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memory-systems

Designs and implements agent memory architectures using vector stores, knowledge graphs, and temporal retrieval.

Updated Apr 17, 2026
Search Tags:#knowledge graph#agent memory#long-term memory#vector store#mem0#memory consolidation#temporal retrieval
One-click install
npx skills add https://github.com/Syedyasir001/RVULibPass --skill memory-systems-syedyasir001
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: memory-systems
Source: https://github.com/Syedyasir001/RVULibPass/tree/main/.agent/skills/library/memory-systems
Command: npx skills add https://github.com/Syedyasir001/RVULibPass --skill memory-systems-syedyasir001

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires numpy, and includes scripts (resource) and references (resource) components.

What problem does it solve? Agents lose all state when sessions end, forcing users to repeat context and preventing long-term knowledge accumulation. This Skill guides the design of layered memory architectures that persist facts, entities, and relationships across sessions with reliable retrieval. ## Core Features & Use Cases - Framework Comparison: Compares production memory frameworks (Mem0, Zep/Graphiti, Letta, LangMem, Cognee) with benchmark data from LoCoMo, LongMemEval, DMR, and HotPotQA to inform selection. - Layered Architecture Guidance: Provides decision tables for working, short-term, long-term, entity, and temporal knowledge graph memory layers, plus retrieval strategies (semantic, entity-based, temporal, hybrid). - Reference Implementations: Ships Python building blocks for vector stores with metadata indexing, property graphs with entity registries, temporal knowledge graphs with validity intervals, and an integrated memory system. - Use Case: When building a support agent that must remember user preferences and track facts that change over time (e.g., addresses, subscriptions), use this Skill to choose a framework, design temporal validity tracking, and implement consolidation that invalidates stale facts without discarding history. ## Quick Start Ask the agent to design a memory architecture for a chatbot that must remember user preferences across sessions and recommend whether to use Mem0, Zep, or a simple file-system approach.

Frequently Asked Questions about memory-systems

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I add long-term memory to an AI agent?▼

Start with the simplest layer that meets your retrieval needs: file-system storage for prototypes, a vector store like Mem0 for semantic search, or a temporal knowledge graph like Zep/Graphiti for facts that change over time. Add complexity only when retrieval quality degrades.

Mem0 vs Zep vs Cognee: which agent memory framework should I choose?▼

Choose Mem0 for fast time-to-production with managed infrastructure, Zep/Graphiti for bi-temporal modeling and relationship traversal, and Cognee for dense multi-layer semantic graphs with customizable ingestion pipelines. Letta fits agents needing deep self-introspection of their own memory.

What is a temporal knowledge graph for agent memory?▼

A temporal knowledge graph stores facts with validity intervals (valid_from, valid_until) so agents can answer point-in-time queries like where a user lived on a specific date. It prevents stale facts from poisoning context by invalidating rather than discarding outdated information.

How do I benchmark an agent memory system?▼

Evaluate against LoCoMo for long-conversation retention, LongMemEval for long-term memory accuracy, DMR for dialogue memory, and HotPotQA for multi-hop reasoning. Treat benchmarks as signals for specific retrieval dimensions rather than absolute rankings.

Why does my agent retrieve stale or conflicting memories?▼

Stale retrieval happens when facts lack temporal validity tracking or consolidation never runs. Prefer the fact with the most recent valid_from timestamp, trigger consolidation when memory counts exceed thresholds, and surface conflicts to the user when confidence is low.

When should I avoid complex memory tooling for agents?▼

Avoid complex tooling during prototyping, since benchmark evidence shows simple approaches can win: Letta's filesystem agents scored 74% on LoCoMo versus Mem0's 68.5%. Escalate to graphs or temporal models only when you need multi-hop reasoning or time-travel queries.

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