hive/ENVIRONMENT_SETUP.md

Agent Development Environment Setup

Complete setup guide for building and running goal-driven agents with the Aden Agent Framework.

Quick Setup

# Run the automated setup script
./scripts/setup-python.sh

Note for Windows Users:
Running the setup script on native Windows shells (PowerShell / Git Bash) may sometimes fail due to Python App Execution Aliases.
It is strongly recommended to use WSL (Windows Subsystem for Linux) for a smoother setup experience.

This will:

• Check Python version (requires 3.11+)
• Install the core framework package (framework)
• Install the tools package (aden_tools)
• Fix package compatibility issues (openai + litellm)
• Verify all installations

Manual Setup (Alternative)

If you prefer to set up manually or the script fails:

1. Install Core Framework

cd core
pip install -e .

2. Install Tools Package

cd tools
pip install -e .

3. Upgrade OpenAI Package

# litellm requires openai >= 1.0.0
pip install --upgrade "openai>=1.0.0"

4. Verify Installation

python -c "import framework; print('✓ framework OK')"
python -c "import aden_tools; print('✓ aden_tools OK')"
python -c "import litellm; print('✓ litellm OK')"

Windows Tip:
On Windows, if the verification commands fail, ensure you are running them in WSL or after disabling Python App Execution Aliases in Windows Settings → Apps → App Execution Aliases.

Requirements

Python Version

• Minimum: Python 3.11
• Recommended: Python 3.11 or 3.12
• Tested on: Python 3.11, 3.12, 3.13

System Requirements

• pip (latest version)
• 2GB+ RAM
• Internet connection (for LLM API calls)
• For Windows users: WSL 2 is recommended for full compatibility.

API Keys (Optional)

For running agents with real LLMs:

export ANTHROPIC_API_KEY="your-key-here"

Running Agents

All agent commands must be run from the project root with PYTHONPATH set:

# From /hive/ directory
PYTHONPATH=core:exports python -m agent_name COMMAND

Example: Support Ticket Agent

# Validate agent structure
PYTHONPATH=core:exports python -m support_ticket_agent validate

# Show agent information
PYTHONPATH=core:exports python -m support_ticket_agent info

# Run agent with input
PYTHONPATH=core:exports python -m support_ticket_agent run --input '{
  "ticket_content": "My login is broken. Error 401.",
  "customer_id": "CUST-123",
  "ticket_id": "TKT-456"
}'

# Run in mock mode (no LLM calls)
PYTHONPATH=core:exports python -m support_ticket_agent run --mock --input '{...}'

Example: Other Agents

# Market Research Agent
PYTHONPATH=core:exports python -m market_research_agent info

# Outbound Sales Agent
PYTHONPATH=core:exports python -m outbound_sales_agent validate

# Personal Assistant Agent
PYTHONPATH=core:exports python -m personal_assistant_agent run --input '{...}'

Building New Agents and Run Flow

Build and run an agent using Claude Code CLI with the agent building skills:

1. Install Claude Skills (One-time)

./quickstart.sh

This installs agent-related Claude Code skills:

• /building-agents-construction - Step-by-step build guide
• /building-agents-core - Fundamental concepts
• /building-agents-patterns - Best practices
• /testing-agent - Test and validate agents
• /agent-workflow - Complete workflow

2. Build an Agent

claude> /building-agents-construction

Follow the prompts to:

1. Define your agent's goal
2. Design the workflow nodes
3. Connect nodes with edges
4. Generate the agent package under exports/

This step creates the initial agent structure required for further development.

3. Define Agent Logic

claude> /building-agents-core

Follow the prompts to:

1. Understand the agent architecture and file structure
2. Define the agent's goal, success criteria, and constraints
3. Learn node types (LLM, tool-use, router, function)
4. Discover and validate available tools before use

This step establishes the core concepts and rules needed before building an agent.

4. Apply Agent Patterns

claude> /building-agents-patterns

Follow the prompts to:

1. Apply best-practice agent design patterns
2. Add pause/resume flows for multi-turn interactions
3. Improve robustness with routing, fallbacks, and retries
4. Avoid common anti-patterns during agent construction

This step helps optimize agent design before final testing.

5. Test Your Agent

claude> /testing-agent

Follow the prompts to:

1. Generate test guidelines for constraints and success criteria
2. Write agent tests directly under exports/{agent}/tests/
3. Run goal-based evaluation tests
4. Debug failing tests and iterate on agent improvements

This step verifies that the agent meets its goals before production use.

6. Agent Development Workflow (End-to-End)

claude> /agent-workflow

Follow the guided flow to:

1. Understand core agent concepts (optional)
2. Build the agent structure step by step
3. Apply best-practice design patterns (optional)
4. Test and validate the agent against its goals

This workflow orchestrates all agent-building skills to take you from idea → production-ready agent.

Troubleshooting

"externally-managed-environment" error (PEP 668)

Cause: Python 3.12+ on macOS/Homebrew, WSL, or some Linux distros prevents system-wide pip installs.

Solution: Create and use a virtual environment:

# Create virtual environment
python3 -m venv .venv

# Activate it
source .venv/bin/activate  # macOS/Linux
# .venv\Scripts\activate   # Windows

# Then run setup
./scripts/setup-python.sh

Always activate the venv before running agents:

source .venv/bin/activate
PYTHONPATH=core:exports python -m your_agent_name demo

"ModuleNotFoundError: No module named 'framework'"

Solution: Install the core package:

cd core && pip install -e .

"ModuleNotFoundError: No module named 'aden_tools'"

Solution: Install the tools package:

cd tools && pip install -e .

Or run the setup script:

./scripts/setup-python.sh

"ModuleNotFoundError: No module named 'openai._models'"

Cause: Outdated openai package (0.27.x) incompatible with litellm

Solution: Upgrade openai:

pip install --upgrade "openai>=1.0.0"

"No module named 'support_ticket_agent'"

Cause: Not running from project root or missing PYTHONPATH

Solution: Ensure you're in the project root directory and use:

PYTHONPATH=core:exports python -m support_ticket_agent validate

Agent imports fail with "broken installation"

Symptom: pip list shows packages pointing to non-existent directories

Solution: Reinstall packages properly:

# Remove broken installations
pip uninstall -y framework tools

# Reinstall correctly
./scripts/setup-python.sh

Package Structure

The Hive framework consists of three Python packages:

hive/
├── core/                    # Core framework (runtime, graph executor, LLM providers)
│   ├── framework/
│   ├── pyproject.toml
│   └── requirements.txt
│
├── tools/                   # Tools and MCP servers
│   ├── src/
│   │   └── aden_tools/     # Actual package location
│   ├── pyproject.toml
│   └── README.md
│
└── exports/                 # Agent packages (your agents go here)
    ├── support_ticket_agent/
    ├── market_research_agent/
    ├── outbound_sales_agent/
    └── personal_assistant_agent/

Why PYTHONPATH is Required

The packages are installed in editable mode (pip install -e), which means:

• framework and aden_tools are globally importable (no PYTHONPATH needed)
• exports is NOT installed as a package (PYTHONPATH required)

This design allows agents in exports/ to be:

• Developed independently
• Version controlled separately
• Deployed as standalone packages

Development Workflow

1. Setup (Once)

./scripts/setup-python.sh

2. Build Agent (Claude Code)

claude> /building-agents-construction
Enter goal: "Build an agent that processes customer support tickets"

3. Validate Agent

PYTHONPATH=core:exports python -m support_ticket_agent validate

4. Test Agent

claude> /testing-agent

5. Run Agent

PYTHONPATH=core:exports python -m support_ticket_agent run --input '{...}'

IDE Setup

VSCode

Add to .vscode/settings.json:

{
  "python.analysis.extraPaths": [
    "${workspaceFolder}/core",
    "${workspaceFolder}/exports"
  ],
  "python.autoComplete.extraPaths": [
    "${workspaceFolder}/core",
    "${workspaceFolder}/exports"
  ]
}

PyCharm

1. Open Project Settings → Project Structure
2. Mark core as Sources Root
3. Mark exports as Sources Root

Environment Variables

Required for LLM Operations

export ANTHROPIC_API_KEY="sk-ant-..."

Optional Configuration

# Credentials storage location (default: ~/.aden/credentials)
export ADEN_CREDENTIALS_PATH="/custom/path"

# Agent storage location (default: /tmp)
export AGENT_STORAGE_PATH="/custom/storage"

Additional Resources

• Framework Documentation: core/README.md
• Tools Documentation: tools/README.md
• Example Agents: exports/
• Agent Building Guide: .claude/skills/building-agents-construction/SKILL.md
• Testing Guide: .claude/skills/testing-agent/SKILL.md

Contributing

When contributing agent packages:

1. Place agents in exports/agent_name/
2. Follow the standard agent structure (see existing agents)
3. Include README.md with usage instructions
4. Add tests if using /testing-agent
5. Document required environment variables

Support

• Issues: https://github.com/adenhq/hive/issues
• Discord: https://discord.com/invite/MXE49hrKDk
• Documentation: https://docs.adenhq.com/