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Update README and docs to attribute maintenance to the LocalAI team (Ettore Di Giacinto and Richard Palethorpe) and drop the autonomous AI dev team section. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: claude-code:claude-opus-4-7 [Edit] [Bash]
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102 lines
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title = "Overview"
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weight = 1
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toc = true
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description = "What is LocalAI?"
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tags = ["Beginners"]
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categories = [""]
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url = "/docs/overview"
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author = "Ettore Di Giacinto"
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icon = "info"
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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.
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## Why LocalAI?
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In today's AI landscape, privacy, control, and flexibility are paramount. LocalAI addresses these needs by:
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- **Privacy First**: Your data never leaves your machine
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- **Complete Control**: Run models on your terms, with your hardware
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- **Open Source**: MIT licensed and community-driven
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- **Flexible Deployment**: From laptops to servers, with or without GPUs
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- **Extensible**: Add new models and features as needed
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## What's Included
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LocalAI is a single binary (or container) that gives you everything you need:
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- **OpenAI-compatible API** — Drop-in replacement for OpenAI, Anthropic, and Open Responses APIs
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- **Built-in Web Interface** — Chat, model management, agent creation, image generation, and system monitoring
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- **AI Agents** — Create autonomous agents with MCP (Model Context Protocol) tool support, directly from the UI
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- **Multiple Model Support** — LLMs, image generation, text-to-speech, speech-to-text, vision, embeddings, and more
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- **GPU Acceleration** — Automatic detection and support for NVIDIA, AMD, Intel, and Vulkan GPUs
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- **Distributed Mode** — Scale horizontally with worker nodes, P2P federation, and model sharding
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- **No GPU Required** — Runs on CPU with consumer-grade hardware
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LocalAI integrates [LocalAGI](https://github.com/mudler/LocalAGI) (agent platform) and [LocalRecall](https://github.com/mudler/LocalRecall) (semantic memory) as built-in libraries — no separate installation needed.
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## Getting Started
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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.
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### Recommended: Docker Installation
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The quickest way to get started with LocalAI is using Docker:
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```bash
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docker run -p 8080:8080 --name local-ai -ti localai/localai:latest-cpu
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```
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Then open **http://localhost:8080** to access the web interface, install models, and start chatting.
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For GPU support, see the [Container images reference]({{% relref "getting-started/container-images" %}}) or the [Quickstart guide]({{% relref "getting-started/quickstart" %}}).
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For complete installation instructions including Docker, macOS, Linux, Kubernetes, and building from source, see the [Installation guide](/installation/).
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## Key Features
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- **Text Generation**: Run various LLMs locally (llama.cpp, transformers, vLLM, and more)
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- **Image Generation**: Create images with Stable Diffusion, Flux, and other models
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- **Audio Processing**: Text-to-speech and speech-to-text
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- **Vision API**: Image understanding and analysis
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- **Embeddings**: Vector representations for search and retrieval
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- **Function Calling**: OpenAI-compatible tool use
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- **AI Agents**: Autonomous agents with MCP tool support
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- **MCP Apps**: Interactive tool UIs in the web interface
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- **P2P & Distributed**: Federated inference and model sharding across machines
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## Community and Support
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LocalAI is a community-driven project. You can:
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- Join our [Discord community](https://discord.gg/uJAeKSAGDy)
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- Check out our [GitHub repository](https://github.com/mudler/LocalAI)
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- Contribute to the project
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- Share your use cases and examples
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## Next Steps
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Ready to dive in? Here are some recommended next steps:
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1. **[Install LocalAI](/installation/)** - Start with [Docker installation](/installation/docker/) (recommended) or choose another method
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2. **[Quickstart guide]({{% relref "getting-started/quickstart" %}})** - Get up and running in minutes
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3. [Explore available models](https://models.localai.io)
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4. [Model compatibility](/model-compatibility/)
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5. [Try out examples]({{% relref "getting-started/try-it-out" %}})
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6. [Join the community](https://discord.gg/uJAeKSAGDy)
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## Team
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LocalAI is created by [Ettore Di Giacinto](https://github.com/mudler) and maintained by the LocalAI team:
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- **[Ettore Di Giacinto](https://github.com/mudler)** — original author and project lead
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- **[Richard Palethorpe](https://github.com/richiejp)** — maintainer
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LocalAI is helped by the wider community of contributors. See the full [contributors list](https://github.com/mudler/LocalAI/graphs/contributors).
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## License
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LocalAI is MIT licensed.
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