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  1. Home
  2. 🧠 Knowledge & Memory
  3. Simplemem
Simplemem logo
Health: ActiveRecent health check succeeded.Last checked 9/9/2026, 3:05:20 PM

Simplemem

User RatingsBe the first to rate and review this MCP server!
View Repository1 GitHub StarsTotal stargazers on GitHub for the source repository (1 stars).Visit Website

An MCP server offering persistent, semantic memory storage with RAG features using DuckDB and Voyage AI embeddings.

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent — or use 1-click editor setup below.

One-click editor setup isn’t available for this listing yet — we don’t have a confirmed install command, and we’d rather show nothing than point your editor at the wrong package or host. Follow the project’s own setup instructions, linked above.

Manual Client & Custom JSON ConfigExpand JSON ▾
No confirmed setup config for this listing yet. We only publish a config block when the install details come from the project itself — its README, its docs, or a verified owner. We haven’t found those for jcdickinson/simplemem, and we’d rather show nothing than a guess you’d paste into your client. Follow the project’s own setup instructions for the current steps.
Install Tool Schemas (7) Directory Badge Claim listing Alternatives🧠 More in Knowledge & Memory

Overview

This MCP server provides persistent memory management for AI agents like Claude, combining file-based storage with retrieval-augmented generation (RAG) capabilities. It supports semantic search, automatic relationship discovery via backlinks, and flexible tagging. Use it when you need an AI-accessible memory system that can store, search, and relate markdown-formatted notes with rich metadata.

Use cases

•Store and retrieve AI agent memories with metadata
•Perform natural language semantic searches over stored memories
•Discover and navigate relationships between related memories automatically
•Organize memories using customizable tags
•Update or delete memories programmatically via MCP

Key features

•Persistent memory storage with markdown content and YAML metadata
•Semantic search powered by Voyage AI vector embeddings
•Automatic semantic backlink generation connecting related memories
•Tag system for flexible memory organization
•DuckDB backend enabling vector similarity search
•MCP protocol integration for seamless use with Claude and other clients

Capabilities & Tool Schemas (7) ~22 tokensApproximate context cost of this server’s tool schemas (~4 chars/token), before any tool is called. Actual usage depends on your client and model.Self-reported Self-reportedParsed from the repository README, not verified against a live server — may be incomplete or out of date.

Inspect callable tools, capabilities, and parameters exposed to AI agents by Simplemem.

create_memory

Callable MCP tool function

read_memory

Callable MCP tool function

update_memory

Callable MCP tool function

delete_memory

Callable MCP tool function

search_memories

Callable MCP tool function

get_backlinks

Callable MCP tool function

Documentation Overview

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

bash
# 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:

bash
# 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):

toml
[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:

server.ts
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:

config.json
{
  "mcpServers": {
    "simplemem": {
      "command": "./simplemem",
      "args": [
        "--db",
        "path/to/your/database.db",
        "--config",
        "path/to/config.toml"
      ]
    }
  }
}

Command Line Testing

bash
# 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

bash
# 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 content
  • read_memory: Read a specific memory by name
  • update_memory: Update existing memory metadata and content
  • delete_memory: Remove a memory and all related data
  • search_memories: Semantic search with optional tag filtering (primary discovery method)
  • get_backlinks: Get memories related to a specific memory
  • change_tag: Modify tags on memories

Note: list_memories has been temporarily removed to encourage efficient semantic search usage instead of token-heavy full listings.

Memory Format

API Structure (for create_memory/update_memory)

config.json
{
  "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:

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

bash
# 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:

bash
# 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:

  1. Make it work first
  2. Make it work well second
  3. Make it pretty... maybe later?
  4. Tests are aspirational
  5. 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." ✨

Read the full README →View source on GitHub →

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Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks — not a rating.

GitHub stars
1
Stargazers on the source repository.
Last commit
1y ago
Most recent push to the default branch.
Tools exposed
7
Callable tools this server registers over MCP.
Directory activity
2 views
Config copies, upvotes, and views on AllMCPs.

Reviews

No reviews yet — be the first to share how this listing worked for you.

Frequently Asked Questions about Simplemem

SimpleMem requires Go 1.21 or later and a Voyage AI API key.

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Technical Specs & Signals

Category🧠Knowledge & Memory
More technical detailsExpand ▾
Last updatedAug 7, 2026
Views2
Unique ViewsTotal visits recorded for this listing page on AllMCPs.
Installs0
Installs & Copy ActionsTotal times users copied install commands or configuration snippets for this server.
GitHub stars1
GitHub Star CountTotal stargazers on GitHub representing community popularity (1 stars).
Last commit1y ago
Last Repository CommitThe most recent commit or push recorded for this server's GitHub repository.Last commit on Sep 11, 2025
39Quality signal: Fair · 39/100How this signal is calculated ▾
Server availabilityNot measured

Not scored for repo-hosted servers — we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.

Verified ownership10/20
Documentation & tools19/30
Adoption & activity0/15
Community engagement0/10

A guidance signal from public completeness & health data — not a user rating. New listings start lower and rise as they add docs, get verified, and grow adoption. Signals we can't observe for a listing are skipped, not counted against it.

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