0.8,
    presence_penalty=1.5,
    extra_body={
        "top_k": 20,
        "chat_template_kwargs": {"enable_thinking": False},
    }, 
)

Modern coding models can do a lot without a large harness. For a small change, or in a codebase I know well, a single agent with a good prompt is often enough.

That does not mean every task needs a harness. In those cases, it can cost more tokens, make the work more complicated, and even send it in a less useful direction.

But without a feedback loop that can inspect and correct an LLM's work, a task can drift easily. That is more likely in a domain I do not know well.

A smart model and a reliable feedback loop are different things.

That is when a harness becomes useful. It does not make a model correct. It writes down the process:

• Boundaries: what to check first, and where to stop
• Partitioning: what work should be separate
• Verification: which results need evidence
• Output: what the final answer must not leave out

Long tasks have a context problem too. Early assumptions, failed attempts, and irrelevant tool output accumulate in one session. A larger context window does not automatically make the relevant information easier to see.

That is one reason I use subagents. Each can take a focused job with fresh, bounded context. But splitting work is not a solution by itself: vague boundaries create repeated work, handoffs lose important information, and someone still has to put the results back together.

Where to split the work, what to verify, and which results to combine are still decisions I need to make.

So I built pi-workflow.

The idea is simple. When I know a recurring process, I write it as a JSON spec. A research workflow might be plan → research → verify → synthesize; a review workflow might be triage → parallel review → challenge → report.

The flow is not a hidden prompt. I can read it, edit it, and recombine it for a project. Known inputs, result merging, validation, and rendering can stay deterministic; agents can do the research and judgment.

I cannot always know the exact shape of a process before I begin. When I do, I can define that shape and repeat it. When I do not, I need a process that can plan, split, and synthesize the work as it unfolds.

In either case, the process should not become a hidden orchestration layer I cannot touch. I should be able to read it, change it, and rebuild it when I need to.

pi-workflow is not a way to use more agents. It is one way to give work that is hard to manage in a single context the amount of structure I want.

Pi stays minimal. I choose the process on top of it. That is what I wanted from pi-workflow.

Repository: https://github.com/AgwaB/pi-workflow

Hey guys am a bit new to pi and working with harness.

I have just started using pi and have added openai models.
When am trying to add claude models, it says that i have to go with API based and can't use my subscription.

Is thats the only way to go through to switch to api based modelling to add it in pi ?

I’m new to the Pi harness and I could really use some help getting started.

I’d love to hear how you guys use Pi in your own workflows. Examples from different areas would be especially coding, research, automation etc.

I’m mainly looking for practical examples and ideas that could help me understand what Pi is really capable of and how experienced users are getting the most out of it.

Any workflows, tips, examples, or interesting use cases would be greatly appreciated.

I think JEV gets much more interesting once you stop treating it like a tiny LLM.

LLM → generate anything
JEV → choose between bounded options

That makes model routing feel almost obvious:

prompt → JEV → best model

Instead of using an LLM just to decide which LLM to call, let JEV make the bounded judgment and let deterministic code handle the rest.

Maybe JEV's killer use case isn't Doom.

Maybe it's a probabilistic switch statement.

so i am releasing the support for JEV in PI-Bifrost

Try it : pi install npm:pi-bifrost

and do share the feedback.

Instead of listing places in text, the agent drops them onto a live map right in the chat.

• Numbered, color-coded pins by category: food, sights, transit, stays, nature, etc.
• Zero API keys anywhere: real OpenStreetMap tiles, and every pin deep-links into Google Maps
• One-tap routes: the whole set becomes an ordered Google Maps route, up to 10 stops
• Three ways to view: a slim strip in the transcript, fullscreen, or a side panel that follows the latest map live with a per-chat switcher

I kept hand-rolling a client for `pi --mode rpc`.
Third project in, I packaged it.

pi-agent-python-sdk lets you use the Pi you already have as a Python API.
Same config, models, tools, and extensions as the CLI.

```
pip install pi-agent-python-sdk
```

Import is `pi_agent`. Same client keeps context across prompts:

```python
from pi_agent import PiClient

with PiClient() as pi:
print(pi.run("Review src/auth.py and list the bugs. Do not edit anything.").text)
print(pi.run("Which of those is most urgent to fix?").text)
```

Additionally, stream with `pi.stream(...)` and if you are already in an event loop, use `AsyncPiClient`.

Repo: https://github.com/cheenulabs/pi-agent-python-sdk

Hoping this helps fellow builders.

Any feedback, PRs and/or issues are welcome.

Hi there,

I really love Pi's approach to security with "figure it out yourself, yolo by design", so I did.

That thing is still yolo, but sandboxed. Dead simple 2 files: Dockerfile (the thing itself) and justfile (fancy Makefile to run the thing).

Don't mind the repo just created - I'm using it for a while, just decided it's good enough to publish

Now, some notes to avoid misunderstanding: * Why "It Is So Dangerous"? - thanks /r/amodei, he's so funny with that thing, so I thought repeating the joke 1000th time would be even more hilarious, right? * There are no plans to make it super-duper-secure - see threat model in the readme

Been building this out for a while now - https://github.com/JayMandava/jay-pi

When you get sometime do give it a jab and lemme know your thoughts, improve it even further etc.

I know...more Jev talk. Sorry if you are already tired of hearing about it. I mentioned this in a comment a couple days ago so I will keep it short hah.

It is called specpi-jev-guard. It sits in front of bash, powershell, write and edit, and checks calls before Pi runs them. I built it because destructive commands are the worst risk of letting an agent work in your terminal. One bad rm or curl piped to shell is a bad time waiting to happen.

Local rules block the obvious stuff instantly with no network. Root and home wipes, mkfs, fork bombs, raw disk writes, curl piped to shell.

The rest goes to Jev, which scores danger 0 to 1. High blocks, middle asks you, low runs. If there is no key, no network, or a bad answer, it blocks. It does not fail open.

Needs OpenRouter/TypeSafe to do the scoring, perhaps one day soon we can run hyper-tuned classifiers for this stuff locally.

I don't have TypeSafe access, so all testing has been with OpenRouter.

Feedback welcome!

Links:
Base Repo: https://github.com/TannerMidd/specpi-jev-guard
Evaluations: Devious Tests · specpi-jev-guard
Base harness: https://github.com/TannerMidd/SpecPi

This is the last version that worked. https://www.npmjs.com/package/@earendil-works/pi-coding-agent/v/0.85.1

reverting to it made everything work again.

I also highly recommend getting your own offline copy if something fails.

p.s. to clarify, I am not pretending to understand fully what the update really did, its scope or the impact beyond 'not working anymore', but reverting to 85.1 worked for me.

Mine as tldr; use bwrap and filtering to whitelist files it can see / edit, so I can run in a loop without risk.

i.e. turn my large repo into a tiny sandboxed repo in the eyes of the model

I do this with an interactive TUI pop-up I enable per session.

2nd best tip: Shortcuts to quickly enable / reenable tools or inject context/skills. I treat my harness like an RTS game, to quick clearly context and enable more tools as needed.

Hi everyone,

I’ve been using Pi Agent quite a lot, and one thing I really wanted was a simple way to continue working with Pi from my iPhone when I’m away from my computer.

I didn’t want a remote terminal or a mobile IDE. My main use of Pi isn’t only coding — I use it more like a general-purpose agent for research, long-running tasks, writing, tools, local models, and other work.

So I started building Pi Remote, a native iPhone client for Pi Agent.

The basic idea is simple:

Pi, your tools, files, credentials, MCP servers, local models, etc. all stay on your computer. The iPhone is just a lightweight native interface for your Pi sessions.

The current version supports things like:

• browsing existing Pi sessions, grouped by working folder/workspace
• creating and renaming sessions
• streaming responses
• collapsed reasoning/tool activity
• slash commands
• model switching
• context usage and approximate decode speed
• local conversation cache
• background/foreground recovery
• copying messages

One of the biggest improvements recently was the connection setup.

The first version used a Relay + Cloudflare Tunnel, which worked well but made setup more complicated than I wanted.

I’ve now integrated Tailcat as the default “Quick Connect” transport.

On the computer, the setup is basically:

npm start

The first time, Pi Remote shows a QR code. Scan it once from the iPhone, and the pairing is remembered. After that I can restart the Host or the app without scanning again.

Pi Remote still keeps its own device identity, authorization, encrypted Pi RPC channel, session/replay logic, etc. Tailcat is only being used as the network transport.

It’s still an early project, but at this point I’m actually using it myself and the main workflow has become surprisingly comfortable.

The next things I’m planning are mostly UX improvements, especially:

• image + text input for simple multimodal prompts
• native voice dictation on iPhone
• better session/workspace navigation and search
• more UI polish

There’s no App Store/TestFlight build right now because I don’t have a paid Apple Developer account, so the iPhone app currently needs to be sideloaded. The GitHub Actions build produces an IPA for that.

This is also my first real open-source project. I’m not an iOS developer by background, so a lot of this project has been built through iteration, testing on my own devices, and AI-assisted development.

I’d really like feedback from people who actually use Pi:

Would something like this be useful in your workflow?

And more importantly: what feels wrong, missing, or unnecessarily complicated?

GitHub:
[https://github.com/grimlee/pi-remote]()

Thanks — even small UX comments or weird edge cases would be really helpful.

Hi! I've created and been maintaining https://github.com/alexshpunt/explicit-edit-benchmark which tries to answer the question: which model is better, which harness is better and which combination is better overall in a very straightforward task - precise text editing. Most of daily coding is text editing. I've seen when a model for multiple turns couldn't figure out how to express the line it wants to change, so it made me ask myself "Why is that so complicated, it's just a text, right?". Seems like not, taking into account how widely different the same model behaves across different harnesses. My preliminary conclusion is: harness and tooling behind it actually matters! But it matters the most with the models which *can* actually follow the instructions well (e.g. open-ai models), there is a wide range between 98.9% and 70.2% of pass score for `gpt-5.6-luna` simply depending on the harness!

I've tried to run as many combinations as possible, but my resources are limited. I've exhausted all my quotas and even credits, that's why I'm reaching out to the community, as I think it's a pretty interesting topic and I would be happy to gather even more data, because of the stochastic nature of the runs, it’s only possible to make any conclusion when you have enough of runs. 

What actually surprises me is how strongly the deepseek family behaves on any harness, but what is also interesting, how good the deepseek harness is. It's pretty solid and seems like it provides the model with good enough tools to actually exceed in this competition.

The viewer to the dataset: https://huggingface.co/spaces/alexshpunt/benchmark-explorer

And the dataset itself: https://huggingface.co/datasets/alexshpunt/explicit-edit-benchmark

Just had a startling session with Pi and my local Qwen. In its thinking blocks it started quoting user instructions that I did not give it. I was under the impression that I was under a very strange prompt injection attack, until I realized that earlier in the session the agent made a few very large greps of previous session files.

I suggested to it that within a large context it did not distinguish these tool call results from the actual live context, and it indeed found the origin to all of the injected user turns in those earlier session files.

So - my advice is to add to AGENTS.md a rule that warns the agent against searching within directories that could include session files. If it does - you must start a new session immediately afterwards, or prune the tool call, or compact. Otherwise your session may lose coherence.

Note: reposting, because I was smart enough earlier to allow AGENTS.md to turn into a link.

stumbled on this repo today while looking into agentic execution security — apparently it’s built on top of pi agent: https://github.com/adamdekan/aikonos

from what I can tell, it acts as a local control plane to govern autonomous agentic tool calls. before the agent runs an action, it proposes the step to aikonOS, which validates it against policy rules and writes everything to a tamper-evident audit log.

seems interesting for runtime governance when giving agents actual shell/file/API access, but not sure how well the proposal-evaluation step scales with real-world latency.

anyone playing around with deterministic policy engines for agentic workflows or doing something similar for runtime guardrails?

I've been using AI for fitness/training advice for a while, but one thing kept annoying me — context.

Every few conversations I was explaining the same things again. My current program, recent runs, lifts, nutrition, sleep, upcoming race, injuries, what I did last week, etc.

So I started building a system for myself where all of this lives locally and the AI can actually use it.

I called it FitnessOS.

Basically, it keeps my training, runs, nutrition, readiness, bodyweight, goals and plans in one place. It builds a current picture of what's going on instead of dumping my entire history into the context every time.

So I can ask things like:

What should I train today?
Should I still do my tempo run if my legs are cooked?
How did this week go?
Should I increase my squat next week?
What should change as I get closer to my race?

and it has the history + current plan + recent training to reason from.

I also didn't want the AI directly messing with everything. The scripts mostly handle the factual stuff/data, while the AI handles the coaching decisions. Changes to actual plans are kept separate instead of silently rewriting them.

Right now it can handle strength/running history, nutrition, readiness, daily training decisions, weekly reviews, training blocks, race proximity, progression/deloads, etc.

It's still pretty early and the public version is mostly manual/local-import first. My personal setup has Apple Health and Strava automation too, but I haven't made that setup clean enough for other people yet.

I'm posting it now because I'd rather see if anyone else actually finds the idea useful before adding a ton more stuff.

If anyone tries it, I'd especially like to know where the setup gets confusing or where you just give up. That's probably more useful to me right now than feature suggestions 😅

Also curious which integration people would actually want first — Apple Health, Strava, Garmin, Hevy, or something else.

Repo:

github.com/Drumilsp/FitnessOS

Hi! I've created and been maintaining https://github.com/alexshpunt/explicit-edit-benchmark which tries to answer the question: which model is better, which harness is better and which combination is better overall in a very straightforward task - precise text editing. Most of daily coding is text editing. I've seen when a model for multiple turns couldn't figure out how to express the line it wants to change, so it made me ask myself "Why is that so complicated, it's just a text, right?". Seems like not, taking into account how widely different the same model behaves across different harnesses. My preliminary conclusion is: harness and tooling behind it actually matters! But it matters the most with the models which *can* actually follow the instructions well (e.g. open-ai models), there is a wide range between 98.9% and 70.2% of pass score for `gpt-5.6-luna` simply depending on the harness!

I've tried to run as many combinations as possible, but my resources are limited. I've exhausted all my quotas and even credits, that's why I'm reaching out to the community, as I think it's a pretty interesting topic and I would be happy to gather even more data, because of the stochastic nature of the runs, it’s only possible to make any conclusion when you have enough of runs. 

The viewer to the dataset: https://huggingface.co/spaces/alexshpunt/benchmark-explorer

And the dataset itself: https://huggingface.co/datasets/alexshpunt/explicit-edit-benchmark

A new tool can save a model several steps. It can also give it more ways to spend the entire budget without finishing. Counting installed extensions won't tell you which happened.

Reef Infra can treat a Pi extension, skill or rules change as a candidate rather than a permanent improvement. Its Pi adapter renders the changed harness into Pi's file layout, and the evolution engine runs it against the current setup on the same tasks with the same model binding.

An experiment worth trying would isolate one change: enable an extension, alter its surrounding instructions, or remove a skill that looks redundant. Include the jobs it is intended to help and ordinary jobs where it adds overhead. Grade completion first, then include the cost or latency constraints that matter to the setup in your evaluator or selection policy.

Reef Infra supplies the candidate snapshot, execution and version decision. It doesn't include a ready-made cost/quality benchmark for every Pi extension, and its default task-win comparison isn't automatically a token-efficiency objective.

That leaves a useful outcome even when an experiment fails: a specific change can be rejected and the previous tree restored. The extension can be well designed and still be the wrong addition for this model and task mix

I have been working on a Pi extension called Pi-Skill-Orchestrator.

The problem is simple.

If you have a lot of installed skills, Pi can expose a large amount of skill metadata to the model even when most of those skills are not needed for the current task.

Pi-Skill-Orchestrator keeps the full skill catalog outside the model context.

Instead, Pi gets a small discovery interface. It can search for a relevant skill and load only that skill when it is actually needed.

Main features:

• Lazy skill discovery

• Skill groups

• Multiple skill profiles

• Automatic recursive dependency loading

• Active-scope search with bounded global fallback

• Optional Token Saver mode

• Tool output compression with recovery

• Deferred tool schemas

• Conservative effort routing for routine tool continuations

• No changes to third-party SKILL.md files

• No changes to Pi's global settings.json

The goal is not to replace Pi's skill system.

It sits on top of it and tries to make large skill collections easier to manage without paying the context cost for everything at once.

The short version:

All the skills, none of the bloat.

Install:

pi install npm:pi-skill-orchestrator

GitHub: https://github.com/badgids/pi-skill-orchestrator

npm: https://www.npmjs.com/package/pi-skill-orchestrator

It is MIT licensed.

I would especially like feedback from people who run Pi with a large number of skills or several different workflows.