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In this sixth episode of this series, Jules Damji dives deep into MLflow's extensive framework integrations. MLflow supports over 30 different open-source agent-building frameworks, allowing you to automatically trace and evaluate complex AI workflows regardless of your chosen architecture. This tutorial provides a hands-on comparison of three open source agent building frameworks and demonstrates how MLflow provides full visibility into their execution: š¹ šš®š»š“ššµš®š¶š»: Learn how to use high-level primitives like ChatPromptTemplate and StringOutputParser to build sequential workflows. We demonstrate both simple chains and complex multi-step sequences connected via the pipeline operator. š¹ šš¹š®šŗš®šš»š±š²š : See how to build a Retrieval-Augmented Generation (RAG) system. We walk through creating an in-memory vector index, generating embeddings with OpenAI, and using a query engine to retrieve document-based answers, all while capturing the entire operation trace in MLflow. š¹ šš®š»š“ššæš®š½šµ: For more advanced use cases, we explore building stateful, hierarchical agent workflows. We demonstrate a customer service triage system that uses a supervisor node to classify queries and route them to specialized handlers for billing, tech support, or general inquiries. Key Takeaways: š¹ šššš¼šŗš®šš¶š° š§šæš®š°š¶š»š“: All frameworks integrated with MLflow are automatically traced, capturing inputs, outputs, and intermediate steps without manual instrumentation.8 š¹ ššæš®šŗš²šš¼šæšø š¦š²š¹š²š°šš¶š¼š»: Choose LangChain for sequential chains, LlamaIndex for heavy document indexing, and LangGraph for complex, stateful branching or looping workflows. š¹ š©š¶šš¶šÆš¶š¹š¶šš: Use the MLflow UI to inspect timelines, verify embeddings, and debug the internal logic of your AI agents. Resources: š Notebook 1.5: https://github.com/dmatrix/mlflow-genai-tutorials/blob/main/06_framework_integrations.ipynb š„ Full Series Playlist: https://youtube.com/playlist?list=PLaoPu6xpLk9EI99TuOjSgy-UuDWowJ_mR&si=jdbAbxTCRuxFxfnG