Background
LangManus' system of agents (coordinators, planners, supervisors, etc.) needs to collaborate efficiently to avoid task blocking or resource wastage. Optimizing allocation efficiency can significantly increase the processing speed of complex tasks.
core element
- Optimization of the division of roles:: Modify agent Markdown prompts (located in src/prompts/) to clarify the boundaries of each agent's responsibilities. For example, limit the planner's strategy development time.
- Model Layering Configuration: Distinguish between task types in .env, assigning BASIC_MODEL for simple tasks (e.g. Qwen-1.8B) and REASONING_MODEL for complex tasks (e.g. GPT-4).
- Monitoring Visualization:: Observe the flow of tasks and identify bottleneck agents through the built-in workflow management feature.
- Resource preloading: Caching of high-frequency tools (e.g., Tavily API) to reduce wait times for researcher agents.
Summary points
It is recommended to test the agent response time through real tasks and prioritize the optimization of the most time-consuming aspects. Community-contributed agent templates may also provide optimization ideas.
This answer comes from the articleLangManus: an open source AI automation framework supporting multi-intelligence collaborationThe
May not be reproduced without permission:AI productivity tools " How to optimize task allocation efficiency for multi-agent collaboration in LangManus framework?
