认知-MCP

cognee MCP 服务器

手动安装

MCP 服务器项目

克隆cognee仓库
安装依赖项

brew install uv

cd cognee-mcp
uv sync --dev --all-extras --reinstall

使用以下命令激活 venv

source .venv/bin/activate

将新服务器添加到您的 Claude 配置中：

该文件应位于此处：~/Library/Application\ Support/Claude/

cd ~/Library/Application\ Support/Claude/

如果不存在，则需要在此文件夹中创建 claude_desktop_config.json 确保将您的路径和 LLM API 密钥添加到下面的文件中使用您选择的编辑器，例如 Nano：

nano claude_desktop_config.json

{
	"mcpServers": {
		"cognee": {
			"command": "/Users/{user}/cognee/.venv/bin/uv",
			"args": [
        "--directory",
        "/Users/{user}/cognee/cognee-mcp",
        "run",
        "cognee"
      ],
      "env": {
        "ENV": "local",
        "TOKENIZERS_PARALLELISM": "false",
        "LLM_API_KEY": "sk-"
      }
		}
	}
}

重新启动您的 Claude 桌面。

通过 Smithery 安装

要通过Smithery自动安装 Cognee for Claude Desktop：

npx -y @smithery/cli install cognee --client claude

在 server.py 中定义 cognify 工具重新启动您的 Claude 桌面。

要使用调试器，请运行：

mcp dev src/server.py

打开检查器并超时：

http://localhost:5173?timeout=120000

要在开发认知时应用新的变化，您需要执行以下操作：

cognee 文件夹中的poetry lock
uv sync --dev --all-extras --reinstall
mcp dev src/server.py

发展

为了使用本地 cognee 构建，请在 cognee repo 的根目录中运行：

poetry build -o ./cognee-mcp/sources

构建过程完成后，将cognee-mcp/pyproject.toml中的 cognee 库依赖项从

cognee[postgres,codegraph,gemini,huggingface]==0.1.38

到

cognee[postgres,codegraph,gemini,huggingface]

之后将以下代码片段添加到同一个文件（ cognee-mcp/pyproject.toml ）。

[tool.uv.sources]
cognee = { path = "sources/cognee-0.1.38-py3-none-any.whl" }

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security – no known vulnerabilities

license - permissive license

quality - confirmed to work

local-only server

The server can only run on the client's local machine because it depends on local resources.

Tools

人工智能应用程序和代理的内存管理器，使用各种图形和矢量存储，并允许从 30 多个数据源提取数据

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cognee-mcp

cognee MCP 服务器

手动安装

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通过 Smithery 安装

发展

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