Give your AI a real memory.

AI Agent Memory Service — cross-platform, model-agnostic, fully under your control.

Why?

ChatGPT, Claude, Gemini — they all start from scratch every conversation. They don't remember who you are, what you prefer, or what decisions you've made.

aimemory fixes that:

• 🧠 Smart extraction — automatically picks out worth-remembering info from conversations
• 🔍 Semantic search — finds memories by meaning, not just keywords (3-engine: local neural network → Gemini API → TF-IDF)
• 📂 Auto-categorization — people, preferences, decisions, events, knowledge
• ⏰ Memory decay — simulates human forgetting curves: important things stick, trivial things fade
• 🔌 MCP protocol — any MCP-compatible AI agent can use it directly
• 🌍 Bilingual — full Chinese + English support, cross-language search
• 🧠 Local neural network — all-MiniLM-L6-v2 (22M params), runs on CPU, zero API cost
• 💰 Zero cost — local SQLite storage, works fully offline

Quick Start

# 一行命令配置 MCP（自动检测 Claude Desktop / Cursor / Windsurf / Cline）
npx aimemory-agent setup

# Or if installed globally
aimem setup

# Add a memory
aimem add "User prefers dark mode and minimal UI" --cat preference --imp 0.8

# Keyword search
aimem search "dark mode"

# Semantic search (finds related meanings)
aimem search -s "what does the user like"

# Export all memories
aimem export backup.json

# Import memories
aimem import backup.json

# Stats
aimem stats

# Apply memory decay
aimem decay

MCP Server

Auto Setup (Recommended)

aimem setup              # 自动检测并配置所有已安装的客户端
aimem setup --force      # 强制覆盖已有配置
aimem setup cursor       # 只配置 Cursor

Manual Config

{
  "mcpServers": {
    "aimemory": {
      "command": "node",
      "args": ["path/to/aimemory/src/mcp-server.js"]
    }
  }
}

Available Tools

Tool	Description
memory_add	Store a new memory
memory_search	Keyword search
memory_semantic_search	Semantic similarity search
memory_forget	Delete a memory
memory_extract	Auto-extract memories from text
memory_rebuild_index	Rebuild vector index
memory_auto	Auto-extract & save memories from conversation summary
memory_stats	Get statistics

Architecture

CLI / MCP Server
      ↓
  MemoryEngine (memory.js)
   ├── Keyword search (FTS5 + LIKE)
   ├── Semantic search (Gemini embedding + TF-IDF fallback)
   ├── Memory decay (forgetting curve)
   └── Entity relations
      ↓
  SQLite (db.js)

Dual Search Engine

• Gemini embedding (3072-dim) — real semantic understanding, cross-language
• TF-IDF fallback — zero-dependency, works offline
• Gemini is used when API key is available; TF-IDF kicks in automatically otherwise

Zero Dependencies

Only uses Node.js built-in modules. No node_modules needed for core functionality.

Data Storage

Default: ~/.aimemory/memories.db

Override with:

export AIMEM_DB=/path/to/your/memories.db

Roadmap

• Core engine (storage + search + extraction + decay)
• Semantic search (dual engine)
• MCP Server
• CLI with export/import
• npm package publish
• Web Dashboard
• Cloud sync
• Chrome extension
• Obsidian plugin

License

MIT

---

中文说明

让你的AI真正记住你。

AI Agent 记忆服务 — 跨平台、跨模型、你完全掌控。

功能

• 🧠 智能记忆提取 — 自动从对话中抽取值得记住的信息
• 🔍 语义搜索 — 双引擎：Gemini embedding（跨语言）+ TF-IDF（离线兜底）
• 📂 自动分类 — 人物、偏好、决策、事件、知识
• ⏰ 记忆衰减 — 模拟人类遗忘曲线
• 🔌 MCP协议 — 任何支持MCP的AI agent都能接入
• 💰 零成本 — 本地SQLite，零外部依赖

快速使用

# 一行命令配置 MCP
aimem setup

aimem add "用户喜欢简洁的UI" --cat preference --imp 0.8
aimem search -s "用户喜欢什么"
aimem export backup.json
aimem import backup.json

详细用法见上方英文文档。