Exploring Multi-Agent Framework CrewAI for Efficient GameFi Data Extraction

Introduction
The rapid evolution of AI technologies has led to the development of sophisticated tools designed to automate and optimize web scraping tasks. Among these innovations is CrewAI, a multi-agent framework that offers powerful capabilities for extracting data from websites. This study explores the use of CrewAI’s ScrapeWebsiteTool to gather GameFi information from Playtoearn, demonstrating its efficiency and potential challenges.

Methodology
The experiment involved utilizing CrewAI’s ScrapeWebsiteTool to scrape data from Playtoearn, a prominent platform for GameFi information. By inputting a URL into ScrapeWebsiteTool, CrewAI’s agent autonomously extracts webpage content, showcasing high efficiency and automation capabilities. This process is akin to delegating the task of page parsing and content extraction to AI, including handling automatic pagination seamlessly.

However, the challenge arises when attempting to navigate to specific subpages within the website. For instance, extracting detailed game information from a list page requires a more complex approach than what a single agent can accomplish. The solution involves deploying two agents: LinkAgent and DetailAgent.

a1.png
Figure 1. The Initial Scrape Page.
a2.png
Figure 2. The target pages from which comprehensive information needs to be scraped.

Agent Definition and Implementation

LinkAgent: This agent’s primary task is to retrieve links to detailed game pages from the list page. It scans the main page, identifies the relevant links, and prepares them for further extraction.

DetailAgent: Using the links provided by LinkAgent, DetailAgent navigates to each detailed game page and scrapes specific information. The extracted data is formatted and structured into JSON, containing fields such as “name”, “url”, “genres”, “blockchain”, “platforms”, “status”, “content”, “NFT_support”, “free_to_play”, “play_to_earn”, “socialscore_number”, and “socialscore_change”.

a3.png
Figure 3. The content extracted for each game.

Discussion
The use of CrewAI’s multi-agent framework for web scraping in the GameFi sector demonstrates notable improvements in efficiency and automation. While minor stability issues persist, the ability to log and address errors ensures that the benefits far outweigh the drawbacks. Future work could focus on enhancing the robustness of AI parsing and exploring additional applications of multi-agent frameworks in other domains.

Reference:
https://www.crewai.com/
https://docs.crewai.com/tools/ScrapeWebsiteTool/

RLHF’s Optimization Assumption of LLM under Web3 Incentive Mechanism Abstract

This article explores the potential and strategies of utilizing Web3 incentive mechanisms to optimize large language models (LLMs). By conducting an in-depth analysis of the concept, principles, and application of Reinforcement Learning from Human Feedback (RLHF) in LLMs, this study aims to address how incentive mechanisms can improve the quality of human feedback, thereby enhancing the performance and accuracy of language models. The article begins by outlining the basics of RLHF, including its relationship with traditional reinforcement learning, advantages, and existing limitations. Subsequently, the paper proposes a series of ideas for employing Web3 incentive mechanisms, aimed at encouraging users to provide higher quality and more honest feedback through economic rewards. These proposals include methods for determining the sincerity of suggested replies and rating feed-back, reward calculation mechanisms, and strategies for enhancing rating judgments using implicit
feedback.

Access Paper:

View PDF