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chanada440824

ebbingflow-mcp

ebbingflow-mcp

EbbingFlow 长期记忆的 MCP 集成桥 —— 一个轻量 stdio MCP Server,把 EbbingFlow 的记忆能力以 MCP 工具形式接入 OpenCode / Claude / 任意 MCP 客户端。

这是什么

针对"对话一长 AI 就丢失前文"的上下文记忆痛点:EbbingFlow 是本地部署的开源 AI 记忆引擎(Neo4j 知识图谱 + 向量 + SQL 证据链 + Ebbinghaus 遗忘曲线)。本仓库提供一个薄桥接层,将记忆能力封装为 4 个 MCP 工具,让 Agent 能跨会话写入、检索、溯源记忆。

┌────────────┐   stdio(MCP)   ┌────────────────┐   HTTP    ┌──────────────┐
│ OpenCode / │ ◄────────────► │  ebbingflow-mcp │ ◄───────► │  EbbingFlow  │
│ 任意 MCP    │   4 个工具      │  (本仓库)        │          │  核心服务      │
│ 客户端      │                │  薄转发,无状态   │          │ localhost:8000│
└────────────┘                └────────────────┘          └──────────────┘

设计要点:本桥不直接触碰 Chroma/Neo4j/SQLite,只把请求转发给已运行的 EbbingFlow HTTP 服务(默认 localhost:8000),避免多进程对本地持久化存储的锁冲突。

Related MCP server: mem0-mcp

提供的工具

工具

说明

remember

写入一条长期记忆(经 EbbingFlow 抽取事实 → 图谱 + 向量 + 证据链)

recall

多轨检索(图谱/向量/BM25/SQL/剧情),按 Ebbinghaus 衰减排序

chat

带完整记忆上下文的对话(回复也会回写记忆)

health

检查 EbbingFlow 核心服务是否在线

前置要求

  1. EbbingFlow 核心服务已启动并监听 localhost:8000(上游仓库:https://github.com/MMX920/ebbingflow)

  2. Python 3.10+

pip install -r requirements.txt

运行与配置

环境变量(均可选,有默认值):

变量

默认

说明

EBBINGFLOW_BASE_URL

http://localhost:8000

EbbingFlow 核心服务地址

EBBINGFLOW_USER_ID

user_001

记忆归属的用户 ID

EBBINGFLOW_MCP_NAME

ebbingflow

MCP Server 名称

EBBINGFLOW_MCP_TIMEOUT

120

请求上游的超时秒数

直接运行:

python mcp_server.py

接入 OpenCode(opencode.jsonc):

{
  "mcp": {
    "ebbingflow": {
      "type": "local",
      "command": ["python", "/path/to/ebbingflow-mcp/mcp_server.py"],
      "enabled": true
    }
  }
}

示例

# 写入
remember: "用户正在求职 AI 应用方向岗位"
→ remembered. engine confirmation: 已记住...

# 检索
recall: "用户求职什么方向"
→ 1. [GRAPH] ... [score=0.9, source=关系网]

许可

  • 本仓库(MCP 集成桥):MIT

  • 上游 EbbingFlow 引擎:Apache-2.0(见 MMX920/ebbingflow

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