Understanding Swarms
Learn how swarms enable coordinated systems of agents working together in SwarmCanvas.
What is a Swarm?
In SwarmCanvas, following OpenAI's Swarm framework, a Swarm represents a coordinated system of agents working together. Swarms enable agent coordination, task distribution, workflow management, and state handling.
Coordination
Swarms manage agent interactions through:
- •Message Passing
Inter-agent communication system
- •State Sharing
Synchronized data across agents
- •Error Recovery
Handling failures and recovery
State Management
- •Conversation Context
Maintaining interaction history
- •Agent State
Individual agent status tracking
- •Shared Resources
Managing common resources
Example Implementation
client = Swarm()
response = client.run(
agent=triage_agent,
messages=[{"role": "user", "content": "I need help"}]
)Swarm Patterns
1. Sequential Processing
- • Step-by-step execution
- • Clear progression
- • Ordered handoffs
2. Parallel Processing
- • Concurrent agent execution
- • Task distribution
- • Result aggregation
3. Dynamic Routing
- • Conditional handoffs
- • Context-based routing
- • Adaptive workflows
Best Practices
Design
- • Clear agent responsibilities
- • Well-defined handoff conditions
- • Proper error handling
Implementation
- • Efficient state management
- • Proper resource usage
- • Effective monitoring
Testing
- • Comprehensive scenarios
- • Edge case handling
- • Performance validation