Complete OpenClaw Multi-Agent Orchestration Guide

Target keywords: OpenClaw multi-agent, agent orchestration, multi-agent workflow, AI agent coordination

Introduction

A single AI Agent has limited capabilities. Multi-agent orchestration lets multiple specialized agents collaborate to complete complex tasks. This guide shows you how to build multi-agent systems with OpenClaw.

What you will learn:

Multi-agent architecture design
Inter-agent communication
Task allocation
Workflow orchestration
Real-world examples

Estimated time: 50 minutes
Difficulty: Advanced

Why Use Multiple Agents?

Limitations of a Single Agent

Problem	Description
Context confusion	Different tasks get mixed together easily
Limited expertise	One agent cannot excel in every domain
Performance bottlenecks	Complex tasks respond more slowly
Error propagation	A single failure affects the whole system

Advantages of Multiple Agents

✅ Specialization - Each agent focuses on one domain
✅ Parallel processing - Multiple tasks run at the same time
✅ Fault tolerance - A single failure does not take down everything
✅ Scalability - Add new agents as needed

Architecture Patterns

Pattern 1: Orchestrator Architecture

┌─────────────────┐
│  Orchestrator   │  ← 主控 Agent
│    (主控)        │
└────────┬────────┘
         │
    ┌────┴────┬────────┬────────┐
    ▼         ▼        ▼        ▼
┌───────┐ ┌───────┐ ┌───────┐ ┌───────┐
│Research│ │Writer │ │Editor │ │Review │
└───────┘ └───────┘ └───────┘ └───────┘

Best for: content creation, report generation

Pattern 2: Pipeline Architecture

Input → [Agent A] → [Agent B] → [Agent C] → Output
        (提取)      (分析)      (生成)

Best for: data processing, ETL workflows

Pattern 3: Mesh Architecture

       ┌───────┐
  ┌────┤Agent A├────┐
  │    └───────┘    │
  │         │       │
┌─┴─┐    ┌─┴─┐   ┌─┴─┐
│ B │◄──►│ C │◄─►│ D │
└─┬─┘    └─┬─┘   └─┬─┘
  │         │       │
  └────┬────┘       │
       └─────────────┘

Best for: complex decisions, negotiation systems

Hands-On: Content Creation Team

Scenario

Create an automated content creation system:

Researcher - Research topics and gather information
Writer - Write the first draft
Editor - Edit and refine the content
Publisher - Publish to target platforms

Step 1: Create the Agents

# Create 4 agents
openclaw agent create researcher
openclaw agent create writer
openclaw agent create editor
openclaw agent create publisher

Step 2: Configure the Researcher

# ~/.openclaw/agents/researcher.yaml
agent:
  name: "Content Researcher"
  description: "Research topics and gather information"
  
  model:
    provider: openai
    model: gpt-4o
    temperature: 0.3  # More precise
    
  system_prompt: |
    You are a research specialist. Your job is to:
    1. Analyze the given topic
    2. Identify key points and subtopics
    3. Gather relevant information
    4. Structure findings for the writer
    
    Output format:
    - Topic Overview
    - Key Points (bullet points)
    - Sources/References
    - Suggested Structure
    
  tools:
    - name: web_search
      description: "Search the web for information"
    - name: read_url
      description: "Read content from a URL"

Step 3: Configure the Writer

# ~/.openclaw/agents/writer.yaml
agent:
  name: "Content Writer"
  description: "Write content based on research"
  
  model:
    provider: openai
    model: gpt-4o
    temperature: 0.7  # More creative
    
  system_prompt: |
    You are a skilled content writer. Your job is to:
    1. Read the research provided
    2. Write engaging content
    3. Follow the suggested structure
    4. Use appropriate tone and style
    
    Guidelines:
    - Write in markdown format
    - Include headers and subheaders
    - Add examples where relevant
    - Target 1000-1500 words
    
  tools:
    - name: save_draft
      description: "Save the written draft"

Step 4: Configure the Editor

# ~/.openclaw/agents/editor.yaml
agent:
  name: "Content Editor"
  description: "Edit and improve content"
  
  model:
    provider: anthropic
    model: claude-3-5-sonnet
    temperature: 0.2
    
  system_prompt: |
    You are a meticulous editor. Your job is to:
    1. Review the draft content
    2. Fix grammar and spelling
    3. Improve clarity and flow
    4. Ensure consistency
    5. Check facts against research
    
    Editing checklist:
    - [ ] Grammar correct
    - [ ] Spelling correct
    - [ ] Clear and concise
    - [ ] Consistent tone
    - [ ] Accurate facts
    - [ ] Proper formatting
    
    Output: Edited content with change summary

Step 5: Configure the Publisher

# ~/.openclaw/agents/publisher.yaml
agent:
  name: "Content Publisher"
  description: "Publish content to platforms"
  
  system_prompt: |
    You are a publishing specialist. Your job is to:
    1. Format content for target platform
    2. Generate metadata (title, tags, excerpt)
    3. Schedule or publish content
    4. Track publication status
    
  tools:
    - name: publish_to_wordpress
      description: "Publish to WordPress"
    - name: publish_to_medium
      description: "Publish to Medium"
    - name: schedule_social
      description: "Schedule social media posts"

Workflow Orchestration

Define the Workflow

# ~/.openclaw/workflows/content-creation.yaml
workflow:
  name: "Content Creation Pipeline"
  description: "Multi-agent content creation workflow"
  
  steps:
    # Step 1: Research
    - name: research
      agent: researcher
      input: "{{topic}}"
      output: research_data
      
    # Step 2: Write (can start in parallel)
    - name: write
      agent: writer
      input: 
        research: "{{steps.research.output}}"
        topic: "{{topic}}"
      output: draft
      depends_on: [research]
      
    # Step 3: Edit
    - name: edit
      agent: editor
      input:
        draft: "{{steps.write.output}}"
        research: "{{steps.research.output}}"
      output: final_content
      depends_on: [write]
      
    # Step 4: Publish
    - name: publish
      agent: publisher
      input:
        content: "{{steps.edit.output}}"
        platforms: "{{platforms}}"
      output: publication_links
      depends_on: [edit]
      
  # Error handling
  error_handling:
    on_failure: notify_admin
    retry_count: 2
    retry_delay: 30s

Run the Workflow

# Run the workflow
openclaw workflow run content-creation \
  --param topic="AI Automation Trends 2026" \
  --param platforms="wordpress,medium"

Inter-Agent Communication

Method 1: Shared Memory

# ~/.openclaw/skills/shared_memory.py
from openclaw import memory

def share_data(agent_id: str, key: str, data: any):
    """Share data between agents"""
    memory.set(f"shared:{agent_id}:{key}", data)

def get_data(agent_id: str, key: str) -> any:
    """Get shared data"""
    return memory.get(f"shared:{agent_id}:{key}")

Method 2: Message Queue

# Use Redis as the message queue
services:
  redis:
    image: redis:7-alpine
    
agents:
  agent_a:
    tools:
      - name: publish_message
        config:
          queue: "agent_b_queue"
          
  agent_b:
    triggers:
      - type: queue
        queue: "agent_b_queue"
        action: process_message

Method 3: Direct Calls

# Agent A calls Agent B
from openclaw import agent

result = agent.call(
    agent_id="writer",
    message="Write based on this research: ...",
    context=research_data
)

Real-World Examples

Example 1: Intelligent Customer Support System

workflow:
  name: "Customer Support"
  
  steps:
    # Intent classification
    - name: classify
      agent: classifier
      input: "{{customer_message}}"
      output: intent
      
    # Route to a specialist agent
    - name: route
      type: switch
      condition: "{{steps.classify.output.category}}"
      cases:
        technical:
          agent: tech_support
        billing:
          agent: billing_support
        sales:
          agent: sales_support
        default:
          agent: general_support
          
    # Generate the reply
    - name: respond
      agent: "{{steps.route.output.agent}}"
      input: "{{customer_message}}"
      output: response
      
    # Quality check
    - name: qc
      agent: quality_checker
      input: "{{steps.respond.output}}"
      condition: "{{steps.classify.output.priority}} == 'high'"

Example 2: Code Review System

workflow:
  name: "Code Review"
  
  steps:
    # Static analysis
    - name: static_analysis
      agent: linter_agent
      input: "{{code}}"
      output: lint_issues
      
    # Security scan (parallel)
    - name: security_scan
      agent: security_agent
      input: "{{code}}"
      output: security_issues
      
    # Architecture review (parallel)
    - name: architecture_review
      agent: architect_agent
      input: 
        code: "{{code}}"
        context: "{{pr_description}}"
      output: architecture_feedback
      
    # Compile the report
    - name: compile_report
      agent: report_compiler
      input:
        lint: "{{steps.static_analysis.output}}"
        security: "{{steps.security_scan.output}}"
        architecture: "{{steps.architecture_review.output}}"
      output: review_report
      depends_on: [static_analysis, security_scan, architecture_review]

Example 3: Data Analysis Pipeline

workflow:
  name: "Data Analysis Pipeline"
  
  steps:
    # Data extraction
    - name: extract
      agent: data_extractor
      input: "{{data_source}}"
      output: raw_data
      
    # Data cleaning
    - name: clean
      agent: data_cleaner
      input: "{{steps.extract.output}}"
      output: clean_data
      
    # Parallel analysis
    - name: analyze_descriptive
      agent: descriptive_analyst
      input: "{{steps.clean.output}}"
      output: descriptive_stats
      
    - name: analyze_predictive
      agent: predictive_analyst
      input: "{{steps.clean.output}}"
      output: predictions
      
    # Visualization
    - name: visualize
      agent: visualization_agent
      input:
        descriptive: "{{steps.analyze_descriptive.output}}"
        predictive: "{{steps.analyze_predictive.output}}"
      output: charts
      depends_on: [analyze_descriptive, analyze_predictive]
      
    # Generate the report
    - name: report
      agent: report_writer
      input:
        data: "{{steps.clean.output}}"
        analysis: "{{steps.analyze_descriptive.output}}"
        predictions: "{{steps.analyze_predictive.output}}"
        charts: "{{steps.visualize.output}}"
      output: final_report

Monitoring and Debugging

Workflow Monitoring

monitoring:
  workflows:
    enabled: true
    
    # Track metrics
    metrics:
      - step_duration
      - agent_utilization
      - error_rate
      - throughput
      
    # Alerts
    alerts:
      - condition: "error_rate > 5%"
        action: notify_admin
        
      - condition: "step_duration > 60s"
        action: log_warning

Debugging Tips

# View workflow execution history
openclaw workflow logs content-creation

# View a specific step
openclaw workflow logs content-creation --step write

# Retry a failed step
openclaw workflow retry content-creation --step edit

# Export execution data
openclaw workflow export content-creation --format json

Best Practices

1. Agent Design Principles

Single responsibility - Each agent should do one thing well
Clear interfaces - Define clear inputs and outputs
Fault-tolerant design - Handle exceptions and edge cases
Observable systems - Record logs and metrics

2. Workflow Optimization

optimization:
  # Parallelization
  parallel:
    max_concurrent: 5
    
  # Caching
  caching:
    enabled: true
    ttl: 3600
    
  # Timeouts
  timeouts:
    default: 60s
    research: 120s
    write: 180s

3. Error Handling

error_handling:
  strategies:
    # Retry
    retry:
      max_attempts: 3
      backoff: exponential
      
    # Fallback
    fallback:
      agent: simple_agent
      
    # Manual intervention
    escalation:
      condition: "retry_exhausted"
      action: create_ticket

Next Steps

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Last updated: March 2026

Multi-Agent Guide