Config is the same across clients — only the file and path differ.
{
"mcpServers": {
"marm-memory": {
"httpUrl": "http://localhost:8001/mcp",
"authentication": {
"type": "oauth",
"clientId": "local_client_b6f3a01e",
"tokenUrl": "http://localhost:8001/oauth/token",
"clientSecret": "local_secret_ad6703cd2b4243ab",
"authorizationUrl": "http://localhost:8001/oauth/authorize"
}
}
}
}Are you the author?
Add this badge to your README to show your security score and help users find safe servers.
Universal MCP Server with advanced AI memory capabilities and semantic search.
Run this in your terminal to verify the server starts. Then let us know if it worked — your result helps other developers.
uvx 'marm-mcp-server' 2>&1 | head -1 && echo "✓ Server started successfully"
After testing, let us know if it worked:
Five weighted categories — click any category to see the underlying evidence.
No known CVEs.
Checked marm-mcp-server against OSV.dev.
Click any tool to inspect its schema.
This server is missing a description.If you've used it, help the community.
Add informationBe the first to review
Have you used this server?
Share your experience — it helps other developers decide.
Sign in to write a review.
Others in ai-ml
Persistent memory using a knowledge graph
Privacy-first. MCP is the protocol for tool access. We're the virtualization layer for context.
An open-source AI agent that brings the power of Gemini directly into your terminal.
Just a Better Chatbot. Powered by Agent & MCP & Workflows.
MCP Security Weekly
Get CVE alerts and security updates for Marm MCP Server and similar servers.
Start a conversation
Ask a question, share a tip, or report an issue.
Sign in to join the discussion.
Your AI forgets everything. MARM MCP doesn't.
Modern LLMs lose context over time, repeat prior ideas, and drift off requirements. MARM MCP solves this with a unified, persistent, MCP‑native memory layer that sits beneath any AI client you use. It blends semantic search, structured session logs, reusable notebooks, and smart summaries so your agents can remember, reference, and build on prior work—consistently, across sessions, and across tools.
| Memory | Multi-AI | Architecture |
|---|---|---|
| Semantic Search - Find by meaning using AI embeddings | Unified Memory Layer - Works with Claude, Qwen, Gemini, MCP clients | 18 Complete MCP Tools - Full Model Context Protocol coverage |
| Auto-Classification - Content categorized (code, project, book, general) | Cross-Platform Intelligence - Different AIs learn from shared knowledge | Database Optimization - SQLite with WAL mode and connection pooling |
| Persistent Cross-Session Memory - Memories survive across agent conversations | User-Controlled Memory - "Bring Your Own History," granular control | Rate Limiting - IP-based tiers for stability |
| Smart Recall - Vector similarity search with context-aware fallbacks | MCP Compliance - Response size management for predictable performance | |
| Docker Ready - Containerized deployment with health/readiness checks |
https://github.com/user-attachments/assets/c7c6a162-5408-4eda-a461-610b7e713dfe
Watch MARM install through Docker, connect to Claude, and share persistent memory across Claude, Gemini, and Qwen.
“MARM successfully handles our industrial automation workflows in production. We've validated session management, persistent logging, and smart recall across container restarts in our Windows 11 + Docker environment. The system reliably tracks complex technical decisions and maintains data integrity through deployment cycles.