Background
When dealing with complex tasks involving multiple domains, a single intelligence is often not up to the task.PraisonAI's multi-intelligence collaboration feature allows intelligences with different specialties to divide up the work and achieve 1+1>2.
Core Solutions
- Building Intelligent Body Teams: Create combinations of intelligences with a clear division of labor through Python code, e.g., set up research intelligences + summarizing intelligences
- Using the PraisonAIAgents class: Multiple Agent instances are passed in as parameters, and the system automatically establishes collaborative relationships.
- Configuring the communication mechanism: Intermediate results can be shared between intelligences through a built-in messaging system
- asynchronous parallel processing: Enable the parallel parameter to let intelligences process different subtasks at the same time.
Example of operation
Suppose a health management app is to be developed:
- Creation of nutritional expert intelligences (to process dietary recommendations)
- Creating Fitness Trainer Intelligence (developing workout programs)
- Creation of Medical Advisor Intelligence (Health Risk Assessment)
- Integrating the output of all three with PraisonAIAgents
caveat
It is recommended to configure explicit instructions parameter for each smartbody and define the interaction logic between them via YAML files.
This answer comes from the articlePraisonAI: A Low-Code Multi-Intelligent Body Framework to Simplify Automation Solutions for Complex TasksThe
May not be reproduced without permission:AI productivity tools " How can PraisonAI be used to enable multi-intelligence collaboration to solve complex cross-domain tasks?
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