This README is the documentation for a Home Assistant (HA) integration called home-generative-agent. This project uses LangChain and LangGraph to create a generative AI agent that interacts with and automates tasks within a HA smart home environment. The agent understands your home's context, learns your preferences, and interacts with you and your home to accomplish activities you find valuable. Key features include creating automations, analyzing images, and managing home states using various LLMs (Large Language Models). The architecture involves both cloud-based and edge-based models for optimal performance and cost-effectiveness. Installation instructions, configuration details, and information on the project's architecture and the different models used are included. The project is open-source and welcomes contributions.
These are some of the features currently supported:
- Create complex Home Assistant automations.
- Image scene analysis and understanding.
- Home state analysis of entities, devices, and areas.
- Full agent control of allowed entities in the home.
- Short- and long-term memory using semantic search.
- Automatic summarization of home state to manage LLM context length.
This integration will set up the conversation platform, a convenient HA component allowing users to converse directly with the Home Generative Assistant.
The snippet below shows that the agent is fluent in yaml based on what it generated and registered as an HA automation.
alias: Check Litter Box Waste Drawer
triggers:
- minutes: /30
trigger: time_pattern
conditions:
- condition: numeric_state
entity_id: sensor.litter_robot_4_waste_drawer
above: 90
actions:
- data:
message: The Litter Box waste drawer is more than 90% full!
action: notify.notify
all_cameras.mov
state_of_home.mov
You can create an automation of the home state summary that runs periodically from the HA Blueprint hga_summary.yaml located in the blueprints folder.
You can see that the agent correctly generates the automation below.
alias: Prepare Home for Arrival
description: Turn on front porch light and unlock garage door lock at 7:30 PM
mode: single
triggers:
- at: "19:30:00"
trigger: time
actions:
- target:
entity_id: light.front_porch_light
action: light.turn_on
data: {}
- target:
entity_id: lock.garage_door_lock
action: lock.unlock
data: {}
Below is the camera image the agent analyzed, you can see that two packages are visible.
Below is an example notification from this automation if any boxes or packages are visible.
The agent uses a tool that in turn uses the HA Blueprint hga_scene_analysis.yaml for these requests and so the Blueprint needs to be installed in your HA installation.
Below is a high-level view of the architecture.
The general integration architecture follows the best practices as described in Home Assistant Core and is compliant with Home Assistant Community Store (HACS) publishing requirements.
The agent is built using LangGraph and uses the HA conversation component to interact with the user. The agent uses the Home Assistant LLM API to fetch the state of the home and understand the HA native tools it has at its disposal. I implemented all other tools available to the agent using LangChain. The agent employs several LLMs, a large and very accurate primary model for high-level reasoning, smaller specialized helper models for camera image analysis, primary model context summarization, and embedding generation for long-term semantic search. The primary model is cloud-based, and the helper models are edge-based and run under the Ollama framework on a computer located in the home. The models currently being used are summarized below.
| Model | Location | Purpose |
|---|---|---|
| GPT-4o | OpenAI Cloud | High-level reasoning and planning |
| llama-3.2-vision-11b | Ollama Edge | Image scene analysis |
| llama-3.2-vision-11b | Ollama Edge | Primary model context summarization |
| mxbai-embed-large | Ollama Edge | Embedding generation for sematic search |
LangGraph powers the conversation agent, enabling you to create stateful, multi-actor applications utilizing LLMs as quickly as possible. It extends LangChain's capabilities, introducing the ability to create and manage cyclical graphs essential for developing complex agent runtimes. A graph models the agent workflow, as seen in the image below.
The agent workflow has five nodes, each Python module modifying the agent's state, a shared data structure. The edges between the nodes represent the allowed transitions between them, with solid lines unconditional and dashed lines conditional. Nodes do the work, and edges tell what to do next.
The __start__ and __end__ nodes inform the graph where to start and stop. The agent node runs the primary LLM, and if it decides to use a tool, the action node runs the tool and then returns control to the agent. The summarize_and_trim node processes the LLM's context to manage growth while maintaining accuracy if agent has no tool to call and the number of messages meets the below-mentioned conditions.
You need to carefully manage the context length of LLMs to balance cost, accuracy, and latency and avoid triggering rate limits such as OpenAI's Tokens per Minute restriction. The system controls the context length of the primary model in two ways: it trims the messages in the context if they exceed a max parameter, and the context is summarized once the number of messages exceeds another parameter. These parameters are configurable in const.py; their description is below.
| Parameter | Description | Default |
|---|---|---|
CONTEXT_MAX_MESSAGES |
Messages to keep in context before deletion | 100 |
CONTEXT_SUMMARIZE_THRESHOLD |
Messages in context before summary generation | 20 |
The summarize_and_trim node in the graph may trim the messages only after content summarization.
The latency between user requests or the agent taking timely action on the user's behalf is critical for you to consider in the design. I used several techniques to reduce latency, including using specialized, smaller helper LLMs running on the edge and facilitating primary model prompt caching by structuring the prompts to put static content, such as instructions and examples, upfront and variable content, such as user-specific information at the end. These techniques also reduce primary model usage costs considerably.
You can see the typical latency performance in the table below.
| Action | Latency (s) | Remark |
|---|---|---|
| HA intents | < 1 | e.g., turn on a light |
| Analyze camera image | < 3 | initial request |
| Add automation | < 1 | |
| Memory operations | < 1 |
The agent can use HA tools as specified in the LLM API and other tools built in the LangChain framework as defined in tools.py. Additionally, you can extend the LLM API with tools of your own as well. The code gives the primary LLM the list of tools it can call, along with instructions on using them in its system message and in the docstring of the tool's Python function definition. If the agent decides to use a tool, the LangGraph node action is entered, and the node's code runs the tool. The node uses a simple error recovery mechanism that will ask the agent to try calling the tool again with corrected parameters in the event of making a mistake.
The LLM API instructs the agent always to call tools using HA built-in intents when controlling Home Assistant and to use the intents HassTurnOn to lock and HassTurnOff to unlock a lock. An intent describes a user's intention generated by user actions.
You can see the list of LangChain tools that the agent can use in the table below.
| Langchain Tool | Purpose |
|---|---|
get_and_analyze_camera_image |
run scene analysis on the image from a camera |
upsert_memory |
add or update a memory |
add_automation |
create and register a HA automation |
get_entity_history |
query HA database for entity history |
I built the HA installation on a Raspberry Pi 5 with SSD storage, Zigbee, and LAN connectivity. I deployed the edge models under Ollama on an Ubuntu-based server with an AMD 64-bit 3.4 GHz CPU, Nvidia 3090 GPU, and 64 GB system RAM. The server is on the same LAN as the Raspberry Pi.
- Using the tool of choice, open your HA configuration's directory (folder) (where you find
configuration.yaml). - If you do not have a
custom_componentsdirectory (folder), you must create it. - In the
custom_componentsdirectory (folder), create a new folder calledhome_generative_agent. - Download all the files from the
custom_components/home_generative_agent/directory (folder) in this repository. - Place the files you downloaded in the new directory (folder) you created.
- Restart Home Assistant
- In the HA UI, go to "Configuration" -> "Integrations" click "+," and search for "Home Generative Agent"
- Install all the Blueprints in the
blueprintsdirectory (folder). - Install
ollamaon your edge device by following the instructions here. - Pull
ollamamodelsllama-3.2-vision-11bandmxbai-embed-large.
Configuration is done in the UI and via the parameters in const.py.
If you want to contribute to this, please read the Contribution guidelines