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
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
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.
