DeepMem

Mem0-compatible persistent memory for AI agents: write facts once, recall them semantically.

我該用這個嗎

品質與安全性

A
說明品質
100%
結構描述完整度
77%
命名品質
80%
汙染風險
100%
權限相符程度
100%
協定合規性
100%

根據工具定義與協定合規性的自動化分析。

上下文成本

~410Token(工具定義)
~1.4 KB典型回應大小
極小的注意力影響(128k 上下文的 0.32%)

這是每次將伺服器的工具載入模型上下文時所消耗的約略 token 數量。數量越高,可用於其他工作的注意力就越少。

安裝

一鍵安裝

將以下內容加入你的 `claude_desktop_config.json` 檔案:

{
  "mcpServers": {
    "deepmem": {
      "url": "https://deepmem.dev/mcp"
    }
  }
}

遠端端點

https://deepmem.dev/mcpstreamable-http

它能做什麼

工具清單

工具(2)

🟢 唯讀🟡 寫入🔴 刪除⚪ 未知
🟡deepmem_write(messages, user_id, infer, agent_id, run_id, ...)

Write conversation messages to DeepMemory for fact extraction and persistent storage. Messages are processed by an LLM to extract structured memories, which are then embedded and stored in a vector database for later semantic search. Set infer=True to enable LLM fact extraction (produces richer memories but costs one LLM call). Set infer=False to store raw messages without extraction. Returns a list of memory IDs for successfully stored facts.

輸入結構描述

{
  "type": "object",
  "properties": {
    "messages": {
      "items": {
        "additionalProperties": true,
        "type": "object"
      },
      "title": "Messages",
      "type": "array"
    },
    "user_id": {
      "default": "default",
      "title": "User Id",
      "type": "string"
    },
    "infer": {
      "default": true,
      "title": "Infer",
      "type": "boolean"
    },
    "agent_id": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "title": "Agent Id"
    },
    "run_id": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "title": "Run Id"
    },
    "api_key": {
      "default": "",
      "title": "Api Key",
      "type": "string"
    }
  },
  "required": [
    "messages"
  ],
  "title": "deepmem_writeArguments"
}
🟢deepmem_search(query, user_id, top_k, threshold, api_key)

Search memories stored in DeepMemory using semantic search. Returns the most relevant memories for the given query, ranked by hybrid scoring (vector similarity + BM25 keyword match + entity boost + time decay). Use this to retrieve context from past conversations before responding to the user. Memories are scoped to the user_id provided during write.

輸入結構描述

{
  "type": "object",
  "properties": {
    "query": {
      "title": "Query",
      "type": "string"
    },
    "user_id": {
      "default": "default",
      "title": "User Id",
      "type": "string"
    },
    "top_k": {
      "default": 10,
      "title": "Top K",
      "type": "integer"
    },
    "threshold": {
      "default": 0.3,
      "title": "Threshold",
      "type": "number"
    },
    "api_key": {
      "default": "",
      "title": "Api Key",
      "type": "string"
    }
  },
  "required": [
    "query"
  ],
  "title": "deepmem_searchArguments"
}

社群

為此伺服器評分

證據

近期觀測

已驗證未記錄版本2 個工具
已驗證未記錄版本2 個工具
已驗證未記錄版本2 個工具