🧪 MLFlow Integration
🧪

MLFlow

Industry-Standard ML Experiment Tracking - Comprehensive artifact management, experiment comparison, and model registry for SuperOptiX agents.

Industry Standard
Excellent Artifact Management
Model Registry
Team Collaboration

🎯 Why MLFlow for SuperOptiX?

MLFlow provides robust experiment tracking and artifact management for your AI agent workflows

Experiment Tracking

Track all agent runs, parameters, and metrics

Artifact Management

Code, models, data versioning

Reproducibility

Detailed experiment tracking and comparison

Team Collaboration

Experiment sharing and model registry

Production ML

Model deployment and lifecycle management

Visualization

Compare experiments with built-in UI

📦 Installation & Setup

1. Install MLFlow

uv pip install mlflow

2. Start MLFlow Server

mlflow server --host 0.0.0.0 --port 5001 \
  --backend-store-uri sqlite:///mlflow.db \
  --default-artifact-root ./mlflow_artifacts

3. Configure Agent Playbook

observability:
  enabled: true
  backends:
    - mlflow
  mlflow:
    experiment_name: "developer_agent"
    tracking_uri: "http://localhost:5001"
    log_artifacts: true
    log_metrics: true
    log_params: true
    tags:
      agent_type: "developer"
      tier: "genies"
      version: "1.0.0"
      environment: "development"

🚀 Complete Workflow

1

Initialize Project

super init mlflow_demo
cd mlflow_demo
2

Pull Agent

super agent pull developer --tier genies
3

Compile Agent

super agent compile developer
4

Run Agent with MLFlow

super agent run developer --goal "Write factorial function" --observe mlflow
5

Optimize with Tracking

super agent optimize developer --auto medium --observe mlflow
6

View in MLFlow UI

# Open http://localhost:5001 in your browser

📊 What Gets Tracked

Agent-Specific Metrics

  • GEPA optimization iterations and improvements
  • Agent accuracy and performance scores
  • Cost tracking (tokens, API costs)
  • Latency and execution time
  • Success rate and error rates

Artifacts Logged

  • Trace files (JSONL format)
  • Agent playbook configurations
  • Generated code outputs
  • Optimization weights
  • Performance reports

🎯 When to Use MLFlow

Choose MLFlow when:

  • You're doing traditional ML experiments
  • You need detailed artifact versioning
  • You want to track model performance over time
  • You're building ML pipelines
  • You need team collaboration on experiments
  • You want model registry and deployment tracking

Consider other options:

LangFuse

For LLM-specific features, real-time cost tracking, A/B testing

Weights & Biases

For research projects, hyperparameter tuning, beautiful visualizations

SuperOptiX Native

For local development, no setup required, works offline

📚 Additional Resources

MLFlow Documentation

Official MLFlow docs

SuperOptiX MLFlow Guide

Complete integration guide

Observability Overview

All observability options

MLFlow vs LangFuse

Comparison guide

🧪 Ready to Track Your Experiments?

Start using MLFlow with SuperOptiX for comprehensive experiment tracking!