DeepAgents' sub-agent system provides a complete solution:
- Role Definition: By
add_subagent(role="角色名",tool="工具类型")Create a dedicated agent, such as a setupresearcher,writeret cetera - communication mechanism: Inter-agent sharing via virtual file system
research_data.jsonIntermediate files such as - Workflow orchestration: Use
StateGraphConstruct a directed acyclic graph that specifies the execution order and dependencies of each agent - load balancing: For computationally intensive tasks, the
max_concurrent=3Parameters control parallelism - Result aggregation: the principal agent through
compile_results()method integrates the output of each subagent
This architecture is particularly well suited to complex tasks that require multidisciplinary collaboration.
This answer comes from the articleDeep Agents: a Python toolkit for rapidly building AI agents for complex tasksThe
May not be reproduced without permission:AI productivity tools " Enabling efficient collaboration and task allocation across multiple AI agents
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