Hive

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An open-source runtime for colonies of AI agents that do real business work.

Tell a queen agent what you need. She does the first piece herself to learn what the job takes, then hands the rest to as many parallel workers as it needs. Every result lands in a shared tracker you can query, audit and resume from.

A real run. Only the typing and the waiting are sped up, and the badge says by how much. Watch the full run (MP4).

What you just watched

A growth team preparing a Show HN wants to know how five developer tools' own launches did on Hacker News. Here's what Hive did with that one message:

1. You hand it to a queen. Queens are long-lived agents with a role (here, Head of Growth) and their own memory and tools.
2. She asks the one question that changes the answer: every launch post, or only each product's original launch? You pick every launch, and she carries on.
3. She proposes a colony, and you approve it. Five products means five parallel jobs, so the chat becomes a colony: the queen plus as many worker agents as the work needs.
4. She does one herself. She takes PostHog first, works out what counts as a genuine launch post rather than a passing mention, checks the numbers against the Hacker News API, and saves the method as a reusable skill.
5. She fans out. A playbook starts one worker per product. Each follows her skill and writes its result to the colony's tracker, a shared SQLite table.
6. She checks every row before she answers, and fixes one. Cal.com's worker counted a post from its old name, Calendso, but that post wasn't a Show HN, Launch HN or YC launch. She clears the row, and the chart shows Cal.com as not found rather than zero.

None of this was wired up in advance. There's no workflow graph to draw: the queen shapes the colony as the work unfolds, and a tracker on disk, not a context window, keeps count of what's done and what's left.

Quick start

You'll need Python 3.11+, Node.js 20+ and git. The quickstart installs uv and ripgrep if they're missing, and offers to install Node.

And a model. The quickstart sets up any of these:

• an API key from Anthropic, OpenAI, Google Gemini, Groq, Cerebras or OpenRouter
• a coding subscription you already pay for: Claude Code, OpenAI Codex, Kimi Code, MiniMax, Z.AI or Antigravity
• Hive LLM
• a local model through Ollama, with no key at all

git clone https://github.com/aden-hive/hive.git
cd hive
./quickstart.sh          # macOS / Linux
.\quickstart.ps1         # Windows (PowerShell 5.1+)

The quickstart creates a single Python environment for the workspace, stores your API key encrypted under ~/.hive, asks which model to use, builds the dashboard and opens it at http://127.0.0.1:8787. Next time, run hive open from the repository.

[!NOTE] This repository is a uv workspace: pip install -e . at its root installs a placeholder that won't run, so use the quickstart. Without the repository, pip install "aden-hive[tools]" installs Hive from PyPI; then run hive open. To build your own project on Hive's agent loop and models, depend on aden-hive: see Building on Hive.

Then type a task on the home screen and choose the queen to give it to, or open the Prompt Library and send a ready-made prompt straight to the queen it was written for.

How it works