swf-monitor

Monitoring and information service for the ePIC streaming workflow testbed.

System Overview

The application is built on Django infrastructure and comprises two main web apps, real-time messaging, and PostgreSQL backend.

Core Components

1. Monitor App (monitor_app): Primary user-facing component

   • Browser UI: Server-side rendered dashboard for viewing agent statuses with Django session authentication
   • REST API: Programmatic interface with token-based authentication and OpenAPI schema

2. MCP App (mcp_app): Real-time communication layer

   • WebSocket Service: Django Channels implementation of Model Control Protocol (MCP)
   • REST Endpoints: HTTP alternative to WebSocket for MCP commands

3. ActiveMQ Integration: Management command (listen_activemq) for agent communications via message queue

4. PostgreSQL Database: Primary data store for agents, logs, runs, and application state

Architecture

The monitor serves as the central hub in a distributed agent-based workflow:

┌─ swf-daqsim-agent (scheduler/generator)
│   ↓ ActiveMQ messages  
├─ [swf-data-agent] → [swf-processing-agent] → [swf-fastmon-agent]
│   ↓ status updates & logs
└─ swf-monitor (dashboard/database)

Key Features

• 🖥️ Real-time Dashboard - Agent status monitoring with live updates
• 🔗 REST API - Complete CRUD operations with OpenAPI documentation
• ⚡ WebSocket Service - Bidirectional real-time communication via MCP
• 📊 Centralized Logging - Agent log collection with swf-common-lib integration
• 🔐 Authentication - Token-based API access and web session management
• 📈 Message Queue Integration - ActiveMQ connectivity for workflow coordination
• 🧪 Comprehensive Testing - 65 tests across API, UI, and integration scenarios

Documentation

📚 Complete technical documentation in docs/ directory:

Guide	Description	Use Case
Setup Guide	Installation, configuration, and development setup	Getting started
API Reference	REST API, WebSocket, database schema, authentication	Integration
MCP Implementation	Model Control Protocol REST API details	Agent communication
Development Roadmap	Future plans, architecture, workflow design	Contributors
Test System	Testing approach, structure, and best practices	Quality assurance

Quick Links

• Interactive API Docs: Swagger UI | ReDoc
• Database Schema: testbed-schema.dbml (auto-generated)
• Parent Project: swf-testbed documentation

Quick Examples

Basic Setup

# See docs/SETUP_GUIDE.md for complete installation
python manage.py runserver          # Start web interface  
python manage.py listen_activemq    # Start message queue listener

API Usage

# Generate token and create agent
python manage.py get_token username --create-user
curl -H "Authorization: Token <token>" -H "Content-Type: application/json" \
     -d '{"instance_name": "my-agent", "agent_type": "test", "status": "OK"}' \
     http://127.0.0.1:8000/api/systemagents/

Agent Integration

# Centralized logging with swf-common-lib
from swf_common_lib.rest_logging import setup_rest_logging
logger = setup_rest_logging(app_name='my_agent', instance_name='agent_001')
logger.info("Agent started")  # Automatically sent to monitor

Testing

See Test System documentation for comprehensive testing guide.

Development

Requirements

• Python 3.9+
• PostgreSQL for data persistence
• ActiveMQ for agent messaging (optional)

Architecture Notes

• Django web framework with Channels for WebSocket support
• Model Control Protocol (MCP) for standardized agent communication
• Token-based REST API with comprehensive OpenAPI documentation
• Shared service architecture between WebSocket and REST implementations

See Setup Guide for detailed development environment configuration.

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For complete technical documentation and implementation details, see the docs/ directory.