iflow-mcp_inardini-vertex-memory-bank-mcp

<mark style="background-color: #e1e100">_This is a personal project by Ivan Nardini to explore how to build a Model Context Protocol (MCP) server for Vertex AI Memory Bank._</mark> <mark style="background-color: #e1e100">_Vertex AI Memory Bank MCP server is not a Google product. And it is not officially support._</mark> --- # Vertex AI Memory Bank MCP Server A simple MCP (Model Context Protocol) server that enables LLMs to generate and retrieve long-term memories using Vertex AI's Memory Bank. ## Why This Project? This server demonstrates how to build an MCP server with Vertex AI Memory Bank. It has been inspired by a developer request and released for developers. ## Prerequisites - Python 3.11 or higher - Google Cloud account with Vertex AI API enabled - Basic understanding of async Python (helpful but not required) ## Quick Start ### Setup Google Cloud ```bash # Install gcloud CLI (if not already installed) # https://cloud.google.com/sdk/docs/install # Authenticate gcloud auth application-default login # Set your project gcloud config set project YOUR_PROJECT_ID # Enable Vertex AI API gcloud services enable aiplatform.googleapis.com ``` ### Install ```bash # Clone the repository git clone https://github.com/yourusername/vertex-ai-memory-bank-mcp.git cd vertex-ai-memory-bank-mcp # Install with pip pip install -r requirements.txt # OR install with uv (faster, recommended) uv sync # For running examples (optional) pip install -e ".[examples]" # OR with uv uv sync --extra examples ``` ### Configure ```bash # Copy the example environment file cp .env.example .env # Edit .env with your project details GOOGLE_CLOUD_PROJECT=your-project-id GOOGLE_CLOUD_LOCATION=us-central1 ``` ### Run Your First Example **Interactive Tutorial (Recommended):** Open `get_started_with_memory_bank_mcp.ipynb` in Jupyter **Or try the command-line examples:** ```bash # Basic MCP Client Usage python examples/basic_usage.py # Gemini Agent with Memory python examples/gem…