Observability
LLM and Agent Observability
Gain visibility into your agent or LLM application's logic to debug issues, improve quality and understand user behavior.
Rich, detailed traces for every request

End to end observability

Capture your agent or LLM application's inputs, outputs, and step-by-step execution: prompts, retrievals, tool calls, and more.

End to end observability screenshot

Visualize execution flow

Deep dive into your agent or LLM application's logic and latency with a comprehensive and intuitive UI for effective debugging.

Visualize execution flow screenshot

Quality monitoring

Track and analyze the quality of your agent or LLM application over time, and take action to fix issues before impact spreads.

Quality monitoring screenshot

Spot trends and patterns at scale

Zoom out with a simplified summary UI to quickly review many traces at once to understand how your agent or LLM application is performing overall.

Spot trends and patterns at scale screenshot
Automatic tracing for your entire stack
Auto-trace 50+ LLM providers and agent frameworks with a single line of code. MLflow is OpenTelemetry compatible, supporting any programming language, agent, or LLM.
Get Started in 3 Simple Steps
From zero to full observability in minutes. No complex setup or major code changes required.Get Started →
1

Start MLflow Server

One command to get started. Docker setup is also available.

bash
uvx mlflow server
~30 seconds
2

Enable Logging

Add minimal code to start capturing traces, metrics, and parameters

python
import mlflow


mlflow.set_tracking_uri(
    "http://localhost:5000"
)
mlflow.openai.autolog()
~30 seconds
3

Run your code

Run your code as usual. Explore traces and metrics in the MLflow UI.

python
from openai import OpenAI


client = OpenAI()
client.responses.create(
    model="gpt-5-mini",
    input="Hello!",
)
~1 minute