The MIST stack is an evaluation and inference platform for AI systems, written in Go with zero external dependencies. It consists of four tools and a shared core library:
- MatchSpec — Eval framework. Define benchmark suites, run against any backend, get structured results.
- InferMux — Inference router. Abstracts LLM providers, routes by model, tracks tokens and cost.
- SchemaFlux — Structured data compiler. Pass pipeline, pluggable backends.
- TokenTrace — Observability. Span collection, latency percentiles, cost tracking, threshold alerts.
- mist-go — Shared library. Protocol, transport, metrics, circuit breakers, checkpointing.
Every component follows eval-driven development: deterministic, automated evaluation as the starting point.