Control your Home Assistant smart home with a completely local Large Language Model. No cloud services and no subscriptions needed. Just privacy-focused AI running entirely on your own hardware.
Home LLM is a complete solution for adding AI-powered voice and chat control to Home Assistant. It consists of two parts:
See the Setup Guide for detailed installation instructions.
Requirements: Home Assistant 2025.7.0 or newer
The integration connects language models to Home Assistant, enabling them to understand your requests and control your smart devices.
Choose how and where you want to run your models:
| Backend | Best For |
|---|---|
| Llama.cpp (built-in) | Running models directly in Home Assistant |
| Ollama | Easy setup on a separate GPU machine |
| Generic OpenAI API | LM Studio, LocalAI, vLLM, and other OpenAI-compatible servers |
| llama.cpp server | Heterogeneous (non-uniform) GPU compute setups, including CPU + GPU inference |
| OpenAI 'Responses' Style API | Cloud services supporting the 'responses' style API |
| Anthropic 'Messages' Style API | Cloud services supporting the 'messages' style API |
| text-generation-webui | Advanced users with existing setups |
NOTE: When utilizing external APIs or model providers, your data will be transmitted over the internet and shared with the respective service providers. Ensure you understand the privacy implications of using these third-party services, since they will be able to see the status of all exposed entities in your Home Assistant instance, which can potentially include your current location.
The integration can control: lights, switches, fans, covers, locks, climate, media players, vacuums, buttons, timers, todo lists, and scripts
As a Conversation Agent: - Chat with your assistant through the Home Assistant UI - Connect to voice pipelines with Speech-to-Text and Text-to-Speech - Supports voice streaming for faster responses
As an AI Task Handler: - Create automations that use AI to process data and generate structured responses - Perfect for dynamic content generation, data extraction, and intelligent decision making - See AI Tasks documentation for examples
The "Home" models are small language models (under 5B parameters) fine-tuned specifically for smart home control. They understand natural language commands and translate them into Home Assistant service calls.
| Model Family | Size | Link |
|---|---|---|
| Llama 3.2 | 3B | acon96/Home-Llama-3.2-3B |
| Gemma | 270M | acon96/Home-FunctionGemma-270m |
Previous Model Versions
Stable Models: - 3B v3 (StableLM-Zephyr-3B): acon96/Home-3B-v3-GGUF - 1B v3 (TinyLlama-1.1B): acon96/Home-1B-v3-GGUF - 3B v2 (Phi-2): acon96/Home-3B-v2-GGUF - 1B v2 (Phi-1.5): acon96/Home-1B-v2-GGUF - 1B v1 (Phi-1.5): acon96/Home-1B-v1-GGUF
Multilingual Experiments: - German, French, & Spanish (3B): acon96/stablehome-multilingual-experimental - Polish (1B): acon96/tinyhome-polish-experimental
Note: Models v1 (3B) and earlier are only compatible with integration version 0.2.17 and older.
Don't have dedicated hardware? You can use any instruction-tuned model with in-context learning (ICL). The integration provides examples that teach general-purpose models (like Qwen3, Llama 3, Mistral) how to control your smart home. See the Setup Guide for configuration details.
The fine-tuning dataset and training scripts are included in this repository: - Dataset: Home-Assistant-Requests-V2 on HuggingFace - Source: data/ directory - Training: See train/README.md
| Version | Highlights |
|---|---|
| v0.4.9 | Relax dependency requirements to avoid conflicting with internal HA lib versions |
| v0.4.8 | OpenAI backends rewritten using the official openai Python library for better reliability and compatibility. New "Use server sampling defaults" to let your backend set the sampling parameters. More robust tool call parsing with auto-repair for malformed JSON, ability to disable streaming for all backends. |
| v0.4.7 | Bug fixes, update default llama_cpp_python version to support new models, and support python 3.14 for new Home Assistant versions |
| v0.4.6 | Anthropic API support, on-disk caching for Llama.cpp, new tool calling dataset |
| v0.4.5 | AI Task entities, multiple LLM APIs at once, official Ollama package |
| v0.4 | Tool calling rewrite, voice streaming, agentic tool use loop, multiple configs per backend |
| v0.3 | Home Assistant LLM API support, improved prompting, HuggingFace GGUF auto-detection |
Full Version History
| Version | Description |
|---|---|
| v0.4.9 | Relax dependency requirements to avoid conflicting with internal HA lib versions |
| v0.4.8 | OpenAI backends rewritten using the official openai Python library for better reliability and compatibility. New "Use server sampling defaults" to let your backend set the sampling parameters. More robust tool call parsing with auto-repair for malformed JSON, ability to disable streaming for all backends. |
| v0.4.7 | Bug fixes, update default llama_cpp_python version to support new models, and support python 3.14 for new Home Assistant versions |
| v0.4.6 | New dataset supporting proper tool calling, Add Anthropic "messages" style API support, Add on-disk caching for Llama.cpp backend |
| v0.4.5 | Add support for AI Task entities, Replace custom Ollama API implementation with the official ollama-python package, Support multiple LLM APIs at once |
| v0.4.4 | Fix issue with OpenAI backends appending /v1 to all URLs |
| v0.4.3 | Fix model config creation during setup |
| v0.4.2 | Fix default model settings, numeric config fields, finish_reason handling |
| v0.4.1 | Fix Llama.cpp models downloaded from HuggingFace |
| v0.4 | Rewrite for tool calling models, agentic tool use loop, voice streaming, multiple config sub-entries |
$ claude mcp add home-llm \
-- python -m otcore.mcp_server <graph>