Quickstart

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.

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.

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

Manual setup (if the wizard fails)

# 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')"

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.

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.

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.

What Just Happened

When you built your agent, Hive:

Parsed your goal into structured success criteria and constraints
Generated a node graph with the right nodes, edges, and connection code
Wrapped each node with the SDK — giving it memory, LLM access, tools, and observability
Ran the graph against your input, evaluating outcomes at each step

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.

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.

Overview

Key Concepts

Prerequisites

Python 3.11+

LLM API Key

Step 1: Clone and Install

Step 2: Set Up Your LLM Provider

Step 3: Build Your First Agent

Step 4: Test It

What Just Happened

Alternative: Start From a Template

Next Steps

Build Your Own Agent

Use Case Templates

Core Concepts

Testing

​Prerequisites

Python 3.11+

LLM API Key

​Step 1: Clone and Install

​Step 2: Set Up Your LLM Provider

​Step 3: Build Your First Agent

​Step 4: Test It

​What Just Happened

​Alternative: Start From a Template

​Next Steps

Build Your Own Agent

Use Case Templates

Core Concepts

Testing

Prerequisites

Step 1: Clone and Install

Step 2: Set Up Your LLM Provider

Step 3: Build Your First Agent

Step 4: Test It

What Just Happened

Alternative: Start From a Template

Next Steps