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Documentation Index

Fetch the complete documentation index at: https://docs.adenhq.com/llms.txt

Use this file to discover all available pages before exploring further.

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.