LocalAI

LocalAI, a self-hosted runtime that runs AI workloads on user-controlled hardware, offers an OpenAI-compatible API supporting text generation, vision, speech, image and video generation, embeddings, and autonomous agents, with backends pulled on demand from engines like llama.cpp, vLLM, SGLang, MLX, and whisper.cpp. It scales from CPU-only laptops to distributed GPU clusters, includes built-in agents with MCP tools and realtime voice via WebRTC, and provides privacy controls such as PII analysis and audit logging. The project is open source and self-hosted, with a repository age and version not specified in the source.

Open Source Alternative to: Last 30 days Last commit Repository age Version License Self-hosted Repository LocalAI is a self-hosted runtime that lets you run virtually any AI workload on hardware you control. Text generation, vision, speech recognition, text-to-speech, image and video generation, embeddings, reranking, and autonomous agents /agentos all run behind a single OpenAI-compatible API. If you're already using OpenAI or Anthropic APIs, switching the endpoint is often all it takes. The core design is deliberately lean. Backends aren't bundled upfront. They're pulled on demand when a model needs them, each one wrapping a best-in-class engine like llama.cpp, vLLM, SGLang, MLX, or whisper.cpp as an isolated service. You can install, update, or remove individual backends without touching the rest of the stack. Hardware mixing is first-class: NVIDIA, AMD, Intel, Apple Silicon, Vulkan, and Jetson all work, and you can route across them in a single cluster. For cases where existing engines are too heavy or too closed, the LocalAI team builds its own: It scales from a CPU-only laptop to a distributed GPU cluster without changing how you interact with it. A single workstation setup can grow into a team server with API keys, roles, quotas, and usage tracking, then further into a multi-worker cluster with model routing and device-spanning inference. Local model runners /categories/ai-machine-learning/machine-learning-infrastructure/local-model-runners rarely cover this range in one package. Agents are built in, not bolted on. You can create agents with MCP tools, memory, RAG, and citations directly from the UI or API. Realtime voice experiences are supported through WebRTC with interruptible STT, LLM output, and TTS pipelines, similar to what LiveKit /livekit handles for general media but focused on AI interaction. Privacy controls go beyond keeping data local: PII analysis, redaction middleware, and audit logging are available at the infrastructure level. The API surface is compatible with OpenAI, Anthropic, Ollama, and ElevenLabs conventions, so existing tooling like LibreChat /librechat or Open WebUI /open-webui connects without custom adapters. Last 30 days Last commit Repository age Version License Self-hosted Repository Every Sunday we deconstruct one proprietary app and pick the best open source alternatives worth switching to. Stars Last commit License Stars Last commit License Stars Last commit License Stars Last commit License Compare with Stars Last commit License Compare with Last commit License Compare with License Compare with