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94d5affcea
* feat(website): split the site, move docs to /docs, add a landing page The Hugo docs site has always been localai.io itself, which left nowhere to explain what LocalAI is or show what the team builds. This adds a separate marketing site at the root and moves the documentation under /docs/. Docs: The existing site keeps its content tree and its Relearn theme, and now builds with baseURL <root>/docs/. Its _index.md, which held a hand written landing page, becomes a real documentation home. Every previously published URL keeps working. GitHub Pages has no server side rewrites, so .github/ci/gen-redirects.sh walks the built docs output and leaves a meta refresh plus a canonical link at each old root path. It covers bare .html files too, which is what keeps /gallery.html alive, and it never overwrites a path the marketing site already owns. Website: A second Hugo site under website/ with its own layouts and no external theme, so the marketing side does not have to fight Relearn's home rooted menu and asset pipeline. CI builds both and merges them into one Pages artifact. The design is derived from the project logo rather than invented: the navy of the triangle, the cyan of the llama, the purple of the speed bars. Those offset bars became the motion signature. The background renders a real depth-anything.cpp depth map as contour lines and switches to a locate-anything.cpp style detection overlay over the engines section. Also included: an /engines/ index driven entirely by data/engines.yaml, a /blog/ section with five posts written from the release notes and the engine benchmark suites, install.sh and a Kubernetes manifest since the site advertises both, and a rule in .agents/ that release preparation now includes a blog post and demo clips. Every figure on the site is derived from the repository or the GitHub API, not from memory. Correcting them against their sources found one error in README.md: voxtral-tts.c is text to speech, not speech to text. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude Code:claude-opus-5 [Bash] [Edit] [Write] [Agent] * feat(website): add a star history chart, rewrite the history post in first person The history post read like a changelog written by a committee. It is now in Ettore's voice, first person, with the admissions left in. The numbers paragraph in particular read like a directory listing. It now says what the figures mean rather than which file they came from. Adds an interactive star history chart, built from the GitHub stargazers API rather than embedded from a third party, so the page makes no external request and cannot break when someone else's service is down. The four releases the post is organised around are marked on the curve, and the labels stack into rows because three of them land within two months of each other. The API stops paginating at 40,000 items, so the curve is measured up to December 2025 and the segment from there to today's total is drawn dashed, labelled as an estimate in the caption and in the tooltip. It is a straight line between two known points, and the chart says so rather than implying it is data. Also drops "marketing site" from the README heading and everywhere else it appeared, and calls it the main site instead. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude Code:claude-opus-5 [Bash] [Edit] [Write] --------- Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
108 lines
5.7 KiB
Markdown
108 lines
5.7 KiB
Markdown
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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 = "/overview"
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author = "Ettore Di Giacinto"
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icon = "info"
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LocalAI is a composable AI stack for running models locally: a small core that speaks the OpenAI and Anthropic APIs, with each model backend added only when you need it. It's simple, efficient, and private by default, and a drop-in replacement that keeps your data on your own hardware.
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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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- **Composable by design**: A small core, not a bundle. Backends are separate and installed on demand, so you only run what you use
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## What's Included
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The LocalAI core is a single small binary (or container). It gives you everything you need to serve models, and pulls each model backend on demand, so you install only what you use:
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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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- **Any Model, Any Modality**: LLMs, image and video, text-to-speech, speech-to-text, vision, and embeddings, each on its own backend, pulled automatically when you load a model
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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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Each backend is a dedicated gRPC service that LocalAI builds around a best-in-class engine (llama.cpp, vLLM, whisper.cpp, stable-diffusion, MLX, and more), exposing it through the unified API. Backends ship as standard OCI images and run as isolated processes, so each one can be installed, upgraded, or removed without touching the core, can even run on a separate machine, and a fault in one never brings down the rest.
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Because the backend contract is a simple gRPC interface, the system is open: bring your own model, or write a custom backend in any language and plug it in, exactly how the built-in backends work. This is what keeps the core small and gives you the flexibility to run precisely the stack you want, instead of compiling every engine into one binary.
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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
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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/containers" %}}) 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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