autogen - 💡(How to fix) Fix 多Agent系统AI成本优化方案建议 [1 participants]

Fix Plan

To address the issue of high AI API costs in multi-agent systems, we can implement the following solutions:

• Agent-level cost tracking: Modify the AutoGen framework to track API usage costs for each agent.
• Conversation flow cost analysis: Develop a tool to analyze the cost distribution of complete conversation flows.
• Intelligent agent scheduling: Implement a cost-optimizing agent scheduling strategy.
• Budget-aware agent behavior: Modify agent behavior to act intelligently within budget constraints.

Example Code

To implement agent-level cost tracking, you can add a cost_tracker module to the AutoGen framework:

import logging

class CostTracker:
    def __init__(self):
        self.agent_costs = {}

    def track_cost(self, agent_id, cost):
        if agent_id not in self.agent_costs:
            self.agent_costs[agent_id] = 0
        self.agent_costs[agent_id] += cost
        logging.info(f"Agent {agent_id} cost: {self.agent_costs[agent_id]}")

# Example usage:
cost_tracker = CostTracker()
cost_tracker.track_cost("agent1", 0.05)

Verification

To verify that the fix worked, you can:

• Monitor agent-level costs using the cost_tracker module.
• Analyze conversation flow costs using the developed tool.
• Evaluate the effectiveness of the intelligent agent scheduling strategy.
• Test budget-aware agent behavior in a controlled environment.

Extra Tips

• Consider using a messaging queue (e.g., RabbitMQ) to handle agent scheduling and cost tracking.
• Use a database (e.g., PostgreSQL) to store agent costs and conversation flow data.
• Implement logging and monitoring tools (e.g., Prometheus, Grafana) to track system performance and costs.