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By the end of this guide, you’ll have Hive installed and your first agent running. That’s your First Success — and everything from here should be smooth.

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Prerequisites

Python 3.11+

Python 3.11, 3.12, or 3.13. Check with python --version.

LLM API Key

An API key for Anthropic, OpenAI, Gemini, or another LiteLLM-supported provider.
Windows users: Use WSL (Windows Subsystem for Linux) or Git Bash. Some automation scripts may not run correctly in standard Command Prompt or PowerShell.

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Step 1: Clone and Install

git clone https://github.com/adenhq/hive.git
cd hive
./quickstart.sh
The setup wizard handles everything:
  • Checks Python and installs uv if needed
  • Installs workspace packages (framework and aden_tools)
  • Installs Playwright browser dependencies when available
  • Verifies imports and local environment health
  • Guides LLM provider configuration
# Install dependencies
uv sync

# Verify everything works
uv run python -c "import framework; print('framework OK')"
uv run python -c "import aden_tools; print('aden_tools OK')"
uv run python -c "import litellm; print('litellm OK')"

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Step 2: Set Up Your LLM Provider

Export at least one provider API key: 
export ANTHROPIC_API_KEY="sk-ant-..."
The quickstart script can detect existing keys in your environment and generate a default Hive provider configuration automatically.

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Step 3: Build Your First Agent

Use your coding agent to create an agent through a guided conversation: 
claude> /hive
This walks you through defining your goal, generating the agent graph, and setting up the required nodes and edges. No manual wiring needed.

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Step 4: Test It

Run your agent interactively to see it work: 
# Interactive dashboard
hive tui

# Or run directly with input
hive run exports/your_agent_name --input '{"task": "Your input here"}'
First Success! If your agent ran and produced output, you’ve reached the milestone. Everything from here — debugging, deployment, iteration — builds on this foundation.

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What Just Happened

When you built your agent, Hive:
  1. Parsed your goal into structured success criteria and constraints
  2. Generated a node graph with the right nodes, edges, and connection code
  3. Wrapped each node with the SDK — giving it memory, LLM access, tools, and observability
  4. Ran the graph against your input, evaluating outcomes at each step

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Alternative: Start From a Template

If you’d rather start from something that already works and customize it: 
# Browse available agents
hive tui

# Or copy an existing template and make it yours
See Build From Existing Agent for the full workflow.

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Next Steps

Build Your Own Agent

Detailed guide to creating custom goal-driven agents.

Use Case Templates

Browse ready-made agents you can deploy or customize.

Core Concepts

Understand goals, graphs, and the runtime model.

Testing

Add goal-based tests before deploying to production.