# mlflow-skinny

> MLflow is an open source platform for the complete machine learning lifecycle

- **URL**: https://www.freshcrate.ai/projects/mlflow-skinny
- **Author**: pypi
- **Category**: Databases
- **Latest version**: `v3.13.0` (2026-06-01)
- **License**: non-standard
- **Source**: https://github.com/mlflow/mlflow/issues
- **Homepage**: https://pypi.org/project/mlflow-skinny/
- **Language**: Python
- **GitHub**: 25,478 stars, 5,612 forks
- **Registry**: pypi (`mlflow-skinny`)
- **Tags**: `ai`, `databricks`, `mlflow`, `pypi`

## Description

<!--  Autogenerated by dev/pyproject.py. Do not edit manually.  -->

📣 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:

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

---

<br>
<br>

<h1 align="center" style="border-bottom: none">
    <a href="https://mlflow.org/">
        <img alt="MLflow logo" src="https://raw.githubusercontent.com/mlflow/mlflow/refs/heads/master/assets/logo.svg" width="200" />
    </a>
</h1>
<h2 align="center" style="border-bottom: none">The Open Source AI Engineering Platform for Agents, LLMs & Models</h2>

MLflow is the largest open source **AI engineering platform for agents, LLMs, and ML models**. MLflow enables teams of all sizes to [debug](https://mlflow.org/llm-tracing),
[evaluate](https://mlflow.org/llm-evaluation), [monitor](https://mlflow.org/ai-monitoring), and [optimize](https://mlflow.org/prompt-optimization) 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](https://mlflow.org/docs/latest/genai/tracing), [evaluation](https://mlflow.org/docs/latest/genai/eval-monitor),
[prompt management](https://mlflow.org/docs/latest/genai/prompt-registry), [prompt optimization](https://mlflow.org/prompt-optimization) and an [AI Gateway](https://mlflow.org/docs/latest/genai/governance/ai-gateway) for managing costs and model access.
Learn more at [MLflow for LLMs and Agents](https://mlflow.org/docs/latest/genai).

<div align="center">

[![Python SDK](https://img.shields.io/pypi/v/mlflow)](https://pypi.org/project/mlflow/)
[![PyPI Downloads](https://img.shields.io/pypi/dm/mlflow)](https://pepy.tech/projects/mlflow)
[![License](https://img.shields.io/github/license/mlflow/mlflow)](https://github.com/mlflow/mlflow/blob/master/LICENSE.txt)
<a href="https://twitter.com/intent/follow?screen_name=mlflow" target="_blank">
<img src="https://img.shields.io/twitter/follow/mlflow?logo=X&color=%20%23f5f5f5"
      alt="follow on X(Twitter)"></a>
<a href="https://www.linkedin.com/company/mlflow-org/" target="_blank">
<img src="https://custom-icon-badges.demolab.com/badge/LinkedIn-0A66C2?logo=linkedin-white&logoColor=fff"
      alt="follow on LinkedIn"></a>
[![Ask DeepWiki](https://deepwiki.com/badge.svg)](https://deepwiki.com/mlflow/mlflow)

</div>

<div align="center">
   <div>
      <a href="https://mlflow.org/"><strong>Website</strong></a> ·
      <a href="https://mlflow.org/docs/latest"><strong>Docs</strong></a> ·
      <a href="https://github.com/mlflow/mlflow/issues/new/choose"><strong>Feature Request</strong></a> ·
      <a href="https://mlflow.org/blog"><strong>News</strong></a> ·
      <a href="https://www.youtube.com/@mlflowoss"><strong>YouTube</strong></a> ·
      <a href="https://lu.ma/mlflow?k=c"><strong>Events</strong></a>
   </div>
</div>

<br>

## Get Started in 3 Simple Steps

From zero to full-stack LLMOps in minutes. No complex setup or major code changes required. [Get Started →](https://mlflow.org/docs/latest/genai/tracing/quickstart/)

**1. Start MLflow Server**

```bash
uvx mlflow server
```

**2. Enable Logging**

```python
import mlflow

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

**3. Run Your Code**

```python
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](https://opentelemetry.io/) and MCP.

<table>
  <tr>
    <td width="50%">
    <img src="https://raw.githubusercontent.com/mlflow/mlflow/refs/heads/master/assets/readme-tracing.png" alt="Observability" width=100%>
    <div align="center">
        <br>
        <a href="https://mlflow.org/docs/latest/genai/tracing/"><strong>Observability</strong></a>
        <br><br>
        <div>Capture complete traces of your LLM applications and agents for deep behavioral insights. Built on OpenTelemetry, supporting any LLM provider and agent framework. Monitor pr

## Recent releases

| Version | Date | Urgency | Changes |
| --- | --- | --- | --- |
| `v3.13.0` | 2026-06-01 | High | MLflow 3.13.0 includes several major features and improvements  ### Major New Features  - **🔐 [Role-Based Access Control & Admin UI](https://mlflow.org/docs/latest/self-hosting/security/role-based-access-control)**: A full RBAC system with reusable roles and workspace-scoped grants, plus a new web Admin UI for managing users, roles, and permissions on self-hosted MLflow. - **🗄️ [Trace Retention & Auto Archival](https://mlflow.org/docs/latest/genai/tracing/observe-with-traces/archive-trace |
| `v3.12.0` | 2026-05-05 | High | MLflow 3.12.0 includes several major features and improvements  ### Major New Features  - **🖼️ Multimodal Tracing**: Users can now store multimodal content in tracing spans as artifact attachments instead of inline binary data. We've also patched the UI to support the new mlflow-attachment:// style URI, with rich rendering available for PDFs, audio, and images. - **🤖 Codex, Gemini, Qwen coding agent tracing support**: Similar to our Claude Code tracing integration, we've now added support |
| `3.11.1` | 2026-04-21 | Low | Imported from PyPI (3.11.1) |
| `ts/v0.2.0-rc.1` | 2026-04-13 | Medium | Release candidate for `@mlflow/vercel` TypeScript package with version 0.2.0: https://github.com/mlflow/mlflow/pull/22105 |
| `v3.11.1` | 2026-04-08 | Medium | MLflow 3.11.1 includes several major features and improvements.  **Major New Features**:  - 🔍 **Automatic Issue Identification**: Automatically identify quality issues in your agent with AI! Use the new "Detect Issues" button in the traces table to analyze selected traces and surface potential problems across categories like correctness, safety, and performance. Issues are linked directly to traces for easy investigation and debugging. [Docs](https://mlflow.org/docs/latest/genai/eval-monito |
| `v3.11.1` | 2026-04-08 | Medium | MLflow 3.11.1 includes several major features and improvements.  **Major New Features**:  - 🔍 **Automatic Issue Identification**: Automatically identify quality issues in your agent with AI! Use the new "Detect Issues" button in the traces table to analyze selected traces and surface potential problems across categories like correctness, safety, and performance. Issues are linked directly to traces for easy investigation and debugging. [Docs](https://mlflow.org/docs/latest/genai/eval-monito |
| `v3.11.1` | 2026-04-08 | Medium | MLflow 3.11.1 includes several major features and improvements.  **Major New Features**:  - 🔍 **Automatic Issue Identification**: Automatically identify quality issues in your agent with AI! Use the new "Detect Issues" button in the traces table to analyze selected traces and surface potential problems across categories like correctness, safety, and performance. Issues are linked directly to traces for easy investigation and debugging. [Docs](https://mlflow.org/docs/latest/genai/eval-monito |
| `v3.11.1` | 2026-04-08 | Medium | MLflow 3.11.1 includes several major features and improvements.  **Major New Features**:  - 🔍 **Automatic Issue Identification**: Automatically identify quality issues in your agent with AI! Use the new "Detect Issues" button in the traces table to analyze selected traces and surface potential problems across categories like correctness, safety, and performance. Issues are linked directly to traces for easy investigation and debugging. [Docs](https://mlflow.org/docs/latest/genai/eval-monito |
| `v3.11.1` | 2026-04-08 | Medium | MLflow 3.11.1 includes several major features and improvements.  **Major New Features**:  - 🔍 **Automatic Issue Identification**: Automatically identify quality issues in your agent with AI! Use the new "Detect Issues" button in the traces table to analyze selected traces and surface potential problems across categories like correctness, safety, and performance. Issues are linked directly to traces for easy investigation and debugging. [Docs](https://mlflow.org/docs/latest/genai/eval-monito |
| `v3.11.1` | 2026-04-08 | Medium | MLflow 3.11.1 includes several major features and improvements.  **Major New Features**:  - 🔍 **Automatic Issue Identification**: Automatically identify quality issues in your agent with AI! Use the new "Detect Issues" button in the traces table to analyze selected traces and surface potential problems across categories like correctness, safety, and performance. Issues are linked directly to traces for easy investigation and debugging. [Docs](https://mlflow.org/docs/latest/genai/eval-monito |

## Citation

- HTML: https://www.freshcrate.ai/projects/mlflow-skinny
- Markdown: https://www.freshcrate.ai/projects/mlflow-skinny.md
- Dependencies JSON: https://www.freshcrate.ai/api/projects/mlflow-skinny/deps

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