Most agent frameworks coordinate calls to a model. The application manages the workflow, while the model s live state sits behind an API. We spent a year building around a different idea: make attention state something application code can program.

In Lloyal, the harness and model run in the same process. Your TypeScript code can fork the KV cache (the model s working memory) into concurrent agents.

Imagine the model has read a set of documents. You fork that state into agents investigating different explanations. They inherit what it has already read, then develop their own reasoning paths. The shared context stays shared; each branch accumulates its own work.

Your application decides what evidence reaches each branch, which tools it can use, when to stop it and which findings should survive at inference-time. It can discard an unproductive branch and reclaim its memory while the others continue.