MLflow

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Integrating Agno with MLflow Observability

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Prerequisites

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1) Install packages

pip install mlflow agno openai

Use your preferred LLM provider package if not OpenAI.

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2) Tracking server / UI

• Local: run an MLflow server/UI or mlflow ui for quick local tests.
• Remote: point to your team’s MLflow Tracking Server.

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3) Authentication & configuration

export MLFLOW_TRACKING_URI=http://localhost:5000   # or your server
export MLFLOW_TRACKING_TOKEN=<token-if-required>   # only if your server uses tokens

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Quickstart: Autologging Agno → MLflow Observability

import os
import mlflow
from agno.agent import Agent
from agno.models.openai import OpenAIChat

mlflow.set_tracking_uri(os.getenv("MLFLOW_TRACKING_URI", "http://localhost:5000"))
mlflow.set_experiment("agno-observation-demo")
mlflow.agno.autolog()

agent = Agent(model=OpenAIChat(id="gpt-4o"), name="horror-writer")

with mlflow.start_run(run_name="two-sentence-horror") as run:
    result = agent.run("Share a 2 sentence horror story")
    print(result.content)

    mlflow.log_text(result.content, artifact_file="outputs/horror.txt")

    trace_id = mlflow.get_last_active_trace_id()
    print(f"Trace ID: {trace_id}")

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View your traces

• Select the agno-observation-demo experiment → open the latest Run.
• Go to Observability / Traces (or the Traces tab in the run) to inspect spans:
  • Agent & model calls (LLM invocations)
  • Tool calls, inputs/outputs
  • Timing, status, and attributes

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Notes

• Autologging: Use mlflow.agno.autolog() before creating/running agents.
• Runs & traces: Wrap your workflow in with mlflow.start_run(...): so runs and traces are grouped.
• Artifacts: Use helpers like mlflow.log_text(...), mlflow.log_dict(...), or mlflow.log_figure(...) to attach outputs to the run.