Local OpenAI LLM (Custom Integration for Home Assistant)

Allows use of generic OpenAI-compatible LLM services, such as (but not limited to):

• llama.cpp
• vLLM
• LM Studio
• Ollama
• OpenRouter
• Scaleway
• DeepSeek

This integration has been forked from Home Assistants OpenRouter integration, with the following changes:

• Added server URL to the initial server configuration
• Made the API Key optional during initial server configuration: can be left blank if your local server does not require one
• Uses streamed LLM responses
• Conversation Agents support TTS streaming
• Automatically strips <think> tags from responses
• Added support for image inputs for AI Task entities
• Added support for reconfiguring Conversation Agents
• Added option to trim conversation history to help stay within your context window
• Added temperature control
• Added option to strip emojis from responses
• Added support for parallel tool calling
• Added experimental Retrieval Augmented Generation capability
• Added chat template arguments support
• Added image generation support for AI Task entities
• Added tools support for Generate Data actions for AI Task entities

Installation

Install via HACS (recommended)

Have HACS installed, this will allow you to update easily.

Adding Tools for Assist to HACS can be using this button:

[!NOTE] If the button above doesn't work, add https://github.com/skye-harris/hass_local_openai_llm as a custom repository of type Integration in HACS.

• Click install on the Local OpenAI LLM integration.
• Restart Home Assistant.

• Copy the local_openai folder from latest release to the custom_components folder in your config directory.
• Restart the Home Assistant.

Integration Configuration

After installation, configure the integration through Home Assistant's UI:

1. Go to Settings → Devices & Services.
2. Click Add Integration.
3. Search for Local OpenAI LLM.
4. Follow the setup wizard to configure your desired services.

Configuration Notes

• The Server URL must be a fully qualified URL pointing to an OpenAI-compatible API.
  • This typically ends with /v1 but may differ depending on your server configuration.
• A Server Type configuration can be set to expose some additional options for different inference servers and providers, where they have been implemented.
• Assist requires a fairly lengthy context for tooling and entity definitions.
  • It is strongly recommended to use at least 10k context size and to limit history length and exposed entities to avoid context overflow issues.
  • This is not configurable through OpenAI-compatible APIs, and needs to be configured with the inference server directly.
• Tool calling must be enabled in your inference engine, eg:
  • vLLM: https://docs.vllm.ai/en/latest/features/tool_calling/
  • llama.cpp: https://github.com/ggml-org/llama.cpp/blob/master/docs/function-calling.md
• Parallel tool calling requires support from both your model and inference server.
  • In some cases, control of this is handled by the server directly, in which case toggling this will not have any result.
• Chat Template Arguments allow you to provide custom arguments to your model
  • Arguments are supplied as key/value pairs and provided to the chat_template_kwargs request parameter
  • Values support Jinja2 templates, in order to provide non-string and more complex data structures
  • Arguments differ per model, and not all models make use of user-provided arguments
  • See your models documentation for what arguments are available to be used
• AI Task entities can be configured for Text and/or Image generation capabilities
  • This capability uses the Images API spec and requires support from your chosen image generation server
  • Support has been developed and tested with StableDiffusion.cpp

DeepSeek Cloud Configuration

Reasoning Effort

When the server type is set to DeepSeek Cloud, both conversation and AI task agents show a new DeepSeek Configuration section with a Reasoning Effort option. This option controls whether thinking is enabled, and what level of reasoning to perform on the request.

• Disabled (default) — no thinking tokens.
• High — enables thinking with standard reasoning effort.
• Max — enables thinking with maximum reasoning effort.

When enabled, thinking content returned by the model is also fed back into the conversation as reasoning content on supported Home Assistant versions (2026.4+).

llama.cpp Configuration

When the server type is set to llama.cpp, both conversation and AI task agents show a llama.cpp Configuration section with the following options.

Enable thinking

Passes enable_thinking=true via chat_template_kwargs to enable reasoning on supported models.

• Disabled (default) — no thinking tokens.
• Enabled — requests reasoning from the model.

Note: This option completely overrides any existing enable_thinking option in your Chat Template Arguments.

Include prior thinking

Controls whether thinking/reasoning content from prior conversation turns is sent back in new completion requests.

Some reasoning models require this enabled, and others require it disabled. Check the documentation for your model if unsure.

• Enabled (default) — prior-turn thinking_content is passed as reasoning_content in the next request, allowing the model to see its own prior reasoning.
• Disabled — prior thinking context is stripped before sending. Use for models that reject prior reasoning context (e.g., Gemma 4).

Slot ID

Pins requests to a specific llama.cpp server slot for prompt-cache reuse. Leave empty to allow any slot to be used.

Model naming

llama.cpp exposes the value supplied via its --alias flag on the model object. When an alias is set it is used as the model's display name; otherwise the raw model id (typically the full model file path) is used, with the path and .gguf extension stripped for a cleaner name.

Sampling Parameters

These options control how llama.cpp selects tokens during text generation. Please refer to the llama.cpp documentation for further information and usage.

Experimental: Date/Time Context Injection Role

This integration supports injecting some dynamic content, presently the date and time, into the active Conversation Agent prompt when making a request. This was added as it is beneficial for the model to be grounded with this context in its role as an assistant, and was previously added to the system prompt by Home Assistant itself before later being removed due to negative effects on prompt caching and performance.

This was previously always-on but has been extracted as an experimental configuration option as this is not a once-size-fits-all for all models. To this end I have provided a number of options so that users can try them out and select the one that works best, or disable entirely if none work well, for their chosen model.

The available options are:

Tool Result:

The date and time are inserted as a Tool Call Result message to the model, before the current user message.

As long as the model does not reject it, this is the recommended method to use and produces the most reliable results during testing.

Assistant:

The date and time are inserted as an additional Assistant message to the model, before the current user message.

In cases where the Tool Call Result role method does not work for a model, this is the next recommended to test with.

User:

The date and time are inserted as an additional User message to the model, before the current user message.

Recommended only where neither the System nor Assistant injection methods work for the model, but may not produce desirable results. Some models have been known to repeat the date/time back to the user without request.

Disabled (no selection):

If your model simply refuses to work well with any method, simply remove the value from the configuration option to disable this again.

Experimental: Retrieval Augmented Generation (RAG) with Weaviate

Retrieval Augmented Generation is used to pre-feed yo