SuperAGI's multi-agent concurrency feature supports efficient collaboration:
- Resource allocation::
- Each agent runs independently in an isolated Docker container
- Real-time monitoring of CPU/Memory usage via GUI
- Mission design::
- Assign special tasks to different agents (e.g. Agent A handles GA data, Agent B synchronizes Notion)
- utilization
SuperCoder+ImproveCodePortfolio enables code review collaboration
- data interoperability::
- Sharing of processing results through the Weaviate vector database
- Passing Output to Other Systems with Webhooks
Measurements show that when 10 agents concurrently process data analysis tasks, the time consumed is only 151 TP3T for a single agent.
This answer comes from the articleSuperAGI: An Open Source Framework for Rapidly Building and Running Autonomous AI AgentsThe
May not be reproduced without permission:AI productivity tools " How to realize teamwork and parallel processing of tasks for AI agents?
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