Integrations

Community integrations

List of projects that are using directly LocalAI behind the scenes can be found here.

The list below is a list of software that integrates with LocalAI.

Feel free to open up a Pull request (by clicking at the “Edit page” below) to get a page for your project made or if you see a error on one of the pages!

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

  1. Edit the OpenCode configuration file

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

  2. 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
          }
        }
      }
    }
  }
}

  3. 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.

  4. 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.

  5. Restart OpenCode

    After saving the configuration file, restart OpenCode for the changes to take effect.

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:

  1. 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/[email protected]
      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/[email protected]
      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

  1. Clone the example repository

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

  2. 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

  3. Install host dependencies

    Install the required host dependencies (sudo is required):

    sudo bash setup.sh

  4. 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

Additional Resources