Pattern Multi Agent

Multi-Agent DSPy

🎯 Trigger Conditions

Use when asked about multi-agent systems, agent coordination, or distributed DSPy execution.

📚 Prerequisites

• dspy package installed
• Understanding of multi-agent architectures
• Coordination mechanism defined

🛠️ Multi-Agent Patterns

1. Agent Specialization

class SpecialistAgent(dspy.Module):
    def __init__(self, specialty):
        self.specialty = specialty
        self.predictor = dspy.Predict(f"input -> {specialty}_output")
    
    def forward(self, input):
        return self.predictor(input=input)

# Create specialized agents
agents = {
    "math": SpecialistAgent("math"),
    "science": SpecialistAgent("science"),
    "general": SpecialistAgent("general")
}

# Route to specialist
def route_to_specialist(input):
    intent = classify_intent(input)
    return agents[intent](input)

2. Agent Debate

class AgentDebate(dspy.Module):
    def __init__(self, n_agents=3):
        self.agents = [
            dspy.Predict("input -> opinion") for _ in range(n_agents)
        ]
        self.judge = dspy.Predict("opinions -> decision")
    
    def forward(self, input):
        # Generate opinions
        opinions = [agent(input=input).opinion for agent in self.agents]
        
        # Judge the debate
        decision = self.judge(opinions=opinions).decision
        return decision

3. Agent Hierarchy

class HierarchicalAgent(dspy.Module):
    def __init__(self):
        self.manager = dspy.Predict("task -> subtasks")
        self.workers = [
            dspy.Predict("subtask -> result") for _ in range(4)
        ]
        self.aggregator = dspy.Predict("results -> final_answer")
    
    def forward(self, task):
        # Manager breaks down task
        subtasks = self.manager(task=task).subtasks
        
        # Workers execute subtasks
        results = [worker(subtask=subtask).result for worker, subtask in zip(self.workers, subtasks)]
        
        # Aggregate results
        return self.aggregator(results=results).final_answer

4. Agent Marketplace

class AgentMarketplace(dspy.Module):
    def __init__(self):
        self.bids = {}
        self.agents = {}
    
    def register_agent(self, agent, cost):
        self.agents[agent] = cost
    
    def bid(self, agent, task):
        self.bids[task] = self.agents[agent]
    
    def execute(self, task):
        # Find lowest cost agent
        cheapest_agent = min(self.bids.items(), key=lambda x: x[1])
        return cheapest_agent[0](task)

⚠️ Pitfalls

• Coordination overhead: Communication between agents adds latency
• Consistency: Ensuring consistent behavior across agents
• Resource usage: Multiple agents consume more resources
• Debugging: Multi-agent systems are harder to debug

📖 References

• Multi-Agent Systems
• DSPy Multi-Agent