justindbca/fleetmux

Run AI teams, not a single assistant. fleetmux runs one supervisor agent and N worker agents as visible
terminal sessions. The supervisor only judges and dispatches; workers only execute; a daemon, not the models,
decides when the work is done. Sessions near their context limit hand over to a fresh session with a written note,
so one task can run for hours across many model sessions. Official Claude Code, official Codex CLI, Pi
(Codex models and local models) and opencode mix in one fleet, in any supervisor × worker pairing that has
passed a real smoke run (cmux_fleet/config/fleet.yaml#cli_pairs).
In daily use. Since October 2026 it has run resident service fleets and scheduled task fleets on three Macs
for a live trading operation: an ops team that starts and stops the other teams, a trading desk, a strategy review
team that answers price and indicator triggers, and a scheduled research team. Each team has an AI lead and AI
workers; the ops team runs the trading desk and the review team as its sub-fleets.
## A project manager, not a secretary
Personal-assistant agents can already spawn sub-agents, run scheduled jobs and fail over between models, but a
sub-agent's result still goes back to a model to judge, long tasks rely on context compaction, and the model is
whatever the caller or the config names. fleetmux adds what a project manager does:
1. Delivery accepted by the program. Whether the outputs exist, whether they contain what they must and whether
the tests pass is decided by the daemon, not taken from the model's own report.
2. Sessions that continue without end. Near the context limit a session writes a handoff note and a fresh session
takes its place; a frozen session restarts in place; daemon and supervisor watch each other.
3. Dispatch by a capability matrix. Before every dispatch the advisor picks the model and effort from a
capability matrix (domain × task type × model, each cell with evidence), and the pick must pass a check against
that matrix; a usage wall moves the slot to the next usable model on its role's chain, or pauses.
4. Visible throughout. Every agent is a visible terminal session you can watch and step into at any time.
5. Official CLIs, your own login. Each agent runs in its vendor's own CLI under your own login; no token is
extracted, no client is impersonated and no API call goes through a proxy.
6. A budget per task. A budget card caps calls, rounds, hours and per-account quota; a usage-wall table drops
a model tier or pauses the run so it can resume later.
7. Teams of teams. A worker can become the lead of its own sub-fleet, so one resident fleet can run several
business lines, each started and stopped on its own clock.
We learned why this matters from our own setup: an assistant gateway we ran lost its chat channels for months
and nothing outside it noticed, because nothing outside it was checking. In a fleet, the daemon checks every
delivery.
Built by Justin Wang (Toronto), AI systems engineer: LinkedIn · X.
Available for freelance work: AI setup from zero, agent harness tuning, LLM capability elicitation and multi-agent orchestration. DM me on X.
中文说明 · Design baseline / 设计基线