Three key steps are required to realize multi-intelligence collaboration:
- Creating Intelligentsia: Each
ChatAgentInstances can be configured with independent LLM and system roles - Task organization: By
TaskClasses establish hierarchical relationships using theadd_sub_task()Structuring parent-child tasks - message delivery: Intelligentsia through
llm_response()Automated Message Exchange (AME)
For example, in the teacher-student scenario, the teacher intelligence first generates a math problem, passes the problem to the student intelligence through task scheduling, and the control returns to the teacher after the student answers. This model can be extended to any number of intelligences and is suitable for complex scenarios such as automated research and process simulation.
This answer comes from the articleLangroid: Easily Navigating Large Language Models with Multi-Intelligent Body ProgrammingThe
May not be reproduced without permission:AI productivity tools " How to implement multi-intelligentsia collaboration in Langroid?
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