DeepAgents offers a complete solution:
- Iterative control: By
max_iterationsparameter (e.g. set to 10 times) to limit the number of task execution rounds to avoid infinite loops - State persistence: Automatically saves intermediate results using a virtual file system, allowing recovery from the most recent state even after an interruption
- asynchronous support: Use
async_execute()method to achieve non-blocking execution, in conjunction with Python'sasynciohandle concurrency - error recovery: the built-in tool call loop automatically retries failed API requests
- Resource Monitoring: It is recommended that external monitoring be added to the production environment so that when an exception is detected the
agent.filesystem.backup()Saving Progress
Together, these mechanisms ensure stable execution of long-duration tasks.
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 " Solve the problem that AI agent tends to crash when dealing with long-time tasks
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