Agentic Loop Memory
Persistent memory and state manager for long-running agentic workflows with self-healing and context compaction.
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Persistent memory and state management system for autonomous AI loops. Provides active state monitoring that triggers compaction cycles based on word counts and enforces self-healing strategies on failures, preventing agents from getting stuck in repetitive loops. Works as a two-part ecosystem combining an MCP server for state management with an agent skill for orchestration, enabling smaller models with limited context windows to maintain persistence across long-running workflows.
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