这是我们模型driaforall/mem-agent的MCP服务器，可以连接到Claude Desktop或Lm Studio等应用，与类似黑曜石的内存系统交互。

支持平台

• macOS（金属后端）
• Linux（配备GPU，vLLM后端）

平台说明：aarch64（ARM64） Linux

• 在 ARM64 Linux 上，默认不安装 vLLM，以避免构建失败（任何稳定轮子和源代码构建都不会失败）。
• 安装无需 vLLM 也能成功;你可以：
• 使用默认的OpenRouter/OpenAI路径（无需本地vLLM），或
• 在兼容的x86_64主机上运行vLLM，并指向客户端（参见agent/model.py create_vllm_client）。

运行指令

使用 LiteLLM 代理（兼容 OpenAI）

• 如果你有本地运行的LiteLLM代理（例如4000端口），请通过.env配置客户端：

• 验证连接性：

• 然后使用以下任一：

• CLI： make chat-cli

• MCP通过STDIO：make serve-mcp

• 通过HTTP的MCP：make serve-mcp-http

注意：在 ARM64 Linux 上，推荐采用这个配置，而不是 vLLM。

1. make check-uv）（如果你已经安装了UV，跳过这一步）。
2. make install：在 MacOS 上安装 LmStudio。

mem-agent-mcp

This is an MCP server for our model driaforall/mem-agent, which can be connected to apps like Claude Desktop or Lm Studio to interact with an obsidian-like memory system.

Supported Platforms

• macOS (Metal backend)
• Linux (with GPU, vLLM backend)

Platform note: aarch64 (ARM64) Linux

• On ARM64 Linux, vLLM is not installed by default to avoid build failures (no stable wheels and source builds can fail).
• Installation will succeed without vLLM; you can:
  • Use the default OpenRouter/OpenAI path (no local vLLM needed), or
  • Run vLLM on a compatible x86_64 host and point the client at it (see agent/model.py create_vllm_client).

Running Instructions

Using a LiteLLM proxy (OpenAI-compatible)

• If you have a LiteLLM proxy running locally (e.g., on port 4000), configure the client via .env:

VLLM_HOST=localhost
VLLM_PORT=4000

• Verify connectivity:

curl http://localhost:4000/v1/models

• Then use either of these:
  • CLI: make chat-cli
  • MCP over STDIO: make serve-mcp
  • MCP over HTTP: make serve-mcp-http

Note: On ARM64 Linux, this is the recommended setup instead of vLLM.

1. make check-uv (if you have uv installed, skip this step).
2. make install: Installs LmStudio on MacOS.
3. make setup: This will open a file selector and ask you to select the directory where you want to store the memory.
4. make run-agent: If you’re on macOS, this will prompt you to select the precision of the model you want to use. 4-bit is very usable as tested, and higher precision models are more reliable but slower.
5. make generate-mcp-json: Generates the mcp.json file. That will be used in the next step.
6. Instructions per app/provider:
   • Claude Desktop:
     • Copy the generated mcp.json to the where your claude_desktop.json is located, then, quit and restart Claude Desktop. Check this guide for detailed instructions.
   • Lm Studio:
     • Copy the generated mcp.json to the mcp.json of Lm Studio. Check this guide for detailed instructions. If there are problems, change the name of the model in .mlx_model_name (found in the root of this repo) from mem-agent-mlx-4bit or mem-agent-mlx-8bit to mem-agent-mlx@4bit or mem-agent-mlx@8bit respectively.

Memory Instructions

• Each memory directory should follow the structure below:

memory/
    ├── user.md
    └── entities/
        └── [entity_name_1].md
        └── [entity_name_2].md
        └── ...

• user.md is the main file that contains information about the user and their relationships, accompanied by links to the enity file in the format of [[entities/[entity_name].md]] per relationship. The link format should be followed strictly.
• entities/ is the directory that contains the entity files.
• Each entity file follows the same structure as user.md.