CybertraceAI-Ops

CybertraceAI-Ops is an open-source AI agent designed to simplify network management through natural language interactions, focusing on network telemetry data analysis. It is one of the products offered by CybertraceAI.

Overview

CybertraceAI-Ops uses local large language models (LLMs) to interpret and analyze network telemetry data, making network management more accessible and efficient. It combines:

• Ollama for local LLM processing (llama 3.1 8B) and embeddings (Nomic)
• Chainlit for interactive chat interface
• Langchain for LLM orchestration
• suzieq for telemetry data analysis
• Dynamic tool selection using embeddings

Installation

1. Prerequisites

   • Python 3.9 or higher
   • Ollama installed and running
   • Git

2. Clone the Repository

   git clone https://github.com/yourusername/CybertraceAI-Ops.git
   cd CybertraceAI-Ops

3. Set Up Virtual Environment

   python -m venv venv
   source venv/bin/activate  # On Windows use: venv\Scripts\activate

4. Install Dependencies

   pip install -r requirements.txt

5. Install and Pull Required Models

   ollama pull llama3.1:8b
   ollama pull nomic-embed-text

Running the Application

1. Start Ollama Ensure Ollama is running in the background

2. Launch the Application

   chainlit run chainlit_app.py --port 8010 -w

   The application will be available at http://localhost:8010

Roadmap

CybertraceAI-Ops development focuses on the following priorities:

1. Enhanced Functionalities

   • Expanding telemetry data analysis capabilities
   • Adding more suzieq-based analysis tools
   • Improving data visualization options

2. Integration with CybertraceAI-Live

   • Seamless integration between telemetry and live data analysis
   • Unified interface for both products
   • Combined insights from historical and real-time data

Features

• Natural language interface for network telemetry analysis
• Local execution using Ollama language models (llama 3.1 8B)
• Dynamic tool selection using Nomic embeddings
• Zero-cloud dependency - runs entirely on your infrastructure
• Secure API token management
• Interactive streaming responses with interpretation
• Powered by suzieq for comprehensive network telemetry analysis:
  • Device information and status
  • Interface analytics
  • Routing table analysis
  • BGP session monitoring
  • OSPF network state
  • LLDP neighbor discovery
  • VLAN configuration
  • MAC address tracking
  • ARP/ND table analysis
  • MLAG status
  • EVPN VNI information
  • Path analysis with EVPN overlay support
  • Network topology visualization
  • File system monitoring
  • Poller statistics

Known Issues

• Assert functionality is currently under active development
• Some complex queries may require multiple interactions for optimal results
• Large dataset queries may experience longer processing times

Special Thanks

Special thanks to Dinesh G Dutt, Justin Pietschand, and the entire suzieq team and contributors for creating the powerful network observability engine that powers CybertraceAI-Ops. Check out the suzieq project at github.com/netenglabs/suzieq.

Security Considerations

• Secure API token management
• No data sent to external servers
• All processing happens locally
• Encrypted API communications

Architecture

Core Components:

• chainlit_app.py: Interactive chat interface and streaming response handler
• app.py: Core logic, LLM orchestration, and tool selection
• tools.py: suzieq API integration and tool registry
• embeddings.py: Dynamic tool selection using vector embeddings

Integration Components:

• Langchain for LLM orchestration and tool management
• Chainlit for interactive chat interface
• suzieq for network telemetry analysis
• Ollama for local LLM processing
• Vector store for intelligent tool selection

Features:

• Streaming responses for real-time feedback
• Comprehensive error handling and debugging
• Dynamic tool selection based on query context
• Session-based state management
• Modular architecture for easy extension

Philosophy

CybertraceAI-Ops shares suzieq's core philosophy about network observability. Like suzieq, we believe that:

• Network observability goes beyond traditional monitoring and alerting
• The true measure of observability is how easily you can answer questions about your network
• Network engineers and designers need tools that enhance their understanding of network behavior
• Multi-vendor support is essential for modern network environments
• Open-source solutions promote transparency and community-driven improvements

As the first open-source, multi-vendor network observability platform, suzieq established a foundation that CybertraceAI-Ops builds upon by adding:

• Natural language interaction with network telemetry data
• AI-powered interpretation of network states
• Dynamic tool selection based on context
• Interactive streaming responses for real-time insights

We believe that combining suzieq's powerful observability engine with AI-driven natural language processing creates a more accessible and efficient way to understand your network.

Contributing

We welcome contributions!

License

This project is licensed under the Apache 2.0 License. See the LICENSE file for more details.

Support

• Create an issue on GitHub
• Join our Discord community
• Email: [email protected]