iflow-mcp_firstbatchxyz-mem-agent-mcp

# mem-agent-mcp This is an MCP server for our model [driaforall/mem-agent](https://huggingface.co/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](https://modelcontextprotocol.io/quickstart/user) for detailed ins…