It does this by simulating production tool responses through text world modeling (similar to QwenAgentWorld, summary here https://x.com/silennai/status/2073887455884058814).
We can then use this to train a router for frontier, OS, and local models (use defaults or pick which ones to optimize against).
wmo ingests agent traces, builds the simulation, embeds the traces, runs different models you choose against the simulation scenarios, and then uses a KNN for model selection (similar to https://arxiv.org/abs/2505.19797).
- Cache aware: cache is taken into account for the effective price in routing.
- Confidence gated: we don't deviate from the best fit model when paired evidence over retrieved neighbors is below 0.5 standard errors or on queries unlike anything in the fit set.
- Optimize for cost or quality: train a balanced, cost max, or quality max router.
Usage
`wmo build` creates the simulation (or add your own benchmark)
`wmo optimize` tunes the router
`wmo serve` starts the server and can run everything fully locally. The simulation and router can update over time as more agent traces are gathered and new models are added.
Router results vs Fable
- RouterBench: -66.5% cost, -1.7% performance, -24.7% latency p50. 77.5% of traffic to Sonnet 5, 16.1% Fable 5.
- TauBench: -44.5% cost, +6.3% performance, -20% latency. 83% to Opus 5, 17% to Kimi-K2.6 (over K3).
- Terminal Bench 2: -64% cost, +8% performance, -50.6% latency. Sonnet 5 is fully along the pareto front. Training a specialized router per task isn't cheap. In sparse data regimes the value can be "here's the best model".
We're working on sample effiient continual learning for agent specific models at experientiallabs.ai"
Show HN: Optimize and serve models with Fable quality at half the cost
github.com