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Agent Memory Persistence

Provide long-term memory persistence for AI agents with SQLite-backed storage, structured metadata, vector embeddings, semantic retrieval, lifecycle manageme...
为AI代理提供基于SQLite的长期记忆持久化、结构化元数据、向量嵌入、语义检索和生命周期管理
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数据分析 clawhub v0.1.0 1 版本 100000 Key: 无需
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概述

Agent Memory Persistence

Use this skill when an agent needs durable memory storage across sessions.

What it provides

  • SQLite-backed persistence for text, metadata, and embedding vectors
  • CRUD operations for memory items
  • Semantic retrieval with cosine-similarity vector search
  • Memory lifecycle operations including expiration cleanup
  • Filters by user, session, type, and time window

Project structure

  • src/MemoryStore.ts: low-level SQLite storage engine
  • src/VectorIndex.ts: vector similarity search over stored embeddings
  • src/MemoryManager.ts: high-level API used by agents
  • src/types.ts: shared TypeScript contracts

Usage pattern

  1. Create a MemoryManager with a SQLite path.
  2. Write memories with content, optional metadata, and optional embedding.
  3. Query memories by session/user or use searchByVector() for semantic lookup.
  4. Periodically call cleanupExpired() to delete stale memories.

Notes

  • Embeddings are stored as JSON arrays in SQLite.
  • Vector search is implemented in TypeScript using cosine similarity, which keeps deployment simple and avoids SQLite extensions.
  • If memory volume grows substantially, replace VectorIndex with an ANN index or SQLite vector extension while preserving the MemoryManager API.

版本历史

共 1 个版本

  • v0.1.0 当前
    2026-03-30 12:53 安全 安全

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