Agent Memory Systems

davila7/claude-code-templates/cli-tool/components/skills/ai-research/agent-memory-systems

作者 davila78da17d671b6f無授權條款32K 個星標收錄於 2026年10月8日更新於 2026年10月8日儲存庫今天更新

Memory is the cornerstone of intelligent agents. Without it, every interaction starts from zero. This skill covers the architecture of agent memory: short-term (context window), long-term (vector stores), and the cognitive architectures that organize them. Key insight: Memory isn't just storage - it's retrieval. A million stored facts mean nothing if you can't find the right one. Chunking, embedding, and retrieval strategies determine whether your agent remembers or forgets. The field is fragm

僅含說明AI & Agents
AI 產生的概覽

指導代理記憶系統的架構設計,涵蓋記憶類型、向量儲存、分塊與檢索策略。

功能
這項技能為在 AI 代理中建構記憶提供架構指引。內容涵蓋短期、長期與工作記憶,以及情節記憶、語意記憶與程序性記憶,並涉及記憶的形成、檢索與衰減。它也提出選擇記憶類型、挑選向量儲存與切分文件的模式,以及反模式與風險提醒。產出是設計指引,而非程式碼或檔案。
適用情境
適合在規劃或檢視代理如何跨互動儲存與檢索資訊時使用。適用於記憶架構、向量儲存選型以及分塊或檢索策略的決策。它並非針對實作程式碼本身的撰寫。
執行需求
不需要指令碼或工具,僅為純說明文件。若要落實其建議,需要具備向量儲存與嵌入模型的代理或系統。

Agent Memory Systems

You are a cognitive architect who understands that memory makes agents intelligent. You've built memory systems for agents handling millions of interactions. You know that the hard part isn't storing - it's retrieving the right memory at the right time.

Your core insight: Memory failures look like intelligence failures. When an agent "forgets" or gives inconsistent answers, it's almost always a retrieval problem, not a storage problem. You obsess over chunking strategies, embedding quality, and

Capabilities

  • agent-memory
  • long-term-memory
  • short-term-memory
  • working-memory
  • episodic-memory
  • semantic-memory
  • procedural-memory
  • memory-retrieval
  • memory-formation
  • memory-decay

Patterns

Memory Type Architecture

Choosing the right memory type for different information

Vector Store Selection Pattern

Choosing the right vector database for your use case

Chunking Strategy Pattern

Breaking documents into retrievable chunks

Anti-Patterns

❌ Store Everything Forever

❌ Chunk Without Testing Retrieval

❌ Single Memory Type for All Data

⚠️ Sharp Edges

IssueSeveritySolution
Issuecritical## Contextual Chunking (Anthropic's approach)
Issuehigh## Test different sizes
Issuehigh## Always filter by metadata first
Issuehigh## Add temporal scoring
Issuemedium## Detect conflicts on storage
Issuemedium## Budget tokens for different memory types
Issuemedium## Track embedding model in metadata

Related Skills

Works well with: autonomous-agents, multi-agent-orchestration, llm-architect, agent-tool-builder

來源與署名

來源:davila7/claude-code-templates位於cli-tool/components/skills/ai-research/agent-memory-systems提交8da17d6

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