domains / families / agent-tooling / memory-systems

memory-systems

combined

This skill should be used for persistent semantic memory in agent systems: cross-session knowledge retention, entity tracking, temporal validity, graph or vector retrieval, memory consolidation, and memory benchmark selection. Route file-backed scratchpads to filesystem-context, handoff summaries to context-compression, and token-efficiency tactics to context-optimization. Design short-term, long-term, and graph-based memory architectures. Use when building agents that must persist across sessions, needing to maintain entity consistency across conversations, or implementing reasoning over accumulated knowledge.

When to use

• Design short-term, long-term, and graph-based memory architectures Use this skill when working with design short-term, long-term, and graph-based memory architectures.
• Building agents that must persist across sessions
• Needing to maintain entity consistency across conversations
• Implementing reasoning over accumulated knowledge
• Designing systems that learn from past interactions
• Creating knowledge bases that grow over time
• Building temporal-aware systems that track state changes

When not to use

• Use this skill only when the task clearly matches the scope described above.
• Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
• Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.

Skill page

Conflict

The first copy (muratcankoylan) is a curated, decision-oriented skill that focuses on framework selection (Mem0, Zep, Letta, Cognee, LangMem), routing to adjacent skills, and a 'start shallow' design heuristic. The second copy (sickn33) is a bulk mirror that includes specific benchmark numbers (DMR accuracy percentages, latency figures) and a more detailed theoretical breakdown of the memory spectrum (Working/Short-term/Long-term/Permanent) and the limitations of vector stores. The first copy lacks the specific performance data, while the second lacks the specific framework selection table and the routing logic to other skills. Combining them provides both the theoretical foundation and the practical selection criteria.