Which integrations are available for this server?

Supports local Ollama instances for LLM and embedding processing, allowing for a private and local-first semantic memory system. Leverages OpenAI's models to provide semantic memory capabilities, facilitating the storage and retrieval of relevant information to enhance chat context.

How do I use MemoVault?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@MemoVault Remember that I prefer using Python for backend development" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

MemoVault

A simplified personal memory system for AI assistants, designed for Claude Code integration via MCP.

Features

MCP Server: First-class integration with Claude Code
Flexible Backends: Support for OpenAI and Ollama (local) LLMs
Vector Search: Semantic memory retrieval using Qdrant
Simple JSON Storage: Lightweight option for basic use cases
Easy Configuration: Environment-based setup

Quick Start

Installation

pip install memovault # For local embeddings (optional) pip install memovault[local]

Basic Usage

from memovault import MemoVault # Initialize with default settings (reads from .env) mem = MemoVault() # Add memories mem.add("I prefer Python for backend development") mem.add("My project deadline is March 15th") # Search for relevant memories results = mem.search("programming preferences") for result in results: print(result.memory) # Chat with memory context response = mem.chat("What language should I use for my backend?") print(response) # Save memories to disk mem.dump("./my_memories")

Claude Code Integration

Configure MemoVault in your Claude Code settings:

{ "mcpServers": { "memovault": { "command": "memovault-mcp", "env": { "MEMOVAULT_LLM_BACKEND": "openai", "MEMOVAULT_OPENAI_API_KEY": "sk-..." } } } }

Use memory commands in Claude Code:
- "Remember that I prefer dark mode"
- "What do you know about my preferences?"

Configuration

Copy .env.example to .env and customize:

# LLM Backend MEMOVAULT_LLM_BACKEND=openai # or "ollama" MEMOVAULT_OPENAI_API_KEY=sk-... MEMOVAULT_OPENAI_MODEL=gpt-4o-mini # Embedder Backend MEMOVAULT_EMBEDDER_BACKEND=openai # or "ollama", "sentence_transformer" # Memory Backend MEMOVAULT_MEMORY_BACKEND=vector # or "simple" # Storage MEMOVAULT_DATA_DIR=./memovault_data

MCP Tools

Tool	Description
`add_memory`	Store new information
`search_memories`	Find relevant memories
`chat_with_memory`	Memory-enhanced chat
`get_memory`	Retrieve specific memory by ID
`delete_memory`	Remove a memory
`list_memories`	Show recent memories
`clear_memories`	Clear all memories

Architecture

MemoVault/ ├── src/memovault/ │ ├── core/ # Main MemoVault class │ ├── memory/ # Memory backends (simple, vector) │ ├── llm/ # LLM providers (OpenAI, Ollama) │ ├── embedder/ # Embedding providers │ ├── vecdb/ # Vector database (Qdrant) │ ├── config/ # Configuration management │ └── api/ # MCP server & REST API