VoltAgent's multi-intelligent body system architecture is specifically designed to handle complex workflow scenarios. The framework introduces the concept of 'Supervisor Intelligence' to coordinate the collaboration of multiple sub-intelligences for task decomposition and parallel processing. In practice, each sub-intelligence focuses on a specific task, while the supervisor is responsible for overall process control and result integration.
Taking the GitHub repository analysis system as an example, VoltAgent can create specialized intelligences such as StarsFetcher (to get the number of stars), ContributorsFetcher (to get the list of contributors), RepoAnalyzer (to analyze the data), and Supervisor (to coordinate the supervisor). When a user sends an analysis request, the supervisor intelligence will call on the capabilities of each sub-intelligence on demand, ultimately generating a comprehensive analysis report.
The advantages of this architecture are: 1) improved task processing efficiency; 2) reduced complexity of individual intelligences; 3) easy horizontal expansion of functionality; and 4) strong fault isolation. The debugging console also provides visual workflow monitoring, which greatly simplifies the development and maintenance of multi-intelligent body systems.
This answer comes from the articleVoltAgent: TypeScript Open Source Framework for Rapidly Building AI IntelligentsiaThe
English 
简体中文 
日本語 
Deutsch 
Português do Brasil