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MLflow 3.11.1 Highlights: Automatic Issue Detection, Gateway Budget Management, and Pickle-Free Models!

· 5 min read
MLflow maintainers
MLflow maintainers
MLflow maintainers

MLflow 3.11.1 is a major release that significantly advances MLflow's AI Observability, security, and governance capabilities. This release brings automated quality issue detection for agents, fine-grained spending controls for AI Gateway, interactive trace graph visualization, native OpenTelemetry GenAI semantic convention support, and safer pickle-free model serialization — alongside broad improvements to tracing integrations, evaluation pipelines, and the MLflow UI.

1. Automatic Issue Identification​

Automatically surface quality problems in your agent without manual inspection! Use the new Detect Issues button in the traces table to analyze selected traces with AI and identify potential problems across categories like correctness, safety, and performance. Detected issues are linked directly to the relevant traces, making it easy to investigate root causes and debug your agent at scale.

Learn more about automatic issue detection

2. Gateway Budget Alerts & Limits​

Gateway Budget Alerts & Limits

Take control of your AI Gateway spending with configurable budget policies. Set spending limits by time window (daily, weekly, or monthly), receive alerts before hitting limits, and block runaway costs automatically when thresholds are exceeded. The new budget management UI lets you track current spending, configure webhook notifications, and monitor violations across all gateway endpoints — all without writing any code.

Learn more about Gateway budget alerts and limits

3. Trace Graph View​

Trace Graph View

Navigate complex agent interactions with a new interactive graph view for traces. Visualize multi-level trace hierarchies, understand parent-child span relationships at a glance, and debug intricate multi-agent systems more effectively with a visual representation of your trace topology.

Learn more about the trace graph view

4. Native OpenTelemetry GenAI Convention Support​

Native OpenTelemetry GenAI Convention Support

MLflow now natively supports the OpenTelemetry GenAI Semantic Conventions for trace export. When exporting traces via OTLP with MLFLOW_ENABLE_OTEL_GENAI_SEMCONV enabled, MLflow automatically translates spans to follow the OTel GenAI semantic conventions — enabling seamless integration with OTel-compatible observability platforms while preserving all GenAI-specific metadata.

Learn more about OTel GenAI semantic convention support

5. OpenCode Tracing Integration​

Debug smarter with the new OpenCode CLI tracing integration. OpenCode is an open-source, terminal-based AI coding assistant. Track and analyze code execution flows directly from your development workflow, making it easier to identify performance bottlenecks and trace issues back to specific code paths without leaving your terminal.

Learn more about OpenCode tracing

6. Native UV Support for Model Dependencies​

Automatic dependency inference now supports UV. MLflow detects UV projects and captures exact, locked dependencies — including SHA-256 hashes for every package — from your lockfile when logging models, ensuring fully reproducible environments when serving or sharing models that were built with UV. This provides a safer approach against supply chain attacks: if an attacker publishes a modified package under an existing version number, the hash check fails and installation is blocked.

Learn more about UV dependency management

7. Pickle-Free Model Serialization​

Enhance the security of your ML pipelines with pickle-free model formats. MLflow now supports safer model serialization using torch.export and skops formats, with improved controls when MLFLOW_ALLOW_PICKLE_DESERIALIZATION=False. Comprehensive documentation guides you through migrating existing models to pickle-free formats for production deployments.

Learn more about pickle-free model formats

Breaking Changes​

  • TypeScript SDK Package Renaming: The MLflow TypeScript SDK packages have been renamed to use npm organization scoping. Update your package.json dependencies: mlflow-tracing → @mlflow/core, mlflow-openai → @mlflow/openai, mlflow-anthropic → @mlflow/anthropic, mlflow-gemini → @mlflow/gemini. All packages are now at version 0.2.0.
  • The MLFLOW_ENABLE_INCREMENTAL_SPAN_EXPORT environment variable has been removed.
  • litellm and gepa have been removed from genai extras.
  • / and : are now blocked in Registered Model names.

Full Changelog​

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

What's Next​

Get Started​

Install MLflow 3.11.1 to try these new features:

pip install mlflow==3.11.1

Share Your Feedback​

We'd love to hear about your experience with these new features:

Learn More​