Today morning when I waked up, my AI agent reviewed 14 restaurant ratings in Indiranagar, updated a shared Google Sheet, signed a 20-page PDF I'd been ignoring for a week, and wrote a bash script to clean up my server logs.
I didn't ask it to do any of that. It just... does things now.
Meet OpenClaw — my long-running autonomous agent that lives on a Raspberry Pi, plugged into Discord, running 24/7. It manages my memory, handles research, writes code, edits documents, finds the best weekend spots in Bangalore by scraping live ratings — basically, it runs half my life on autopilot.
But a few weeks ago, I noticed something that bothered me.
I asked it: "Write a Python script to parse JSON logs." Simple coding task. It sent that request to a cloud API, waited 3 seconds, burned tokens I paid for, and came back with an answer — when I had a perfectly capable local LLM sitting idle on my Mac Mini, three feet away.
Then I asked: "Think step by step about the trade-offs between event-driven vs polling architecture for my notification system." That's a hard reasoning question. I want that going to a frontier model. That's worth the tokens.
Same agent. Same endpoint. Completely different needs.
And that's when a stupid idea hit me:
What if the system could figure out which brain to use — before the request even reaches a model?
Turns out, it's not stupid at all. And it took me a weekend, a Raspberry Pi, a Mac Mini, 50 lines of Python, and an open-source gateway to build it.
Here's how.
The Setup
Three models. Three price points. One unified endpoint. OpenClaw just hits
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