Maintain persistent tiered cognitive memory with relevance-scored retrieval and token budgets.
npx skills add https://github.com/selfagency/agentsy --skill memory-selfagency
Please help me install this Agent Skill. Skill: memory Source: https://github.com/selfagency/agentsy/tree/main/packages/memory/skill Command: npx skills add https://github.com/selfagency/agentsy --skill memory-selfagency
Long-running agents lose important context, waste context-window tokens, and struggle to consistently remember preferences, facts, and tool outcomes across sessions.
Run the memory MCP server to initialize and operate persistent agent memory for your workflow.
High-intent search queries and answers about installing and using this skill.
Persistent agent memory is maintained by ingesting events into sensory-to-long-term cognitive tiers with promotion, decay, and token budget enforcement, enabling context retention and recall across sessions.
Agent memory token budgeting is managed through tiered recall with pending queues and awaken cycles, which consolidate queued events, apply decay, and enforce token limits to maintain healthier long-horizon knowledge without overflowing the context window.
Tiered cognitive memory recall works by allowing agents to query memories by relevance across sensory-to-long-term tiers, including cross-tier search and targeted listing within specific tiers, applying token budgets to manage retrieval scope.
Yes, the memory MCP server initializes persistent cognitive memory that includes lightweight knowledge-graph queries and persona storage, supporting agent orchestration by capturing episodic events from tool calls and responses for contextual grounding.
Agents lose context and waste tokens because they lack persistent memory consolidation; applying tiered cognitive memory with awaken cycles, decay, and relevance-scored retrieval prevents context loss and optimizes token usage across long-running sessions.
Tiered memory consolidation is needed when long-running agents must retain preferences, facts, and tool outcomes across sessions, requiring awaken/sleep lifecycle management and decay to maintain relevant long-horizon knowledge within token budgets.
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jieni777Automate dynamic context management and orchestration across multi-agent AI workflows.
melikhanmutluStructure agent memory with short-term, long-term, and retrieval strategies.
jinwoonghongManage token budgets and persist session state for multi-agent workflows.
