mlflow-skinny

📣 This is the mlflow-skinny package, a lightweight MLflow package without SQL storage, server, UI, or data science dependencies. Additional dependencies can be installed to leverage the full feature set of MLflow. For example:

To use the mlflow.sklearn component of MLflow Models, install scikit-learn, numpy and pandas.
To use SQL-based metadata storage, install sqlalchemy, alembic, and sqlparse.
To use serving-based features, install flask and pandas.

Note: When using mlflow-skinny, set the tracking URI to your remote MLflow server:

export MLFLOW_TRACKING_URI="http://your-mlflow-server:5000"

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

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}

Gateways

_Databricks	_{LiteLLM Proxy}	_{Vercel AI Gateway}	_OpenRouter	_Portkey
_Helicone	_{Kong AI Gateway}	_{PydanticAI Gateway}	_TrueFoundry

Tools & No-Code

_Instructor	_{Claude Code}	_Opencode	_Langfuse	_{Arize / Phoenix}	_Goose
_Langflow

Hosting MLflow

MLflow can be used in a variety of environments, including your local environment, on-premises clusters, cloud platforms, and managed services. Being an open-source platform, MLflow is vendor-neutral — whether you're building AI agents, LLM applications, or ML models, you have access to MLflow's core capabilities.

_Databricks

_{Amazon SageMaker}

_{Azure ML}

_Nebius

_{Red Hat OpenShift AI}

_Self-Hosted

💭 Support

For help or questions about MLflow usage (e.g. "how do I do X?") visit the documentation.
In the documentation, you can ask the question to our AI-powered chat bot. Click on the "Ask AI" button at the bottom right.
Join the virtual events like office hours and meetups.
To report a bug, file a documentation issue, or submit a feature request, please open a GitHub issue.
For release announcements and other discussions, please subscribe to our mailing list (mlflow-users@googlegroups.com) or join us on Slack.

🤝 Contributing

We happily welcome contributions to MLflow!

Submit bug reports and feature requests
Contribute for good-first-issues and help-wanted
Writing about MLflow and sharing your experience

Please see our contribution guide to learn more about contributing to MLflow.

⭐️ Star History

✏️ Citation

If you use MLflow in your research, please cite it using the "Cite this repository" button at the top of the GitHub repository page, which will provide you with citation formats including APA and BibTeX.

👥 Core Members

MLflow is currently maintained by the following core members with significant contributions from hundreds of exceptionally talented community members.

Metadata

Release files for mlflow-skinny 3.17.0

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for mlflow-skinny 3.17.0
File	Size	Uploaded
mlflow_skinny-3.17.0.tar.gz	3.5 MB	Oct 7, 2026	Details

Built distribution (wheel)

Table of built distributions (wheels) for mlflow-skinny 3.17.0
File	Interpreter	ABI	Platform	Reset
mlflow_skinny-3.17.0-py3-none-any.whl 4.1 MB Oct 7, 2026	Python 3	none	any	Details

Total release size: 7.6 MB

Release files / mlflow_skinny-3.17.0.tar.gz

Download URL	mlflow_skinny-3.17.0.tar.gz
Size	3.5 MB
Tags	Source
SHA-256 checksum How to use checksums	`e887675d9f5ab57ec70860247b1cd5682bb719f626c69979ca7c99b5439877df`
BLAKE2b-256 checksum How to use checksums	`185adea201b3cec8a3a1d97b042684113df54e79681f4bc051e807a13dd164b2`
Upload date	Oct 7, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/6.1.0 CPython/3.13.14`

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Publishing platform: GitHub Actions
Publishing repository: github.com/mlflow/releases
Publishing commit: github.com/mlflow/releases/tree/95f00d90bc84d798a8ba3dc12ab77f9ddc6e5fbc

Workflow

Publishing configuration: github.com/mlflow/releases/blob/95f00d90bc84d798a8ba3dc12ab77f9ddc6e5fbc/.github/workflows/python.yml
Publishing logs: github.com/mlflow/releases/actions/runs/37576712009/attempts/1

Artifact

Subject: mlflow_skinny-3.17.0.tar.gz
SHA-256 checksum: e887675d9f5ab57ec70860247b1cd5682bb719f626c69979ca7c99b5439877df Verifying attestations

Release files / mlflow_skinny-3.17.0-py3-none-any.whl

Download URL	mlflow_skinny-3.17.0-py3-none-any.whl
Size	4.1 MB
Tags	Python 3
SHA-256 checksum How to use checksums	`443c1e8cbf04edc71434754e0dae2846db91814b342c65bbc05b131c2a68b7ba`
BLAKE2b-256 checksum How to use checksums	`17366c4cb2aa855061705f0295228654cfca564a0a2425d5e5484f187e4aa44d`
Upload date	Oct 7, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/6.1.0 CPython/3.13.14`

Provenance

Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/mlflow/releases
Publishing commit: github.com/mlflow/releases/tree/95f00d90bc84d798a8ba3dc12ab77f9ddc6e5fbc

Workflow

Publishing configuration: github.com/mlflow/releases/blob/95f00d90bc84d798a8ba3dc12ab77f9ddc6e5fbc/.github/workflows/python.yml
Publishing logs: github.com/mlflow/releases/actions/runs/37576712009/attempts/1

Artifact

Subject: mlflow_skinny-3.17.0-py3-none-any.whl
SHA-256 checksum: 443c1e8cbf04edc71434754e0dae2846db91814b342c65bbc05b131c2a68b7ba Verifying attestations