Fleets

A fleet is N governed sandboxes coordinated by an atomic task board. It's how you run many agents in parallel (or iterate on one app) with the same isolation, policy, egress, and audit guarantees as a single cell.

The runeward fleet command is a client to a running control plane (runeward serve), so start that first.

Subcommands

Command	What it does
runeward fleet up [profile]	Create a fleet of workers from a profile.
runeward fleet add <prompt>	Queue a task (its own workspace when a worker picks it up).
runeward fleet run	Let idle workers claim and execute queued tasks in parallel.
runeward fleet build <prompt>	Shorthand: add a task and run it.
runeward fleet exec <prompt>	Run a prompt pinned to a single sandbox (accumulates in one workspace).
runeward fleet status	Show workers and task board state.
runeward fleet export [dir]	Copy the finished workspace(s) back to the host.
runeward fleet down	Tear the fleet down.

Select the agent and model with environment variables, e.g. AGENT=claude, MODEL=sonnet, AGENT=codex MODEL=gpt-5-codex, or AGENT=cursor.

Two ways to work

Each worker has its own /workspace (fleet isolation), so it matters whether follow-ups land on the same one.

A) Iterate on one app

Use exec, which pins to a single sandbox — the "pass a prompt, then add more prompts/changes" flow where the same code accumulates:

AGENT=claude MODEL=sonnet runeward fleet up
runeward fleet exec "Build a FastAPI todo API in app/ with SQLite and pytest"
runeward fleet exec "Now add a PUT /todos/{id} endpoint and tests"   # same code
runeward fleet exec "Add a Dockerfile and a README"
runeward fleet export ./out

Switch agent/model any time by changing the env vars.

B) Fan-out independent pieces

Use add + run; each prompt goes to whichever worker is free, in its own workspace, and they build in parallel:

AGENT=codex runeward fleet up
runeward fleet add "Build the auth module in auth/ with tests"
runeward fleet add "Build the billing module in billing/ with tests"
runeward fleet add "Build the CLI in cmd/ with tests"
runeward fleet run          # all workers build in parallel
runeward fleet export ./out

Inter-agent coordination

Agents don't talk peer-to-peer. Coordination happens through the control plane's atomic task board: workers claim tasks under a lease, heartbeat while working, and mark completion. If a worker dies, its lease expires and the task is reclaimed by another. This is the same on Docker and Kubernetes.

Multiple keys and local LLMs

A fleet can mix providers (Cursor, Codex, Claude) and keys, and can target a local LLM exposed over an OpenAI-compatible endpoint (e.g. Ollama, LM Studio) by adding the endpoint to the profile's egress allowlist and pointing the agent's base-URL env var at it. See examples/build-fleet.toml and examples/build-fleet-k8s.toml.