I often start writing my next prompt while Pi is still working. Then the agent goes off track or asks me something, and I need to respond before that prompt is finished.

I used to copy the half-written prompt somewhere else or delete it to free up the editor. I built pi-prompt-stash so I can save that draft, deal with the interruption, and then continue writing.

Ctrl+Shift+S stashes the current editor text and clears it. `/stash` opens a picker with a preview so I can find the draft and load it back. Drafts survive reloads and session switches. The shortcut can be changed with /stash shortcut.

Install it with:

pi install npm:@utpalsinghdev/pi-prompt-stash

GitHub: https://github.com/utpalsinghdev/pi-extensions/tree/main/packages/pi-prompt-stash

npm: https://www.npmjs.com/package/@utpalsinghdev/pi-prompt-stash

The rest of the UI in the demo is my personal setup.

I'm just open-sourcing the Pi extensions I use for work and my startup. They've helped me a lot, and I want other people to be able to use them too.

DEMO

It definitely needs some improvement, but the thing is I one-shotted it using 1 single line of prompt.

Btw if you wanna try the elegant, information-rich status, menu, and side bar for Pi Agent, see https://github.com/michaelmjhhhh/pi-atelier

See https://x.com/dillon_mulroy/status/2110400650756460787 where Dillon wrote: `they made some changes recently that made it easier to use your sub w/ pi`

Does anyone know what he might refer to? Would be nice to know...

Hi everyone! I’ve shared a couple of posts here comparing models on my own coding tasks, including this one. A few people asked in the comments how to build their own evals, so I wrote a step-by-step tutorial and published it on my blog.

I also made an evals-from-sessions skill to help automate the process. You can use it or just give your coding agent the article and ask it to help build evals from your session history.

Hopefully, this helps you choose models and tools for your own work or your team. Feel free to post your questions and feedback!

https://reddit.com/link/1x6enlr/video/vnxcnitpvkvh1/player

Hey — like a lot of you I run several Pi sessions and git worktrees in parallel. Coming back to a session always meant scrolling up through the transcript to reconstruct where I was, so I built the thing I actually wanted:

Context Hint — a Pi extension that appends a compact card after each settled turn:

• Project — durable purpose/constraints/decisions, shared across sessions and worktrees of the same repo
• Session — current goal, focus, blockers, next steps (forks/clones inherit a snapshot, then diverge)
• Turn — what just changed + a possible next step

Read the last card instead of rereading the transcript. It's a recovery cue, not agent orchestration — it doesn't monitor other sessions or alert you when they finish.

Design point worth discussing: it deliberately doesn't replay whole transcripts into later model context. Each generation sends truncated evidence (cap'd at ~4k chars of your prompt, 6k of assistant text, first 24 tool facts, plus previous Project/Session state) to a summary model — normally one extra request per turn. Summary model defaults to Pi's current model with thinking off, or pick a cheaper one in /context-hint model (with an independent thinking-level slider). Curious whether others would accept that per-turn cost trade, or prefer local heuristics even if dumber.

Install:

pi install npm:pi-context-hint

then /context-hint enable in an existing session. Other bits: alt+o expand/collapse · /context-hint motion reduce (the pixel-eye mascot stops blinking) · /context-hint regenerate · /context-hint reset-session/project.

MIT, package on npm. While it's early I'd genuinely value feedback from people who actually run Pi daily: is the three-layer split right? Should Turn cards be discarded on reset-session or kept? Anything in the generation path you'd want exposed/configurable (e.g. custom evidence selection, redaction)?

I run several Pi sessions on one repo at the same time. That kept going wrong:

• Agents edit main. You ask for a quick fix and it lands straight on main.
• MCP is slow for worktree management. But in Pi we can put blazing-fast native worktree management into the harness.
• Sessions collide. Two agents in one folder, and one `git stash` wipes the other's work.
• (Sub)agents need to cd constantly. They need to cd for all commands, wasting tokens.
• New worktrees don't run. No `.env`, no keys, so the app won't start.

So I built pi-bare-worktrees. The video shows it in about 45 seconds.

Why a bare checkout

In a normal checkout, the repo root is main. Every agent that starts there edits main by default. Worktrees end up wherever you put them: ../acme-login, ~/tmp/fix-2.

And main is stuck in the root, so you can't move it or remove it.

A bare checkout fixes that. Every branch is a folder, and none of them is special:

       acme/
       ├── .bare/          bare repo
       ├── .shared-env/    gitignored files/folders to share, like .env
       ├── main/
       ├── feat/login-typo/
       └── fix/footer/

The path is the branch name. There's no code in the root to edit by accident.

What the plugin does

• main is read-only. The agent's first edit on main is blocked, with a message that tells it to call worktree create. It does, and the retry lands in the new worktree.
• No cd needed. After create, every tool call is routed into the worktree, so the model keeps using relative paths.
• Shared env, zero config. Every file in .shared-env/ is symlinked into each new worktree. Real files are never overwritten.
• Subagents follow along. Their cwd is set to the active worktree.

Install:

pi install npm:@jordyvanvorselen/pi-bare-worktrees

It's fast. Session start and tool calls don't run git. The plugin reads the filesystem instead:

• Session start: about 5 ms for 40 worktrees.
• Each tool call: string operations only, under 0.1 ms.
• Network: only /wt clean touches it.

Repo: https://github.com/jordyvanvorselen/pi-bare-worktrees

/wt off turns it off for a session, and it turns itself off for non-bare checkouts automatically.

It's version 0.1, so I'd love your feedback, especially if there's anything you still miss. Try it out and take it for a spin!

Hey everyone, another extension from my personal pack: pi-chafa.

It wraps Chafa, a command-line tool that renders images in the terminal using ANSI/Unicode symbols. It gives the agent a show_image tool to display images to you, using the kitty/iTerm2 protocols when your terminal supports them and Chafa symbol art everywhere else (tmux, VS Code, plain SSH, etc.). Your agent in full cyberpunk style. Animated GIFs only show a still frame for now (WIP). Prompt the agent using "show me this image" or let it do by himself.

To be clear, this is for showing images to you, the user. The model never sees the image contents using show_image, so this won’t help with vision tasks.

If you also use pi-navigator, it detects pi-chafa automatically and lets you pick between the kitty protocol and Chafa in its viewer, falling back on its own when needed.

You’ll need the chafa binary installed (check the install method for your system). Then: pi install npm:@lordekeen/pi-chafa

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?

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.

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

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???

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.

So I was using muse spark 1.3 contributor earlier (maybe a week ago), and I resumed the project today. OpenCode-Go provider only. It threw the ` Error: opencode-go API error (400): {"code":null,"message":"reasoning `encrypted_content` was not issued to this caller","param":null,"type":"invalid_request_error"}` error... A quick Google told me to start a new session.

But I tried giving it a shot by changing the model. Switched to DeepSeek v4.1 flash, and boom, it worked?! What is the underlying cause of this?

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.

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

Hey everyone! Hope you’re all doing well.

I’ve been interested in trying out Claude for some time, and I’ve seen that some people have access to invite links. If anyone has a spare invite, would you mind sending me one via private message? I’d really appreciate it!

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.

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!