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...
The entire agent system prompt can be seen here:
https://github.com/earendil-works/pi/blob/main/packages%2Fco...
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.
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.)
Though, imo, the fact that pi maintains its "we only include the bare minimum!" statement is part of the draw for me. Especially considering that im in an enterprise env; being able to internally share custom implementations of out-of-the-box Codex/CC stuff is really nice.
I do wonder how they'd go about shipping a default web search tool. Big problem there is the lethal trifecta. Shipping something that arbitrarily allows untrusted content to be retrieved non-deterministically I'm sure is a long conversation on Pi's end. Pushing it off to the user to decide is easy.
"Pi has *ALL* the tools, can you name one it does not".
I've heard really good things but that being my first experience with pi didn't fill me with confidence about it's code quality either.
For now I stay with OpenCode I think - I was using zed editor and agent for the longest time anyway and think I will go back to that. CLI tools for me seem a bit too disconnected from the code.
Every time I read this comments I have the feeling you are talking about mcp or sub agents, otherwise this makes no sense at all.
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.
To learn yourself:
<$20 on a cloud AI api for a chunk of tokens and have the AI teach you. "help me write an AI Agent using (language) and walk me through the steps"
Realize that these agent are REPL/while loops that maintain a conversation state and then based upon the tagging syntax like <TOOL:bash:uptime>uptime for system run time</TOOL> and the agent extracts the tool and then does sub commands.
Incidentally, I also have zero supply chain attack surface as I have zero dependencies in my agent, just go stdlib. Pi, again, has 130+ transitive dependencies asking me to trust the security of my system to 150+ additional people I've never met in exchange for a bunch of bloat I do not want.
For reference, pi-coding-agent, by itself (not including dependencies, tests, or pi-ai, pi-tui, pi-agent-core, etc), is ~41,653 SLOC taking up ~1658.9 KiB across 163 files.
My agent, excluding dependencies (all go stdlib) and tests, is 3 files, 946 SLOC, taking up 36.3 KiB, and includes a basic TUI and an XMPP transport channel (including TLS for XMPP), with dynamically configurable delivery to and receipt from either or both, including allowlists for XMPP message partners. It has tool calling, a permission model with whitelisting and interactive permission querying on a per-tool basis, full thinking support, including the ability to toggle hiding or showing it across either or both transports repeatedly throughout an individual session, the same tools as pi comes with out of the box, plus web search, and a tool to vet, build, and git commit golang projects all in one go, stopping if errors are observed. Configurable model and endpoint, too.
Incidentally, the open source xmpp server (prosody) and metasearch engine (SearXNG) are both self-hosted, too.
I guess the cache would only be invalid if the day changed or the root directory, which would technically happen infrequently enough.
But I'll investigate how that works in a session. You got me curious.