Agent traces you already capture are opportunities to get signal on how to make your model cheaper, faster, better.
We do this by continuously a) distilling relevant chain of thought from larger open source models into smaller ones, b) model routing to frontier + OS models, and c) token compaction to remove noise and save on tokens.
`wmo build` allows you to build a simulation to optimize against with your agent traces with your OpenRouter key
`wmo optimize` trains a router, compaction, and distills chain of thought from a larger model into your specialized model
`wmo serve` gives you an endpoint for your model
When you call your model, behind the scenes a router decides which tasks should go to the frontier versus your model. Tinker continually trains as new traces arrive.
We're also working on a hosted solution that does continual training + serving for you https://experientiallabs.ai