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Roadmap

The core API (questions, the engine, decisions, traces) is intended to stay stable; providers, adapters and benchmarks will grow. Plans change with what people build, so open a discussion if something here matters to you, or if something that is not here should be.

Done in 0.3

  • Shadow mode with agreement, projected savings, fitted thresholds and a verdict per question, and an audit mode for live engines.
  • Adapters for LangGraph and the OpenAI Agents SDK, and framework-neutral route and gate helpers (tested with LangChain tools and Pydantic AI).
  • Deadlines and spend limits on the engine, and per-run spend caps.
  • A decision server with a System One compatible endpoint, and MCP tools.
  • Traces to labeling rows, and a drift report between two periods.

Next

  • A neutral decision-model benchmark. A public, provider-neutral benchmark for decision models (Jev, Kev, OpenJev, Laya, GLiNER, Hugging Face classifiers, LLM deciders) on public multi-domain datasets, with multi-turn cases, that anyone can submit a model to.
  • Cross-request batching in the server. Collect the questions of concurrent requests for a few milliseconds and send them to each local model as one batch, which needs a batch entry point on providers.
  • Calibrated LLM confidence. Several hosted models expose token log probabilities; an LLMDecider that reads them would give LLM answers a real confidence, so they could be thresholded like every other provider.
  • Jev and the direct Anthropic SDK path in the published benchmarks. Both are unit tested (Jev against TypeSafe's own SDK models) but have not been benchmarked live.

Later

  • A trace server with search across runs, for teams that outgrow single HTML files.
  • Learned routing: pick the provider order per question from calibration data instead of by hand.
  • More demos: document processing and an IT operations agent.

Not planned

  • A general agent framework. ThinkLess is a decision plane and a tracer that work inside whatever loop you already have.
  • Hosting models. Providers call models; running them is left to the tools that do it well.