mlflow/mlflow

▲ 12 stars today★ 28,700⑂ 6,589

The open source AI engineering platform for agents, LLMs, and ML models. MLflow enables teams of all sizes to debug, evaluate, monitor

agentops agents ai ai-governance apache-spark evaluation langchain llm-evaluation llmops machine-learning ml mlflow

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About mlflow/mlflow

mlflow/mlflow is an open-source project on GitHub, mainly written in Python. The open source AI engineering platform for agents, LLMs, and ML models. MLflow enables teams of all sizes to debug, evaluate, monitor It currently holds 28,700 stars and 6,589 forks with 2,187 open issues, and was last pushed on 2026-11-06 (repository created 2018-06-05).

Project Overview

AI Homed tracks it on the Today's Trending board, currently at rank #53 with 12 new stars today.

GitHub Repository Details

Repository mlflow/mlflow · default branch master · size 1514651 KB · watchers 324 · source: GitHub REST API and repository README

README

The Open Source AI Engineering Platform for Agents, LLMs & Models

MLflow is the largest open source AI engineering platform for agents, LLMs, and ML models. MLflow enables teams of all sizes to debug, evaluate, monitor, and optimize production-quality AI applications while controlling costs and managing access to models and data. With over 60 million monthly downloads, thousands of organizations rely on MLflow each day to ship AI to production with confidence.

MLflow's comprehensive feature set for agents and LLM applications includes production-grade observability, evaluation, prompt management, prompt optimization and an AI Gateway for managing costs and model access. Learn more at MLflow for LLMs and Agents.

Website · Try Demo · Docs · News · Events

Get Started in 3 Simple Steps

From zero to full-stack LLMOps in minutes. No complex setup or major code changes required. Get Started →

Fastest start — set up tracing with the MLflow setup wizard

> curl -LsSf https://mlflow.org/wizard/setup.sh | sh

> Run this from your project's Git repository with Claude Code, Codex, or OpenCode installed. The wizard guides you through connecting to an MLflow server or Databricks workspace, then launches your coding agent to add tracing to your app. Prefer to wire it up yourself? Follow the three steps below.

1. Start MLflow Server

uvx mlflow server

2. Enable Logging

import mlflow
mlflow.set_tracking_uri("http://localhost:5000")
mlflow.openai.autolog()

3. Run Your Code

from openai import OpenAI
client = OpenAI()
client.responses.create(
    model="gpt-5.4-mini",
    input="Hello!",
)

Explore traces and metrics in the MLflow UI at http://localhost:5000.

LLMs & Agents

MLflow provides everything you need to build, debug, evaluate, and deploy production-quality LLM applications and AI agents. Supports Python, TypeScript/JavaScript, Java and any other programming language. MLflow also natively integrates with OpenTelemetry and MCP.

Observability Capture complete traces of your LLM applications and agents for deep behavioral insights. Built on OpenTelemetry, supporting any LLM provider and agent framework. Monitor production quality, costs, and safety. Getting Started → Try Demo →	Evaluation Run systematic evaluations, track quality metrics over time, and catch regressions before they reach production. Choose from 50+ built-in metrics and LLM judges, or define your own. Getting Started → Try Demo →
Prompts & Optimization Version, test, and deploy prompts with full lineage tracking. Automatically optimize prompts with state-of-the-art algorithms to improve performance. Getting Started → Try Demo →	AI Gateway Unified API gateway for all LLM providers. Route requests, manage rate limits, handle fallbacks, and control costs through an OpenAI-compatible interface with built-in credential management, guardrails and traffic splitting for A/B testing. Getting Started →

Model Training

For machine learning and deep learning model development, MLflow provides a full suite of tools to manage the ML lifecycle:

Experiment Tracking — Track models, parameters, metrics, and evaluation results across experiments
Model Evaluation — Automated evaluation tools integrated with experiment tracking
Model Registry — Collaboratively manage the full lifecycle of ML models
Deployment — Deploy models to batch and real-time scoring on Docker, Kubernetes, Azure ML, AWS SageMaker, and more

Learn more at MLflow for Model Training.

Integrations

MLflow supports all agent frameworks, LLM providers, tools, and programming languages. We offer one-line automatic tracing for more than 60 frameworks. See the full integrations list.

OpenTelemetry

_{OpenTelemetry}

Agent Frameworks (Python)

_LangChain	_LangGraph	_{OpenAI Agent}	_DSPy	_PydanticAI	_{Google ADK}
_{Microsoft Agent}	_CrewAI	_LlamaIndex	_AutoGen	_Strands	_{LiveKit Agents}
_Agno	_{Bedrock AgentCore}	_Smolagents	_{Semantic Kernel}	_DeepAgent	_AG2
_Haystack	_Koog	_txtai	_Pipecat	_Watsonx

Agent Frameworks (TypeScript)

_LangChain

_LangGraph

_{Vercel AI SDK}

_Mastra

_VoltAgent

_Flue

Agent Frameworks (Java)

_{Spring AI}

_{Quarkus LangChain4j}

Model Providers

_OpenAI	_Anthropic	_Databricks	_Gemini	_{Amazon Bedrock}	_LiteLLM
_Mistral	_{xAI / Grok}	_Ollama	_Groq	_DeepSeek	_Qwen
_{Moonshot AI}	_Cohere	_BytePlus	_{Novita AI}	_FireworksAI	_{Together AI}