Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add LuizEduPP/Rememb --skill memory-systemsgit clone --depth 1 https://github.com/LuizEduPP/RemembWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/luizedupp/rememb/memory-systems)<a href="https://agentmods.dev/skills/luizedupp/rememb/memory-systems"><img src="https://agentmods.dev/badge/skills/luizedupp/rememb/memory-systems/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/luizedupp/rememb/memory-systems"><img src="https://agentmods.dev/badge/skills/luizedupp/rememb/memory-systems.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00115 | $0.03311 |
| Opus 5.5 | $0.00046 | $0.01324 |
| Sonnet 5.5 | $0.00023 | $0.00662 |
| Haiku 4.5 | $0.00012 | $0.00331 |
Grade A, and why
memory-systems scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 238 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Memory System Design
Memory provides the persistence layer that allows agents to maintain continuity across sessions and reason over accumulated knowledge. Simple agents rely entirely on context for memory, losing all state when sessions end. Sophisticated agents implement layered memory architectures that balance immediate context needs with long-term knowledge retention. The evolution from vector stores to knowledge graphs to temporal knowledge graphs represents increasing investment in structured memory for improved retrieval and reasoning.
When to Use
Activate this skill when:
- Building agents that must persist knowledge across sessions
- Choosing between memory frameworks (Mem0, Zep/Graphiti, Letta, LangMem, Cognee)
- Needing to maintain entity consistency across conversations
- Implementing reasoning over accumulated knowledge
- Designing memory architectures that scale in production
- Evaluating memory systems against benchmarks (LoCoMo, LongMemEval, DMR)
- Building dynamic memory with automatic entity/relationship extraction and self-improving memory (Cognee)
Core Concepts
Think of memory as a spectrum from volatile context window to persistent storage. Default to the simplest layer that meets retrieval needs, because benchmark evidence shows tool complexity matters less than reliable retrieval — Letta's filesystem agents scored 74% on LoCoMo using basic file operations, beating Mem0's specialized tools at 68.5%. Add structure (graphs, temporal validity) only when retrieval quality degrades or the agent needs multi-hop reasoning, relationship traversal, or time-travel queries.
Detailed Topics
Production Framework Landscape
Select a framework based on the dominant retrieval pattern the agent requires. Use this table to narrow the shortlist, then validate with the benchmark data below.
| Framework | Architecture | Best For | Trade-off |
|---|---|---|---|
| Mem0 | Vector store + graph memory, pluggable backends | Multi-tenant systems, broad integrations | Less specialized for multi-agent |
| Zep/Graphiti | Temporal knowledge graph, bi-temporal model | Enterprise requiring relationship modeling + temporal reasoning | Advanced features cloud-locked |
| Letta | Self-editing memory with tiered storage (in-context/core/archival) | Full agent introspection, stateful services | Complexity for simple use cases |
| Cognee | Multi-layer semantic graph via customizable ECL pipeline with customizable Tasks | Evolving agent memory that adapts and learns; multi-hop reasoning | Heavier ingest-time processing |
| LangMem | Memory tools for LangGraph workflows | Teams already on LangGraph | Tightly coupled to LangGraph |
| File-system | Plain files with naming conventions | Simple agents, prototyping | No semantic search, no relationships |
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 238 lines · 115 tokens per session scan A cd0fbc8fc3ab
memory-systems is a skill published in the GitHub repository LuizEduPP/Rememb (5 stars, last pushed 12d ago), licensed MIT. It adds 115 tokens to every session and 3,311 once invoked, about $0.0005 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
memory
Persist and retrieve agent memory across sessions — recall via the three-stage protocol (structure route → semantic search → navigation fallback), read via memoryload, see the wiki structure via memorybrowse, write well-placed pages via memorywrite (including section-level append/rewrite), maintain via memorycurate.…
memory-import
Bulk-import an external knowledge base into Prismer memory via the three-layer import pipeline (07 §3.1). Use when the user asks to import/migrate documents, a knowledge base, or notes into workspace memory. Layer 1 lands raw documents (zero LLM, instantly searchable); Layer 2 is YOUR distillation work — you read the…
memory
Project memory powered by Hippo. Use when starting a session, after learning something, after hitting an error, or when the user mentions memory/remember/recall. Auto-invoked at session start.
prismer-obsidian
Read, search, create, and edit notes in the Obsidian vault.
plur-create-engrams
Create or improve PLUR engrams from conversations, documents, decisions, observations, and explicit preferences. Use for memory extraction, engram authoring, or reviewing proposed memories, including global, scoped, pinned, retrieved, and provisional knowledge. Ordinary use of existing memories does not require this…
recall
Recall this repository's OwnMem local memory before changing code, and keep it healthy. Use when a repository contains .ownmem/, when past debugging lessons could apply ("have we hit this before", "why is it done this way"), or when the user mentions ownmem, project memory, or recalling across sessions.