This page holds the project overview that used to live in the repository README: what Hive does, how it differs from a terminal agent, the hub/spoke model, the contributor relay, the ACMM trust levels, and the questions people ask before adopting it. The README itself is kept short and carries the measured outcomes; everything else is here or linked from here.
Hive watches the work that piles up in a repository — issues, pull requests, failing CI, CVEs, and dependabot alerts — and sends AI coding agents (Claude, Copilot, Codex, Antigravity, Goose…) to deal with it: triage, fixes, reviews, releases. You decide how much it is allowed to do. Humans keep every decision that matters; agents do the chores.
A terminal agent is a tool you hold, aim, and stop. Hive runs those tools for you, on three rules:
- Short-lived agents. Every task starts a fresh agent with a clean context and ends it. No long-running agent drifting for hours.
- External context. What the agent needs to know lives outside the model — in your repo, your knowledge base, your audit trail — and is re-injected each time, so nothing is forgotten and nothing is reconstructed from stale memory.
- Prompts driven by deterministic facts. The instructions an agent gets are templates filled from real data (the issue, the CI log, the diff, the trust level), not hand-tuned prose.
Around that, Hive picks work from your real queue, sets limits in code with six trust levels from the AI Codebase Maturity Model (ACMM) — a framework for how much autonomy a codebase is ready to give agents (levels · paper), checks before and after with deterministic filters and gates, writes every action to an audit log owned by a named human, and runs around the clock. Same agents you use today — governed, scheduled, scoped, and accountable.
- Spoke — one running Hive, serving your repo(s). It is where agents actually work. Runs on your Kubernetes cluster, Podman, or Docker Compose.
- Hub — the control plane for many spokes: registers, provisions, upgrades and watches them; fleet view, leaderboards, routes contributor compute to the hives that need it.
A spoke works alone. A hub is how you see and manage a fleet of them.
Your own agents are only the start. The contributor relay (ClankeR) lets anyone who wants to help your project donate their compute and model tokens without you handing out anything:
- They bring their own agent. A contributor runs one command on their laptop, cluster, or Compose box; it connects outbound to your hive, takes one curated task at a time, and runs their CLI with their model and their subscription.
- Their secrets never reach you. API keys and CLI logins stay on the contributor's machine. The hive never sees them.
- Your forge settings never change. Nobody is added as a collaborator. For each task the hive mints a short-lived, task-scoped GitHub token so the PR can be opened, then it expires. Revoke a contributor and their access is gone.
- Your prompts, your limits. Contributors work the queue you curated, under the same trust level, filters and gates as your resident agents.
The result is an elastic swarm: a three-person project can have a dozen contributors' agents working its backlog overnight, each accountable to a named human, while the maintainers hold every merge decision. See contributor-relay.md.
Community members can contribute compute to any hive through ClankeR: it hands tasks from a hive's backlog to the CLI agent running on your own machine, under the hive's prompts and limits, with no repo permissions and no shared secrets.
Start with contributor-relay.md or the Hive Hub contribute page.
Homebrew is an additional install option:
brew install hivecommons/hive/contributeFollow the Homebrew setup instructions to
register without a checkout before running hive-contribute. The
just workflow remains the from-source
path.
- Hive the project: Apache-2.0 code in this repo. Run it yourself.
- Hive the hosted service: hive.hivecommons.dev — a hub we operate so you can onboard a spoke without a cluster. Same software, one deployment of it. See hosted-hub.md.
Do I need a paid agent subscription? No. Hive drives the agent CLIs you already have (Claude, Copilot, Codex, Antigravity, Goose) or any OpenAI-compatible model through its Model Gateway (OpenRouter key, no CLI login). Contributors can bring their own.
Who tells it what to work on? Your backlog does. The Governor reads your issues/PRs/CI, filters them deterministically (labels, areas, complexity), and only hands agents work you have allowed. You choose the areas and the trust level. Nothing is invented.
We require design discussion before non-trivial PRs. That is a trust
level, not a workaround: Measured mode = agents may only file findings and
proposals as issues — no code, no PRs — so the discussion happens first;
Holdgated = PRs are opened with a hold label and a human must review and
release; Full (L6) = merge on green. Start at Measured. You never have to
move up.
Can it open PRs to our upstream repo? Yes, if you install it there. Or run
it on a fork (upstream_watch keeps the fork current) and push reviewed PRs
upstream yourself.
More answers: https://docs.hivecommons.dev/docs/community/what-is-hive-commons#does-hive-fit-my-project
| Path | Start here |
|---|---|
| Docker Compose | Quick Start (Docker Compose) |
| Podman | Quick Start (Podman) |
| Kubernetes | Kubernetes deployment |
| Hosted hub | https://hive.hivecommons.dev and the hosted hub guide |
| Getting started | Zero to Automation: Getting Started with Hive |
Configuration lives in hive.yaml; see the
operator reference,
environment variable reference, and
agent configuration.
Hive uses an AI Codebase Maturity Model (ACMM) with six levels that control what agents are allowed to do:
| Level | Name | What agents can do |
|---|---|---|
| L1 | Inception (Assisted) | Interactive advisor and project inception. Advisory beads only. |
| L2 | Advisory (Instructed) | Observe and report findings as dashboard beads. No GitHub interaction. |
| L3 | Quality-Gated (Measured) | Quality agent opens issues and PRs gated by the literal hold level-gate label. Others remain advisory. |
| L4 | Security-Aware (Adaptive) | All agents file issues. Quality, sec-check, and CI open PRs gated by hold. |
| L5 | Semi-Autonomous (Semi-Automated) | All agents open PRs gated by hold. Humans batch-review and approve. |
| L6 | Autonomous | Agents open PRs and auto-merge when checks pass. Non-outreach PRs have no level hold; outreach PRs remain held. |
See acmm-policy-matrix.md for the full matrix and labels-and-control-signals.md for label behavior.
Hive runs as a single container with a Go control plane, an authenticated Node.js gateway, and ttyd for web terminal access. The Governor evaluates queue depth, deterministic filters decide what an agent may see, scoped tokens and a MITM proxy enforce what it may do, and gates decide what may land.
See architecture.md for process model, governor loop, guardrails, hub/spoke, and walkthrough diagrams.
hivectl tui is a full-screen terminal view of the same dashboard API the web
UI uses: agents, governor, token spend, live activity, pause/resume, kick,
ACMM level, and hive-profile actions. See
hivectl tui in the command reference.
Hive drives Claude Code, GitHub Copilot CLI, Codex, Goose, Antigravity and OpenAI-compatible gateways; reads work from GitHub Issues, GitHub Projects, Linear and Jira; and runs on GitHub today with GitLab and Gitea/Forgejo planned. See the integrations page for the full logo grid with status.
- Goose, an agent backend whose upstream moved to the Agentic AI Foundation (AAIF). Hive integrates Goose as an independent CNCF project; Hive does not join AAIF, seek AAIF membership, or rely on AAIF to host or govern Hive. See unattended Goose with Hive and Goose upstream tracking.
- Repositories — the other repositories in the hivecommons organization, what each is for, and which ship inside a release.
- ADOPTERS.md — organizations running Hive. Add yours with a pull request.
- CONTRIBUTING.md — contributing to Hive itself. Contributors follow the CNCF Code of Conduct, adopted in CODE_OF_CONDUCT.md. Recent changes are in CHANGELOG.md.
- Security model — code-grounded inventory of agent safety controls, enforcing versus advisory modes, known gaps, and audit steps. Vulnerability disclosure: SECURITY.md; telemetry and data collection: telemetry.md. Do not report security vulnerabilities through public issues or pull requests.