MLflow 2.22.1 brings important bug fixes and improvements.

Features:

• [Scoring] For DBConnect client, make spark_udf support DBR 15.4 and DBR dedicated cluster (#15938, @WeichenXu123)

Bug Fixes:

• [Model Registry] Log Resources from SystemAuthPolicy in CreateModelVersion (#15485, @aravind-segu)
• [Tracking] Trace search: Avoid spawning threads for span fetching if include_spans=False (#15635, @dbczumar)

For a comprehensive list of changes, see the release change log, and check out the latest documentation on mlflow.org.

MLflow 2.22.0 brings important bug fixes and improvements to the UI and tracking capabilities.

Features:

• [Tracking] Supported tracing for OpenAI Responses API.
  (#15240, @B-Step62)
• [Tracking] Introduced get_last_active_trace, which affects model serving/monitoring logic.
  (#15233, @B-Step62)
• [Tracking] Introduced async export for Databricks traces (default behavior).
  (#15163, @B-Step62)
• [AI Gateway] Added Gemini embeddings support with corresponding unit tests.
  (#15017, @joelrobin18)
• [Tracking / SQLAlchemy] MySQL SSL connections are now supported with client certs.
  (#14839, @aksylumoed)
• [Models] Added Optuna storage utility for enabling parallel hyperparameter tuning.
  (#15243, @XiaohanZhangCMU)
• [Artifacts] Added support for Azure Data Lake Storage (ADLS) artifact repositories.
  (#14723, @serena-ruan)
• [UI] Artifact views for text now auto-refresh in the UI.
  (#14939, @joelrobin18)

Bug Fixes:

• [Tracking / UI] Fixed serialization for structured output in langchain_tracer + added unit tests.
  (#14971, @joelrobin18)
• [Server-infra] Enforced password validation for authentication (min. 8 characters).
  (#15287, @WeichenXu123)
• [Deployments] Resolved an issue with the OpenAI Gateway adapter.
  (#15286, @WeichenXu123)
• [Artifacts / Tracking / Server-infra] Normalized paths by stripping trailing slashes.
  (#15016, @tarek7669)
• [Tags] Fixed a bug where tag values containing ": " were being truncated.
  (#14896, @harupy)

For a comprehensive list of changes, see the release change log, and check out the latest documentation on mlflow.org.

MLflow 2.21.2 is a patch release that introduces minor features and bug fixes.

• Fix connection exhausting when exporting traces to Databricks (#15124, @B-Step62)
• Add logging of result table for DSPy optimizer tracking (#15061, @TomeHirata)

For a comprehensive list of changes, see the release change log, and check out the latest documentation on mlflow.org.

MLflow 2.21.1 is a patch release that introduces minor features and addresses some minor bugs.

Features:

• Introduce support for logging evaluations within DSPy (#14962, @TomeHirata)
• Add support for run creation when DSPy compile is executed (#14949, @TomeHirata)
• Add support for building a SageMaker serving container that does not contain Java via the --install-java option (#14868, @rgangopadhya)

Bug fixes:

• Fix an issue with trace ordering due to a timestamp conversion timezone bug (#15094, @orm011)
• Fix a typo in the environment variable OTEL_EXPORTER_OTLP_PROTOCOL definition (#15008, @gabrielfu)
• Fix an issue in shared and serverless clusters on Databricks when logging Spark Datasources when using the evaluate API (#15077, @WeichenXu123)
• Fix a rendering issue with displaying images from within the metric tab in the UI (#15034, @TomeHirata)

Documentation updates:

• Add additional contextual information within the set_retriever_schema API docs (#15099, @smurching)

For a comprehensive list of changes, see the release change log, and check out the latest documentation on mlflow.org.

We are excited to announce the release of MLflow 2.21.0! This release includes a number of significant features, enhancements, and bug fixes.

Major New Features​

• 📚 Documentation Redesign: MLflow documentation is fully revamped with a new MDX-based website that provides better navigation and makes it easier to find the information you need! (#13645, @daniellok-db)
• 🤖 Prompt Registry: MLflow Prompt Registry is a powerful tool that streamlines prompt engineering and management in your GenAI applications. It enables you to version, track, and reuse prompts across your organization. (#14795, #14834, #14936, @B-Step62, #14960, #14984, @daniellok-db, #14909, @hubertzub-db)
• 🖥️ OpenAI Agent SDK: MLflow Tracing now supports OpenAI Agent SDK, a multi-agent framework developed by OpenAI. (#14987, @B-Step62)
• ⚡️ FastAPI Scoring Server: The MLflow inference server has been migrated from Flask to FastAPI, enabling ASGI-based scalable inference for improved performance and throughput. (#14307, @TomeHirata)
• 🔍 Enhanced Tracing Capabilities: MLflow Tracing now supports synchronous/asynchronous generators and auto-tracing for Async OpenAI, providing more flexible and comprehensive tracing options. (#14459, #14400, #14793, #14792, @B-Step62)

Features:

• [Sqlalchemy / Tracking] Support mysql ssl connections with client certs (#14839, @aksylumoed)
• [Artifacts] Supports ADLS artifact repo (#14723, @serena-ruan)
• [Tracking] Add import and docs for txtai integration (#14712, @B-Step62)
• [Models] Introduce User Auth Policy for Pyfunc Models (#14538, @aravind-segu)
• [Tracking] Support new Google GenAI SDK (#14576, @TomeHirata)
• [Tracking] Support generating traces from DSPy built-in compilation and evaluation (#14400, @B-Step62)
• [Tracking] Add mlflow.log_trace API (#14418, @TomeHirata)
• [Models] ChatAgent LangGraph and LangChain Connectors (#14215, @bbqiu)

Bug fixes:

• [Models] Fix infinite recursion error with warning handler module (#14954, @BenWilson2)
• [Model Registry] Fix invalid type issue for ModelRegistry RestStore (#14980, @B-Step62)
• [Tracking] Fix: ExperimentViewRunsControlsActionsSelectTags doesn't set loading state to false when set-tag request fails. (#14907, @harupy)
• [Tracking] Fix a bug in tag creation where tag values containing ": " get truncated (#14896, @harupy)
• [Tracking] Fix false alert from AMD GPU monitor (#14884, @B-Step62)
• [Tracking] Fix mlflow.doctor to fall back to mlflow-skinny when mlflow is not found (#14782, @harupy)
• [Models] Handle LangGraph breaking change (#14794, @B-Step62)

Documentation updates:

• [Tracking] Add guide about using MLflow tracing across threads (#14881, @B-Step62)
• [Docs] Add guide for tracing deepseek (#14826, @B-Step62)
• [Docs] Update llama Jupyter notebook source (#14754, @emmanuel-ferdman)
• [Docs] Replace Databricks Community Edition with Lighthouse (#14642, @TomeHirata)
• [Docs] Update models from code guide and chat model guide to always recommend models from code (#14370, @smurching)
• [Artifacts] [DOC-FIX #14183] Improve documentation for 'artifact_uri' in 'download_artifacts' (#14225, @vinayakkgarg)

For a comprehensive list of changes, see the release change log, and check out the latest documentation on mlflow.org.

MLflow 2.20.4 is a tiny patch release to include a bug fix:

• [Tracking] fix: remove log_trace at top level module (#14873, @yxtay)

For a comprehensive list of changes, see the release change log, and check out the latest documentation on mlflow.org.

MLflow 2.20.3 is a patch release includes several major features and improvements

Features:

• Implemented GPU metrics for AMD/HIP GPUs (#12694, @evenmn)
• Add txtai tracing integration (#14712, @B-Step62)
• Support new Google GenAI SDK (#14576, @TomeHirata)
• Support the new thinking content block in Anthropic Claude 3.7 models (#14733, @B-Step62)

Bug fixes:

• Resolve LangGraph tracing bug with astream_event API (#14598, @B-Step62)

For a comprehensive list of changes, see the release change log, and check out the latest documentation on mlflow.org.

MLflow 2.20.2 is a patch release includes several bug fixes and features

Features:

• [Tracing] Support tracing sync/async generator function with @mlflow.trace (#14459, @B-Step62)
• [Tracing] Support generating traces from DSPy built-in compilation and evaluation (#14400, @B-Step62)
• [Models] ChatAgent interface enhancements and Langgraph connectors updates (#14368, #14567, @bbqiu)
• [Models] VariantType support in spark_udf (#14317, @serena-ruan)

Bug fixes:

• [Models] DSPy thread issue fix (#14471, @chenmoneygithub)

Documentation updates:

• [Docs] ChatAgent documentation updates (#14367, @bbqiu)

For a comprehensive list of changes, see the release change log, and check out the latest documentation on mlflow.org.

MLflow 2.20.1 is a patch release includes several bug fixes and features:

Features:

• Spark_udf support for the model signatures based on type hints (#14265, @serena-ruan)
• Helper connectors to use ChatAgent with LangChain and LangGraph (#14215, @bbqiu)
• Update classifier evaluator to draw RUC/Lift curves for CatBoost models by default (#14333, @singh-kristian)

Bug fixes:

• Fix Pydantic 1.x incompatibility issue (#14332, @BenWilson2)
• Apply temporary fix for LiteLLM tracing to workaround https://github.com/BerriAI/litellm/issues/8013 (#14340, @B-Step62)
• Fix false alert from type hint based model signature for ChatModel (#14343, @B-Step62)

For a comprehensive list of changes, see the release change log, and check out the latest documentation on mlflow.org.

Major New Features​

• 💡Type Hint-Based Model Signature: Define your model's signature in the most Pythonic way. MLflow now supports defining a model signature based on the type hints in your PythonModel's predict function, and validating input data payloads against it. (#14182, #14168, #14130, #14100, #14099, @serena-ruan)

• 🧠 Bedrock / Groq Tracing Support: MLflow Tracing now offers a one-line auto-tracing experience for Amazon Bedrock and Groq LLMs. Track LLM invocation within your model by simply adding mlflow.bedrock.tracing or mlflow.groq.tracing call to the code. (#14018, @B-Step62, #14006, @anumita0203)

• 🗒️ Inline Trace Rendering in Jupyter Notebook: MLflow now supports rendering a trace UI within the notebook where you are running models. This eliminates the need to frequently switch between the notebook and browser, creating a seamless local model debugging experience. (#13955, @daniellok-db)

• ⚡️Faster Model Validation with uv Package Manager: MLflow has adopted uv, a new Rust-based, super-fast Python package manager. This release adds support for the new package manager in the mlflow.models.predict API, enabling faster model environment validation. Stay tuned for more updates! (#13824, @serena-ruan)

• 🖥️ New Chat Panel in Trace UI: THe MLflow Trace UI now shows a unified chat panel for LLM invocations. The update allows you to view chat messages and function calls in a rich and consistent UI across LLM providers, as well as inspect the raw input and output payloads. (#14211, @TomuHirata)

Other Features:

• Introduced ChatAgent base class for defining custom python agent (#13797, @bbqiu)
• Supported Tool Calling in DSPy Tracing (#14196, @B-Step62)
• Applied timeout override to within-request local scoring server for Spark UDF inference (#14202, @BenWilson2)
• Supported dictionary type for inference params (#14091, @serena-ruan)
• Make context parameter optional for calling PythonModel instance (#14059, @serena-ruan)
• Set default task for ChatModel (#14068, @stevenchen-db)

For a comprehensive list of changes, see the release change log, and check out the latest documentation on mlflow.org.