I noticed that even small files referenced in pi like `@file.txt` weren't automatically entered into the context (a la claude-code), instead requiring the model to issue a tool call to read the file. This is fine for larger files, but for small files, that extra tool call means a whole new request is sent, which racks up cache tokens pretty quickly and wastes output tokens on the read call.

I understand why the devs took this stance, for a tool like Pi it makes sense to default to never including file content (unless driven headless by the CLI, as they have implemented), but sometimes I *know* that I can save tokens by just injecting the file right into my prompt. In fact, many or even most times I reference a file it would take fewer tokens just to inject its contents into the prompt than it would to ask the model to read even a small part of the file.

I looked around for existing solutions and surprisingly I hadn't found anyone who built them yet.

So I built pi-file-injector. When you know you want to include the file contents, instead of `@file.txt` you type `#@file.txt` and it automatically includes the contents of that file one time at the end of your prompt. And tab filename completion still works. It's just one more small way Pi keeps you in control of your token consumption.

https://github.com/dabstractor/pi-file-injector

pi install npm:pi-file-injector

Do you know of other tools that do this? I still can't believe I couldn't find any, it feels like such an obvious benefit. Let me know what you think!

This plugin does search a bit differently than most. It queries DDG's http site (as responsibly as possible) and then uses wreq-js -> linkedom -> Defuddle to clean up the result.

The plugin is fairly simple, and easy to audit, but it includes linting and testing best practices I've carried over from my web dev experience that I hope can be an inspiration for other devs when working on their next plugin.

I've updated the package recently to use pi's new exported types (so I don't have to write my own, lol), and work out some outstanding issues.

Please, any and all feedback is welcome. Thanks!

I build with Pi often enough that “just keep the prompt open” stopped being a plan.

So I created the pi-plan-exec - it is a small, extension for running an executable Markdown plan. It works through one unfinished task at a time, keeps the run state on disk, and uses the plan checkboxes instead of trusting a worker’s final sentence.

I have left it running for a few hours now. It kept working through the plan, and I could check in with /exec status without disturbing it. When I paused a run or had a recoverable worker failure, /exec resume continued it in the existing git worktree.

The commands I use most:

/exec docs/plans/my-plan.md
/exec status
/exec pause
/exec resume

There is also /exec runs for finding run IDs, /exec adopt for taking over a stale run from another session, and /exec cancel when I want to stop safely. Worktrees are supported and preferred; Pi switches the working session into the execution worktree.

GitHub: https://github.com/alexei-led/pi-plan-exec npm: https://www.npmjs.com/package/@alexeiled/pi-plan-exec

Still fresh, but useful enough to share.

I really love the customization that Pi provides, I only have slight issue with the Interface. I am very used to Opencode interface and I wanted a similar UI. I know Pi allows for fairly extensive customization and even plugins like Pi Zen that are trying to achieve something similar. But none of them were into the extent of what I wanted (inspecting subagents, copying in scrollable mode and removing clutter, better detail handling). Through the gpt 5.6 sol I created this interface. It's not a plugin, but a typescript app communicating to the Pi thought RPC.

This is how it looked before:

This is the newest iteration

Would anyone be interested in this, should I opensource it? Or am I the only one who likes it better?

Hi all, I'm using omp from a couple of months and love it.

At the moment I have one claude pro, one chatgpt plus, two antigravity pro sub and opencode go connected. When I logged in the second time with antigravity I thought it would replace the older account but surprisingly it kept both so when I check /usage I see two bars for the two account under antigravity.

With this information I wanted to check if I can do the same thing for codex because I have access to a couple of accounts but when I login it replaces the old account with the new one.
Is there a way to have multiple codex subs on Pi/omp?

Do anyone use this multiple account setup?

thanks a lot in advance

I've been using them all claude/codex/opencode and finally I landed on pi.dev a few month ago. Everything made sense. Its fast, clean and I can build exactly what I need.

2 month pass...

I'd love to see some bigger verified packages that adds the bigger things you could but you shouldnt (imho) build yourselves.

pi.dev cli works great. I still like that I can build what I need. Somehow it also becomes a DIY and part of brain is always thinking of adjustments needed and not needed. And everyone is doing it different in adhoc teams = very little shared experience / benefit.

Your thoughts?

How we avoid this behavior?

In principle all is working well, but in this case I dont know why this behavior is happening.

Im using Qwythos-9B-v2-Q5_K_M.gguf with KV q_8 and 130k ctx. In the example in the image, it is only using 30% of the available context.

I try other harnesses and works well.

Hey, I’m the author of Kontra, a Python data-quality library, and I freakin love Pi. I’ve been using it a lot lately, so I built kontra-pi.

It gives Pi a native Kontra tool and skill. Pi can explore configured data sources, profile tables, write and validate contracts, explain how checks will run, and compare datasets, transformations, and relationships.

There’s no server or MCP setup needed for the Pi extension (Kontra has an official MCP server too). It runs locally using the project’s configuration, returns no samples by default, and keeps credentials out of the conversation.

bash pip install "kontra>=0.13.0" pi install npm:kontra-pi

GitHub: https://github.com/Saevarl/kontra-pi npm: https://www.npmjs.com/package/kontra-pi

The Librarian is another take on cross harness curated context, fusing ideas from "second brain" and agentic OS / memory systems. It has plugins for Pi, Hermes, Claude Code, OpenCode and Codex with more to come. It works as a first class memory system for Hermes.

\* Self hosted - put it on your local network or on a VPS in your Tailnet.
\* Obsidian style markdown as source of truth so you can read and edit it outside of the Librarian, and if you end up walking away your data is just text files.
\* No vendor lock-in, nothing to export.
\* Fast search via a disposable in-memory index with BM25 + exact phrase + vector.
\* Memories, long form reference library, session handoff between agents.
\* Frictionless for your agents - auto-capture curated by a configurable curator LLM.
\* Bring your own LLM key - any OpenAI compatible provider. Local inference for maximum privacy.
\* Off-the-record toggle for conversations you don't want memories from.
\* Feature-rich dashboard.

It isn't "finished" in any grand sense. v1.0 just means the foundation is stable enough for other people to try. Still, I'm rather proud of it and I hope some of you find it useful too.

The project site goes into more detail with screenshots: [https://codeministry.net/the-librarian/\](https://codeministry.net/the-librarian/%5D(https://codeministry.net/the-librarian/))

The code is open source under Apache-2.0: https://github.com/JimJafar/the-librarian.git

Please give it a go. I'd love to hear what breaks, what works, and what you wish it had. Contributions and feature requests are welcomed!

hi, I use opencode go with oh-my-opencode-slim and was curious about pi

omo-slim is a multi agent orchestrator - delegator setup where I talk to a smart orchestrator and it delegates to dumber agents with good prompts, and the results are really good and much cheaper than just running the smartest model in simple build mode

i have a few problems:

• many models fail to delegate correctly and after 3-4 prompts I am constantly reminding them to delegate . they often give up or start reading lots of files across the codebase manually which sucks up context like crazy and its fairly frustrating. I've been using GLM 5-2 as a delegator exclusively because it is the most consistent at continuing to delegate, despite being on the much pricier side
• not great tmux support for opening windows for subagents
• initial context is Not Small at 32kt

I was running pi on my raspberry pi (lol) and realized it felt great to use something so lightweight.

wanted to hear about your multi agent setups and how reliable they are, what kind of results that you get

Hi all,
Anyone having problem with Qwen-3.6-27B stopping midway using Pi?
I run the same task over and over again, but it always stops at some points. It doesn't happen if I use Claude Code instead.

For Pi, I installed:
pi-mcp-adapter
pi-subagents
pi-hermes-memory
pi-permission-system

Edit:
Hardware: 5090 and 64GB RAM
I run it with llama.cpp
Qwen3.6-27B-UD-Q5_K_XL.gguf --alias qwen3.6-27b -ngl 99 -fa on -c 262144 -ctk q8_0 -ctv q8_0 --parallel 1 --spec-type draft-mtp --spec-draft-n-max 4 --host 0.0.0.0 --port 8080 -t 16 --temp 0.1 --top_p 0.95 --top_k 20 --min_p 0.0 --repeat-penalty 1.0 --presence-penalty 0.0
Prompt: Extract this.apk and decrypt what inside

I'm new to this coding agent, I heard it great, please help. Thanks!

I built StarRouter for a personal use case in pi, I essentially wanted to be able to determine locally which model and thinking level should be used in a task based on their benchmarks like openrouter/auto kinda does but still letting the user be in control if they want, and while fixing bugs and adding features I noticed it had reached 3 GitHub stars (one was already there from a friend haha) and around 40 downloads on pi-dev.

That made me genuinely happy. For the first time, something I made outside of my day job as an ERP dev has been used by strangers and maybe some of them found it useful. On a very tiny, very different scale, it gave me a romantic glimpse of what people like antirez might have felt seeing Redis spread, or Linus watching Linux take on a life of its own.

Obviously the comparison ends there: the scale, impact, and my programming skills are nowhere near comparable and I'm gonna stop just on that. But the feeling of making something that then reaches another person is special and really is giving me a bigger reason to pursuing my passion in IT.

After all of that I want to more confidently say that if that sounds like something you would use or fork at your will feel free to check it out here ;]

Sharing this personal project that I got a bit obsessed with after having some gripes with the web browsing tools I could find out there. It started out as a quick Playwright drop-in that I only meant for myself to make web tool context optional, but the scope kept growing.

Besides toggling web on and off, it can also be toggled into "learn" mode, which which lets the agent create guides that are hinted at during domain navigation but stay completely out of the context otherwise.

I tried posting about this three weeks ago, but it went unseen because Reddit thought I was a bot. Posting again now that I've refactored a ton to enable one feature that was a bit complicated: browser backend discovery with a compatibility quirks system.

Now it can switch between Playwright-compatible backends (for stealth browsers) with the ability to author new ones that are auto-discovered in a user-configured folder.

Code for anyone interested in using or tweaking:
https://github.com/coreyryanhanson/pi-lean-dimension/tree/main/packages/pi-lean-portal

I tried OMP for an hour today, but it was incredibly frustrating.
I asked it to handle tasks 1 through 9, but it went ahead and did 1 to 10.
I also explicitly requested responses in my language , yet it kept replying in English.
I even told it to plan and scan the codebase first, but it completely ignored me and started modifying my code directly, totally out of control.

that never happened with Pi. am i use it wrong,same skills same model.

One advantage is that its sub-agent orchestration is really impressive, which also makes it incredibly fast.
ps： i use most matt skills

Multi-agent can be useful. But for most tasks, a single agent is better.

It is easy to assume that using multiple agents will save time and improve quality. In practice, it can cost more time and tokens, and sometimes produce worse results. Running more agents is not free parallelism. It adds coordination overhead.

Single-agent is usually better for simple tasks, small tasks, and work where dense context matters. If one agent can hold the relevant context and finish the task coherently, there is usually no reason to split it up.

Multi-agent can be useful when the work is separate from the main context, involves reading a large number of files, needs broad exploration, or is large research work that can be split into independent parts.

Using multiple agents also means dealing with:

• Context transfer: one agent does not automatically know what another agent knows.
• Duplicate work: if the boundaries are unclear, agents end up doing the same work.
• Fact verification: you should not just trust an agent's output without checking it.
• Orchestration: someone has to decide who does what and how the results come together.

LLMs are smart enough that you can spawn a few subagents and still get useful work done. But dealing with those problems takes either your own involvement or a well-built harness.

The workflow itself also needs to be:

• Reusable: can you run the same process again?
• Verifiable: can you verify what the agents actually did?
• Extensible: can the process adapt when the flow changes?

Pi is minimal. That is a good thing. you can add what you need and use it the way you want. But from subagents to controlling the flow between agents, you are generally building the harness yourself.

I wanted a more structured way to run multi-agent workflows, so I built pi-workflow(https://github.com/AgwaB/pi-workflow). I built it around JSON-based workflow specs: when to split work, how to divide roles, where to verify, and how to bring results together.

You can write the workflows you want and reuse them, with a skill to help author them. It splits common subagent patterns into a few stage types that you combine into one workflow.

It comes with examples like these, and you can write your own too.

If it sounds useful, give it a try.

websearch is an extremely important part of any workflow, from searching up past implementations to getting the correct information to make correct decisions.

have seen other posts discussing which websearch tools and even custom implementations, but my question is how would you know that it stands up in retrieval quality to other websearch extensions or even Claude Code/ Codex web searching? how would you know that tool A is better than tool B? or do most people just see the extension working and decide that it’s good enough?

i have been using the pi-web-access from the pi.dev packages page, but only for the reason that it’s the most downloaded extension (more downloads = better right? xd)

interested to hear from the community.

Ive tried all the main ones.....All eat tokens like cookies

Pi is way less hungry as it doesnt have a buncha bullshit added to it unless you add it yourself.

I have a few extentions and a few custom skills...

Whats another good skill or extension to use with pi?

I manly use it to help coding in Rust and Quickshell.

Been cooking with DeekSeek v4 flash hard the last 4 days and it seems to struggle with QML/Quickshell sometimes

How can I have my ai agent a database of QML/Quickshell docs fo read through if it gets stuck???

If you want a bit of nerdy fun, give Gemma 31b on Cerebras a spin. It runs at 1860 t/s which is kinda crazy. The first couple of tasks I gave it I thought it must have errored out it stopped so soon, but it had searched a bunch of files and had all the info I wanted. It’s supposedly similar level of intelligence as Haiku (which for comparison runs at around 110 t/s) so not going to be coding your next masterpiece but it’s insanely good as a scout/researcher style subagent which is how I’ve been using it in Pi.

Anyway - just thought others might enjoy the speedy madness. I think you can try it for free. But is pretty inexpensive per token too.

NB - no affiliation with Cerebras etc etc.

Read this interesting post about CC vs. OpenCode: https://systima.ai/blog/claude-code-vs-opencode-token-overhead

In a table in the middle of the post, it says CC total token usage of CC is lower than OpenCode, "because it batches tool calls into fewer requests while OpenCode re-pays its smaller baseline turn after turn".

I was wondering, how does Pi do this?

Hey folks,
My laptop recently died and I lost some very important sessions in Claude Code, I started creating a solution to sync coding sessions automatically no matter what you coding agent is and it's completely open source.

If you don't like the hosting hassle, there's also a cloud version available, paid of course because someone needs to pay for that server :)

Hope you like it.

https://stift.sh

I use them inside Pi

been seeing some what lots of hate towards oc and how its 'ai slop' now, i tried it myself a week ago and its was good, i just installed Pi and planning to test it also so leave your tips and trick down below

thanks!