Integrations

Community Integrations

The lists below cover software and community projects that integrate with LocalAI.

Feel free to open up a Pull request (by clicking at the “Edit page” below) to get your project added!

Build & Deploy

aikit — Build and deploy custom LocalAI containers
Helm chart — Deploy LocalAI on Kubernetes
GitHub Actions — Use LocalAI in CI/CD workflows

Web UIs

localai-admin
LocalAI-frontend
QA-Pilot — Interactive chat for navigating GitHub code repositories
Big AGI — Powerful web interface running entirely in the browser

Agentic Libraries & Assistants

cogito — Agentic library for Go
LocalAGI — Local smart assistant with autonomous agents

MCP Servers

MCPs — Model Context Protocol servers

OS Assistants

Keygeist — AI-powered keyboard operator for Linux

Voice

VoxInput — Use voice to control your desktop

IDE & Editor Plugins

VSCode extension
GPTLocalhost (Word Add-in) — Run LocalAI in Microsoft Word locally

Framework Integrations

Langchain (Python) — pypi
langchain4j — Java LangChain
lingoose — Go framework for LLM apps
LLPhant — PHP library for LLMs and vector databases
FlowiseAI — Low-code LLM app builder
LLMStack
Midori AI Subsystem Manager

Terminal Tools

ShellOracle — Terminal utility
Shell-Pilot — Interact with LLMs via pure shell scripts
Mods — AI on the command line

Chat Bots

Home Automation

hass-openai-custom-conversation — Home Assistant integration
ha-llmvision — Home Assistant LLM Vision
HA-LocalAI-Monitor — Home Assistant monitoring
Nextcloud integration plugin and AI assistant

Automation & DevOps

Reflexia — Auto-documentation
GitHelper — GitHub bot for issues with code and documentation context
kairos — Immutable Linux OS

Other Integrations

AnythingLLM
Logseq GPT3 OpenAI plugin
CodeGPT (JetBrains) — Custom OpenAI-compatible endpoints
Wave Terminal — Native LocalAI support
Obsidian BMO Chatbot
spark
openops (Mattermost)
Model Gallery
Examples

Configuration Guides

This section provides step-by-step instructions for configuring specific software to work with LocalAI.

OpenCode

OpenCode is an AI-powered code editor that can be configured to use LocalAI as its backend provider.

Prerequisites

LocalAI must be running and accessible (either locally or on a network)
You need to know your LocalAI server’s IP address/hostname and port (default is 8080)

Configuration Steps

Edit the OpenCode configuration file
Open the OpenCode configuration file located at ~/.config/opencode/opencode.json in your editor.

Add LocalAI provider configuration

Add the following configuration to your opencode.json file, replacing the values with your own:

{
  "$schema": "https://opencode.ai/config.json",
  "provider": {
    "LocalAI": {
      "npm": "@ai-sdk/openai-compatible",
      "name": "LocalAI (local)",
      "options": {
        "baseURL": "http://127.0.0.1:8080/v1"
      },
      "models": {
        "Qwen3-Coder-30B-A3B-Instruct-i1-GGUF": {
          "name": "Qwen3-Coder-30B-A3B-Instruct-i1-GGUF",
          "limit": {
            "context": 38000,
            "output": 65536
          }
        },
        "qwen_qwen3-30b-a3b-instruct-2507": {
          "name": "qwen_qwen3-30b-a3b-instruct-2507",
          "limit": {
            "context": 38000,
            "output": 65536
          }
        }
      }
    }
  }
}

Customize the configuration
- baseURL: Replace http://127.0.0.1:8080/v1 with your LocalAI server’s address and port.
- name: Change “LocalAI (local)” to a descriptive name for your setup.
- models: Replace the model names with the actual model names available in your LocalAI instance. You can find available models by checking your LocalAI models directory or using the LocalAI API.
- limit: Adjust the context and output token limits based on your model’s capabilities and available resources.
Verify your models
Ensure that the model names in the configuration match exactly with the model names configured in your LocalAI instance. You can verify available models by checking your LocalAI configuration or using the /v1/models endpoint.
Restart OpenCode
After saving the configuration file, restart OpenCode for the changes to take effect.

Charm Crush

You can ask Charm Crush to generate your config by giving it this documentation’s URL and your LocalAI instance URL. The configuration will look something like the following and goes in ~/.config/crush/crush.json:

{
  "$schema": "https://charm.land/crush.json",
  "providers": {
    "localai": {
      "name": "LocalAI",
      "base_url": "http://localai.lan:8081/v1",
      "type": "openai-compat",
      "models": [
        {
          "id": "qwen3-coder-480b-a35b-instruct",
          "name": "Qwen 3 Coder 480b",
          "context_window": 256000
        },
        {
          "id": "qwen3-30b-a3b",
          "name": "Qwen 3 30b a3b",
          "context_window": 32000
        }
      ]
    }
  }
}

A list of models can be fetched with https://<server_address>/v1/models by crush itself and appropriate models added to the provider list. Crush does not appear to be optimized for smaller models.

GitHub Actions

You can use LocalAI in GitHub Actions workflows to perform AI-powered tasks like code review, diff summarization, or automated analysis. The LocalAI GitHub Action makes it easy to spin up a LocalAI instance in your CI/CD pipeline.

Prerequisites

A GitHub repository with Actions enabled
A model name from models.localai.io or a Hugging Face model reference

Example Workflow

This example workflow demonstrates how to use LocalAI to summarize pull request diffs and send notifications:

Create a workflow file
Create a new file in your repository at .github/workflows/localai.yml:

name: Use LocalAI in GHA
on:
  pull_request:
     types:
       - closed

jobs:
  notify-discord:
    if: ${{ (github.event.pull_request.merged == true) && (contains(github.event.pull_request.labels.*.name, 'area/ai-model')) }}
    env:
        MODEL_NAME: qwen_qwen3-4b-instruct-2507
    runs-on: ubuntu-latest
    steps:
    - uses: actions/checkout@v4
      with:
        fetch-depth: 0 # needed to checkout all branches for this Action to work
    # Starts the LocalAI container
    - id: foo
      uses: mudler/localai-github-action@v1.1
      with:
        model: 'qwen_qwen3-4b-instruct-2507' # Any from models.localai.io, or from huggingface.com with: "huggingface://<repository>/file"
    # Check the PR diff using the current branch and the base branch of the PR
    - uses: GrantBirki/git-diff-action@v2.7.0
      id: git-diff-action
      with:
            json_diff_file_output: diff.json
            raw_diff_file_output: diff.txt
            file_output_only: "true"
    # Ask to explain the diff to LocalAI
    - name: Summarize
      env:
        DIFF: ${{ steps.git-diff-action.outputs.raw-diff-path }}
      id: summarize
      run: |
            input="$(cat $DIFF)"

            # Define the LocalAI API endpoint
            API_URL="http://localhost:8080/chat/completions"

            # Create a JSON payload using jq to handle special characters
            json_payload=$(jq -n --arg input "$input" '{
            model: "'$MODEL_NAME'",
            messages: [
                {
                role: "system",
                content: "Write a message summarizing the change diffs"
                },
                {
                role: "user",
                content: $input
                }
            ]
            }')

            # Send the request to LocalAI
            response=$(curl -s -X POST $API_URL \
            -H "Content-Type: application/json" \
            -d "$json_payload")

            # Extract the summary from the response
            summary="$(echo $response | jq -r '.choices[0].message.content')"

            # Print the summary
            echo "Summary:"
            echo "$summary"
            echo "payload sent"
            echo "$json_payload"
            {
                echo 'message<<EOF'
                echo "$summary"
                echo EOF
              } >> "$GITHUB_OUTPUT"
    # Send the summary somewhere (e.g. Discord)
    - name: Discord notification
      env:
        DISCORD_WEBHOOK: ${{ secrets.DISCORD_WEBHOOK_URL }}
        DISCORD_USERNAME: "discord-bot"
        DISCORD_AVATAR: ""
      uses: Ilshidur/action-discord@master
      with:
        args: ${{ steps.summarize.outputs.message }}

Configuration Options

Model selection: Replace qwen_qwen3-4b-instruct-2507 with any model from models.localai.io. You can also use Hugging Face models by using the full huggingface model url`.
Trigger conditions: Customize the if condition to control when the workflow runs. The example only runs when a PR is merged and has a specific label.
API endpoint: The LocalAI container runs on http://localhost:8080 by default. The action exposes the service on the standard port.
Custom prompts: Modify the system message in the JSON payload to change what LocalAI is asked to do with the diff.

Use Cases

Code review automation: Automatically review code changes and provide feedback
Diff summarization: Generate human-readable summaries of code changes
Documentation generation: Create documentation from code changes
Security scanning: Analyze code for potential security issues
Test generation: Generate test cases based on code changes

Additional Resources

Realtime Voice Assistant

LocalAI supports realtime voice interactions , enabling voice assistant applications with real-time speech-to-speech communication. A complete example implementation is available in the LocalAI-examples repository.

Overview

The realtime voice assistant example demonstrates how to build a voice assistant that:

Captures audio input from the user in real-time
Transcribes speech to text using LocalAI’s transcription capabilities
Processes the text with a language model
Generates audio responses using text-to-speech
Streams audio back to the user in real-time

Prerequisites

A transcription model (e.g., Whisper) configured in LocalAI
A text-to-speech model configured in LocalAI
A language model for generating responses

Getting Started

Clone the example repository

git clone https://github.com/mudler/LocalAI-examples.git
cd LocalAI-examples/realtime

Start LocalAI with Docker Compose
```
docker compose up -d
```
The first time you start docker compose, it will take a while to download the available models. You can follow the model downloads in real-time:
```
docker logs -f realtime-localai-1
```
Install host dependencies
Install the required host dependencies (sudo is required):
```
sudo bash setup.sh
```
Run the voice assistant
Start the voice assistant application:
```
bash run.sh
```

Configuration Notes

CPU vs GPU: The example is optimized for CPU usage. However, you can run LocalAI with a GPU for better performance and to use bigger/better models.
Python client: The Python part downloads PyTorch for CPU, but this is fine as computation is offloaded to LocalAI. The Python client only runs Silero VAD (Voice Activity Detection), which is fast, and handles audio recording.
Thin client architecture: The Python client is designed to run on thin clients such as Raspberry PIs, while LocalAI handles the heavier computational workload on a more powerful machine.

Key Features

Real-time processing: Low-latency audio streaming for natural conversations
Voice Activity Detection (VAD): Automatic detection of when the user is speaking
Turn-taking: Handles conversation flow with proper turn detection
OpenAI-compatible API: Uses LocalAI’s OpenAI-compatible realtime API endpoints

Use Cases

Voice assistants: Build custom voice assistants for home automation or productivity
Accessibility tools: Create voice interfaces for accessibility applications
Interactive applications: Add voice interaction to games, educational software, or entertainment apps
Customer service: Implement voice-based customer support systems