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Ray

Ray is an open-source unified compute framework for scaling AI and Python workloads, including data processing, model training, model serving, hyperparameter tuning, and reinforcement learning. The full documentation lives at https://docs.ray.io/.

For agents: Ray, an open-source unified compute framework for scaling Python and AI workloads: data processing, training, serving, tuning, RL. Full docs at docs.ray.io, llm

distributed computingpythonmodel trainingmodel servingreinforcement learning

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The beginning of the file, from our validated copy saved 2026-10-25T10:56:32.185Z. Cached copy · Source on ray.io

# Ray

> Ray is an open-source unified compute framework for scaling AI and Python workloads, including data processing, model training, model serving, hyperparameter tuning, and reinforcement learning. The full documentation lives at https://docs.ray.io/.

Full page text, grouped by section, is in [llms-full.txt](https://docs.ray.io/en/latest/llms-full.txt).

Request the links that follow with the HTTP header `Accept: text/markdown` to retrieve a Markdown rendering of the page.

## Overview

- [Overview](https://docs.ray.io/en/latest/ray-overview/index.html): Introduction to Ray, the open-source framework for scaling AI and Python workloads — its core primitives, AI libraries, and cluster integrations.
- [Ray Examples](https://docs.ray.io/en/latest/ray-overview/examples.html): Gallery of end-to-end Ray examples spanning data processing, model training, serving, and hyperparameter tuning workloads.

## Getting Started

- [Getting Started](https://docs.ray.io/en/latest/ray-overview/getting-started.html): Quickstart paths for Ray: scale ML workloads with the AI libraries, parallelize Python with Ray Core, or deploy to the cloud with Ray Clusters.

## Installation

- [Installing Ray](https://docs.ray.io/en/latest/ray-overview/installation.html): Install Ray and its AI libraries with pip across Linux, Windows (beta), and macOS on x86_64, ARM, and Apple silicon hardware.

## Use Cases

- [Ray Use Cases](https://docs.ray.io/en/latest/ray-overview/use-cases.html): Common Ray use cases: LLMs and generative AI, batch inference, distributed training, hyperparameter tuning, reinforcement learning, and ML platforms.
- [Ray for ML Infrastructure](https://docs.ray.io/en/latest/ray-air/getting-started.html): Overview of using Ray as ML infrastructure, covering how Ray Train, Ray Data, and Ray Serve compose end-to-end ML workflows on a unified compute runtime.

## Examples

- [Examples](https://docs.ray.io/en/latest/ray-overview/examples/index.html): End-to-end Ray examples: multimodal AI, audio and timeseries, XGBoost, object detection, RAG, and serving LLM agents with MCP and LangChain.
- [Multi-modal AI pipeline](https://docs.ray.io/en/latest/_collections/ray-overview/examples/e2e-multimodal-ai-workloads/README.html): 💻 Run this entire tutorial on Anyscale for free: https://console.anyscale.com/template-preview/image-search-and-classification or access the repository here.
- [LLM training and inference](https://docs.ray.io/en/latest/_collections/ray-overview/examples/entity-recognition-with-llms/README.html): This end-to-end tutorial fine-tunes an LLM to perform batch inference and online serving at scale. While entity recognition (NER) is the main task in this tutorial, you can easily extend these end-to-end workflows to any use case.
- [Audio batch inference](https://docs.ray.io/en/latest/_collections/ray-overview/examples/e2e-audio/README.html): This tutorial demonstrates a batch inference pipeline that converts raw audio files into a curated subset using
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