Knowledge Scraping

Introduction

Knowledge scraping is a critical component of the Alith AI agent ecosystem, enabling agents to access up-to-date information from various sources automatically. This documentation covers the knowledge scraping framework that automatically collects, processes, and maintains structured data for Alith AI agents.

In the era of AI and Web3 convergence, timely and accurate information is essential for AI agents to provide value. The knowledge scraping system addresses this need by:

• Automating Data Collection: Eliminating manual data entry and updates
• Standardizing Data Format: Ensuring consistent structure for agent consumption
• Maintaining Freshness: Regularly updating information from authoritative sources
• Preserving Provenance: Tracking the origin and history of collected knowledge

The framework currently supports two primary sources:

• Metis Blog - Scrapes blog posts from the Metis.io website
• CEG Forum - Collects proposals and discussions from the CEG governance forum

Why Knowledge Scraping?

In the decentralized AI ecosystem that LazAI and Alith are building, knowledge scraping serves multiple purposes:

1. Data Sovereignty: Helps maintain control over what data sources are trusted and used
2. Resource Efficiency: Reduces redundant data collection efforts across multiple agents
3. Transparency: Creates clear lineage of information through structured collection methods
4. Customization: Allows developers to tailor knowledge sources to specific agent needs

Knowledge scraping is designed to be extendable, allowing developers to add new sources as needed while maintaining a consistent interface for Alith agents to consume.

Getting Started

The knowledge scraping system is fully automated through GitHub Actions workflows that run daily. This ensures your AI agents have access to the latest information without manual intervention.

To start using knowledge scraping in your project:

1. Reference the Knowledge Files: Direct your Alith agents to consume the JSON files in the knowledge/metis directory
2. Customize Sources: Modify existing scrapers or create new ones following the templates provided
3. Configure Update Frequency: Adjust GitHub Actions workflows based on how often your sources update

Detailed guides for each of these steps are available in the respective technical documentation sections.