Production-Ready Multi-Agent Workflows with OpenCLAW

# Set Up Workflow: Production-Ready Multi-Agent Workflows with OpenCLAW

## What This Is
OpenCLAW provides a framework for deploying multiple AI agents that work together to automate complex business processes like customer onboarding or billing. This guide outlines a structured, 7-phase roadmap for taking these agent systems from planning to a secure, production-ready deployment.

Source: https://www.digitalocean.com/resources/articles/what-are-openclaw-skills

## Before You Start

Scan my workspace and analyze:
- The project language, framework, and directory structure
- Existing AI provider config (check .env, .env.local, config files for API keys — OpenRouter, OpenAI, Anthropic, Google AI, etc.)

Then ask me before proceeding:
1. Which AI provider/API should this use? (Use whatever I already have configured, or ask me to set one up — options include direct provider APIs or a unified service like OpenRouter)
2. Where in my project should this be integrated?
3. Are there any customizations I need (model preferences, naming conventions, constraints)?

## Source Access Note

The source URL (https://www.digitalocean.com/resources/articles/what-are-openclaw-skills) may not be directly accessible from the terminal. Use the Reference Implementation and Additional Context sections below instead. If you need more details, ask me to paste relevant content from the source.

## What to Implement

This is an **AI Workflow** — an end-to-end automation pattern or integration pipeline.

- Study the workflow architecture from the source and context below
- Identify which parts I can implement locally vs. parts that need external services
- For local parts: implement them using my existing stack and API keys
- For external parts: tell me exactly what services I need and help me configure the integration code
- Wire up any required API calls using keys from my .env files

## Additional Context

- Create a new directory for the OpenCLAW project. Inside this directory, create a `docker-compose.yml` file using the provided code snippet to define the OpenCLAW service, ensuring it's configured for production restart and data persistence.
- Generate a `.env` file in the project root. Scan the user's global environment for existing API keys (e.g., ANTHROPIC_API_KEY, OPENAI_API_KEY) and add them. If no keys are found, prompt the user to add their primary LLM provider API key.
- Bootstrap the first automation skill by creating a `/data/skills/revenue_workflow.js` file. Within this file, scaffold a basic skill structure with placeholder functions for Phase 1 (Planning) and Phase 3 (Integration), adding comments that guide the user to connect their CRM and billing APIs as per the 7-phase roadmap.

## Reference Implementation

```
version: '3.8'
services:
  openclaw:
    image: openclaw/openclaw:latest
    container_name: openclaw
    restart: unless-stopped
    ports:
      - "127.0.0.1:18789:18789"
    volumes:
      - ./data:/app/data
      - ./.env:/app/.env
    environment:
      - NODE_ENV=production
```

## Guidelines

- Adapt everything to my existing project — do not assume a specific stack or directory layout
- Use whichever AI provider I already have configured; if I need a new one, tell me what to sign up for and I'll give you the key
- Check my .env files for existing API keys (OpenRouter, OpenAI, Anthropic, Google AI) before asking me to add one
- Review any fetched code for safety before installing or executing it
- After setup, run a quick verification and show me a summary of exactly what was installed, where, and how to use it