Collaboration pain point analysis
When multiple Agents need to work together to accomplish complex tasks, traditional solutions face problems such as difficulty in state sharing and complex invocation logic, etc. Lang-Agent's SupervisorAgent and state variable mechanism provide system-level support for this.
Specific methods of implementation
- architectural design: Use SupervisorAgent as a scheduling center and its state variables as shared memory
- interface specification: Define standard input/output variable naming for subagents, e.g. {{subtask_input}}/{{subtask_result}}
- error handling: Configure the monitoring node to check {{execution_status}} and trigger the standby Agent in case of an exception.
- Performance monitoring: record {{execution_time}} of each Agent by counter node for subsequent optimization
best practice
Develop generic log nodes that record the complete collaboration process to the {{audit_trail}} variable. It is recommended to validate the communication mechanism in small processes before extending it to complex scenarios. Detailed comments should be added to all collaboration related state variables.
This answer comes from the articleLang-Agent: a LangGraph-based platform for visualizing the configuration of AI intelligencesThe
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