This can be efficiently accomplished using the DeepAgents toolkit in the following steps:
- Installation Configuration: By
pip install deepagentsInstallation, and configuration of API keys for LLMs such as OpenAI - Agent Initialization: Use
DeepAgent(task="研究目标")Creating a Proxy Instance - automated programming: Call
plan_and_execute()methodology, the tool automatically breaks down complex research tasks into subtasks such as searching, analyzing, summarizing, etc. - Collaborative implementation: By
add_subagent()Configure specialized sub-agents (e.g., search agent, summary agent) to work together - Getting results: The final study is stored in a virtual file system that can be accessed via the
filesystem.read_file()gain
The entire process utilizes DeepAgents' built-in planning tools and sub-agent collaboration mechanisms, greatly reducing the development threshold.
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 " How to quickly build an AI agent that can handle complex research tasks?
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