application scenario
For scenarios that require real-time response and continuous data processing (e.g., financial monitoring, real-time customer service, etc.), the traditional batch processing model cannot meet the demand.
program of implementation
- Leveraging streaming processing support: LangGraph Supervisor natively supports streaming, you can set the stream=True parameter in app.invoke.
- Optimization of memory mechanisms: Combined use of short-term memory (to process current data streams) and long-term memory (to maintain the knowledge base)
- Implementation of incremental renewal: Design workflows that enable supervisory agents to continuously receive new inputs and generate incremental outputs
- Setting the timeout mechanism: Setting time limits for task processing per agent
- stress test: Validate system stability by simulating highly concurrent streaming data
Example of implementation
Taking real-time data analysis as an example, it can be configured as follows: sensor data → streaming input → supervisory agent allocation → specialized agent processing → real-time dashboard output. The supervisory agent in this process continuously monitors the status of each agent to ensure real-time performance.
This answer comes from the articleLangGraph Supervisor: a tool for managing multi-intelligence collaboration using supervising intelligencesThe
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