blog.matterxiaomi.com

part 7 - Build Your Own Private AI Station: Llama server with Docker+ Open WebUI with Docker

2026-08-20T15:18:45Z

I am running llama.cpp server with Gemma 4 (E2B) model on an rp55 8GB.

Connecting llama.cpp running Gemma 4 (E2B) to Home Assistant via HA-MCP allows the AI model to perform local, tool-calling smart home control.

RPi5 8GB +docker llama.cpp server + Gemma 4 E2B + docker Home Assistant + ha-mcp-integration(build-in ha-mcp server )+docker Open WebUI.

Open WebUI will begin using your local Llama.cpp server as a backend!

Overview
How it work?
Install
Connections
- Step 4: Connect Llama.cpp server via Open WebUI
- Step 5: Connect ha-mcp server via Open WebUI

Overview

Runs the full ha-mcp server inside Home Assistant.Transport: use Streamable HTTP

Set up your Llama.cpp server with docker

Llama.cpp server Load large models locally

Enable 4GB of ZRAM swap space to prevent hard OOM crashes during heavy context processing.

Integrate with Open WebUI for a seamless interface

#docker images
REPOSITORY                      TAG       IMAGE ID       CREATED         SIZE
ghcr.io/ggml-org/llama.cpp      server    bde659bfc300   8 days ago      1.21GB
ghcr.io/open-webui/open-webui   v0.11.0   72c0ba641ba7   2 weeks ago     7.15GB

How it work?

Architecture Overview

Open WebUI Docker(port 3000)
    ↓ OpenAI-compatible API
llama.cpp server (port 8080) ← Gemma 4 E2B Q4
    ↓ (tool calls via MCP)
ha-mcp (inside HA or add-on)
    ↓
Home Assistant Docker

┌─────────────────┐       MCP       ┌─────────────────┐      REST/WS      ┌─────────────────┐
│ MCP Host Client │ ──────────────> │  HA-MCP Server  │ ────────────────> │ Home Assistant  │
│ (Open WebUI /   │                 │   (:9584)       │                   │   (:8123)       │
│  MCP Agent)     │                 └─────────────────┘                   └─────────────────┘
└────────┬────────┘
         │ OpenAI API
         ▼
┌─────────────────┐
│  llama.cpp      │ (Gemma 4 E2B)
│  (:8080)        │
└─────────────────┘

Note

1.llama.cpp acts as an inference server (provider)

2.HA-MCP acts as an MCP tool provider

3.Open WebUI sends user prompts to llama.cpp server

Install

Step 1: Install Llama.cpp server with docker

Step 2: Download a Supported Model

cd /datadocker/llama-cpp

docker pull ghcr.io/ggml-org/llama.cpp:server-b10499

docker run \
  --name llama \
  --restart=unless-stopped \
  -v /datadocker/llama-cpp/models:/models \
  -p 8091:8080 \
  ghcr.io/ggml-org/llama.cpp:server:server-b10499 \
  -m /models/google_gemma-4-E2B-it-Q4_0.gguf \
  --host 0.0.0.0 \
  --port 8080 \
  --threads 4 \
  --jinja \
  --reasoning-budget 0 \
  --chat-template-kwargs '{"enable_thinking": false}' \
  --ui-mcp-proxy

you will get api:

http://192.168.2.134:8091/v1

Step 2: Install Open WebUI with docker

docker pull ghcr.io/open-webui/open-webui:v0.11.0

cd /datadocker/open-webui/

docker run -d -p 3000:8080 --name open-webui \
 -v /datadocker/open-webui/data:/app/backend/data \
 ghcr.io/open-webui/open-webui:v0.11.0


web ui

http://192.168.2.125:3000/

Step 3: Install ha-mcp-integration(build-in ha-mcp server ) in home assistant

first,Install HA-MCP Custom Component integration in ha

then Add HA-MCP Server in HA-MCP Custom Component integration.

you will get api:

http://192.168.2.125:9584/private_QQz2CLU3n8xR

Connections

Step 4: Connect Llama.cpp server via Open WebUI

connect to a AI Provider

Add the API Connection in Open WebUI

Connect Llama.cpp server to Open WebUI

Open WebUI will begin using your local Llama.cpp server as a backend

Open Open WebUI in your browser

Go to ⚙️ Admin Settings → Connections → OpenAI.

Click ➕ Add Connection.

http://192.168.2.134:8091/v1

add http://192.168.2.134:8091/v1,it is llama.cpp with gemma4 model

Step 5: Connect ha-mcp server via Open WebUI

add ha mcp server

Open Open WebUI in your browser

Go to ⚙️ Admin Settings → Tools → Integrations.

Click ➕ Add External Tool Servers.

http://192.168.2.125:9584/private_QQz2CLU3n8xR

Note

you can get Connect URL via HA-MCP Custom Component integration in ha.

http://192.168.2.125:8123/config/integrations/integration/ha_mcp_tools

debug step by step

http://192.168.2.134:8091/v1

cqrs

0.00.013.586 W srv  llama_server: CORS is set to allow all origins ('*') and no API key is set
0.00.013.586 W srv  llama_server: this can be a security risk (cross-origin attacks)
0.00.013.587 W srv  llama_server: more info: https://github.com/ggml-org/llama.cpp/pull/25655

useful links

https://docs.openwebui.com/getting-started/quick-start/connect-a-provider/starting-with-llama-cpp/

add ha mcp server

https://homeassistant-ai.github.io/ha-mcp/setup/?method=ha-component&client=open-webui&scope=local

Docker HA + HA Custom Component (in-process server)+ Docker Open WebUI + Local access on same host.

https://homeassistant-ai.github.io/ha-mcp/setup/?method=ha-component&client=open-webui&scope=local

Part 12 - update Raspberry Pi OS Trixie (2025) and increase swap to 4 GB

2026-08-15T19:41:27Z

Raspberry Pi OS Trixie (2025)

Because the RPi 5 shares system RAM between the CPU and AI workloads, careful configuration is required to prevent out-of-memory (OOM) crashes.

System Requirements
Raspberry Pi OS Trixie (2025) uses rpi-swap:
Check Current Swap Configuration

Expand Swap Using dphys-swapfile (Bookworm and Older).

Expand Swap Using rpi-swap (Trixie and Newer).

Raspberry Pi OS Trixie (2025) or newer, the system uses rpi-swap instead of dphys-swapfile.

rpi-swap is a new package that replaces/deprecates dphys-swapfile for configuring swap on your Raspberry Pi.it is a standard swap management solution, providing better performance and reliability than the old dphys-swapfile approach.

Supports: Compressed RAM swap (zram), file-based swap, or hybrid modes.

Using zram avoids small/frequent writes to /var/swap so as to increase SD card longevity.

Raspberry OS Version

cat /etc/os-release
PRETTY_NAME="Debian GNU/Linux 13 (trixie)"
NAME="Debian GNU/Linux"
VERSION_ID="13"
VERSION="13 (trixie)"
VERSION_CODENAME=trixie
DEBIAN_VERSION_FULL=13.4
ID=debian
HOME_URL="https://www.debian.org/"
SUPPORT_URL="https://www.debian.org/support"
BUG_REPORT_URL="https://bugs.debian.org/"

System Requirements

Raspberry Pi OS Trixie (2025) or newer

rpi-swap is installed.

Raspberry Pi OS Trixie (2025) uses rpi-swap:

Config Directory: /etc/rpi/swap.conf (drop-in configuration files).

Default Swap Type: Compressed RAM swap (zram).

Default Swap File: /dev/zram0.

Default Size: Up to 2048MB, sized to match RAM (capped at 50% of disk space).

Supports: Compressed RAM swap (zram), file-based swap, or hybrid modes.

rpi-swap

rpi-swap GitHub Repository (Official):https://github.com/raspberrypi/rpi-swap

Modern swap management for Raspberry Pi systems, designed to replace dphys-swapfile with a more flexible and efficient approach.

Check Current Swap Configuration

Check total swap size and usage:

free -h

 free -h
               total        used        free      shared  buff/cache   available
Mem:           7.9Gi       5.7Gi       342Mi       2.7Mi       2.0Gi       2.2Gi
Swap:          2.0Gi       2.0Gi       656Ki

Check swap details

cat /proc/meminfo | grep Swap

 cat /proc/meminfo | grep Swap
SwapCached:           48 kB
SwapTotal:       2097136 kB
SwapFree:          26944 kB

see where the swap file is located:

swapon --show

 swapon --show
NAME       TYPE      SIZE USED PRIO
/dev/zram0 partition   2G   0B  100

rpi-swap uses systemd generators so 'rpi-zram-writeback' will not appear in the list of package files.

tree /run/systemd/generator/

 tree /run/systemd/generator/
/run/systemd/generator/
├── boot-firmware.mount
├── dev-zram0.swap
├── getty.target.wants
│   └── serial-getty@ttyAMA10.service -> /usr/lib/systemd/system/serial-getty@.service
├── local-fs.target.requires
│   ├── boot-firmware.mount -> ../boot-firmware.mount
│   └── -.mount -> ../-.mount
├── local-fs.target.wants
│   ├── systemd-fsck-root.service -> /usr/lib/systemd/system/systemd-fsck-root.service
│   └── systemd-remount-fs.service -> /usr/lib/systemd/system/systemd-remount-fs.service
├── -.mount
├── netplan.stamp
├── rpc_pipefs.target
├── rpi-setup-loop@var-swap.service.d
│   ├── fast-symlink.conf
│   ├── no-dependency-cycle.conf
│   └── resize-swap.conf
├── rpi-zram-writeback.timer
├── run-rpc_pipefs.mount
├── sockets.target.wants
│   └── sshd-unix-local.socket -> ../sshd-unix-local.socket
├── sshd-unix-local@.service -> /usr/lib/systemd/system/sshd@.service
├── sshd-unix-local.socket
├── swap.target.wants
│   └── dev-zram0.swap -> ../dev-zram0.swap

install/configure rpi-swap (Trixie only):

sudo apt install rpi-swap
sudo reboot

Configuration files are located at:

/etc/rpi/swap.conf

detailed configuration options

man swap.conf

verify that swap has been configured properly:

systemctl list-units --type swap

systemctl list-units --type swap
  UNIT           LOAD   ACTIVE SUB    DESCRIPTION                             
  dev-zram0.swap loaded active active rpi-swap managed swap device (zram+file)

Legend: LOAD   → Reflects whether the unit definition was properly loaded.
        ACTIVE → The high-level unit activation state, i.e. generalization of SUB.
        SUB    → The low-level unit activation state, values depend on unit type.

1 loaded units listed. Pass --all to see loaded but inactive units, too.
To show all installed unit files use 'systemctl list-unit-files'.

References

rpi-swap GitHub Repository (Official):https://github.com/raspberrypi/rpi-swap

run-local-llm-server-part6 debian vmware on windows 10

2026-08-23T11:26:51Z

Llama.cpp is a high-performance inference engine written in C/C++ designed for running Llama and compatible models in the GGUF format.

vmware on windows 10

Overview

Set up your Llama.cpp server

Load large models locally

docker pull ghcr.io/ggml-org/llama.cpp:server-b10499
docker pull ghcr.io/open-webui/open-webui:v0.11.0

Step 1: Download a model in GGUF format from Hugging Face.

# llama.cpp only works with GGUF

# Create a dedicated folder for models:
mkdir -p /datadocker/llama-cpp

cd /datadocker/llama-cpp

mkdir models

cd /datadocker/llama-cpp/models


https://huggingface.co/unsloth/gemma-4-E2B-it-GGUF/tree/main

https://huggingface.co/bartowski/google_gemma-4-E2B-it-GGUF/tree/main

google_gemma-4-E2B-it-Q4_0.gguf

wget https://huggingface.co/bartowski/google_gemma-4-E2B-it-GGUF/resolve/main/google_gemma-4-E2B-it-Q4_0.gguf?download=true

Step 2: Install Llama.cpp server with docker

cd /datadocker/llama-cpp

docker run \
  --name llama \
  --restart=unless-stopped \
  -v /datadocker/llama-cpp/models:/models \
  -p 8091:8080 \
  ghcr.io/ggml-org/llama.cpp:server \
  -m /models/google_gemma-4-E2B-it-Q4_0.gguf \
  --host 0.0.0.0 \
  --port 8080 \
  --threads 4 \
  --jinja \
  --reasoning-budget 0 \
  --chat-template-kwargs '{"enable_thinking": false}' \
  --webui-mcp-proxy

Step 4: Connect Llama.cpp server via WebUI

Claude Code for Home Assistant(ha-mcp Server directly inside Home Assistant)

2026-08-20T17:31:46Z

Claude Code for Home Assistant

Is there a guide to setup home assistant with MCP and integrate with Claude？

I created an guide that integrates Claude Code (Anthropic’s AI assistant) directly into Home Assistant. With it, you can create automations, debug configurations, and manage your smart home using natural language through a browser-based terminal.

Install type
Usage examples
How does it work?
Requirements
Inside home assistant
- Installation ha-mcp-integration
Copy the connect URL for AI Client
Verify the Target Server is Active:

Install type

Run MCP Server directly inside Home Assistant.

Note

Newly solutions usually ran as an internal Python process inside Home Assistant.

ha-mcp-integration is a native Home Assistant integration that embeds an MCP (Model Context Protocol) Server directly inside Home Assistant.

offcial website:https://github.com/homeassistant-ai/ha-mcp-integration

download:https://codeload.github.com/homeassistant-ai/ha-mcp-integration/zip/refs/tags/v1.3.0

Usage examples

claude “Create an automation to turn off lights when I leave home”

Create an automation that turns on the bedroom AC when the temperature exceeds 28°C.

How does it work?

The ha-mcp-integration uses ha-mcp (Model Context Protocol) server which allows Claude to:

Query entity states

Control devices

List available services

Access the entire Home Assistant API

Besides the built-in tools, the server can expose tools coming from MCP servers.

ha-mcp-integration

The integration starts an MCP Server inside Home Assistant..

The integration registers MCP Tools

AI Mcp client connects via MCP.

Architecture

                 AI Client
       (ChatGPT / Claude / Cursor)
                    │
          MCP Protocol (HTTP/SSE)
                    │
        ┌────────────────────────┐
        │ Home Assistant         │
        │ ha-mcp-integration     │
        │                        │
        │  MCP Server            │
        │  Tool Registry         │
        │  Authentication        │
        └──────────┬─────────────┘
                   │
          Home Assistant Core
                   │
      ┌────────────┴─────────────┐
      │ States                   │
      │ Services                 │
      │ Devices                  │
      │ Areas                    │
      │ Automations              │
      │ Scripts                  │
      │ History                  │
      └──────────────────────────┘

ha-mcp-integration:Full MCP Server running inside Home Assistant. AI clients connect directly to it.

ha_mcp_tools:A library of MCP tools used by the MCP Server.

ha-mcp-integration
        │
        ├── starts MCP Server
        │
        └── uses ha_mcp_tools
                 │
                 ├── call_service
                 ├── get_state
                 ├── list_entities
                 ├── search_devices
                 └── ...

ha-mcp-integration does not "think up" automations by itself. Instead, it exposes Home Assistant capabilities as MCP tools, allowing an AI (ChatGPT, Claude, Cursor, etc.) to inspect your Home Assistant configuration and then create or modify automations.

Requirements

Home Assistant Docker

Anthropic account

Inside home assistant

Installation ha-mcp-integration

ha-mcp-integration=HA-MCP Custom Component

Step 1: Download the HA-MCP Custom Component:

https://github.com/homeassistant-ai/ha-mcp-integration/releases

wget https://github.com/homeassistant-ai/ha-mcp-integration/archive/refs/tags/v1.3.0.zip

copy custom_components/ha_mcp_tools/ into config/custom_components/

Step 2: Restart home assistant:

Step 3: Install the HA-MCP Custom Component:

Choose Add HA-MCP Server.it

Copy the connect URL for AI Client

http://192.168.2.125:8123/config/integrations/integration/ha_mcp_tools

Copy the connect URL from the entry's Configure screen (Settings → Devices & Services → HA-MCP Custom Component → HA-MCP Server → Configure)

Verify the Target Server is Active:

http://192.168.2.125:9584/private_QQz2CLU3n8xRQ1

HA-MCP server is up and running!

To connect, paste the full URL (including the /private_... key) into the
connector or MCP settings of your AI/LLM client. No username or password required.
Setup instructions: https://homeassistant-ai.github.io/ha-mcp/

--- Seeing this page? Your URL is set up correctly ---

If this page loads in your browser, the MCP server is reachable and the
URL is correct. If your AI client still cannot connect, the problem is
not on HA-MCP's side. Common causes:

- Geo / country blocking in your reverse proxy / CDN (Cloudflare, NGINX,
  Traefik, Zoraxy, etc.). Most AI/LLM services connect from US-based
  cloud infrastructure, so if you block US IP addresses (or only allow
  your own country), that is why your client cannot connect. Allow your
  provider's IP ranges (or your client's egress IPs). For example,
  Claude.ai connects from Anthropic's network, 160.79.104.0/21.
- WAF, bot-blocking, or rate-limiting rules on the proxy that drop or
  challenge the request.
- The AI client's network can sometimes be spotty -- you may just need
  to try connecting again.
- The AI client itself refusing certain domains or proxy providers on
  its end. This is rare and outside your control; try a different
  hostname or proxy if you suspect it.

Your proxy's access logs will show the blocked attempt -- look for the
request from your AI provider's IP (e.g. an Anthropic 160.79.x.x address).

--- Cloudflare Users ---

If your LLM cannot connect, Cloudflare's "Block AI training bots"
setting is the most common cause. To disable it:

1. Log in to Cloudflare (https://dash.cloudflare.com)
2. In the left sidebar, click Domains, then click Overview
3. Click on the domain you use for connecting to Home Assistant
4. On the right side, find "Control AI Crawlers"
5. Under "Block AI training bots", open the dropdown
6. Select "do not block (allow crawlers)"

Screenshot of the setting:
https://homeassistant-ai.github.io/ha-mcp/images/cloudflare-ai-crawlers-setting.jpg

Using Claude Code to manage your Home Assistant config step by step(docker ha-mcp server))

2026-08-20T17:24:01Z

Install type

Run Standalone ha-mcp MCP Server outside Home Assistant.

Note

Earlier solutions (such as the standalone ha-mcp project) usually ran as an external Python process:

Claude
      │
Standalone ha-mcp Server
      │
REST/WebSocket
      │
Home Assistant

This project provides a seamless integration between Home Assistant and Claude AI via the Model Context Protocol (MCP), enabling using Claude Code to manage your Home Assistant config through natural language step by step.

Prerequisites
Architecture
Installation
Usage Examples

Prerequisites

Home Assistant ha-mcp MCP Server

https://github.com/homeassistant-ai/ha-mcp

Integration

https://github.com/homeassistant-ai/ha-mcp-integration

AI client

Claude Desktop

Architecture

┌─────────────────┐
│   Claude AI     │
│  (Sonnet 4.5)   │
└────────┬────────┘
         │ MCP Protocol
         │
┌────────▼────────┐
│ Standalone  MCP Server    │
│  (WebSocket)    │
└────────┬────────┘
         │ REST API
         │
┌────────▼────────┐
│ Home Assistant  │
│   (v2026.7)    │
└─────────────────┘

MCP server connects to my HA instance through a Long-lived access token to access the Home Assistant API, giving it the ability to search entities, read automation traces, reload configs, and query device states.

ha-mcp is a standalone server built for configuring, building, and debugging your smart home

Installation

step 1.Start MCP server

Runs the ha-mcp server.

docker pull ghcr.io/homeassistant-ai/ha-mcp:latest


docker run -d \
  --name ha-mcp \
  --restart unless-stopped \
  -p 9000:9000 \
  -e HOMEASSISTANT_URL=http://192.168.1.100:8123 \
  -e HOMEASSISTANT_TOKEN=xxxxxxxxxxxxxxxx \
  -e MCP_PORT=9000 \
  ghcr.io/homeassistant-ai/ha-mcp:latest \
  ha-mcp-web

Docker (HTTP server): run ghcr.io/homeassistant-ai/ha-mcp in HTTP mode, pointed at your Home Assistant URL and a long-lived token.

Start Docker Container

docker run -d --name ha-mcp -p 8086:8086 -v ha-mcp-data:/home/mcpuser/.ha-mcp -e HOMEASSISTANT_URL={{HOMEASSISTANT_URL}} -e HOMEASSISTANT_TOKEN={{HOMEASSISTANT_TOKEN}} ghcr.io/homeassistant-ai/ha-mcp:latest ha-mcp-web

Server will be at: http://YOUR_IP:8086/mcp

detail:https://homeassistant-ai.github.io/ha-mcp/setup/?method=docker&client=claude-desktop&scope=local&platform=linux

step 2.Install HA-MCP Custom Component in Home Assistant

To add the Model Context Protocol Server service to your Home Assistant instance, use this Integration:

https://github.com/homeassistant-ai/ha-mcp-integration

source code:https://github.com/homeassistant-ai/ha-mcp/tree/master/custom_components/ha_mcp_tools

step 3.Create a long-lived access token in Home Assistant

You may create a Long-lived access token to allow the client to access the Home Assistant API.

Go to User profile > Security tab,select Create token.

step 4.Configure Claude

An LLM application acts as a client and can connect to multiple MCP servers to provide context.

{
  "mcpServers": {
    "homeassistant": {
      "type": "http",
      "url": "http://localhost:9090",
      "headers": { "Authorization": "Bearer your-ha-access-token" }
    }
  }
}

Usage Examples

Automation Creation

"Create an automation that turns off all lights when I leave home"
"Set up a morning routine at 7 AM with lights and coffee machine"
"Alert me when the washing machine finishes"

useful links

https://homeassistant-ai.github.io/ha-mcp

download stable release from the latest release

https://github.com/homeassistant-ai/ha-mcp/releases

https://github.com/homeassistant-ai/ha-mcp/releases/tag/v8.0.0

Home Assistant MCP Server Add-on doc

https://github.com/homeassistant-ai/ha-mcp/blob/master/homeassistant-addon/README.md

Runs the full ha-mcp server inside Home Assistant.

Home Assistant MCP Setup：https://homeassistant-ai.github.io/ha-mcp/setup/?method=ha-component&client=open-webui&scope=local

LM Studio +ollama+unofficial HA-MCP Server+ Claude Code

https://www.xda-developers.com/i-connected-my-local-llm-to-home-assistant-through-mcp/

Ha Template

2026-07-17T18:55:21Z

The Template integrationIntegrations connect and integrate Home Assistant with your devices, services, and more. [Learn more] allows creating entities which derive their values from other data.

Legacy template entities (alarm_control_panel, binary_sensor, cover, fan, light, lock, sensor, switch, vacuum, weather) have been removed in 2026.6. You have to migrate to modern template entity syntax.

ha 2026.5
ha 2026.6

ha 2026.5

Old Syntax

#
# ---------------------------------- vacuum Template -----------------------------------
#
# https://www.home-assistant.io/integrations/vacuum.template/  old
# https://www.home-assistant.io/integrations/template/#state-based-vacuum---custom-attributes  new
vacuum:
  - platform: template
    vacuums:
      virtual_hallway_room_vacuum:
      #  unique_id:  virtual_hallway_room_vacuum

        start:
            action: script.vacuum_start
        pause:
            action: script.vacuum_pause
        stop:
            action: script.vacuum_stop
        return_to_base:
            action: script.vacuum_return_to_dock
        clean_spot:
            action: script.vacuum_clean_spot
        locate:
            action: script.vacuum_locate_vacuum
        set_fan_speed:
            action: script.vacuum_set_fan_speed
            data:
              speed: "{{ fan_speed }}"
        fan_speeds:
            - Low
            - Medium
            - High
        value_template: "{{ states('sensor.deebotx5pro') }}"
        # battery_level_template: "{{ states('sensor.deebotx5pro_battery')|int }}"
        fan_speed_template: "{{ states('sensor.vacuum_fan_speed') }}"
        attribute_templates:
          status: >-
            {% if (states('sensor.robot_vacuum_robot_cleaner_movement') == "after" and states('sensor.robot_vacuum_robot_cleaner_cleaning_mode') == "stop")  %}
              Charging to Resume
            {% elif states('sensor.vacuum.deebotx5pro') == "auto" %}
              Cleaning
            {% else %}
              Charging
            {% endif %}

Legacy (removed in 2026.6)

Unsupported YAML configuration for the command_line

Configuring the template integration by adding platform: template under the vacuum: key is not supported. The template integration must be configured under its own template: key instead.

To resolve this:

Remove the following from your YAML configuration file:
vacuum:
  - platform: template
Move the configuration under the template: key instead.

Restart Home Assistant.

ha 2026.6

New Syntax

#
# ---------------------------------- Template -----------------------------------
#
# https://www.home-assistant.io/integrations/template
# https://community.home-assistant.io/t/how-to-define-area-in-configuration-yaml/240296/13
template:
  # https://www.home-assistant.io/integrations/template/#vacuum   new template 20260711
  - vacuum:
      - name: Living Room Vacuum
        clean_segments:
          action: script.vacuum_start
          data:
            segment_ids: "{{ segment_ids }}"
        segments: >-
          {{ [
            {'id': '1', 'name': 'Kitchen'},
            {'id': '2', 'name': 'Living room', 'group': 'Upstairs'},
          ] }}
        start:
          action: script.vacuum_start
        unique_id: living_room_vacuum

useful links

https://smarthomeassistant.co.uk/articles/home-automation/home-assistant-2026-6-template-migration

Ecovacs in Home Assistant Part10 - extend ecovacs build-in integration for the Ecovacs X5 Pro( )

2026-07-06T21:45:49Z

Ecovacs in Home Assistant Part10 - extend ecovacs build-in integration for the Ecovacs X5 Pro(mxse7w.py )

DEEBOT X5 PRO (China Version - MXSE7W)

This article specifically targets the Chinese Mainland version of the ECOVACS DEEBOT X5 PRO.

Unlike the global models, the Chinese version has several hardware and software differences that are important when integrating it with Home Assistant.

By extending the device capability definition, Home Assistant can access several additional OMNI station features that are already supported by the robot firmware.

The model covered in this article is:

Product: DEEBOT X5 PRO
Model: MXSE7W
Region: China

The capability definition for the Chinese version is located in:

https://github.com/DeebotUniverse/client.py/blob/dev/deebot_client/hardware/mxse7w.py

This file acts as the capability map between Home Assistant and the robot.

It tells Home Assistant:

which commands the robot supports

which settings can be modified

which events should be monitored

which sensors should be exposed

how the OMNI station can be controlled

"""Deebot DEEBOT X5 PRO OMNI Capabilities."""

from __future__ import annotations

# Import various "Capability" base classes and device type definitions from the deebot_client library
from deebot_client.capabilities import (
    Capabilities,
    CapabilityClean,
    CapabilityCleanAction,
    CapabilityCustomCommand,
    CapabilityEvent,
    CapabilityExecute,
    CapabilityExecuteTypes,
    CapabilityLifeSpan,
    CapabilityMap,
    CapabilitySet,
    CapabilitySetEnable,
    CapabilitySettings,
    CapabilitySetTypes,
    CapabilityStation,
    CapabilityStats,
    CapabilityWater,
    DeviceType,
)
# Import relevant commands for controlling station actions and JSON serialization
from deebot_client.commands import StationAction
from deebot_client.commands.json import station_action
from deebot_client.commands.json.advanced_mode import GetAdvancedMode, SetAdvancedMode
from deebot_client.commands.json.auto_empty import GetAutoEmpty, SetAutoEmpty
from deebot_client.commands.json.battery import GetBattery
from deebot_client.commands.json.carpet import (
    GetCarpetAutoFanBoost,
    SetCarpetAutoFanBoost,
)
from deebot_client.commands.json.charge import Charge
from deebot_client.commands.json.charge_state import GetChargeState
from deebot_client.commands.json.child_lock import GetChildLock, SetChildLock
from deebot_client.commands.json.clean import CleanAreaV2, CleanV2
from deebot_client.commands.json.clean_count import GetCleanCount, SetCleanCount
from deebot_client.commands.json.clean_logs import GetCleanLogs
from deebot_client.commands.json.clean_preference import (
    GetCleanPreference,
    SetCleanPreference,
)
from deebot_client.commands.json.continuous_cleaning import (
    GetContinuousCleaning,
    SetContinuousCleaning,
)
from deebot_client.commands.json.custom import CustomCommand
from deebot_client.commands.json.efficiency import GetEfficiencyMode, SetEfficiencyMode
from deebot_client.commands.json.error import GetError
from deebot_client.commands.json.fan_speed import GetFanSpeed, SetFanSpeed
from deebot_client.commands.json.life_span import GetLifeSpan, ResetLifeSpan
from deebot_client.commands.json.map import (
    GetCachedMapInfo,
    GetMajorMap,
    GetMapInfoV2,
    GetMapSetV2,
    GetMapTrace,
    GetMinorMap,
    SetMajorMap,
)
from deebot_client.commands.json.multimap_state import (
    GetMultimapState,
    SetMultimapState,
)
from deebot_client.commands.json.network import GetNetInfo
from deebot_client.commands.json.ota import GetOta, SetOta
from deebot_client.commands.json.play_sound import PlaySound
from deebot_client.commands.json.pos import GetPos
from deebot_client.commands.json.relocation import SetRelocationState
from deebot_client.commands.json.stats import GetStats, GetTotalStats
from deebot_client.commands.json.sweep_mode import GetSweepMode, SetSweepMode
from deebot_client.commands.json.true_detect import GetTrueDetect, SetTrueDetect
from deebot_client.commands.json.voice_assistant_state import (
    GetVoiceAssistantState,
    SetVoiceAssistantState,
)
from deebot_client.commands.json.volume import GetVolume, SetVolume
from deebot_client.commands.json.water_info import GetWaterInfo, SetWaterInfo
from deebot_client.commands.json.work_mode import GetWorkMode, SetWorkMode
from deebot_client.commands.json.work_state import GetWorkState
from deebot_client.const import DataType

# Import "Event" and enum definitions used to notify the client when the vacuum state changes
from deebot_client.events import (
    AdvancedModeEvent,
    AvailabilityEvent,
    BatteryEvent,
    CachedMapInfoEvent,
    CarpetAutoFanBoostEvent,
    ChildLockEvent,
    CleanCountEvent,
    CleanLogEvent,
    CleanPreferenceEvent,
    ContinuousCleaningEvent,
    CustomCommandEvent,
    EfficiencyModeEvent,
    ErrorEvent,
    FanSpeedEvent,
    FanSpeedLevel,
    LifeSpan,
    LifeSpanEvent,
    MajorMapEvent,
    MapChangedEvent,
    MapTraceEvent,
    MultimapStateEvent,
    NetworkInfoEvent,
    OtaEvent,
    PositionsEvent,
    ReportStatsEvent,
    RoomsEvent,
    StateEvent,
    StationEvent,
    StatsEvent,
    SweepModeEvent,
    TotalStatsEvent,
    TrueDetectEvent,
    VoiceAssistantStateEvent,
    VolumeEvent,
    WorkMode,
    WorkModeEvent,
    auto_empty,
    water_info,
)
from deebot_client.events.auto_empty import AutoEmptyEvent
from deebot_client.events.efficiency_mode import EfficiencyMode
from deebot_client.models import StaticDeviceInfo


def get_device_info() -> StaticDeviceInfo:
    """Get all static device information and capability mappings supported by the DEEBOT X5 PRO OMNI model."""
    return StaticDeviceInfo(
        DataType.JSON,  # This model uses JSON data format for communication
        Capabilities(
            device_type=DeviceType.VACUUM,  # Device type: Robotic Vacuum Cleaner
            
            # --- Basic Status Capabilities ---
            availability=CapabilityEvent(
                AvailabilityEvent, [GetBattery(is_available_check=True)]
            ),  # Availability status (determines if the device is online by checking the battery status)
            battery=CapabilityEvent(BatteryEvent, [GetBattery()]),  # Battery level status
            charge=CapabilityExecute(Charge),  # Charging control command (return to dock)
            
            # --- Cleaning Control Capabilities ---
            clean=CapabilityClean(
                action=CapabilityCleanAction(command=CleanV2, area=CleanAreaV2),  # Global cleaning and area cleaning
                continuous=CapabilitySetEnable(
                    ContinuousCleaningEvent,
                    [GetContinuousCleaning()],
                    SetContinuousCleaning,
                ),  # Continuous cleaning / Resume cleaning (Get/Set/Event)
                count=CapabilitySet(CleanCountEvent, [GetCleanCount()], SetCleanCount),  # Cleaning cycle count (e.g., clean 1 or 2 times)
                log=CapabilityEvent(CleanLogEvent, [GetCleanLogs()]),  # Cleaning logs / historical records
                preference=CapabilitySetEnable(
                    CleanPreferenceEvent, [GetCleanPreference()], SetCleanPreference
                ),  # Cleaning preference settings
                work_mode=CapabilitySetTypes(
                    event=WorkModeEvent,
                    get=[GetWorkMode()],
                    set=SetWorkMode,
                    types=(
                        WorkMode.MOP,               # Mop only
                        WorkMode.MOP_AFTER_VACUUM,  # Mop after vacuum
                        WorkMode.VACUUM,            # Vacuum only
                        WorkMode.VACUUM_AND_MOP,    # Vacuum and mop simultaneously
                    ),
                ),  # Working mode switching
            ),
            
            # --- Custom Commands & Error Handling ---
            custom=CapabilityCustomCommand(
                event=CustomCommandEvent, get=[], set=CustomCommand
            ),  # Passthrough / custom low-level command capability
            error=CapabilityEvent(ErrorEvent, [GetError()]),  # Malfunction and error events
            
            # --- Fan Speed Settings ---
            fan_speed=CapabilitySetTypes(
                event=FanSpeedEvent,
                get=[GetFanSpeed()],
                set=SetFanSpeed,
                types=(
                    FanSpeedLevel.QUIET,     # Quiet
                    FanSpeedLevel.NORMAL,    # Normal
                    FanSpeedLevel.MAX,       # Max
                    FanSpeedLevel.MAX_PLUS,  # Max Plus
                ),
            ),
            
            # --- Consumables & Maintenance Lifecycle ---
            life_span=CapabilityLifeSpan(
                types=(
                    LifeSpan.BRUSH,              # Main brush
                    LifeSpan.FILTER,             # Dustbin filter
                    LifeSpan.HAND_FILTER,        # Handheld / fine filter
                    LifeSpan.SIDE_BRUSH,         # Side brush
                    LifeSpan.UNIT_CARE,          # Unit maintenance
                    LifeSpan.CLEANING_SOLUTION,  # Cleaning solution
                    LifeSpan.SEWAGE_BOX,         # Wastewater tank maintenance
                ),
                event=LifeSpanEvent,
                get=[
                    GetLifeSpan(
                        [
                            LifeSpan.BRUSH,
                            LifeSpan.FILTER,
                            LifeSpan.HAND_FILTER,
                            LifeSpan.SIDE_BRUSH,
                            LifeSpan.CLEANING_SOLUTION,
                            LifeSpan.SEWAGE_BOX,
                        ]
                    )
                ],
                reset=ResetLifeSpan,  # Reset consumable lifecycle timer command
            ),
            
            # --- Map Capabilities ---
            map=CapabilityMap(
                cached_info=CapabilityEvent(CachedMapInfoEvent, [GetCachedMapInfo()]),  # Cached map information
                changed=CapabilityEvent(MapChangedEvent, []),  # Map update / change events
                info=CapabilityExecute(GetMapInfoV2),  # Get basic map details
                major=CapabilitySet(MajorMapEvent, [GetMajorMap()], SetMajorMap),  # Main map management
                minor=CapabilityExecute(GetMinorMap),  # Secondary / multi-floor map management
                multi_state=CapabilitySetEnable(
                    MultimapStateEvent, [GetMultimapState()], SetMultimapState
                ),  # Multi-map management switch state
                position=CapabilityEvent(PositionsEvent, [GetPos()]),  # Real-time vacuum coordinates on the map
                relocation=CapabilityExecute(SetRelocationState),  # Relocation command
                rooms=CapabilityEvent(RoomsEvent, [GetCachedMapInfo()]),  # Room / zone information on the map
                set=CapabilityExecute(GetMapSetV2),  # Map configurations and settings
                trace=CapabilityEvent(MapTraceEvent, [GetMapTrace()]),  # Cleaning path / movement trace
            ),
            
            # --- Network & Audio ---
            network=CapabilityEvent(NetworkInfoEvent, [GetNetInfo()]),  # Wi-Fi and network status information
            play_sound=CapabilityExecute(PlaySound),  # Find my robot / play prompt tone
            
            # --- Advanced Settings ---
            settings=CapabilitySettings(
                advanced_mode=CapabilitySetEnable(
                    AdvancedModeEvent, [GetAdvancedMode()], SetAdvancedMode
                ),  # Advanced mode switch
                carpet_auto_fan_boost=CapabilitySetEnable(
                    CarpetAutoFanBoostEvent,
                    [GetCarpetAutoFanBoost()],
                    SetCarpetAutoFanBoost,
                ),  # Auto-boost suction on carpets switch
                child_lock=CapabilitySetEnable(
                    ChildLockEvent, [GetChildLock()], SetChildLock
                ),  # Child lock switch
                efficiency_mode=CapabilitySetTypes(
                    event=EfficiencyModeEvent,
                    get=[GetEfficiencyMode()],
                    set=SetEfficiencyMode,
                    types=(
                        EfficiencyMode.ENERGY_EFFICIENT_MODE, # Energy-saving mode
                        EfficiencyMode.STANDARD_MODE,         # Standard mode
                    ),
                ),  # Cleaning efficiency / power consumption mode
                ota=CapabilitySetEnable(OtaEvent, [GetOta()], SetOta),  # Firmware OTA update status and settings
                sweep_mode=CapabilitySetEnable(
                    SweepModeEvent, [GetSweepMode()], SetSweepMode
                ),  # Sweeping mode settings
                true_detect=CapabilitySetEnable(
                    TrueDetectEvent, [GetTrueDetect()], SetTrueDetect
                ),  # TrueDetect 3D structured light obstacle avoidance switch
                voice_assistant=CapabilitySetEnable(
                    VoiceAssistantStateEvent,
                    [GetVoiceAssistantState()],
                    SetVoiceAssistantState,
                ),  # YIKO voice assistant status and switch
                volume=CapabilitySet(VolumeEvent, [GetVolume()], SetVolume),  # Voice broadcast volume settings
            ),
            
            # --- Operation & Station Status Monitoring ---
            state=CapabilityEvent(StateEvent, [GetChargeState(), GetWorkState()]),  # Vacuum main state (Charging/Working)
            station=CapabilityStation(
                action=CapabilityExecuteTypes(
                    station_action.StationAction, types=(StationAction.EMPTY_DUSTBIN,)
                ),  # Control station actions (e.g., manually trigger dust collection)
                auto_empty=CapabilitySetTypes(
                    event=AutoEmptyEvent,
                    get=[GetAutoEmpty()],
                    set=SetAutoEmpty,
                    types=(
                        auto_empty.Frequency.AUTO,  # Automatic dust collection
                        auto_empty.Frequency.SMART, # Smart dust collection
                    ),
                ),  # Auto-empty frequency settings
                state=CapabilityEvent(StationEvent, [GetWorkState()]),  # OMNI station's own working state
            ),
            
            # --- Cleaning Statistics Data ---
            stats=CapabilityStats(
                clean=CapabilityEvent(StatsEvent, [GetStats()]),  # Current / single cleaning session stats (area, time)
                report=CapabilityEvent(ReportStatsEvent, []),  # Data reporting events
                total=CapabilityEvent(TotalStatsEvent, [GetTotalStats()]),  # Lifetime cumulative cleaning statistics
            ),
            
            # --- Water Tank & Mop Module Control ---
            water=CapabilityWater(
                amount=CapabilitySetTypes(
                    event=water_info.WaterAmountEvent,
                    get=[GetWaterInfo()],
                    set=SetWaterInfo,
                    types=(
                        water_info.WaterAmount.LOW,    # Low water level
                        water_info.WaterAmount.MEDIUM, # Medium water level
                        water_info.WaterAmount.HIGH,   # High water level
                    ),
                ),  # Mopping water flow adjustment
                mop_attached=CapabilityEvent(
                    water_info.MopAttachedEvent, [GetWaterInfo()]
                ),  # Detect whether the mopping pad/bracket is installed
            ),
        ),
    )

Overall Structure

The file follows a simple three-level hierarchy:

StaticDeviceInfo
        │
        └── Capabilities
                │
                ├── clean
                ├── map
                ├── station
                ├── water
                ├── settings
                ├── battery
                ├── stats
                └── ...

1.Root Node —— StaticDeviceInfo

StaticDeviceInfo is the root object.

It defines the robot's communication protocol (JSON) and registers every capability supported by the device.

2.Branch Nodes

Branch Nodes — Capabilities

Each branch groups together a specific category of functionality.

Each branch contains one or more commands, events, and configuration options.

3.Leaf Nodes

Leaf Nodes — Individual Commands

The leaf nodes are the actual commands and events exchanged with the robot.

These commands correspond to the JSON requests sent through the official ECOVACS Open Platform API.

When Home Assistant calls one of these commands, the robot executes the requested action and returns the corresponding status or result.

Extend the Built-in Home Assistant Ecovacs Integration for the DEEBOT X5 PRO (MXSE7W, China Version)

step 1.Copy the original hardware definition from the Home Assistant container.

cd /data/homeassistant202405

docker cp  ha:/usr/local/lib/python3.14/site-packages/deebot_client/hardware/mxse7w.py .

This creates a local copy that can be edited safely.

step 2.Add new station actions for StationAction.CLEAN_BASE functionality

Locate the following section:

station=CapabilityStation(
    action=CapabilityExecuteTypes(
        station_action.StationAction,
        types=(
            StationAction.EMPTY_DUSTBIN,
        ),
    ),
)

Replace it with:

            station=CapabilityStation(
                action=CapabilityExecuteTypes(
                    station_action.StationAction, 
                    types=(
                           StationAction.EMPTY_DUSTBIN,
                           StationAction.DRY_MOP,
                           StationAction.WASH_MOP,
                           StationAction.CLEAN_BASE,
                     ) # add StationAction.WASH_MOP, 20260715
                ),

step 3.Replace the Original File

Copy the modified file back into the container.

docker cp mxse7w.py ha:/usr/local/lib/python3.14/site-packages/deebot_client/hardware/

docker restart ha

step 4.Verify in Home Assistant

The newly added station services should now appear in Home Assistant.

output

This approach extends the existing integration without modifying Home Assistant's core logic, making it a simple and maintainable customization for the Chinese Mainland MXSE7W model.

Yeelight pro devices to Home Assistant

2026-06-29T21:06:12Z

Yeelight pro gateway and sub devices to Home Assistant.

Yeelight Pro Gateway and sub devices can be integrated into Home Assistant (HA) natively via a custom local integration.you can use a custom integration dedicated to the Yeelight Pro Local API.

https://en.yeelight.com/product/yeelight-pro-s20-gateway-ble-mesh/

To successfully integrate the Yeelight Pro S20 Gateway (Enhanced Edition) into Home Assistant, you need to complete the following steps:

step 1.Download the Yeelight Pro app.

step 2.Add the gateway and its sub-devices within the Yeelight Pro app.

step 3.Enable the local area network (LAN) control settings for the gateway inside the Yeelight Pro app.

step 4.Integrate the gateway into Home Assistant using the https://github.com/hasscc/yeelight-pro custom integration.

Enabe LAN mode:You must enabe LAN mode for the yeelight pro gateway via the yeelight pro app.

Connect call ('192.168.2.126', 65443)

Integration is attempting to establish a local network connection using specific parameters.

Integration

Yeelight Pro Integration for Home Assistant

https://github.com/hasscc/yeelight-pro

Truncate the home-assistant.log file Using a Shell Command

2026-06-21T20:07:18Z

When my Home Assistant instance goes a couple weeks between restarts, the home-assistant.log file can grow to nearly a gigabyte in size if there are any integrations that are throwing error messages regularly. Even on an Intel NUC with a 128GB SSD this was making my snapshots/backups too large in size and using up all of my hard drive space (I run Home Assistant OS). I needed to find a way to automatically reduce the size of this file when it grew too large. I used the following approach.

A command_line sensor to monitor the home-assistant.log file size
A shell_command to truncate the log file to 100MB
An automation to execute the shell command when the file grows to 200MB

File Size Sensor

Creating a sensor to monitor the size of the log file is pretty straightforward. Although there is a file_size sensor I went with a command_line sensor because it was easier to test and debug using the Reload Command Line Entities button in the Server Controls panel. This is the YAML code I used to get the file size in megabytes in my configuration.yaml file:

# https://joshmccarty.com/truncate-the-home-assistant-log-file-using-a-shell-command/
# change to as flow
# https://www.home-assistant.io/integrations/command_line/
command_line:
  - sensor:
      name: "Ha Log File Size"
      command: "du -m home-assistant.log | cut -f1"
      unit_of_measurement: MB
      value_template: "{{ value|int(0) }}"
      scan_interval: 3600

Shell Command

The shell_command to truncate the log file size took a some trial and error. Ultimately I created a bash script file that the shell_command executed so I could make changes to the script without restarting Home Assistant every time I wanted to try a different shell command to get things working right.

I created the shell script file using the Visual Studio Code Add-on, but you could do this however you edit YAML files in your current Home Assistant instance. I named it truncate_log.sh and saved it in the config folder for Home Assistant. The file contains the following script:

#!/usr/bin/env sh

truncate -s 100MB /config/home-assistant.log

Once the shell script file is created, you have to make it executable using the following command in the config directory: chmod +x truncate_log.sh. I did this via the Terminal/SSH Add-on. Then you can create the shell_command to call the bash script file in your configuration.yaml file:

shell_command:
  truncate_log: /config/truncate_log.sh

Automating Things

Finally, I created an automation that uses a numeric_state trigger to run the shell_command when the log file gets over 200MB in size. I did this via the UI, but the following YAML will work:

alias: 'Truncate Log File'
description: >-
  This truncates the home-assistant.log file to 100MB when it grows past 200MB
  to prevent it from ballooning in size between restarts.
trigger:
  - platform: numeric_state
    entity_id: sensor.log_file_size
    above: '200'
    for:
      hours: 0
      minutes: 10
      seconds: 0
      milliseconds: 0
action:
  - service: shell_command.truncate_log
mode: single

Part 11 - How to Free Disk Space on Raspberry Pi OS

2026-06-13T08:03:19Z

If you are on Home Assistant Docker,the possible sources of large disk space usage:

Inside Home Assistant

Auto Backups

Home Assistant Database

home-assistant.log too large

Media or share (if not network attached storage)

tts

core file too large

Outside Home Assistant

.git

docker images

docker logs

Addons data cache (eg. ESPHome Addon)

os system log

If you have access to a shell(eg. using the Terminal&SSH Addon), make sure to run

To get full access.

You can first try

du -h -d 1 /

You can play with variations of this, for example

du -ah -d4 / | sort -hr | head -n 20

Useful links

How to Truncate the home-assistant.log file Using a Shell Command in home assistant

https://joshmccarty.com/truncate-the-home-assistant-log-file-using-a-shell-command/

blog.matterxiaomi.com

part 7 - Build Your Own Private AI Station: Llama server with Docker+ Open WebUI with Docker

Table of Contents

Overview

How it work?

Install

Step 1: Install Llama.cpp server with docker

Step 2: Install Open WebUI with docker

Step 3: Install ha-mcp-integration(build-in ha-mcp server ) in home assistant

Connections

Step 4: Connect Llama.cpp server via Open WebUI

Step 5: Connect ha-mcp server via Open WebUI

Part 12 - update Raspberry Pi OS Trixie (2025) and increase swap to 4 GB

Table of Contents

System Requirements

Raspberry Pi OS Trixie (2025) uses rpi-swap:

Check Current Swap Configuration

run-local-llm-server-part6 debian vmware on windows 10

Overview

Claude Code for Home Assistant(ha-mcp Server directly inside Home Assistant)

Table of Contents

Install type

Usage examples

How does it work?

Requirements

Inside home assistant

Installation ha-mcp-integration

Copy the connect URL for AI Client

Verify the Target Server is Active:

Using Claude Code to manage your Home Assistant config step by step(docker ha-mcp server))

Install type

Table of Contents

Prerequisites

Architecture

Installation

step 1.Start MCP server

step 2.Install HA-MCP Custom Component in Home Assistant

step 3.Create a long-lived access token in Home Assistant

step 4.Configure Claude

Usage Examples

Ha Template

Table of Contents

ha 2026.5

ha 2026.6

Ecovacs in Home Assistant Part10 - extend ecovacs build-in integration for the Ecovacs X5 Pro( )

DEEBOT X5 PRO (China Version - MXSE7W)

Extend the Built-in Home Assistant Ecovacs Integration for the DEEBOT X5 PRO (MXSE7W, China Version)

Yeelight pro devices to Home Assistant

Integration

Truncate the home-assistant.log file Using a Shell Command

File Size Sensor

Shell Command

Automating Things

Part 11 - How to Free Disk Space on Raspberry Pi OS

Inside Home Assistant

Outside Home Assistant