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So think of all this as LLM orchestration runtimes. None of these actually run an LLM, it's just taking care of the interface spaghetti.
Main components they all have:
- LLM API integration - you pick your API platform, the model to use and you give it a key
- Chat channels with owner - a gazillion built-in integrations with every thinkable comms platform (This is what I'm looking at to remove bloating from in #1444916)
- Built-in tools to communicate with the OS (from
lsandcatto interacting with your browser - not all of them have all options) - Skills (markdown instructions)
- Memory (most often just markdown files it reads and writes)
- "Bot Identity" (markdown)
- Scheduling: heartbeat and crontabs
That's really all it is and why you can fit it in a 600kb, 10MB or 300MB runtime. Smaller if you remove bloat, and the smallest runtimes often have already removed a lot of bloat.
Out of the box, picoclaw takes about 20MB RAM on x86_64. zeroclaw was said to be better but honestly, having a million rust crates as dependency is awful and the binary linking took infinite RAM and froze my dev box for a few minutes, so I figured that that was too much work. It's all vibe-coded anyway, so all of this stuff is fully disposable.
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What does this thing actually do?
My understanding of an autonomous AI agent is that it calls a LLM to run system commands basically. Typically, it'd be calling Linux commands which LLMs are pretty good at.
But I don't know what this thing runs on and whether LLMs have well trained models for whatever language it uses. And i'm prety sure it's not running a local AI, right?