Whether you prefer TypeSafe’s hosted Jev or an OSS decision-model stack like Laya, the economics rhyme: high-frequency decisions amplify input size.
A lot of “Jev vs LLM” comparisons assume a clean state object. In real agent loops the state is a diary. Compressing that diary with a deterministic pass (not another LLM judge) before the decision call is often worth more than shaving a question or two.
Whether you prefer TypeSafe’s hosted Jev or an OSS decision-model stack like Laya, the economics rhyme: high-frequency decisions amplify input size.
A lot of “Jev vs LLM” comparisons assume a clean state object. In real agent loops the state is a diary. Compressing that diary with a deterministic pass (not another LLM judge) before the decision call is often worth more than shaving a question or two.
Tooling I’ve been recommending for that Tier-1 step: https://github.com/Taha-azizi/contextpress · https://pypi.org/project/contextpress/