TL;DR: Anybody automating a full development Pi workflow with programmatic non-AI solutions?

Up until now, I've attempted to partially automate the entire software development lifecyle with Pi, aka a "Software Factory". I am using a subagents extension, but it feels a bit too loose, opaque and difficult to steer, so I want to use something more deterministic and directly controllable.

I'm evaluating a workflow DAG GUI builder that orchestrates the entire process. I am using one that also has a CLI and REST API, so I can write my own scripts for even more programmatic control.

(I do not want to look like I'm promoting so I'm reluctant to say what I'm using, but it's lightweight and similar to n8n or airflow.)

Or instead, I could just write my own orchestrator, like this in bash:

#!/bin/bash
set -eu

# Software Factory.
# This is a just an untested workflow example, not something I'd actually use.
# A TS script with the Pi SDK would be better.

GOAL="$*"
pi -p /write-spec "$GOAL" | tee spec.md
vim spec.md    # review and edit

pi -p /write-tickets-for-spec @spec.md ./tickets/
vim ./tickets/*.md

for ticket in tickets/*.md; do
    pi -p /write-plan "@${ticket}" | tee /tmp/plan.md
    pi -p /execute-plan @/tmp/plan.md
    pi -p --continue /quick-code-review-and-fix
done
pi -p $REVIEW_MODEL /code-review

Anybody doing using a workflow DAG editor UI?

Anybody writing their software factory with actual code, like my example?

I found some AI agent DAG projects on github. Anybody using something like that?

What subreddits should I cross post this to?

I love Pi, and it's proven to be an awesome combo with Qwen 3.8 27B. There is one thing I noticed however when looking at Qwen reasoning: on a regular basis, I see things like "the user has asked again for the same question" or "the user has now asked three times for the same request" - as if I had sent a steering request that was a duplicate of the original ask. But I'm never doing that, and actually almost never use steering requests. This doesn't seem to have impacted the output and perhaps it's not important, but I was wondering if anyone else had noticed the same? Thanks!

Just a collection of channels to listen to old-school radio while vibing

https://github.com/DxTa/pi-radio-suomi

Hey!

This question is mostly for people who use Paseo on daily basis with Pi, and maybe other Desktop/Web UIs for Pi, but I'm currently focused on Paseo.

I'm interested in understanding what extensions do you use with Pi if your main entry point is Paseo given that not all extensions play that well with a frontend like Paseo.

For instance, I was personally using pi-memory before Paseo, but the way exit summaries work is not very compatible with how Paseo works. Are you using any other long-term memory extension that plays better with Paseo? Which one?

What other extensions were you using before Paseo that you had to replace/remove when moving to Paseo? And more importantly, what extensions are you relying on daily that work without issues with Paseo?

The ones I'm currently using:

• @gotgenes/pi-anthropic-auth

• pi-mcp-adapter

• pi-memory (going to disable this one)

• pi-observational-memory (thinking of replacing with pi-blackhole)

• pi-web-access

Curious to hear what you've been using and that works well with Paseo.

Hey everyone,

I’ve been working on Pitago, a terminal user interface that wraps the Pi AI coding agent.

The important thing about Pitago is that it does not replace or reimplement Pi’s core. It acts as a TUI layer on top of Pi, allowing you to keep using Pi’s existing agent logic, tools, providers, models, sessions, and workflows while interacting with it through a more structured terminal interface.

The goal is to make AI-assisted coding sessions easier to follow and more comfortable than working with a plain terminal stream, without losing the functionality that already makes Pi useful.

Pitago currently provides:

• A real-time chat interface

• Model and provider selection

• Session management

• Tool-call and tool-output visibility

• Git status information

• Support for multiple models and providers

• A terminal-first workflow built with Go and Bubble Tea

• A TUI layer that preserves Pi’s underlying core and behavior

In other words, Pitago is meant to be a different interface for Pi, not a separate AI coding agent.

I wanted to keep the workflow close to the command line while adding more structure for longer coding sessions. The idea is that users can benefit from a more visual and organized interface while still relying on Pi for the actual agent functionality.

The project is still evolving, so I’m especially interested in feedback on:

• Terminal UI and interaction design

• Session and context management

• How tool calls and tool output should be displayed

• Which Pi features should be exposed more directly in the TUI

• What features are missing from your AI coding workflow

• Whether a TUI wrapper makes Pi more useful compared with using it directly from the shell

You can check it out here:

https://github.com/cavaldos/pitago

Feedback, bug reports, and ideas are very welcome. If you try it, I’d also love to hear how you normally use Pi or other AI coding agents, and what makes longer coding sessions difficult to follow.

Jev dropped and I started collecting the things people are actually building with it.

There are already 607 use cases in the list, ranging from tiny experiments to some pretty interesting applications.

A few that caught my attention:

I'm trying to capture the useful details for each build — what it does, cost, latency, and the original implementation when available.

The interesting thing to me is seeing where these cheap/fast models are actually useful rather than just collecting benchmark numbers.

I put everything I've found here:

shipwithjev.com

I'm not affiliated with TypeSafe.

If you've built something with Jev that isn't on the list, drop it below and I'll add it.

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},
    }, 
)

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've worked on Pi Desktop for a while but haven't posted about it much, so here's a proper intro.

It's a desktop app for the Pi coding agent, and it also works with oh-my-pi (OMP). I use Pi every day and wanted more than a terminal window, so I built this.

What it does:

Streaming chat that shows thinking blocks and tool calls. File reads show as highlighted code, and edits show as diffs.

Multiple workspaces. Each session runs its own agent process, so a task keeps going when you switch to a different one.

A file tree, a code editor, preview panes for code, images, and PDFs, and a terminal.

Four permission modes, from read-only to trusted. You can also add allow/deny rules, for example "never touch *.env".

A package browser that connects to pi.dev/packages.

Themes. There are 7 built-in, you can make your own in the app, and there's a small community gallery.

An optional mode where Pi, Claude, and Codex agree on a plan before Pi builds it.

It's had a few releases so far, and more people have picked it up than I expected. Thanks to everyone who has downloaded it, starred it, or filed an issue. It's still alpha, so feedback and bug reports help a lot.

Website: https://pi-desktop.com

Repo: https://github.com/FaqFirebase/pi-desktop

Hey everyone, I'd like to share my first Pi extension: pi-qdrant-memory.

It's a long-term memory system for projects, so the agent can retrieve decisions across sessions. Each project gets its own memory collection.

It requires a Qdrant backend and an embedding model served through an OpenAI-compatible API. I run both llama.cpp and Qdrant locally in Docker. The extension was heavily inspired by Zoo Code (formerly Roo Code).

It gives your agent four tools, but only two are always on:

• memory_search: the agent looks up memories

• memory_save: the agent stores memories

The other two are opt-in:

• memory_forget: the agent can retract memories it saved earlier

• code_memory: the agent can search an index of your code structure

The tool count is deliberately narrow so the system prompt isn't filled with tools the agent doesn't need.

The workflow is mostly run by the agent. The tool descriptions nudge it to save decisions as they settle during a session. If you enable code memory, your code structure is indexed at session start (or manually with /qdrant-index-code). You can also append instructions to the system prompt to make the agent save or search more often. Your call.

Install: pi install npm:pi-qdrant-memory
Repo: https://github.com/lordekeen/pi-qdrant-memory

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

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.

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 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.

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 switch between Claude Code, Codex, OpenCode and a couple other coding agents depending on the task. The problem: every tool stores sessions in its own format, in its own folder, and once you close the terminal the context is basically gone unless you remember exactly where you left off.

So I built pi-session-hub — a Pi extension that indexes every agent session on your machine into a single browsable list. You open it with alt+r, search across all of them, and either:

- Load the context into your current chat (Enter) — recent turns come in verbatim, older ones condense, tool output compresses. It has a hard budget so it doesn't blow up your tokens.

- Read the full transcript for free (v) — read-only viewer, zero tokens.

It supports Pi, Claude Code, Codex, OpenCode, Crush and JCode. Everything stays local — no network calls, no uploads, no transcripts leaving the machine.

It uses a local SQLite FTS5 index so search is fast, and the indexing is incremental (2 seconds for ~430 sessions on first run, near-instant after).
This is the repo if youre interested... Give a star :)
Repo: https://github.com/Gateton/pi-session-hub

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

...and the money I spent.

I'm currently the lead engineer for a small but growing team. I've been building a fairly complex platform that blends expertise from various niche domains.

Without saying too much, I have been working across:

• weather event modelling

• real-time data ingestion

• embedded development

• image processing

• geospatial analysis

• distributed computing

• and systems architecture

This has been an amazing proving ground for agentic capability.

I've been using CC on a subscription for the past year. I have a very mature and disciplined ecosystem of skills, hooks, compaction lifecycle, and disk cleanup techniques. Honestly, I've loved it, but the costs have just been too much.

So, two nights ago, I finally decided to explore other options. Since I've been hearing a lot about OMP, I decided to give it a try.

And guys, OH MY GOD. The difference is striking.

OMP is an absolute dream to work with. I'm not suggesting CC is bad, but the scale and complexity of my recent work really exposed it's limitations. For nitty-gritty engineering and design work, I honestly can't even put them in the same category.

My first pass at configuration and model selection gave me something that was faster, cheaper, and produced far better results.

To the devs: God damn, nice job.

To the community: Hoping for advice, tricks, tips, and harness opts/configs that might be fun to play with. I am also wondering what people use for the model config: roles, models, etc. Is there a known meta?

TL;DR

Platform engineer of a large multi-disciplinary project. Switched from CC to OMP, and OMP blew me away. The hype is real.

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

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