记忆层级: session (会话级) → global (全局共享)，经 sleep() 归档
检索:     hybrid 评分 (BM25 词频 + 重要性 + 可选时间衰减)
图谱:     subject-predicate-object 三元组 + 多跳 BFS
归档:     sleep() 把低重要性 session 记忆提升为 global/episodic

# 安装
pip install mnemosyne-mcp

# 初始化记忆库
mnemosyne-mcp init --path ~/.mnemosyne/data

# 启动 MCP Server (stdio)
mnemosyne-mcp serve

# 或 SSE 模式（本地回环）
mnemosyne-mcp serve --transport sse --port 8080

{
  "mcpServers": {
    "mnemosyne": {
      "command": "mnemosyne-mcp",
      "args": ["serve"]
    }
  }
}

from mnemosyne_mcp import MnemosyneClient

client = MnemosyneClient(path="~/.mnemosyne/data/mnemosyne.db")

# 批量写入 —— 单个事务，全有或全无
results = client.batch([
    {"action": "remember", "content": "用户偏好 Python", "scope": "global", "importance": 0.9},
    {"action": "triple_add", "subject": "user", "predicate": "prefers", "object": "Python"},
    {"action": "graph_link", "source_id": "...", "target_id": "...", "relationship": "references"},
])

from mnemosyne_mcp import MnemosyneClient

client = MnemosyneClient(path="./my_memory.db")

# 写入
mem_id = client.remember(
    content="用户喜欢简洁的代码风格",
    memory_type="user",
    scope="global",
    importance=0.8,
)

# 检索
results = client.recall("代码风格偏好", limit=5)

# 知识图谱
client.triple_add("user", "prefers", "Python", confidence=1.0)
graph = client.graph_query(seed_id=mem_id, max_hops=2)

# 记忆归档
client.sleep(force=True)

mnemosyne-mcp/
├── src/mnemosyne_mcp/
│   ├── __init__.py      # 公共 API 导出
│   ├── core.py          # 客户端 + MCP Server + 工具路由（0.2.0 起合并）
│   └── client.py        # 兼容别名模块（转发到 core）
├── tests/
│   ├── conftest.py
│   ├── test_client.py
│   └── test_tools.py
├── examples/
│   ├── basic_usage.py
│   └── knowledge_graph.py
└── .github/
    ├── workflows/ci.yml
    └── ISSUE_TEMPLATE/