融合代理内核

fak — the **F**used **A**gent **K**ernel

fak in one line: fak is a fused agent kernel: one Go binary that sits in front of an agent's tool calls, checks each call, and reuses the stable work in long sessions so the same agent loop is safer, cheaper, and faster.

Put one binary in front of the agent you already run — Claude Code, Codex, Cursor, or any OpenAI / Anthropic / MCP client — and the same long session gets cheaper and faster, with nothing else changed.

fak guard -- claude wraps your normal agent in one command. It keeps your model, your IDE, and your keys exactly as they are. You get back the parts of the agent loop that got expensive. fak points one base URL at itself for you; nothing else in your setup changes.

What you get, in numbers. Every figure traces to BENCHMARK-AUTHORITY.md, and the honesty ledger is CLAIMS.md:

- ~4.1× less work than a tuned warm-cache stack on a 50-turn × 5-agent run. fak reuses the shared prompt prefix across agents (the system prompt + tools, the KV cache of the work so far) instead of re-paying for it. That reuse factor climbs to 6.95× across the model ladder. (Against the naive re-send loop it is ~60×; the tuned number is the honest one to beat.) - ~120 tok/s in-kernel GPU decode on an RTX 4070 (SmolLM2-135M, f32 weights, gated FAK_CUDA_GRAPH=1), landing inside llama.cpp's Q8_0 range of 120 ± 15 tok/s — so a full-precision kernel reaches parity with a quantized llama.cpp. - The provider cache discount survives a long session. fak sheds old turns while keeping the prompt-cache prefix byte-identical, so the rebate holds instead of breaking the moment the conversation sprawls. - The guard tax is ~362 ns per call. The kernel's allow/deny decision runs in-process (measured, Apple M3 Pro), not as a network hop.

fak in one line: put fak in front of the agent you already run. It makes long sessions cheaper, routes each call to the right model, and on the same boundary enforces a safety floor and records every decision. One binary, no rewrite, no key to start.

Who is this for? Pick your path: run your agent through it now · run the modular Colab quickstart · run a model in the kernel · the performance story · a hard security floor.