Self-Hosted
Runs entirely on your machine — no telemetry, no cloud. All state lives in local SQLite (scheduler, sessions, budgets, audit) you can open and query yourself. A cooperative control plane, not an OS sandbox.
The environment your agents run in — not another agent to learn.
A self-hosted control plane that gives any AI agent persistent context, room-scoped tool access, and a full audit trail — entirely on your machine, with no vendor lock-in.
Every AI coding tool wants to own your workflow. Harbor inverts that — it's the environment your agents run inside, not another agent you have to learn. Bring your own models, your own tools, your own way of working.
Agents don't bulk-load skills. Harbor serves a ~50-token index first, a ~3K digest when a skill looks relevant, and the full SKILL.md only when it's actually used — so a large skill pool costs almost nothing until something needs it.
Runs entirely on your machine — no telemetry, no cloud. All state lives in local SQLite (scheduler, sessions, budgets, audit) you can open and query yourself. A cooperative control plane, not an OS sandbox.
Works with Claude Code, Codex, OpenCode, Cursor, goose — or any MCP-compatible agent. Harbor provides the runtime, context, and tool wiring underneath, so you switch agents without rebuilding your setup.
Every session carries a token budget, checked and debited in-process on each skill load. Tracked in real time on the local dashboard — no agent quietly blows past its limit.
Tools are just MCP servers or local scripts. Harbor discovers and wires them — filesystem, web search, code execution — no framework gymnastics.
A file-watcher daemon keeps your agent context in sync: when a skill or room changes, Harbor regenerates the AGENTS.md / CLAUDE.md beacons and per-room skill indexes automatically — no manual step. Recurring upkeep runs through the SQLite-backed scheduler.
Harbor doesn't run your agent — it stands in front of the one thing that matters: what the agent can load. Every request is checked against the room, debited from the budget, and written to the audit trail before it returns.
Most setups paste every skill into the prompt up front. Harbor serves skills in tiers — agents read a tiny index first and expand only what they actually use, so a large skill pool stays nearly free until something needs it.
The full file is fetched through the same gate you saw above — progressive loading and access control are the same mechanism. You pay a skill's tokens only at the moment an agent actually opens it.
Harbor governs how cooperating agents load skills, spend budget, and enter rooms — and records every decision. It is not a cage for a hostile process. Here is exactly where the line sits.
AGENT_ENV_ROOM; Harbor trusts it.0.1.No accounts to create. No SaaS to sign up for. Just install, configure, and go.
Alpha ships as a tarball — npm i -g ./harbor-tugboat-0.1.0.tgz. Needs Bun ≥ 1.1. No Docker, no cloud, no account.
config.toml defines your rooms, the skills and MCP servers each can reach, and per-session budgets.
Harbor exposes an MCP server and a CLI. Connect Claude Code, Codex, Cursor, goose, or any MCP-native agent. No adapter needed.
Your agent gets persistent context, gated tool access, in-process budgets, and a full audit trail — out of the box.
Harbor was designed, coded, and shipped by AI agents — the same kind it's designed to manage. A dogfood product in the truest sense.