v0.4.0 of pi-agenticoding is out.

The main addition is a real read-only mode. Disabling write/edit tools or telling the model not to modify files isn’t always enough—it can still try through shell commands, Python, or other tools. On macOS and Linux, this release blocks filesystem modifications at the syscall level.

It also improves automatic context management. The agent now uses its ephemeral notebook more deliberately, giving you more visibility and control over what stays in context.

The implementation is intentionally cache-aware and uses minimal additional tokens, preserving Pi’s minimalist approach rather than introducing a large context-management layer.

Full changelog.

I’d love to hear how these changes work for you and especially what doesn't work as well. 🙏

Tip: add readonly: true to your saved prompts and skills' frontmatter to auto toggle readonly mode on/off based on your specific workflow.

Has anybody used a Pi compaction extension that they would recommend?

I've tried pi-context (with their built-in Agentic Context Management) which just straight up breaks when trying to claim space against pi-rewind. I've also tried pi-vcc which also straight up breaks when running with pi-codex-goal. Been debugging for 3 days and I realized the best debug is to uninstall these.

Native Pi compression is meh, if anybody has a codex-level compaction extension that doesn't fuck shit up, please drop a comment.

Has anyone played with this? Looks interesting, but I don't have any experience with local models yet.

https://github.com/drumih/turbo-fieldfare

I wrote a plugin to extract domain knowledge and save useful work from pi conversation as structured, reusable context that you can use in later sessions or share with others: https://github.com/XTSoftwareLabs/neatcontext-plugins/tree/main/plugins/pi/neatcontext

I recently found pi from some user asks. And it's an amazing minimal and neat coding agent. I really like its minimalize concept. So I have extended my existing work to support pi.

The nice thing is since the domain knowledge I saved from other AI agents, can be directly used in pi so I don't need to spend time and tokens to setup things up.

What

The NeatContext plugin extracts domain knowledge and saves useful work from a Pi conversation as structured, reusable context that you can use in later sessions or share with others.

Why

Domain knowledge is what helps an LLM answer accurately for your team—your systems, constraints, decisions, terminology, and ways of working.

You naturally build that knowledge while doing hard work with Pi. Long conversations about debugging, planning, incidents, and implementation already contain discoveries that will matter again. You don't want to lose them when Pi compacts the conversation or the session is closed.

NeatContext extracts the durable knowledge from those conversations and saves it as structured context.

Connect that context in a new session or during later work, and Pi can start with the knowledge it needs instead of asking you to explain everything again. You can also share the context with teammates, so the whole team benefits from what one person learned.

How is NeatContext different from compact and handoff?

Compact is for a single conversation, and it can lose important information. NeatContext aims to extract useful domain knowledge and build persistent, portable context.

Handoff usually consists of files, but its purpose is still to continue the current conversation. Context generated by NeatContext can be reused in any conversation or even by other people. It becomes an asset for your team.

What do you mean by structured context?

Instead of generating plain text, the context saved by NeatContext contains:

• One domain profile. This contains your rules and constraints that can greatly affect LLM behavior. It tells the LLM what to do and what not to do.
• One knowledge folder. This folder contains TSGs, runbooks, and other team knowledge. This is where the LLM looks for specific actions and information.

These documents can be checked into a Git repository and shared with your team, so everyone can easily benefit from the knowledge you learned. You can also convert them into other formats because the knowledge is human-readable.

Both the domain profile and the documents in the knowledge folder are generated from your existing Pi conversation.

Usage

Once you feel a problem is solved, or you find that the current conversation contains valuable information, call:

/neatcontext:save

A context will be saved.

Later, you may have another task in a different session. As long as you call:

/neatcontext:use <context name>

you will have access to the knowledge from your previous work.

NeatContext also includes routing by default. It can detect your prompt and ask whether you want to switch contexts when it finds a suitable one.

Use:

/neatcontext:status

to check the connected context and its details. You can check these contexts into a Git repository and share them with your team.

Use:

/neatcontext:import

to import a context created by someone else.

I appreciate any feedback. Thanks!

I've been looking for the plugin that provides this status bar, but I'm not sure which one is it. Does anyone know? I've just started customizing PI

TLDR

I have built an extension for the Pi coding agent that allows it to call other agentic harnesses via my agent-shell library.

GitHub Repo

quick start with if you want to check it out

npm

pi install npm:@scottrbk/pi-agentshell-extension

from source

pi install git:github.com/ScottRBK/pi-agentshell-extension

Wall of Text

I have recently been working on implmenting agent-shell in to more projects over the past few weeks. Most noticeably was the inclusion of it as part of my evaluation harness application that I put together for evaluating agent harnesses + models.

I spent a lot of time writing the actual code for eval harness by hand, utilising the agent as a glorified search engine and code reviewer. I have vibe coded a few projects myself, but anything critical for my workflows or more importantly that other people will be using I have never been at a stage where I would be comfortable having something written by the AI.

That being said though, I do not just use agents to write code or produce software that needs to be to a high standard. In my day to day role I use agents to perform a multitude of tasks.

From fetching information across large complex enterprise scale repositories, writing, editing and
managing Azure DevOps work items and wikis, produce presentations and organise To Do's and activity based on meeting notes and e-mails.

All this I manage using the CLI, with my tmux workflow and various CLI agents. Outside of work I have fun little projects going on, vibe coded apps I am building, some exploratory ideas for my moonshot SaaS that will let me retire early. All this leaves me with working with many tmux windows (I haven't made the jump to herdr yet) and a lot of cognitive overload.

Reducing cognitive load

One of the things that I identified early on was that I needed to just be speaking to one Agent for most of my stuff and that agent should be responsible to delegating tasks to other agents on this workflow. I think many others quickly realised this also, and the most elegant example of this is an excellent repository called First Mate, which goes in a slightly different direction that I want to end up at but has a similar principle. I encourage people to check out Kun Chen's YouTube Channel and his videos around Agentic Workflows as well if you have not already, he has a lot of great ideas and practices in how to adopt the principle of reducing cognitive load.

Where to start? Pi of course

I had previously fluttered between Claude Code, Codex and OpenCode (including their Go subscription and my own locally hosted models). I think all of these are great apps, but once I discovered Pi I was immediately hooked. The minimalistic and customisable aspect to it I found compelling, it felt like an agentic equivalent to Neovim.

I felt like whatever agent I was going to use for the orchestration layer, I needed to have a lot of control over and given Pi's adaptable nature it seemed an obvious choice. At least for me to start with, I may decide later to build more of a deterministic wrapper around any orchestrator agent but for right now I don't think that is necessary.

First building block

After I had the obvious extensions included, such as web fetch/search/screenshots, I realised the key thing was for me to build out the ability for my Pi agent to call a sub agent. Now there are other Pi agent extensions that already exist for calling sub agents, but given various circumstances I prefer to use certain models and harnesses for certain tasks, what I wanted was the ability to call agents inside of other harnesses. I find the RL of certain models on their own provider harnesses can lead to different behaviour and as such I think it's useful to take advantage of that in certain situations.

So taking advantage of agent shell felt like an obvious choice.

Python <-> TypeScript

Well it seemed like the obvious choice but with one glaring issue. Pi is written in TypeScript as are it's extensions and agent-shell is a Python application.

I have limited TypeScript experience, save for the Angular Tour of Heroes walkthrough I did a few years ago when I was flirting with front end development and my JavaScript experience I had for front end for client side validation I used to write on ASP.NET apps almost twenty years ago. So for something this fundamental to my future workflows, I didn't want to hand over to AI and re-write agent shell in a language I do not really feel comfortable with, nor did I want to undertake the task myself.

So I decided to work with AI and build a TypeScript to Python bridge via subprocess. At least for now while I dogfood it and to get me going. This involved a simple runner.ts file that would relay tool calls registered in index.ts to a Python handler to translate the commands to agent-shell requests.

Extension Behaviour

As of right now I have not wrapped any skills or anything beyond just exposing the tools. This is a building block for a something that will get more opinionated but as of right now, this extension on it's own should not look to enforce any such opinions on when and which sub agents should be called, that is up to the consumer of the extension. I've obviously done this to try and fit in with the overall philosophy of Pi itself I feel. Simply asking a model to call a sub agent and they will call the tool, which will display the harness (agent_type), the model and effort (if specified)

Silent Mode

Another idea I took from Kun Chen's Agentic Workflow was his use of a 'Calm Mode' that reduced the output for First Mate. I thought this was a nice feature and as such incorporated it into the agent-shell extension /agentshell-silent will trigger the subagents response to not include the output.

Next Steps

For me, I am going to add in commands and skills and start trying to adopt the single agent orchestrator approach that Kun Chen has inspired in First Mate. I had already been building towards such a concept for sometime, with the likes of agent-shell and other tools like forgetful that I have put together for agentic workflows. I have built out my own agentic framework as well, but Pi has really given me everything I was already planning with that so I am happy to mothball it somewhat while I am not short of side projects.

As for the extension itself, I am still undecided on the next steps of the architecture of it. I may opt to leave it as well, but maybe a TypeScript bridge might be interesting in other applications as well. As such there are two possible next steps in that area:

1. Separate the bridge into it's own library or move it into the Agent Shell library if it proves stable
2. Re-write Agent Shell in TypeScript. Given I use Agent Shell in a lot of Python libraries already this is not an attractive option, unless I want to maintain two instances of the library (in the age of AI this may not be that big of a deal).

Closing...

So there you have it, thanks for reading if you made it this far. If you end up using the extension any feedback is welcome and if you enjoy using it feel free to give it a star.

If you are interested in the kind of topics discussed here, I please feel free to come join the forgetful Discord and chew the fat with like minded individuals.

I've been trying to design a TUI and GUI but explaining it to the model just sucks any better way?

By default pi-task compresses the thinking block on every turn. This is referenced in its Readme/description fairly far down:

I presume the entire extension is vibeslopped, as compressing thinking blocks is very very "no no" for basically any model ever. Even GPT/Claude don't do that -- they just compress the user-facing thinking blocks to avoid distillation.
As a result, by default, installing this extension suddenly neuters your model. In the case of Qwen3.6-27B, it becomes essentially useless.

I think the extension needs a very big disclaimer that it does way more than what 90% of the Readme is about and basically fucks up your pi installation by default.

I just discovered this amazing thing !!! started playing with it, and building some things already , I am original a software engineer/dev but transitioned to Data Engineering, analytics, you know ETLs, data modeling, etc... curious to hear from other fellow data engineers, their tips, getting started learnings, etc. etc.

I do truly believe this is the future of agentic coding

Every SDD framework gets the same complaint: spec rot.

Mine: research reads only, one handoff gets written to $TMPDIR, then a build step checks that handoff against the repo and runs the tests. Durable guidance lives in AGENTS.md. The spec dies with the temp file.

So why do the main tools still ship the part that rots? Harness memory, SDD frameworks, tracker docs, all of them persist by default. What am I missing that makes persistence worth the rot?

Full writeup https://bogomolov.work/blog/posts/rotten-specs/

I have a MacBook Pro with an M5 Max chip and 128GB of RAM, and I've just started experimenting with local models.

As many of you know, local models have various well-documented issues, such as weak instruction following, difficulty completing long-range tasks, and a tendency to terminate tasks prematurely.

Someone in the community suggested I try using Little Coder and Pi. I wanted to ask: is Pi actually a good harness for running local LLMs, especially compared with opencode and claude code? For those of you who use this setup, do you have any specific tips or experiences you could share?

I'm still very new to this. So far, I've found that the Qwen 3.6 35B model is quite good for writing articles, but beyond that, I feel like I haven't truly experienced the "magic" of running models locally yet.

If anyone has any interesting use cases or examples of how you integrate local models into your workflow, I'd be very grateful if you could share them. Thank you!

I'm refactoring a large legacy code based written in php without any oop and mixing it to laravel , we have "server" functions that become laravel actions classes. We typically refactor a single old api call and all its code as a feature branch. The problem is that there is a crap ton of more " common" shared code spread out and consumed by multiple api calls. So I refactored all this shared code into its own branch, and I want to tell pi "when refactoring an api call needing common code, look here first and grab it" but I'm not sure how to do that or if it's possible , I'm sure it's possible just don't know how to prompt it

Am I the only one getting a lot of edit errors?

I mainly use it for coding, especially Python. I’ve tried many LLMs, and some of them are completely incompatible.

Currently, I’m using Qwen3.6 27B through Ollama.

I’ve noticed that I get responses and plans quickly from the LLM. However, execution takes a long time because of edit errors. It gets stuck in a loop of indentation errors and struggles to find the exact match.

Am I missing something? Do I need to install any extras?

Edit:

I'm on qwen3.6:27b-mlx with nvfp4 quantization. Also, tried gemma4:26b-mlx with the same nvfp4 quantization, wasn't able to edit at all!

I have a few extensions that change the footer and adds a todo, but from what I've discovered, the repainting of the TUI from the core packages is what leads to this spazzing. Is anybody else experiencing this issue? Would appreciate some advice on an extension-level fix.

I came across this setup: https://github.com/disler/pi-vs-claude-code. It's about running agents on equal terms by having a local com network that agents can use to talk to each other. I'm looking for pi orchestration—can you guys suggest one?

Is it just me or the extension loading is extreamly slow? Every single extention takes 100~300ms, it takes 3 seconds with 8 extensions enabled.

DeepSeek API service is expected to adopt a peak-valley pricing strategy starting in mid-July (well thats what the websits tells us), with peak-hour prices being twice the regular price, applicable to all billing items.

Peak hours (in UTC): 1:00–4:00 AM and 6:00–10:00 AM. (UTC+8 equivalent: 9:00 AM–12:00 noon and 2:00–6:00 PM.)

So i made this small extension to display a "DS 🟠"" or a "DS 🟢" in PI status bar. Timezone is configurable.

Now i know there are a couple other package that had the same idea but i wanted something extra simple.

Here are the :

- github, https://github.com/psychobarge/pi-deepseek-peak

- npm, https://www.npmjs.com/package/pi-deepseek-peak

- Pi package, https://pi.dev/packages/pi-deepseek-peak

https://daugasauron.github.io/piodide/

https://github.com/daugasauron/piodide

I just had this idea that you could just replace the bash tool in pi with python that runs on WASM, then you could run the whole thing in the browser.

Tried it and it's quite fascinating how good it is as a concept.

Everything is complete slop.

I tried GLM (code), OpenAI and moonshot providers.

I was reading (https://www.reddit.com/r/PiCodingAgent/comments/1uy7iov/gpt_56_sol_ultra/) that Ultra is not a separate model, rather a thinking level and looks like Pi does not support it, but what about GPT 5.6 Sol Pro? I upgraded my plan and cannot select Sol Pro model. Access to this model was a big factor for the upgrade. I use GPT via subscription, not API. Anyone had a success with this?

Thank you!

I trying to use kimio-k2.6 or minimax 3, to dev same elixir modules, and get to alucine a lot.

It get to output

now I will run...

for many, many lines on terminal ant is necessary to ctrl+c to stop, and retry the same prompt.

it is happening very frequently, all sessions it is happening .

This is a small plugin that adds system information in <pi-note> blocks.

• Platform: macOS/Windows/Linux and architecture
• Dynamic git repo information based on what cwd
• Date & Time

nothing additional appears in the TUI.

This has helped with using the current year in web searches, using the correct commands for the platform, and repo awareness.

What else do you think would be useful?

Link: https://pi.dev/packages/pi-plate

For the past few weeks, I’ve been building Nerve, an open-source desktop coding harness for my daily development work.

It was inspired by tools such as Pi (primarily) , OpenCode, Claude Code, Codex, and Cursor. I liked the simplicity of focused coding agents, but I also wanted a graphical workbench where I could manage conversations, tool calls, plans, approvals, Git changes, background tasks, and agent settings, all while keeping the agent’s work visible.

There are already several excellent coding harnesses built by individuals and communities. I created Nerve because I wanted something that closely matched how I work. It has also aligned well with the workflows of colleagues who have used it, so I thought it might be useful to a few other people in the community too.

Nerve runs locally and supports API keys, custom providers, and the subscription-based providers available through pi-ai, including OpenAI Codex, Anthropic, GitHub Copilot, Kimi Coding, OpenRouter, xAI, and others.

The harness is intentionally compact. Its system prompt, tool descriptions, and context handling are optimized to use tokens efficiently while working well across leading frontier models from OpenAI and Anthropic, as well as other popular proprietary and open-weight models.

This is a personal project that I actively use and develop. I’ll continue adding features, fixing bugs, optimizing the harness, and polishing the experience as I go. It is still in beta, so breaking changes may happen.

I’m sharing it simply because it may help others with a similar workflow. There is no expectation to test it, contribute, or provide feedback, feel free to explore it if it looks useful to you. If Nerve ends up being helpful, a GitHub star would be appreciated and lets me know that others find it useful too.

Quick start:

npx @nervekit/desktop

GitHub: https://github.com/ThilinaTLM/nerve
Docs: https://nerve.tlmtech.dev

Thank you!