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by tosh·11d ago·view on hn ↗
I use it for smaller changes (no compaction yet)

If I have agents.md or other context I want it to read I mention it at the beginning of the session

re MCP: I am not using an MCP with smol

but there are ways to convert MCPs into CLI tools or typed js

I imagine that would work well/more token efficient with smol (or most harnesses actually)

The best thing I found so far re smol is that it fits into the context window with plenty of room to spare

So it is easy to adapt (and add stuff to it, even stuff you only need specifically for just 1 project)

Whereas adapting a more complex harness is more error prone

1 comments
I didn't realize you were the author. Super cool! I'll be trying it out.

I do like the grug-brain approach of keeping things extremely simple and easy to reason about.

One thing that I don't like about Pi is that it's almost too extensible, in the sense that I can add a lot of shit into it without really understanding what a given extension is doing. And both from a security and token efficiency standpoint, I like the premise of converting things like MCPs into CLIs. It might be worth investing in tooling that works nicely with the agent harness, but that is not directly integrated with it. I'd be glad to work on that for smol if I can get a workflow going.

I'm glad there are quite a few good open source ones by now.

Also happy with how much love codex gets from OpenAI.

That said: I was looking at existing agents to find one to build upon and to me they were all too complex and were leaning too heavily into 3rd party dependencies.

Nothing I could understand comfortably in an afternoon (that's also on me I guess). Pi was closest to what I was looking for but still too big and too modular.

(It's hard to come up with good abstractions that work well across all major models + keep up with new concepts that come and go all the time with new releases.)

The more complex agents err on the side of supporting many models 'ok' instead of focusing on taking advantage of a specific model.

With a tiny implementation it is easier to adapt it.

Adding new stuff, removing stuff again, changing it from working well specifically with GPT 5.6 Sol to working with the exact model I want.