Overview

LocalAI is your complete AI stack for running AI models locally. It’s designed to be simple, efficient, and accessible, providing a drop-in replacement for OpenAI’s API while keeping your data private and secure.

Why LocalAI?

In today’s AI landscape, privacy, control, and flexibility are paramount. LocalAI addresses these needs by:

  • Privacy First: Your data never leaves your machine
  • Complete Control: Run models on your terms, with your hardware
  • Open Source: MIT licensed and community-driven
  • Flexible Deployment: From laptops to servers, with or without GPUs
  • Extensible: Add new models and features as needed

Core Components

LocalAI is more than just a single tool - it’s a complete ecosystem:

  1. LocalAI Core

    • OpenAI-compatible API
    • Multiple model support (LLMs, image, audio)
    • Model Context Protocol (MCP) for agentic capabilities
    • No GPU required
    • Fast inference with native bindings
    • Github repository

  2. LocalAGI

    • Autonomous AI agents
    • No coding required
    • WebUI and REST API support
    • Extensible agent framework
    • Github repository

  3. LocalRecall

    • Semantic search
    • Memory management
    • Vector database
    • Perfect for AI applications
    • Github repository

Getting Started

LocalAI can be installed in several ways. Docker is the recommended installation method for most users as it provides the easiest setup and works across all platforms.

The quickest way to get started with LocalAI is using Docker:

docker run -p 8080:8080 --name local-ai -ti localai/localai:latest

For complete installation instructions including Docker, macOS, Linux, Kubernetes, and building from source, see the Installation guide.

Key Features

  • Text Generation: Run various LLMs locally
  • Image Generation: Create images with stable diffusion
  • Audio Processing: Text-to-speech and speech-to-text
  • Vision API: Image understanding and analysis
  • Embeddings: Vector database support
  • Functions: OpenAI-compatible function calling
  • MCP Support: Model Context Protocol for agentic capabilities
  • P2P: Distributed inference capabilities

Community and Support

LocalAI is a community-driven project. You can:

Next Steps

Ready to dive in? Here are some recommended next steps:

  1. Install LocalAI - Start with Docker installation (recommended) or choose another method
  2. Explore available models
  3. Model compatibility
  4. Try out examples
  5. Join the community
  6. Check the LocalAI Github repository
  7. Check the LocalAGI Github repository

License

LocalAI is MIT licensed, created and maintained by Ettore Di Giacinto.