HomeFleet

Your coding agent, but your other PCs do the heavy lifting.

HomeFleet turns the computers in your home into a fleet your AI coding agent can use. Install a small daemon on each machine, pair them once, and any MCP-capable agent (Claude Code, LM Studio, goose, Cline, ...) gains tools to see every machine in the house and delegate work to them — powered entirely by local models, entirely on your LAN, with no cloud in the loop.

Status: pre-alpha. Under active development toward v0.1. Nothing here is ready to use yet — watch the repo if the idea speaks to you.

Why

Local agents are getting genuinely useful, but a single machine is always the bottleneck — and most of us have more than one computer sitting around. Existing multi-machine tools either pool GPUs to serve one bigger model (exo, GPUStack, llama.cpp RPC) or replace your whole workflow with a new platform (dashboards, kanbans, custom protocols). Nothing lets the agent you already use simply reach over and put your other machines to work.

HomeFleet is that missing thin layer:

• MCP-native — appears as list_nodes / delegate_task tools inside your existing agent session; results stream back into its context
• LAN auto-discovery — daemons find each other via mDNS; pair with a short code, Syncthing-style (device ID = certificate fingerprint, mutual TLS, no CA, no accounts)
• Local models by default — worker machines drive tasks with whatever OpenAI-compatible server they have (Ollama, LM Studio, llama.cpp server)
• Capability-aware — nodes advertise what they can do; a weak GPU box still earns its keep as an execution node (tests, builds, file ops) while stronger machines do the thinking

How it works

┌─────────── Machine A (you) ───────────┐      ┌────────── Machine B (worker) ─────────┐
│                                        │      │                                        │
│  Your agent (Claude Code, goose, ...)  │      │  homefleetd                            │
│        │  MCP (localhost)              │      │   ├─ executor: minimal agent loop ──►  │
│        ▼                               │ mTLS │   │    local model (OpenAI-compat API) │
│  homefleetd  ◄──── discovery/pairing ──┼──────┼─► ├─ executor: command runner         │
│   ├─ list_nodes                        │ LAN  │   └─ workspace cache (git bundles)     │
│   └─ delegate_task ────────────────────┼──────┼─►                                      │
└────────────────────────────────────────┘      └────────────────────────────────────────┘

One daemon per machine. On your machine it faces your agent as an MCP server; on workers it executes delegated jobs — read-only repo recon driven by a local model, or allowlisted commands (test suites, builds). Code travels as git bundles; nothing needs a shared remote.

v0.1 scope

• Delegate recon tasks ("explore this repo, summarize the auth flow") to a worker's local model
• Delegate command runs ("run the test suite") to any paired machine
• Live node list with capability info, job status/streaming, cancellation
• Windows-first reference setup; code is cross-platform TypeScript

Explicit non-goals for v0.1: code-writing delegation, GUI, cloud relay. See the design doc and roadmap.

Repository layout

Path	What
packages/protocol	HomeFleet Protocol (HFP) — zod schemas + types; spec in docs/rfc/
packages/daemon	homefleetd — MCP front, node service, discovery, dispatch
packages/executors	Command executor + minimal agent loop
docs/rfc/	Versioned RFC-style protocol spec
docs/adr/	Architecture Decision Records
docs/specs/	Design documents
devlog/	Findings, benchmarks, lessons learned along the way

Development

pnpm install
pnpm test        # vitest
pnpm typecheck   # tsc across packages
pnpm lint        # biome

Everything is testable on a single machine — integration tests run multiple daemons as local processes with faked capability profiles.

Roadmap

v0.1 (recon + command delegation) → code-writing delegation (diffs/branches back) → tray app + web dashboard → macOS/Linux polish → multi-node fan-out → model-pool orchestration on the same fabric.

License

Apache-2.0 — © 2026 Hugo Deziel