# fak · the multi-agent context demo

… hardware: …
Many agents · many turns · long context · and a different-sized tool result every turn. The honest baseline is a warm per-agent KV cache — each agent already reuses its own KV across turns (what vLLM / SGLang / provider prompt-caching give you). fak's win is the step that cache can't take: cross-agent prefix sharing — one copy of the shared setup serves the whole fleet. Pick an agentic shape and watch the gap.

## ① Pick an agentic shape exact · timing-free · model-independent

## ② How each agent's context assembles tool results drawn to scale

shared prefix (system + tool schema) decode (model output)

## ③ Live race — fak vs the tuned warm-cache baseline (SOTA) real in-kernel model, this box

model
fak idle—
prefilled 0 · decoded 0
tuned warm cache (SOTA) per-agent KV · prefix once/agent idle—
prefilled 0 · decoded 0
The HEADLINE is fak vs the tuned warm-cache baseline (a persistent per-agent KV cache — what vLLM / SGLang prefix-caching or provider prompt-caching give you). Both run fully live here — the warm-cache arm has no quadratic re-prefill, so it never needs projecting.
fak in-kernel engine · pure-Go Q8 forward pass · tokens are real model output. No network, no API — all on this box.