How to Build an OpenClaw
Agent Team
Technical guide to multi-agent setup with OpenClaw. Learn agent orchestration, communication patterns, and team coordination for scalable AI systems.
Agent Team Architecture Patterns
Choose the right architecture pattern for your multi-agent system
Hierarchical Team
Supervisor agent coordinates specialized worker agents
Best for: Complex workflows requiring central coordination
Structure:
supervisor: Main coordinator agent
workers: Data agent, Analysis agent, Report agent
communication: Top-down task delegation
Advantages:
- ✓Clear hierarchy
- ✓Centralized control
- ✓Easy monitoring
Disadvantages:
- •Single point of failure
- •Bottleneck potential
Peer-to-Peer Network
Agents communicate directly without central coordinator
Best for: Collaborative tasks requiring agent autonomy
Structure:
agents: Agent A, Agent B, Agent C
communication: Direct peer communication
coordination: Consensus-based decisions
Advantages:
- ✓No bottlenecks
- ✓Fault tolerant
- ✓Autonomous operation
Disadvantages:
- •Complex coordination
- •Potential conflicts
Pipeline Chain
Sequential processing through specialized agents
Best for: Data processing workflows with clear stages
Structure:
stages: Input agent, Processing agent, Output agent
communication: Sequential handoffs
coordination: Event-driven progression
Advantages:
- ✓Clear data flow
- ✓Specialized processing
- ✓Easy scaling
Disadvantages:
- •Sequential bottlenecks
- •Limited parallelism
Hub and Spoke
Central hub agent routes tasks to specialized agents
Best for: Service-oriented architectures with task routing
Structure:
hub: Central router agent
spokes: Service agent 1, Service agent 2, Service agent N
communication: Hub-mediated routing
Advantages:
- ✓Service isolation
- ✓Dynamic routing
- ✓Easy scaling
Disadvantages:
- •Hub complexity
- •Routing overhead
Step-by-Step Team Setup
Build your multi-agent system with this comprehensive setup guide
Plan Agent Team Architecture
30 minutesDesign the structure and responsibilities of your agent team
Tasks:
Define team objectives
Identify what your agent team needs to accomplish
Example: Customer support: handle inquiries, escalate issues, generate reports
Map workflow processes
Break down the workflow into discrete steps
Example: 1. Receive inquiry → 2. Classify type → 3. Generate response → 4. Update records
Identify agent specializations
Determine what specialized agents are needed
Example: Router agent, Support agent, Escalation agent, Analytics agent
Design communication patterns
Plan how agents will communicate and coordinate
Example: Message queue for async tasks, direct calls for real-time needs
Create Individual Agent Configurations
45 minutesSet up each specialized agent with appropriate skills and settings
Configuration Examples:
router Agent:
{
"name": "router-agent",
"role": "coordinator",
"description": "Routes incoming requests to appropriate specialist agents",
"skills": ["classification", "routing", "queue-management"],
"personality": "Efficient, decisive coordinator",
"capabilities": ["request-classification", "agent-coordination"],
"maxConcurrentTasks": 10,
"priority": "high"
}support Agent:
{
"name": "support-agent",
"role": "specialist",
"description": "Handles customer support inquiries",
"skills": ["customer-service", "knowledge-base", "response-generation"],
"personality": "Helpful, empathetic support specialist",
"capabilities": ["inquiry-handling", "solution-generation"],
"maxConcurrentTasks": 5,
"escalationThreshold": 3
}analytics Agent:
{
"name": "analytics-agent",
"role": "background",
"description": "Processes data and generates insights",
"skills": ["data-analysis", "reporting", "visualization"],
"personality": "Analytical, detail-oriented researcher",
"capabilities": ["data-processing", "report-generation"],
"schedule": "0 */6 * * *",
"batchSize": 100
}Implement Inter-Agent Communication
60 minutesSet up message passing and coordination between agents
Communication Methods:
Message Queue
Async communication for task distribution
# Configure message queue openclaw queue create team-tasks openclaw agent config router-agent --queue team-tasks # Agent sends message openclaw agent send router-agent support-agent "customer-inquiry" payload.json # Agent subscribes to messages openclaw agent subscribe support-agent team-tasks --filter "customer-inquiry"
Direct Agent Calls
Synchronous communication for immediate responses
# Direct agent-to-agent call
openclaw agent call router-agent support-agent "classify_inquiry" --data inquiry.json
# Response handling in agent code
async function handleClassification(data) {
const result = await this.callAgent('support-agent', 'classify_inquiry', data);
return result;
}Shared Data Store
Common data access for coordinated work
# Configure shared storage
openclaw storage create team-data --type redis
# Agents access shared data
openclaw agent config router-agent --storage team-data
openclaw agent config support-agent --storage team-data
# Access from agent code
const sharedData = await this.storage.get('current-workload');
await this.storage.set('task-status', 'completed');Configure Team Coordination
45 minutesSet up coordination rules and conflict resolution
Coordination Features:
Load Balancing
Distribute work evenly across agents
{
"loadBalancer": {
"strategy": "round-robin",
"healthChecks": true,
"maxQueueSize": 50,
"agents": ["support-agent-1", "support-agent-2", "support-agent-3"]
}
}Priority Management
Handle urgent tasks first
{
"priorityQueue": {
"levels": ["urgent", "high", "normal", "low"],
"escalationRules": {
"urgent": "immediate",
"high": "within-15min",
"normal": "within-1hour"
}
}
}Conflict Resolution
Handle competing agent decisions
{
"conflictResolution": {
"strategy": "supervisor-override",
"supervisor": "router-agent",
"votingThreshold": 0.7,
"timeoutAction": "escalate"
}
}Deploy and Monitor Team
30 minutesLaunch the agent team and set up monitoring
Deployment Steps:
Start all agents
openclaw team start customer-support-teamConfigure monitoring
openclaw monitor setup --team customer-support-teamTest team workflow
openclaw team test customer-support-team --scenario basic-inquirySet up alerting
openclaw alerts create --team customer-support-team --conditions failure,overloadReady-to-Use Team Templates
Start with these proven multi-agent configurations
Customer Support Team
Multi-agent customer service automation
Team Agents:
Intake Agent
Receives and categorizes incoming requests
Support Agent
Handles routine support inquiries
Escalation Agent
Manages complex issues requiring human intervention
Analytics Agent
Tracks metrics and generates insights
Workflow Process:
Intake Agent receives customer inquiry
Classifies inquiry type and urgency
Routes to Support Agent or Escalation Agent
Agent processes and responds to customer
Analytics Agent logs interaction for reporting
Content Production Team
Automated content creation and publishing
Team Agents:
Research Agent
Gathers information and insights for content
Writing Agent
Creates written content based on research
Editor Agent
Reviews and refines content quality
Publisher Agent
Distributes content across platforms
Workflow Process:
Research Agent gathers topic information
Writing Agent creates initial content draft
Editor Agent reviews and improves content
Publisher Agent distributes to target platforms
Data Processing Team
Large-scale data analysis and reporting
Team Agents:
Collector Agent
Gathers data from multiple sources
Processor Agent
Cleans and transforms raw data
Analyzer Agent
Performs statistical analysis and modeling
Reporter Agent
Creates reports and visualizations
Workflow Process:
Collector Agent extracts data from sources
Processor Agent cleans and validates data
Analyzer Agent performs statistical analysis
Reporter Agent generates final reports
Team Management Commands
Team Lifecycle
openclaw team create [name]Create new agent team configurationopenclaw team start [name]Start all agents in the teamopenclaw team stop [name]Stop all agents in the teamopenclaw team restart [name]Restart entire agent teamMonitoring & Status
openclaw team status [name]Show status of all team agentsopenclaw team metrics [name]Display team performance metricsopenclaw team logs [name]View aggregated team logsopenclaw team health [name]Check overall team healthConfiguration & Management
openclaw team add-agent [team] [agent]Add agent to existing teamopenclaw team remove-agent [team] [agent]Remove agent from teamopenclaw team scale [team] --agents [count]Scale team size up or downopenclaw team config [team] --file config.jsonUpdate team configurationBest Practices & Guidelines
Agent Specialization
Design agents with clear, focused responsibilities
Guidelines:
- ▸Single responsibility principle - one primary function per agent
- ▸Clear skill boundaries - avoid overlapping capabilities
- ▸Appropriate sizing - not too broad or too narrow
- ▸Reusable components - design for team flexibility
Example:
Instead of one 'customer service agent', create 'intake agent', 'support agent', and 'escalation agent'
Communication Design
Plan efficient inter-agent communication patterns
Guidelines:
- ▸Minimize communication overhead - avoid chatty protocols
- ▸Use appropriate patterns - async for background tasks, sync for real-time
- ▸Handle failures gracefully - timeout and retry logic
- ▸Message versioning - plan for protocol evolution
Example:
Use message queues for task distribution, direct calls for status checks
Error Handling & Recovery
Build resilience into team operations
Guidelines:
- ▸Circuit breakers - prevent cascade failures
- ▸Graceful degradation - maintain partial functionality
- ▸Dead letter queues - handle failed messages
- ▸Health checks - monitor agent availability
Example:
If support agent fails, route urgent requests to escalation agent
Scaling & Performance
Design for growth and efficiency
Guidelines:
- ▸Horizontal scaling - add more agents vs. bigger agents
- ▸Load balancing - distribute work evenly
- ▸Resource monitoring - track CPU, memory, and task queues
- ▸Performance profiling - identify and optimize bottlenecks
Example:
Deploy multiple support agents behind a load balancer for high volume
Troubleshooting Common Issues
Agent Communication Failures
Symptoms:
- •Messages not being delivered
- •Agents timing out on calls
- •Queue backlog building up
Solutions:
- ✓Check network connectivity between agents
- ✓Verify message queue health and capacity
- ✓Review timeout settings and retry logic
- ✓Monitor agent resource usage for bottlenecks
Team Coordination Conflicts
Symptoms:
- •Duplicate task processing
- •Conflicting agent decisions
- •Deadlock situations
Solutions:
- ✓Review task distribution logic
- ✓Implement proper locking mechanisms
- ✓Add conflict resolution protocols
- ✓Increase coordination timeout values
Performance Degradation
Symptoms:
- •Slow team response times
- •High resource usage
- •Queue backlog growth
Solutions:
- ✓Profile individual agent performance
- ✓Scale up bottleneck agents
- ✓Optimize communication patterns
- ✓Review and tune team configuration
Team Scalability Issues
Symptoms:
- •Cannot handle increased load
- •Resource exhaustion
- •Management overhead
Solutions:
- ✓Implement horizontal scaling patterns
- ✓Review agent resource requirements
- ✓Optimize coordination overhead
- ✓Consider team architecture redesign
Need Help Building Your
Agent Team?
Get expert assistance designing and implementing multi-agent systems. From architecture planning to deployment and optimization.
- ✓Multi-agent architecture design
- ✓Team coordination and communication setup
- ✓Performance optimization and scaling
Multi-Agent Development
Build sophisticated agent teams with expert guidance