1557 Environment Setup 88dddc86

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
./quickstart.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

Windows Setup

Windows users should use WSL (Windows Subsystem for Linux) to set up and run agents.

1. Install WSL 2 if you haven't already:

   wsl --install
   

2. Open your WSL terminal, clone the repo, and run the quickstart script:

   ./quickstart.sh
   

Alpine Linux Setup

If you are using Alpine Linux (e.g., inside a Docker container), you must install system dependencies and use a virtual environment before running the setup script:

1. Install System Dependencies:

apk update
apk add bash git python3 py3-pip nodejs npm curl build-base python3-dev linux-headers libffi-dev

2. Set up Virtual Environment (Required for Python 3.12+):

python3 -m venv venv
source venv/bin/activate
pip install --upgrade pip setuptools wheel

3. Run the Quickstart Script:

./quickstart.sh

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"

Windows (PowerShell):

$env: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

Windows (PowerShell):

$env:PYTHONPATH="core;exports"
python -m agent_name COMMAND

Example: Support Ticket Agent

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

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

# Run agent with input
PYTHONPATH=core:exports python -m your_agent_name run --input '{
  "task": "Your input here"
}'

# Run in mock mode (no LLM calls)
PYTHONPATH=core:exports python -m your_agent_name run --mock --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 verifies agent-related Claude Code skills are available:

• /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
./quickstart.sh

Always activate the venv before running agents:

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

PowerShell: “running scripts is disabled on this system”

Run once per session:

Set-ExecutionPolicy -Scope Process -ExecutionPolicy Bypass

"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:

./quickstart.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 'your_agent_name'"

Cause: Not running from project root, missing PYTHONPATH, or agent not yet created

Solution: Ensure you're in /hive/ and use:

Linux/macOS:

PYTHONPATH=core:exports python -m your_agent_name validate

Windows:

$env: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
./quickstart.sh

Package Structure

The Hive framework consists of three Python packages:

hive/
├── core/                    # Core framework (runtime, graph executor, LLM providers)
│   ├── framework/
│   ├── .venv/              # Created by quickstart.sh
│   └── pyproject.toml
│
├── tools/                   # Tools and MCP servers
│   ├── src/
│   │   └── aden_tools/     # Actual package location
│   ├── .venv/              # Created by quickstart.sh
│   └── pyproject.toml
│
└── exports/                 # Agent packages (user-created, gitignored)
    └── your_agent_name/     # Created via /building-agents-construction

Separate Virtual Environments

The project uses separate virtual environments for core and tools packages to:

• Isolate dependencies and avoid conflicts
• Allow independent development and testing of each package
• Enable MCP servers to run with their specific dependencies

How It Works

When you run ./quickstart.sh or uv sync in each directory:

1. core/.venv/ - Contains the framework package and its dependencies (anthropic, litellm, mcp, etc.)
2. tools/.venv/ - Contains the aden_tools package and its dependencies (beautifulsoup4, pandas, etc.)

Cross-Package Imports

The core and tools packages are intentionally independent:

• No cross-imports: framework does not import aden_tools directly, and vice versa
• Communication via MCP: Tools are exposed to agents through MCP servers, not direct Python imports
• Runtime integration: The agent runner loads tools via the MCP protocol at runtime

If you need to use both packages in a single script (e.g., for testing), you have two options:

# Option 1: Install both in a shared environment
python -m venv .venv
source .venv/bin/activate
pip install -e core/ -e tools/

# Option 2: Use PYTHONPATH (for quick testing)
PYTHONPATH=core:tools/src python your_script.py

MCP Server Configuration

The .mcp.json at project root configures MCP servers to use their respective virtual environments:

{
  "mcpServers": {
    "agent-builder": {
      "command": "core/.venv/bin/python",
      "args": ["-m", "framework.mcp.agent_builder_server"]
    },
    "tools": {
      "command": "tools/.venv/bin/python",
      "args": ["-m", "aden_tools.mcp_server", "--stdio"]
    }
  }
}

This ensures each MCP server runs with its correct dependencies.

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)

./quickstart.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 your_agent_name validate

4. Test Agent

claude> /testing-agent

5. Run Agent

PYTHONPATH=core:exports python -m your_agent_name 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/