AgentVerse: 1 Million AI Agents Platform

🚀 Overview

AgentVerse is a scalable platform for managing and orchestrating AI agents with direct LLM and MCP (Model Context Protocol) integration. The platform demonstrates how to build production-ready AI agents that can integrate with real enterprise systems.

✨ Key Achievement

Successfully implemented a clean SRE (Site Reliability Engineer) agent that integrates:

• OpenAI LLM (GPT-4) for intelligence
• ServiceNow for incident management
• Function calling for tool execution

🏗️ Architecture

User → Agent → OpenAI LLM → Tools → Real Systems (ServiceNow)
         ↓
      Response ← LLM ← Real Data

📁 Core Components

Working Implementation

• sre_agent_clean.py - Clean SRE agent with OpenAI + ServiceNow integration
• test_sre_openai_simple.py - Direct OpenAI function calling test
• load_single_sre_agent.py - Load single agent for testing
• unload_all_agents.py - Clean slate utility
• spawn_fresh_agents.py - Generate 1000 agents

Configuration

• .env - Environment variables (OpenAI API key, ServiceNow credentials)
• src/config/agentverse_agents_1000.json - Agent definitions

🚦 Quick Start

1. Setup Environment

cp .env.example .env
# Add your OpenAI API key and ServiceNow credentials

2. Test SRE Agent

python sre_agent_clean.py

3. Load Agents

python load_single_sre_agent.py  # For testing
python spawn_fresh_agents.py      # For full 1000 agents

🔧 Environment Variables

OPENAI_API_KEY=your-openai-key
SERVICENOW_INSTANCE_URL=https://your-instance.service-now.com
SERVICENOW_USERNAME=admin
SERVICENOW_PASSWORD=your-password

📊 Current Status

• ✅ SRE Agent working with real ServiceNow integration
• ✅ OpenAI function calling implemented
• ✅ Clean architecture ready for scaling
• 🔄 Ready to extend to 1 million agents

🎯 Next Steps

1. Add more agent types (DevOps, Security, Data, etc.)
2. Implement MCP server connections
3. Build agent orchestration layer
4. Scale to production workloads

📝 License

MIT

👥 Contributors

• A\V