The AGIXBT framework is a highly modular and customizable system designed to launch individual or ecosystem-specific agents, such as Twitter-based AI agents. These agents leverage cutting-edge Language Models (LLMs), data ingestion pipelines, and on-chain activity monitoring to provide real-time, contextually relevant insights and interactions. The framework is engineered to minimize third-party dependencies while maximizing scalability, adaptability, and operational efficiency.

The Framework is developed with the following client requirements in mind:

  1. Twitter Interaction: The agent must scrape tweets from top accounts within a specified ecosystem and post relevant information based on predefined characteristics.

  2. Multi-LLM Architecture: Utilize multiple Language Learning Models (LLMs) or processors to clean and analyze data, minimizing reliance on third-party services.

  3. Customizability: The system should be customizable using character cards, allowing for tailored behavior without heavy operational overhead.

  4. Sustainability: Design a sustainable system with low operational demands, easy to run and maintain.