Your data leaves your machine.
Capable agents usually mean large, remote models. How comfortable are you with the email to your therapist being a line item in someone else's training or eval dataset?
LILA
Lean Intelligent Local Agent
Private by design, explicit in every action, light enough for your laptop.
Get in touchToday's AI agents ask for a lot of trust.
You can't see what it did.
The same task can run differently twice, a small wording change can alter behavior, and figuring out what went wrong means reading a chat transcript.
It needs a data center.
Every step piles more messages and outputs into the context window, so the model has to be big enough to hold it all — usually bigger than your laptop can run.
Why it happens
Most agent harnesses encode skills as natural-language instructions and run a loop — think, act, observe, repeat — until the model decides it's done. That's easy to start with and fine for one-off tasks, but it leaves out structure by design.
Visibility isn't built in. It's bolted on after the fact.
What if an agent worked on a graph?
LILA brings structure to agent work. Tasks run on an explicit graph of small steps, and skills are graphs too. Because each step is small, a small model can handle work normally reserved for big ones — so both the harness and the model run 100% on your computer.
Already have natural-language skills? Compile them into a graph.
What you get
Private
The harness and the model both run on your hardware. Your data doesn't need to leave your computer.
Explicit
Every step is a node you can see. Debug with a picture, not a chat log — and run the same task twice to get the same shape of result.
Light
Small steps need only a small model, so commodity hardware is enough. No GPU cluster required.
Get in touch
LILA is in early development. Have a workflow that needs a private agent, or want early access? Tell me about it.