forkd is a fork-on-write microVM primitive — the "for AI agents" framing on the front page is one prominent use case, not the ceiling. Anything that wants N isolated children spawned from a warmed parent in milliseconds fits.

If you're plugging forkd into an existing agent framework, these are ~150-250 lines of Python with a --dry-run mode so you can verify the forkd plumbing without an LLM key.

Rootfs recipes turn a public Docker / OCI image into a forkd parent snapshot. Same shape across all of them:

# 1. Build a parent rootfs from an upstream image
sudo bash recipes/<name>/build.sh

# 2. Snapshot the warmed parent (one-time per image version)
sudo forkd snapshot --tag <name> \
    --kernel /var/lib/forkd/kernels/vmlinux \
    --rootfs recipes/<name>/parent.ext4 \
    --tap forkd-tap0

# 3. Fork N children, fan-out workload
sudo -E forkd fork --tag <name> -n 100 --per-child-netns

The first-time build.sh of each recipe takes a few minutes (pulling the Docker image + converting to ext4). The snapshot step is ~10 s. After that, forking children is the published benchmark cost.

• Recipes are tested on Ubuntu 24.04 / Linux 6.14 / x86_64. Other distros may need adjustments to scripts/build-rootfs.sh.
• Each recipe is self-contained — pick one, run it; you don't need to understand the others.
• The "AI agent" framing on the project front page is the dominant use case today but not the only one — the technology is fork(2) for KVM microVMs. If your workload needs N hardware-isolated children spawned from a warmed parent in milliseconds, forkd is the primitive.