And pi agent is even less.

The entire agent system prompt can be seen here:

https://github.com/earendil-works/pi/blob/main/packages%2Fco...

Maybe related to this minimalism, Pi doesn't come with most of the tools an LLM needs to function efficiently or effectively. I get that a blank slate is the paradigm, and you can add whatever you want, but it's too blank IMO.

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arcanemachiner 2 hours ago | parent | next [-]

I have a functional Pi config, mostly self-made (it has everything I want, incl. subagents, web search, a /btw command, and other misc. addons), and my system prompt is ~3k.

	▲	mft_ an hour ago \| parent [-]
		Would you mind sharing?

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bel8 2 hours ago | parent | prev | next [-]

It's easy to add using plugins.

What do you miss? I ask because I do some heavy work with pi + GLM 5.2 (using opencode Go subscription) and my workflow is plan -> implement.

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mft_ an hour ago | parent [-]

> It's easy to add using plugins.

Sure, but you have to add almost everything, no? It deliberately only comes with read, write, edit, and bash. My point wasn't that you can't add stuff, but that I'd just rather use an harness that's a bit more full featured from the start.

(Pi is a bit like old 3D printing where fettling the printer to work is a central part of the hobby. I'd rather just buy a Prusa.)

	▲	agentdev001 41 minutes ago \| parent [-]
		I'd like to understand what features you're referring to that are missing from base-install Pi CLI.

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azuanrb 2 hours ago | parent | prev [-]

[dead]

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ppeetteerr 2 hours ago | parent | prev | next [-]

Read through it an I'm curious whether setting the date and cmd on every system prompt call will cause the cache to invalidate.

I guess the cache would only be invalid if the day changed or the root directory, which would technically happen infrequently enough.

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AndreiCalazans 44 minutes ago | parent | prev | next [-]

I was here looking for this comment = )

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anonym29 2 hours ago | parent | prev [-]

If you really want a minimal agent that you heavily customize, just skip pi (130+ transitive dependencies on the "minimal" pi-coder package) and write your own. You learn a bunch, and it's not hard. You can even ask another LLM to help you get started.

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novaRom an hour ago | parent | next [-]

Exactly! I just vibe coded (with GPT Sol and Claude whatever-number) my own agent, it's trivial to add now any feature I want - simply ask more powerful model to do it for you. I am happy with end result, however it looks indeed these tools are trained to increase token count - they do quite stupid token-spending steps while making code, but the code itself is also a bit weird - it's like they intentionally do code which is hard to modify on your own without using exactly those authoring models. Interestingly, when I am using DeepSeek with OpenCode, I don't see that - it understands my intent well enough and overall code quality is not bad. I recently switched to local Gemma 4, and I often switch (in opencode) to just that less powerful model, because it understands my intent and has enough skills to provide good quality solution although it's rather for small size projects, and for not coding from scratch, but it's also free and private. It feels slower than any big cloud model, so my model switching is probably most quickest path to robust end result :)

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tmalsburg2 2 hours ago | parent | prev | next [-]

I wrote my own harness in Emacs and it’s completely ridiculous how well it works. Auto-compact is the only missing feature on my list. Claude‘s approach, if I understand it correctly, invalidates a lot of cached context, and I‘m thinking about a more cache-friendly strategy.

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wolttam 2 hours ago | parent | prev | next [-]

This is a truly underrated approach IMO

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amunozo an hour ago | parent | prev [-]

Any tips on how to get started?

	▲	anonym29 an hour ago \| parent [-]
		At a minimum, you need an inference endpoint: either cloud or local. If going local, llama.cpp is going to be the more beginner friendly local inference engine that supports more processor types (AMD GPUs, Intel GPUs, CPUs, anything that supports Vulkan, not just Nvidia). LM Studio is a nice wrapper for this if you'd rather avoid cloning repo and compiling yourself, provided you don't mind closed source software; it's much less enshittified than Ollama. If going local, you will also need model weights in the right format for your inference engine, and with a model that can fit on your hardware. This is going to be .GGUF files if you're using llama.cpp or a wrapper for it like LM Studio. From there, pick a language, go look up the OpenAI /chat/completions API format (or Anthropic's "Responses" API format), create a DS or array or slice to store messages, and build a loop that accepts user input, formats it according to the API format, sends it to the inference server, retrieves and parses the response, adds the response to the DS/array/slice, and repeat. There's a lot more beyond this - tool calling, other API formats (optionally), MCP servers, transport layers besides terminal stdin/stdout, permission models, starting with a system message, clearing your message stack correctly (hint: don't reset it mid tool-call), message compaction, web searching and page fetching, semantic search RAG over embeddings, memory layers - way too much to cover exhaustively in a single message.