Integrate MLflow for MLOps Tracking of Agent Policies

Set up MLflow to track your AutoGen multi-agent system's performance across different simulation runs and agent configurations. Log agent conversations, critical decisions, and simulation metrics (e.g., collisions, travel time, safety scores) as MLflow artifacts. This will allow you to compare different versions of your autonomous driving policies and ensure reproducibility. ```python
import mlflow
import random # For simulating metrics # Set MLflow tracking URI (e.g., to a local directory or remote server)
# mlflow.set_tracking_uri("http://localhost:5000")
mlflow.set_experiment("Autonomous Vehicle Agent Simulation") def run_simulation_with_mlflow(config: dict, agents: list): with mlflow.start_run(): mlflow.log_params(config) # Log agent configuration as parameters # Simulate a conversation/driving scenario # manager.initiate_chat(user_proxy, message="Start simulation...") # Log simulated metrics mlflow.log_metric("collisions", random.randint(0, 1)) mlflow.log_metric("travel_time_seconds", random.randint(200, 500)) mlflow.log_metric("safety_score", random.uniform(0.8, 0.99)) # Log agent conversation as an artifact # with open("agent_conversation.json", "w") as f: # json.dump(groupchat.messages, f) # mlflow.log_artifact("agent_conversation.json") # Example usage:
# agent_config = {"planner_model": "gpt-5-pro", "decision_model": "claude-4-sonnet"}
# run_simulation_with_mlflow(agent_config, [sensor_interpreter, path_planner, decision_maker])
```