fak — the fast local runtime for coding agents

fak is an agent runtime: one binary puts a fast, cache-accelerated boundary between your coding agent and every tool call.

In short: run coding agents locally with workflow batching and cache reuse, protected by a default-deny capability floor (blocking unauthorized actions).

Try fak

Run the offline proof with no key, model, or GPU:

go build -o fak ./cmd/fak
./fak agent --offline  # -> task completed (booked)

The poisoned result and destructive operation are blocked; safe tasks complete normally.

Or wrap the agent you already run with one command. In this example, fak forwards Codex subscription credentials with no API key required and blocks tools outside the allowed policy. The capability floor stops unsafe calls without breaking the task:

fak guard -- codex

The agent keeps working inside that boundary. See the interactive showcase for the guided tour.

Latest hardware results — 2026-09-04

The front page shows one row per supported hardware family. Latest means the newest committed performance receipt for that platform, not the newest code change. A row can be historical or held when no newer quality-complete measurement exists. The table reports measured throughput, for example 2.3–2.9 decode tok/s on Mac, with claim boundaries beside each result and links to its receipt.

Read the status column before comparing rates: the held NVIDIA cache-hit path measured about 0.2 tok/s, but failed exact-output quality.

Use the benchmark index for hardware history and model-specific results. Use BENCHMARK-AUTHORITY.md for claim boundaries and canonical receipts. For newcomer Mac guidance and head-to-head Apple Silicon Metal measurements, see the Mac agent UI guide and the three-way Mac benchmark.

Open-source memory overflow landscape

Most LLM serving engines treat memory overflow as a slow host-memory fallback with multiple CPU bounce copies. fak implements hardware-native, zero-copy peer-to-peer DMA directly between NVMe storage and GPU VRAM:

Why run coding agents on fak

• Workflow batching and cache reuse: Multi-agent coding loops reuse prompt context across turns, achieving 4.1× vs tuned baselines with 86.7% cache hit rates. Instead of re-reading codebases on every turn, fak keeps shared prefixes hot and trims stale context.
• Zero-copy GPU Direct storage overflow: Run models far exceeding physical GPU VRAM without CPU memory thrashing. Built on a BaM-style accelerator storage architecture, fak maps NVMe submission queues directly in GPU VRAM and streams paged KV caches and hybrid linear attention states over peer-to-peer PCIe DMA without host DRAM bounce buffering (StagingCopyCount == 0). See the GPU Direct overflow specification.
• Local execution on your hardware: Run models directly with native inference across Apple Silicon, AMD, and NVIDIA. Cut per-token API bills and keep your code private on your own machine.
• Default-deny capability floor: Protect your workspace from unintended terminal commands or file edits. Every tool call is checked against a default-deny (block everything unless allowed) policy before it runs. Drop-in support wraps existing agents like Claude Code, Codex, Aider, and Cursor with zero rewrites.

Native inference provides direct execution on local silicon, with external engines supported as an explicit reference; see the native inference goal for details.

Default priorities & operating modes

fak is organized around a focused four-tier default priority hierarchy:

1. fak all in one (serving and harness + memory — the "one touch" thing): The primary focus — a single-binary "one touch" deployment (fak up) bundling model serving, agent harness governance, and persistent memory.
2. fak serving only: High-performance model inference runtime (fak serve), disaggregated gateway, KV-cache/context MMU acceleration, and native model execution.
3. fak harness only: Standalone agent harness and governance substrate (fak guard), default-deny capability floor, and tool adjudication over external models.
4. other things: Standalone utilities, peripheral tools, benchmarks, and off-spine extensions.

Install and configure

# macOS / Linux
curl -fsSL https://raw.githubusercontent.com/anthony-chaudhary/fak/main/install.sh | sh

# Any host with Go 1.26+
go install github.com/anthony-chaudhary/fak/cmd/fak@latest

# Inspect the shipped profiles
fak agent profiles

Tune agent execution with built-in work and output profiles that cut token waste and resist unnecessary dependencies:

fak manage --output-profile caveman:medium --work-profile ponytail:high -- codex \
  "Remove the duplicate cache without adding a dependency."

Balanced defaults are ponytail:medium for work discipline and caveman:medium for concise responses. See work profiles, response profiles, or the harness guide to build a named agent around the same boundary.

Going deeper

Apache-2.0 licensed.