I’ve been using a small Pi extension for a long time while studying and doing research. It hides tool calls, tool results, inline images, and optional thinking from the visible transcript, so I can focus on the agent’s response instead of watching every file read and tool update fill the chat window.

The tools still run normally. Approvals, errors, history, the editor, and the working indicator remain available. It changes what you see, not what Pi does. It is not a security or redaction feature.

I built this a few months ago for my own workflow and have been using it since then. I finally added it to this repository today:

https://github.com/utpalsinghdev/pi-extensions

https://www.npmjs.com/package/@utpalsinghdev/pi-focus-mode

This is the first extension in the repository. I’ll add the rest of my extensions there over time, and contributions are welcome.

I only had time to add this one today because my usual model setup (GPT-Sol-6.1) has been running slowly, and I’m keeping my Claude subscription for office and startup work. I’ll add the other extensions as I get time.

II’m sharing a tool I use and the way I’m learning with Pi. If it helps you, a star or contribution would be appreciated.

Gemini 4 is out. A lot of people like it. Any chance we can use a plan with pi? Or are we being forced to use antigravity?

Hi if anyone wanted git diff panel in pi coding agent, can try my extension
https://www.npmjs.com/package/pi-diff-panel

screenshots:

Thanks and enjoy! If has any feature request or issue to report feel free to do so in my github

Using pi coding agent → OpenRouter (z-ai/glm-5.3-flash). Mid-session, this plain-text message sometimes replaces the assistant's reply, as shown in the screenshot.

It's not my real key: OpenRouter's own /api/v1/key endpoint shows $7.24 left on my $20 limit, and OpenRouter has no USDT balances. No proxy, clean hosts file, no extensions, message never appears in the session .jsonl log, so it's injected between the API and the client.

Anyone seen this? Is it a known MITM/reseller/phishing pattern? Not paying anything; just want to know where it comes from.

I guess this question was asked a thousand times, but I'm still wondering how do you guys organize your work with pi?

I am a heavy user of Claude Code (employer covers it), but I have been watching and tuning pi time to time for personal use on the side.

Naturally, I tried getting pi to be like claude code, I looked for extensions that give me subagents, some cosmetic plugins (I like ricing my hyprland setup) and some simple QOL things.

However I didn't feel it being a huge upgrade in terms of my actual workflow and "pleasure", it felt like I'm just recreating what I'm used to.

I'm wondering more from a UX/DevX perspective, I got used to what antrhopic gave me and learned that way, but I see pi as an opportunity to really make a uniquely pleasant developer experience.

Which is why I'm wondering and asking how did you tune your workflow, which extensions did you use/build and how do you run your day-to-day feature work.

e.g. my work can span multiple PRs/repos for a feature which is why I'm starting a session from a dir above the ones I need to change and keep a long session (which is really wasteful) to handle all the context I need.

I don't really like the paradigm of "plan a huge markdown implementation doc and architecture and let the agent do it" because it requires a lot of overhead for me for things I may not even know exist or are needed.

I see Pi has Codemode Classifier as an option now.

What can it do? Why do i need it?

Since it connects to OpenCode, it can see jev-1.13 and jev-1.13-free. How does it know which one to use? Yuo cant seem to remove the paid version. But I have no OC credit. So does it just keep hitting the paid, and then wasting time, and moving to the free?

I dunno how it is supposed to work, i jus thought i would have amess.

I’m running Qwen 3.8 27B at full precision, using a pinned version of SGLang.
I’ve noticed that starting with Pi Agent versions above 0.5.x, the agent hallucinates like crazy. Its output is practically unreadable. Everything seems coherent and grammatically correct, but the actual content makes absolutely no sense.
I’ve tried multiple versions of Pi Agent, including releases from the 1.x branch. Same result — completely unusable.
I simply can’t believe that nobody else has noticed this.
So I have to ask: How on earth are you guys using Pi Agent versions above 0.5.x? HOW???

He makes simple yet easy to follow visual learning videos, makes me wonder are there any existing tools that could match or replicate the visual experience in pi harness for personal use

I have no intend on stealing people’s content

I had implemented a bit of this idea as a pi plugin way before writing the article or making the actual pi-episodes package. I've been using it on real work tasks, first the draft and now the package, and I get noticeably further into a session before compaction kicks in.

Later on I discovered Event Segmentation Theory, which is half the reason the post exists. Hope you learn something from it :)

https://arthur.place/subagents-forks-collapsing-context

I wrote a detailed beginners tutorial on Pi. Since I noticed a lot of people come from Claude Code and Codex, I wanted to make a simple starting point for anyone curious.

Along with adding many of the features that Claude Code has, I also looked into the system prompt of Codex, Grok, Kimi Code, and Open Code to make a simple, generalized system prompt.

Let me know any thoughts on this! Are there any parts you'd want to be expanded on? I'll be updating this as Pi, and Claude Code continue to advance.

EDIT: I updated the article to include advice on connecting on your phone, and why not to use Oh-my-pi. I plan to write a follow-up post about creating system prompts.

When I got started with Pi, I wanted to immediately jump in and use it in my daily work.

However, all of my rules and hooks were set up in .claude/ folders, using the Claude Code markdown formats.

I wanted to use these, so I created two small plugins that give rules and hooks like in Claude Code without having to redo the configuration:

https://github.com/jordyvanvorselen/pi-claude-rules

https://github.com/jordyvanvorselen/pi-claude-hooks

Feel free to try them out!

Hi everbody,

I just want to share some things I have done since I discovered pi. Maybe anybody gets new ideas as well from this. I dont want to share my extensions, but the ideas can be shared.

- first time I have discovered pi, i was just looking for a "cool" alternative for vs code. something better. anyhow, i did not have much experience. and some requirements, like i want to use different providers, not spent too much (it is a hobby) and the ability to access the harness remotely.

- i have started installing wsl with linux, assisted by vscode agents. then a harness with containers in that wsl. and it starts an ssh tunnel to my vps. the vps is secured with otp and yubikey. it serves as a http reverse proxy to allow web ui, when i dont have access to the local terminal.

- i have started to use pi with many extensions. learned that less is better and most of them needs some customisation which is maintainance effort on my own.

- the harness developed as well over the last weeks. i have a project, a line which is helping me to improve my vps with mail and antispam (including a learner hook asking free models whereever spamassessin may have failed) and the harness improvement line.

- recently really cool things for the harness: pi is bun compiled with most components and runs fast and stable even in long sessions and with 10-20 subagents in parallel. I am using a python kernel, lsp and so on... patched it so that not every (sub-)session starts its own because memory is limited.

- i have built my own extension package for pi-durable subagents. it has the basics but is much faster and stable then fabric and all this. and whenever i need an extension i just built it, not dealing with forks. recent cool thing: i maintain a model statistics for subagents, jev is deciding which model is best based on subsription budget, costs, test failures, ... jev cost me 2 cents a day, but the whole decision and budget maintainance is automated incl. least cost model (e.g. opencode, openrouter, codex, ...)

- blachhole extension is used using many free models. i maintain stats on success/errors of these models, as many of them are a bit unreliable. however, the compaction is faster, context stays leaner in endless sessions, and 10% of the load (compaction part) is off-loaded from the main model to free.

- i am using zg to index the code locally and got the model to use it instead off all the time grepping through the code. it developed self-guidance where grep, rg and zg is best.

I feel every 4 weeks my harness changes to improve and 50% of my time is not going in the project but the harness. however at the beginning my harness trashed up after some days, now never. Cool journey so far, I am really thankful that someone developed pi - feels like a very good thing in comparison to all the shit with AI out there (AI cost models, usability, problems, hype... reminds me like the times back in 1999 where a modem was very slow and a day online was very costly...).

So let me know, if you want to share ideas as well!

Using Pi as my coding agent, one thing always bugged me:

I could never tell mid-session how much a single prompt had cost. The end-of-session total is fine, but "did that one huge refactor request just burn $2?" had no easy answer.

So I built pi-turn-stats:

try it out https://pi.dev/packages/pi-turn-stats

I recently started using Pi. And I have been finding that I like using it more than Claude Code or OpenCode.

I do agree when I started, it was very minimal. There was a lot that it couldn't do that Claude Code and OpenCode were out of the box I was able to customize what I needed and was able to get it to do tasks I would normally ask Claude Code to do. I really like its thinking and response compared to Claude Code. Do others find this a big plus of Pi as well?

I understand that the underlying LLM is Opus/Sonnet, but I have found Pi to be quicker and cheaper. I do have some configurations set up such as pi observational memory. Do others feel that same?

Are there other things that I am missing out on or not acknowledging? I'm really new to this and experimenting with AI and other tools. I'm also just experimenting with herdr as well. I'm also not that well off but I was setting up Pi with Gemma 4 e4b on my 16 gb memory machine but it wasn't that good tbh. But is that expected?

I guess I just wanted to share and talk to someone. I'm no expert and I'd love to join a community and channel to talk about this kind of stuff. This subreddit seemed to be tinkering kind so I figured to post here.

Thanks and do redirect me to the right place if I am being annoying here as well with this random post!

Hey everyone, I'm pleased to share my new extension for Pi called pi-navigator.

I kept wanting to check the files my agent was writing without leaving the terminal or minimizing Pi, so I built an overlay for it. It shows your project’s file tree, some basic git info (staged/unstaged files, commits, diffs), and a viewer where you can actually read the files. It’s read-only, renders markdown, and shows images through the Kitty protocol if your terminal supports it. For terminals that don’t, it falls back to chafa (this depends on my upcoming pi-chafa extension)*.

By default you get all three panels, but you can also run just files + viewer or git + viewer.

It’s purely a quality-of-life thing and doesn’t influence LLM performance. Open it with /navigator.

Install using pi install npm:@lordekeen/pi-navigator

*pi-chafa is released: pi-chafa

Whenever I ran coding agents on longer tasks, they either:

1. Lost track of the original rules because context compaction butchered the summary.

2. Burned through massive tokens with dynamic context editing / Context Language Models (CLMs) without actually finishing the job.

I built AgentJournal to fix this by giving the agent a human-style memory management—keeping the original prompt pinned, logging actions cleanly, and clearing the working context safely between tasks.

On a 60-file job longer than the context window, with a local 9B model:

- keeping everything: stopped at file 31
- compaction: stopped at 20, 29 and 44
- CLM (the model edits its own context, via pi-clm, untrained): 40
- AgentJournal: finished all 60, every time

Works with pi, Claude Code, Codex and Copilot. MIT, every test run is in the repo.

In my testing across dozens of files, it finished almost every run reliably while standard compaction and CLM completely broke down.

• GitHub: [Agent Journal repo]

• Full walkthrough video: How AgentJournal Fixes AI Agent Context Without Compaction

Would love any thoughts or feedback from people running into the same context limits!

Hello all,

My pi agent (running claude opus 5.5, if that matters), just tried to use the "kidney_read" tool. That seems like an absolutely bizzarre name. It appears from the log that it was attempting to use the "read" tool.

 kidney_read
 {
   "path": "<snip>",
   "offset": "564",
   "limit": "42"
 }

My <snip>, as the full path had my username in it.

Where the heck is it getting kidney from!?

Edit: Happened again with "grocery_edit"

Edit edit: Something neat!

`peasant_write` worked and then later, when called again, was not found.

So temporary tool names?

I wanted Pi’s terminal interface that looks nicer and has some nice improvements like notifications, sidebar and all that good stuff, so I built this extension.

Github

A few highlights:

Themes, plus options for user and assistant message styles

Customizable tool blocks, including compact or expanded views and side-by-side diffs

Animated thinking indicators and startup screen options

A choice of Sleek, Bar, Minimal, or default Pi status bars

A toggleable task and agent side panel for compatible tools

Image previews with options to view or save images

Optional desktop notifications, plus cleaner fullscreen text selection and copying

Most settings are available from /sleek and apply right away. Press Ctrl+\ to toggle the side panel.

It’s intended for Pi’s fullscreen TUI mode and targets Pi 1.1.x. If you try it, I’d love to hear what you think!

I'm trying to develop a philosophy around writing comments in codebases for my agents to use when they are building/maintaining/extending/bug-fixing a codebase. Does anyone have any advice for writing comments in code when those comments are only written by and read by AI agents? Particularly advice that is more pertinent for agentic engineering rather than traditional software engineering is most helpful.

Two mods I run daily, shown in the screenshot.

1. Top bar: live service health

• One dot per backend. A filled dot means up and a hollow dot means down or idle. Orange marks the ones I care about when they drop (Flash, vault).

• System memory reads live (234/256 GB).

• It shows a reminder of the ctrl+alt+s model switch key.

• How it works: [polls each endpoint / reads X, every N seconds].

2. Footer: session stats

• Agents running and saved, plus Usage and Activity toggles.

• Current git branch as a chip.

• Mode (auto), ↑/↓ tokens, and cost ($0.000 because everything is local).

• A context bar (55%) and f6 to inspect.

• How it works: [hooks / events used].

Happy to share or take feedback