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"> [](https://pypi.org/project/mlflow/) [](https://pepy.tech/projects/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> [](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