Agent Memory Systems

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

作者 davila78da17d671b6f无许可证32K 个星标收录于 2026年10月8日更新于 2026年10月8日仓库今天更新

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

仅含说明AI & Agents
AI 生成的概览

指导智能体记忆系统的架构设计,涵盖记忆类型、向量存储、分块与检索策略。

功能
该技能为在 AI 智能体中构建记忆提供架构指导。内容涵盖短期、长期与工作记忆,以及情景记忆、语义记忆和程序性记忆,并涉及记忆的形成、检索与衰减。它还给出选择记忆类型、挑选向量存储和切分文档的模式,以及反模式和风险提示。其产出是设计指导,而非代码或文件。
适用场景
适用于规划或评审智能体如何跨交互存储与检索信息时。适合记忆架构、向量存储选型以及分块或检索策略方面的决策。它并不针对实现代码本身的编写。
运行要求
无需脚本或工具,仅为纯说明文档。若要落地其建议,需要具备向量存储与嵌入模型的智能体或系统。

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

来源与署名

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

许可证: 无许可证

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