I've seen a couple of power users already switching to Pi [1], and I'm considering that too. The premise is very appealing:

- Minimal, configurable context - including system prompts [2]

- Minimal and extensible tools; for example, todo tasks [3]

- No built-in MCP support; extensions exist [4]. I'd rather use mcporter [5]

Full control over context is a high-leverage capability. If you're aware of the many limitations of AI agents (in-context retrieval limits [6], context rot [7], etc.), you'd truly appreciate Pi lets you tune context for optimal performance.

It's clearly not for everyone, but I can see how powerful it can be.

---

[1] https://lucumr.pocoo.org/2026/1/31/pi/

[2] https://github.com/badlogic/pi-mono/tree/main/packages/codin...

[3] https://github.com/mitsuhiko/agent-stuff/blob/main/pi-extens...

[4] https://github.com/nicobailon/pi-mcp-adapter

[5] https://github.com/steipete/mcporter

[6] https://github.com/gkamradt/LLMTest_NeedleInAHaystack

[7] https://research.trychroma.com/context-rot

> If you look at the security measures in other coding agents, they're mostly security theater. As soon as your agent can write code and run code, it's pretty much game over.

At least for Codex, the agent runs commands inside an OS-provided sandbox (Seatbelt on macOS, and other stuff on other platforms). It does not end up "making the agent mostly useless".

Pi has probably the best architecture and being written in Javascript it is well positioned to use the browser sandbox architecture that I think is the future for ai agents.

I only wish the author changed his stance on vendor extensions: https://github.com/badlogic/pi-mono/discussions/254

I dont know how to feel about being the only one refusing to run yolo mode until the tooling is there, which is still about 6 months away for my setup. Am I years behind everyone else by then? You can get pretty far without completely giving in. Agents really dont need to execute that many arbitrary commands. linting, search, edit, web access should all be bespoke tools integrated into the permission and sandbox system. agents should not even be allowed to start and stop applications that support dev mode, they edit files, can test and get the logs what else would they need to do? especially as the amount of external dependencies that make sense goes to a handful you can without headache approve every new one. If your runtime supports sandboxing and permissions like deno or workerd this adds an initial layer of defense.

This makes it even more baffling why anthropic went with bun, a runtime without any sandboxing or security architecture and will rely in apple seatbelt alone?

Being minimalist is real power these days as everything around us keeps shoving features in our face every week with a million tricks and gimmicks to learn. Something minimalist like this is honestly a breath of fresh air!

The YOLO mode is also good, but having a small ‘baby setting mode’ that’s not full-blown system access would make sense for basic security. Just a sensible layer of "pls don't blow my machine" without killing the freedom :)

I did something similar in Python, in case people want to see a slightly different perspective (I was aiming for a minimal agent library with built-in tools, similar to the Claude Agent SDK):

https://github.com/NTT123/nano-agent

Glad to see more people doing this!

I built on ADK (Agent Development Kit), which comes with many of the features discussed in the post.

Building a full, custom agent setup is surprisingly easy and a great learning experience for this transformational technology. Getting into instruction and tool crafting was where I found the most ROI.

Can I replace Vercel’s AI SDK with Pi’s equivalent?

Armin Ronacher wrote a good piece about why he uses Pi here: https://lucumr.pocoo.org/2026/1/31/pi/

I hadn't realized that Pi is the agent harness used by OpenClaw.

I always wonder what type of moat systems / business like these have

edit: referring to Anthropic and the like

An excellent piece of writing.

One thing I do find is that subagents are helpful for performance -- offloading tasks to smaller models (gpt-oss specifically for me) gets data to the bigger model quicker.

>The only way you could prevent exfiltration of data would be to cut off all network access for the execution environment the agent runs in

You can sandbox off the data.

"Also, it [Claude Code] flickers" - it does, doesn't it? Why?.. Did it vibe code itself so badly that this is hopeless to fix?..

As a user of a minimal, opinionated agent (https://exe.dev) I've observed at least 80% of this article's findings myself.

Small and observable is excellent.

Letting your agent read traces of other sessions is an interesting method of context trimming.

Especially, "always Yolo" and "no background tasks". The LLM can manage Unix processes just fine with bash (e.g. ps, lsof, kill), and if you want you can remind it to use systemd, and it will. (It even does it without rolling it's eyes, which I normally do when forced to deal with systemd.)

Something he didn't mention is git: talk to your agent a commit at a time. Recently I had a colleague check in his minimal, broken PoC on a new branch with the commit message "work in progress". We pointed the agent at the branch and said, "finish the feature we started" and it nailed it in one shot. No context whatsoever other than "draw the rest of the f'ing owl" and it just.... did it. Fascinating.