SimpleMem
A vibe-coded memory management system with RAG capabilities for Claude via the Model Context Protocol (MCP).
⚠️ Warning: This project is completely vibe-coded. It works, but don't look too closely at the implementation details. The code was written by an AI having a good time, not by someone following best practices.
What is SimpleMem?
SimpleMem is an MCP server that provides persistent memory storage and retrieval for Claude and other MCP clients. It combines traditional file-based storage with modern RAG (Retrieval-Augmented Generation) capabilities, including semantic search and automatic relationship discovery.
Think of it as Claude's personal notebook that never forgets and can find connections between ideas automatically.
Features
- 📝 Persistent Memory Storage: Store and retrieve memories with rich metadata
- 🔍 Semantic Search: Find memories using natural language queries
- 🔗 Automatic Relationship Discovery: Semantic backlinks connect related memories
- 🏷️ Tag System: Organize memories with flexible tagging
- 🎯 Vector Embeddings: Powered by Voyage AI for high-quality semantic understanding
- 📊 DuckDB Backend: Fast, efficient storage with vector similarity search
- 🔧 MCP Protocol: Seamless integration with Claude and other MCP clients
Quick Start
Prerequisites
- Go 1.21+
- A Voyage AI API key (see Configuration section below)
Installation
Option 1: Download Pre-built Binaries
Download the latest release for your platform from GitHub Releases:
- Linux AMD64:
simplemem-vX.X.X-linux-amd64.tar.gz
Extract and place the binary in your PATH.
Option 2: Build from Source
# Clone the repository
git clone https://github.com/jcdickinson/simplemem
cd simplemem
# Install dependencies
just deps
# Build the binary
just build
Option 3: Nix
If you have Nix with flakes enabled:
# Run directly from GitHub
nix run github:jcdickinson/simplemem
# Or clone and run locally
git clone https://github.com/jcdickinson/simplemem
cd simplemem
nix run
# Install to profile
nix profile install github:jcdickinson/simplemem
# Run in development shell
nix develop
Configuration
SimpleMem uses TOML-based configuration with flexible options for API keys and settings.
Configuration File
Create a config.toml file (see config.toml.example for reference):
[voyage_ai]
model = "voyage-3.5"
rerank_model = "rerank-lite-1"
api_key = { "path" = "~/.config/simplemem/voyage_api_key.txt" }
# OR
api_key = "api-key-here"
Environment Variables
You can also configure using environment variables:
export SIMPLEMEM_VOYAGE_AI_API_KEY="your-api-key"
export SIMPLEMEM_VOYAGE_AI_MODEL="voyage-3.5"
export SIMPLEMEM_VOYAGE_AI_RERANK_MODEL="rerank-lite-1"
Configuration Options
- Custom config file: Use
--config path/to/config.toml - Database path: Use
--db path/to/database.db(can also be set in config) - API key sources: File path (recommended) or direct value
- Multiple configs: Different configs for different projects
Usage
As an MCP Server
Add to your MCP client configuration:
{
"mcpServers": {
"simplemem": {
"command": "./simplemem",
"args": [
"--db",
"path/to/your/database.db",
"--config",
"path/to/config.toml"
]
}
}
}
Command Line Testing
# Create a memory
just test-json '{"jsonrpc":"2.0","method":"tools/call","params":{"name":"create_memory","arguments":{"name":"my-memory","metadata":{"title":"My First Memory","description":"A test memory","tags":{"category":"test","priority":"low"}},"content":"# Hello World\n\nThis is my first memory!"}},"id":1}'
# Search memories (primary discovery method)
just test-json '{"jsonrpc":"2.0","method":"tools/call","params":{"name":"search_memories","arguments":{"query":"hello world"}},"id":1}'
# Read a specific memory
just test-json '{"jsonrpc":"2.0","method":"tools/call","params":{"name":"read_memory","arguments":{"name":"my-memory"}},"id":1}'
Testing Semantic Backlinks
# Run comprehensive semantic backlinks test
just test-backlinks
# Verify backlinks are working
just test-json '{"jsonrpc":"2.0","method":"tools/call","params":{"name":"get_backlinks","arguments":{"name":"my-memory"}},"id":1}'
Available Tools (MCP)
create_memory: Create a new memory with metadata object and markdown contentread_memory: Read a specific memory by nameupdate_memory: Update existing memory metadata and contentdelete_memory: Remove a memory and all related datasearch_memories: Semantic search with optional tag filtering (primary discovery method)get_backlinks: Get memories related to a specific memorychange_tag: Modify tags on memories
Note:
list_memorieshas been temporarily removed to encourage efficient semantic search usage instead of token-heavy full listings.
Memory Format
API Structure (for create_memory/update_memory)
{
"name": "my-memory-name",
"metadata": {
"title": "A Human-Readable Title",
"description": "Brief description of the memory",
"tags": {
"category": "personal",
"priority": "high",
"status": "active"
},
"custom_field": "any_value",
"version": "1.0"
},
"content": "# Memory Content\n\nYour memory content in **Markdown** format.\n\n- Clean markdown without frontmatter\n- Links to [[other-memories]]\n- Whatever you need!"
}
On-Disk Storage
Memories are stored as YAML frontmatter + Markdown:
---
title: A Human-Readable Title
description: Brief description of the memory
tags:
category: personal
priority: high
status: active
custom_field: any_value
version: "1.0"
created: 2025-01-24T12:00:00Z
modified: 2025-01-24T12:00:00Z
---
# Memory Content
Your memory content in **Markdown** format.
- Clean markdown without frontmatter
- Links to [[other-memories]]
- Whatever you need!
Development
Prerequisites
- Just command runner
Common Commands
# Show available commands
just
# Run tests (when they exist)
just test
# Format code
just fmt
# Run with verbose logging
just run-verbose
# Test semantic backlinks functionality
just test-backlinks
# Clean up build artifacts
just clean
Debug Testing
For rapid development iteration:
# Test any JSON-RPC call with custom database
just test-json '<json>' /tmp/test.db
# Quick minimal test
just test-clean /tmp/debug.db
# Build and test in one command
just build-test
Architecture
- Frontend: MCP JSON-RPC 2.0 protocol server
- Storage: File-based memory storage with DuckDB backend
- Search: Voyage AI embeddings + vector similarity
- Relationships: Automatic semantic backlink discovery
- Memory: YAML frontmatter + Markdown content
Known Issues
- No proper test suite (it's vibe-coded, remember?)
- Error handling could be more robust
- Some edge cases might crash things
- Documentation is whatever this README covers
- Database migrations? What are those?
Contributing
This is a vibe-coded project, so contributions should match the energy:
- Make it work first
- Make it work well second
- Make it pretty... maybe later?
- Tests are aspirational
- If it breaks, fix it with more vibes
License
LGPL 3.0 - Use it, modify it, vibe with it (but share improvements back to the community).
Credits
- Built with love and caffeine
- Powered by Voyage AI embeddings
- Uses DuckDB for storage
- MCP integration via mark3labs/mcp-go
"It works on my machine, and that machine has good vibes." ✨

