Building a Personal Automation Stack with Signal and FastAPI

Compose wiring for a personal automation stack integrating Signal messaging, an assistant core, and skill-based microservices.

Features

• Signal messaging integration using signal-cli REST API
• Notifier gateway to send and receive Signal messages securely
• Assistant core built on FastAPI with LangChain/LlamaIndex for AI-powered automation
• Modular skill services (e.g., weather service) with cron scheduling and webhook support
• Docker Compose setup for easy local deployment and development
• Secure token-based authentication and sender allowlists

Tech Stack

• Python 3.x
• FastAPI for API services
• signal-cli-rest-api Docker image for Signal messaging
• LangChain / LlamaIndex for AI assistant capabilities
• Docker Compose for multi-service orchestration
• Flask for notifier-gateway microservice

Getting Started

Prerequisites

• Docker and Docker Compose installed
• Clone this repo alongside assistant-core and weather-service repos

Installation & Run

# Copy environment template and edit
cp .env.example .env
# Edit .env to configure phone numbers, tokens, API keys, etc.

# Start all services
docker compose up -d --build

# Verify services
curl http://localhost:8088/status  # assistant-core status
curl http://localhost:8789/run      # weather-service run endpoint
curl http://localhost:8787/healthz  # notifier-gateway health

Project Structure

automation-stack/
├── assistant-framework/       # Assistant core source
├── notifier-gateway/          # Signal notifier gateway microservice
├── signal-gateway/            # (Assumed Signal API related, not detailed)
├── weather-servb/             # Weather service skill container
├── weather-service-old/       # Legacy weather service implementation
├── docker-compose.yml         # Compose file wiring all services
├── env-example.txt            # Environment variables template
├── PLAN.md                    # Project plan and architecture notes
├── README.md                  # This file

Future Work / Roadmap

• Add more skill containers (metrics, reminders, etc.)
• Improve assistant-core with additional AI models and retrieval-augmented generation
• Enhance security with more granular sender whitelisting and token management
• Add observability features such as centralized logging and metrics
• Support multi-replica deployments with leader election for polling
• Refactor legacy weather-service and unify skill APIs

Note: Some assumptions were made about the signal-gateway directory and certain internal APIs based on the provided context.