MLflow is an open-source platform for managing and tracking machine learning experiments. When used with the OpenAI Agents SDK, MLflow automatically: Logs all agent interactions and API calls Captures tool usage, input/output messages, and intermediate decisions Tracks runs for debugging, performance analysis, and reproducibility This is especially useful when you’re building multi-agent systems where different agents collaborate or call functions dynamically In this tutorial, we’ll walk through two key examples: a simple handoff between agents, and the use of agent guardrails — all while tracing their behavior using MLflow. Setting up the dependencies Installing the libraries pip install openai-agents mlflow…
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