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 latestaiagents/agent-skills --skill agent-memory-systemsgit clone --depth 1 https://github.com/latestaiagents/agent-skillsWrote 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/latestaiagents/agent-skills/agent-memory-systems)<a href="https://agentmods.dev/skills/latestaiagents/agent-skills/agent-memory-systems"><img src="https://agentmods.dev/badge/skills/latestaiagents/agent-skills/agent-memory-systems.svg" alt="Measured on agentmods" 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 | $0.00051 | $0.02854 |
| Opus 5 | $0.00026 | $0.01427 |
| Sonnet 5 | $0.00010 | $0.00571 |
| Haiku 4.5 | $0.00005 | $0.00285 |
Grade A, and why
agent-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 8d 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 — 471 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Memory Systems
Design and implement memory systems that give agents persistent knowledge and context.
When to Use
- Agents need to remember across sessions
- Multiple agents share knowledge
- Long-running tasks require state persistence
- Building agents that learn from experience
Memory Types
┌─────────────────────────────────────────────────────────────┐
│ AGENT MEMORY TAXONOMY │
├─────────────────────────────────────────────────────────────┤
│ │
│ Working Memory (Active Context) │
│ └─ Current conversation, immediate task state │
│ │
│ Short-Term Memory (Session) │
│ └─ Recent interactions, temporary facts │
│ │
│ Long-Term Memory (Persistent) │
│ ├─ Episodic: Past events, experiences │
│ ├─ Semantic: Facts, knowledge, learned info │
│ └─ Procedural: How to do things, skills │
│ │
└─────────────────────────────────────────────────────────────┘
Working Memory
The agent's current context window.
interface WorkingMemory {
// Current task context
task: {
description: string;
requirements: string[];
progress: string[];
};
// Active entities being discussed
entities: Map<string, Entity>;
// Recent messages
conversationWindow: Message[];
// Scratchpad for reasoning
scratchpad: string;
}
class WorkingMemoryManager {
private memory: WorkingMemory;
private MAX_MESSAGES = 20;
addMessage(message: Message) {
this.memory.conversationWindow.push(message);
// Evict old messages
if (this.memory.conversationWindow.length > this.MAX_MESSAGES) {
const evicted = this.memory.conversationWindow.shift();
// Optionally summarize and store
this.summarizeToShortTerm(evicted);
}
}
updateEntity(id: string, entity: Entity) {
this.memory.entities.set(id, entity);
}
getContext(): string {
return `
Task: ${this.memory.task.description}
Progress: ${this.memory.task.progress.join(', ')}
Key Entities: ${[...this.memory.entities.values()].map(e => e.summary).join('\n')}
Recent Discussion: ${this.memory.conversationWindow.slice(-5).map(m => m.content).join('\n')}
`;
}
}
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.
- 8d ago First seen · 471 lines · 51 tokens per session scan A 566fb8ad5578
agent-memory-systems is a skill published in the GitHub repository latestaiagents/agent-skills (5 stars, last pushed 4mo ago), licensed MIT. It adds 51 tokens to every session and 2,854 once invoked, about $0.0003 per session on Opus 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
agent-collective-intelligence-coordinator
Agent skill for collective-intelligence-coordinator - invoke with $agent-collective-intelligence-coordinator.
agent-memory-coordinator
Agent skill for memory-coordinator - invoke with $agent-memory-coordinator.
mem0-integration
Mem0 memory layer integration for AI agents. Implement persistent, semantic memory for long-term context retention and personalization.
langchain-memory
LangChain memory integration including ConversationBufferMemory, ConversationSummaryMemory, and vector-based memory.
memory-summarization
Conversation summarization for memory compression and context management.
knowledge-graph-management
Capture, validate, query, and sync architectural patterns and design decisions in the knowledge graph.