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Master OpenClaw installation and configuration with this comprehensive technical guide. Learn setup, customization, skill development, and advanced deployment strategies for your personal AI assistant.
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OpenClaw has captured the imagination of developers worldwide, but getting from "wow, this looks amazing" to "I have my own AI assistant running" can seem daunting. This comprehensive guide will take you from complete beginner to OpenClaw power user, covering everything from basic installation to advanced customization and deployment strategies.
Whether you're setting up OpenClaw for personal use, deploying it for your team, or contributing to the project, this guide provides the technical depth you need to succeed.
Before diving into installation, it's crucial to understand OpenClaw's architecture:
System Architecture:
Technology Stack:
Minimum Requirements:
Recommended Production Setup:
The fastest way to get OpenClaw running:
# Clone the repository git clone https://github.com/openclaw/openclaw.git cd openclaw # Run the automated installer ./install.sh # Follow the interactive setup wizard python setup.py --interactive
What the installer does:
For users who want full control over the installation process:
# 1. Clone and enter directory git clone https://github.com/openclaw/openclaw.git cd openclaw # 2. Create virtual environment python3 -m venv venv source venv/bin/activate # On Windows: venv\Scripts\activate # 3. Install dependencies pip install -r requirements.txt # 4. Install additional dependencies for specific features pip install -r requirements-optional.txt # For advanced features # 5. Set up environment variables cp .env.example .env nano .env # Edit configuration # 6. Initialize database python manage.py migrate # 7. Create admin user python manage.py create-admin # 8. Start the service python main.py
For containerized deployment:
# docker-compose.yml version: '3.8' services: openclaw: image: openclaw/openclaw:latest ports: - "8000:8000" environment: - DATABASE_URL=postgresql://user:pass@db:5432/openclaw - REDIS_URL=redis://redis:6379 volumes: - ./data:/app/data - ./logs:/app/logs depends_on: - db - redis restart: unless-stopped db: image: postgres:15 environment: POSTGRES_DB: openclaw POSTGRES_USER: user POSTGRES_PASSWORD: pass volumes: - postgres_data:/var/lib/postgresql/data restart: unless-stopped redis: image: redis:7-alpine restart: unless-stopped volumes: postgres_data:
# Deploy with Docker Compose docker-compose up -d # Check status docker-compose ps # View logs docker-compose logs -f openclaw
The main configuration file controls OpenClaw's behavior:
# config.yaml core: name: "My OpenClaw Assistant" personality: "helpful, friendly, and professional" timezone: "America/New_York" language: "en" ai: provider: "openai" # openai, anthropic, local, azure model: "gpt-4" temperature: 0.7 max_tokens: 2000 memory: type: "persistent" # persistent, session, hybrid retention_days: 365 max_context_length: 10000 security: encryption: true auth_required: true rate_limiting: true allowed_users: [] # Empty = all users allowed
# Install WhatsApp Web dependencies pip install selenium webdriver-manager # Configure WhatsApp settings
# In config.yaml messaging: whatsapp: enabled: true session_path: "./whatsapp_session" auto_reply: true group_support: true media_handling: true
# 1. Create bot with @BotFather on Telegram # 2. Get your bot token # 3. Configure in OpenClaw
# In config.yaml messaging: telegram: enabled: true bot_token: "YOUR_BOT_TOKEN" allowed_chats: [] # Chat IDs that can use the bot admin_users: [] # User IDs with admin privileges
# In config.yaml messaging: discord: enabled: true bot_token: "YOUR_DISCORD_BOT_TOKEN" guild_id: "YOUR_SERVER_ID" command_prefix: "!" allowed_channels: []
ai: provider: "openai" api_key: "sk-your-api-key" model: "gpt-4" organization: "org-your-org-id" # Optional # Advanced settings temperature: 0.7 max_tokens: 2000 top_p: 1.0 frequency_penalty: 0.0 presence_penalty: 0.0
ai: provider: "anthropic" api_key: "sk-ant-your-api-key" model: "claude-3-sonnet-20240229" # Claude-specific settings max_tokens: 4000 temperature: 0.7
# Install Ollama curl -fsSL https://ollama.ai/install.sh | sh # Pull a model ollama pull llama2 # Start Ollama service ollama serve
ai: provider: "ollama" base_url: "http://localhost:11434" model: "llama2" # Local model settings temperature: 0.7 context_length: 4096
OpenClaw's power comes from its modular skill system. Skills are Python modules that extend OpenClaw's capabilities:
Skill Structure:
# skills/weather_skill.py from openclaw.skill import Skill from openclaw.decorators import command, requires_api_key class WeatherSkill(Skill): name = "weather" description = "Get weather information for any location" def __init__(self): super().__init__() self.api_key = self.config.get('weather_api_key') @command("weather", "Get current weather") @requires_api_key("weather_api_key") async def get_weather(self, location: str) -> str: """Get current weather for a location.""" # Implementation here return f"Weather in {location}: Sunny, 72°F" @command("forecast", "Get weather forecast") async def get_forecast(self, location: str, days: int = 3) -> str: """Get weather forecast for multiple days.""" # Implementation here return f"{days}-day forecast for {location}"
# skills/file_manager.py import os import shutil from pathlib import Path from openclaw.skill import Skill from openclaw.decorators import command, safe_path class FileManagerSkill(Skill): name = "file_manager" description = "Manage files and directories" @command("ls", "List directory contents") @safe_path async def list_directory(self, path: str = ".") -> str: """List contents of a directory.""" try: items = os.listdir(path) return "\n".join(items) except Exception as e: return f"Error: {str(e)}" @command("mkdir", "Create directory") @safe_path async def create_directory(self, path: str) -> str: """Create a new directory.""" try: os.makedirs(path, exist_ok=True) return f"Directory created: {path}" except Exception as e: return f"Error: {str(e)}" @command("cp", "Copy file") @safe_path async def copy_file(self, source: str, destination: str) -> str: """Copy a file from source to destination.""" try: shutil.copy2(source, destination) return f"Copied {source} to {destination}" except Exception as e: return f"Error: {str(e)}"
# skills/github_skill.py import aiohttp from openclaw.skill import Skill from openclaw.decorators import command, requires_api_key class GitHubSkill(Skill): name = "github" description = "Interact with GitHub repositories" @command("repo_info", "Get repository information") @requires_api_key("github_token") async def get_repo_info(self, repo: str) -> str: """Get information about a GitHub repository.""" headers = { 'Authorization': f'token {self.config.get("github_token")}', 'Accept': 'application/vnd.github.v3+json' } async with aiohttp.ClientSession() as session: async with session.get( f'https://api.github.com/repos/{repo}', headers=headers ) as response: if response.status == 200: data = await response.json() return f""" Repository: {data['full_name']} Description: {data['description']} Stars: {data['stargazers_count']} Forks: {data['forks_count']} Language: {data['language']} Last Updated: {data['updated_at']} """ else: return f"Error: {response.status}" @command("create_issue", "Create a GitHub issue") async def create_issue(self, repo: str, title: str, body: str) -> str: """Create a new issue in a GitHub repository.""" # Implementation here pass
# Install a skill from the community repository python manage.py install-skill weather # List installed skills python manage.py list-skills # Enable/disable skills python manage.py enable-skill weather python manage.py disable-skill weather # Update all skills python manage.py update-skills
# config.yaml performance: memory: max_conversation_length: 10000 cleanup_interval: 3600 # seconds cache_size: 1000 processing: max_concurrent_requests: 10 request_timeout: 30 retry_attempts: 3
# database_config.py DATABASE_CONFIG = { 'postgresql': { 'host': 'localhost', 'port': 5432, 'database': 'openclaw', 'user': 'openclaw_user', 'password': 'secure_password', 'pool_size': 20, 'max_overflow': 30, 'pool_timeout': 30, 'pool_recycle': 3600 } }
# config.yaml security: authentication: method: "jwt" # jwt, oauth, api_key jwt_secret: "your-secret-key" token_expiry: 86400 # 24 hours authorization: roles: admin: - "manage_skills" - "view_logs" - "modify_config" user: - "use_assistant" - "create_skills" guest: - "use_assistant"
# rate_limiting.py RATE_LIMITS = { 'default': { 'requests_per_minute': 60, 'requests_per_hour': 1000, 'requests_per_day': 10000 }, 'premium': { 'requests_per_minute': 120, 'requests_per_hour': 5000, 'requests_per_day': 50000 } }
# logging.yaml version: 1 formatters: detailed: format: '%(asctime)s - %(name)s - %(levelname)s - %(message)s' handlers: console: class: logging.StreamHandler level: INFO formatter: detailed file: class: logging.handlers.RotatingFileHandler filename: logs/openclaw.log maxBytes: 10485760 # 10MB backupCount: 5 level: DEBUG formatter: detailed loggers: openclaw: level: DEBUG handlers: [console, file] propagate: false
# monitoring.py from prometheus_client import Counter, Histogram, Gauge # Metrics REQUEST_COUNT = Counter('openclaw_requests_total', 'Total requests') REQUEST_DURATION = Histogram('openclaw_request_duration_seconds', 'Request duration') ACTIVE_CONVERSATIONS = Gauge('openclaw_active_conversations', 'Active conversations') SKILL_USAGE = Counter('openclaw_skill_usage_total', 'Skill usage', ['skill_name'])
For personal or small team use:
# systemd service file # /etc/systemd/system/openclaw.service [Unit] Description=OpenClaw AI Assistant After=network.target [Service] Type=simple User=openclaw WorkingDirectory=/opt/openclaw ExecStart=/opt/openclaw/venv/bin/python main.py Restart=always RestartSec=10 [Install] WantedBy=multi-user.target
# Enable and start service sudo systemctl enable openclaw sudo systemctl start openclaw sudo systemctl status openclaw
For production environments:
# kubernetes/deployment.yaml apiVersion: apps/v1 kind: Deployment metadata: name: openclaw spec: replicas: 3 selector: matchLabels: app: openclaw template: metadata: labels: app: openclaw spec: containers: - name: openclaw image: openclaw/openclaw:latest ports: - containerPort: 8000 env: - name: DATABASE_URL valueFrom: secretKeyRef: name: openclaw-secrets key: database-url resources: requests: memory: "512Mi" cpu: "250m" limits: memory: "1Gi" cpu: "500m"
# Using AWS ECS with Fargate aws ecs create-cluster --cluster-name openclaw-cluster # Create task definition aws ecs register-task-definition --cli-input-json file://task-definition.json # Create service aws ecs create-service \ --cluster openclaw-cluster \ --service-name openclaw-service \ --task-definition openclaw:1 \ --desired-count 2
# Build and deploy to Cloud Run gcloud builds submit --tag gcr.io/PROJECT-ID/openclaw gcloud run deploy --image gcr.io/PROJECT-ID/openclaw --platform managed
Issue: Python dependency conflicts
# Solution: Use virtual environment python -m venv openclaw-env source openclaw-env/bin/activate pip install --upgrade pip pip install -r requirements.txt
Issue: Database connection errors
# Check database status sudo systemctl status postgresql # Reset database python manage.py reset-db python manage.py migrate
Issue: High memory usage
# Adjust memory settings in config.yaml performance: memory: max_conversation_length: 5000 cleanup_interval: 1800
Issue: Slow response times
# Check system resources htop iostat -x 1 # Optimize database python manage.py optimize-db
# View real-time logs tail -f logs/openclaw.log # Search for errors grep -i error logs/openclaw.log # Analyze performance grep "REQUEST_DURATION" logs/openclaw.log | awk '{print $NF}' | sort -n
# Enable profiling python -m cProfile -o profile.stats main.py # Analyze profile python -c " import pstats p = pstats.Stats('profile.stats') p.sort_stats('cumulative').print_stats(20) "
# Regular maintenance script #!/bin/bash # Update OpenClaw git pull origin main pip install -r requirements.txt # Update skills python manage.py update-skills # Clean up logs find logs/ -name "*.log" -mtime +30 -delete # Backup database python manage.py backup-db # Restart service sudo systemctl restart openclaw
OpenClaw represents a new paradigm in personal AI assistants, offering unprecedented control, customization, and capability. This guide has covered everything from basic installation to advanced deployment strategies, giving you the knowledge to build and maintain your own OpenClaw instance.
The key to success with OpenClaw is starting simple and gradually adding complexity as you become more comfortable with the system. Begin with a basic installation, experiment with existing skills, and then start creating your own custom capabilities.
Remember that OpenClaw is an active open-source project with a vibrant community. Don't hesitate to ask questions, contribute improvements, and share your experiences with others.
Your journey with OpenClaw starts now. Build something amazing.
This guide will be updated regularly as OpenClaw evolves. Check the official documentation and community forums for the latest information and best practices.