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MLflow 3.6.0

· 3 min read
MLflow maintainers
MLflow maintainers
MLflow maintainers

MLflow 3.6.0 includes several major features and improvements for AI Observability, Experiment UI, Agent Evaluation and Deployment.

#1: Full OpenTelemetry Support in MLflow Tracking Server​

OpenTelemetry Trace Example

MLflow now offers comprehensive OpenTelemetry integration, allowing you to use OpenTelemetry and MLflow seamlessly together for your observability stack.

  • Ingest OpenTelemetry spans directly into the MLflow tracking server
  • Monitor existing applications that are instrumented with OpenTelemetry
  • Choose Arbitrary Languages for your AI applications and trace them, including Java, Go, Rust, and more.
  • Create unified traces that combine MLflow SDK instrumentation with OpenTelemetry auto-instrumentation from third-party libraries

For more information, please check out the blog post for more details.

#2: Session-level View in Trace UI​

Session-level View in Trace UI

New chat sessions tab provides a dedicated view for organizing and analyzing related traces at the session level, making it easier to track conversational workflows.

See the Track Users & Sessions guide for more details.

#3: New Supported Frameworks in TypeScript Tracing SDK​

Auto-tracing support for Vercel AI SDK, LangChain.js, Mastra, Anthropic SDK, Gemini SDK in TypeScript, expanding MLflow's observability capabilities across popular JavaScript/TypeScript frameworks.

For more information, please check out the TypeScript Tracing SDK.

#4: Tracking Judge Cost and Traces​

Comprehensive tracking of LLM judge evaluation costs and traces, providing visibility into evaluation expenses and performance with automatic cost calculation and rendering

See LLM Evaluation Guide for more details.

#5: New experiment tab bar​

The experiment tab bar has been fully overhauled to provide more intuitive and discoverable navigation of different features in MLflow.

Upgrade to MLflow 3.6.0 to try it out!

#6: Agent Server for Lightning Agent Deployment​

import agent
from mlflow.genai.agent_server import AgentServer

agent_server = AgentServer("ResponsesAgent")
app = agent_server.app

def main():
    agent_server.run(app_import_string="start_server:app")

if __name__ == "__main__":
    main()
python3 start_server.py

curl -X POST http://localhost:8000/invocations \
   -H "Content-Type: application/json" \
   -d '{
    "input": [{ "role": "user", "content": "What is the 14th Fibonacci number?"}],
    "stream": true
    }'

New agent server infrastructure for managing and deploying scoring agents with enhanced orchestration capabilities.

See Agent Server Guide for more details.

Breaking Changes and deprecations​

  • Drop numbering suffix (_1, _2, ...) from span names (#18531)
  • Deprecate promptflow, pmdarima, and diviner flavors (#18597, #18577)

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