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Systems that perform multi-step tasks and maintain state without manual intervention.
Distinguishing note: Focuses on the execution of multi-step plans rather than the planning logic itself.
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This project is an autonomous agent framework designed to integrate large language models with popular messaging platforms. It functions as a middleware platform that enables automated, multimodal interactions by decomposing complex user goals into sequential plans, executing them through external tools, and maintaining persistent context across sessions. The framework distinguishes itself through a modular skill architecture and a hybrid memory system. Users can extend system capabilities by installing custom logic modules from community hubs or generating them through natural language. The
Agent framework decomposes user intent into multi-step plans, invokes system tools, and maintains long-term memory across sessions to complete complex objectives.