daryl-memories
Mnemosyne
Servidor MCP de memoria GraphRAG compartido para 3 agentes Hermes. Totalmente local -- ningún dato sale de la red.
Arquitectura
Machine 1 (HOST: this PC) Machine 2 Machine 3
+--------------------------+ +-------------+ +-------------+
| Neo4j (Docker) | | Hermes | | Hermes |
| Ollama (native) |<---| Agent | | Agent |
| MCP server (Python) | MCP| (client) | | (client) |
| Port 8080 | +-------------+ +-------------+
+--------------------------+La máquina 1 ejecuta todo. Las máquinas 2 y 3 son clientes MCP puros.
Neo4j maneja grafo + vectores + texto completo en un solo contenedor.
Ollama se ejecuta de forma nativa (no Docker) por simplicidad.
MCP es el protocolo nativo de Hermes -- los agentes obtienen herramientas de memoria como capacidades de primera clase.
Related MCP server: Knowledge Graph Memory Server
Inicio Rápido
# 1. Clone and configure
git clone https://github.com/DarylAndrian/Mnemosyne.git
cd Mnemosyne
cp .env.example .env
# Edit .env with your passwords
# 2. Start Neo4j
docker compose up -d
# 3. Install Python deps
uv venv .venv
uv pip install -r requirements.txt
# 4. Start the server
python -m server.mainEl servidor arranca en http://0.0.0.0:8080/mcp. Comprobación de estado en /health.
Variables de Entorno
Variable | Default | Descripción |
|
| URI bolt de Neo4j |
|
| Nombre de usuario de Neo4j |
| (required) | Contraseña de Neo4j |
|
| URL de la API de Ollama |
|
| LLM para extracción de entidades |
|
| Modelo de embeddings (768-dim) |
|
| Dirección de enlace |
|
| Puerto de escucha |
| (required) | Clave API compartida para autenticación |
Herramientas MCP
remember(content, agent_id, session_id?, tags?)
Almacena una memoria. El servidor extrae entidades, relaciones y hechos al grafo de conocimiento. Detecta conflictos con hechos existentes.
recall(query, top_k?, agent_id?)
Búsqueda RAG híbrida: similitud vectorial + palabras clave + expansión de vecindario del grafo. Combinada con refuerzos de recencia y recuento de accesos.
context(entity_name, depth_limit?)
Recorrido del vecindario del grafo. Devuelve todas las aristas conectadas a una entidad dentro de 1-3 saltos.
resolve(entity_a, entity_b)
Fusiona entidades duplicadas. Reapunta todas las aristas y conserva ambos nombres como alias.
forget(memory_id)
Elimina de forma suave un episodio. Conserva la procedencia.
Configuración del Agente
Añade a tu configuración de Hermes:
{
"mcpServers": {
"mnemosyne": {
"url": "http://<HOST_IP>:8080/mcp",
"headers": {
"Authorization": "Bearer <MCP_API_KEY>"
}
}
}
}Infraestructura
Neo4j 5.26 (Community) -- grafo + vectores + texto completo en un solo contenedor
Ollama 0.32+ -- qwen2.5:3b (extracción) + nomic-embed-text (embeddings)
Python 3.11+ -- servidor FastMCP con transporte HTTP Streamable
Docker Compose -- solo Neo4j (Ollama permanece nativo)
Desarrollo
# Run integration tests (requires live stack)
.venv/Scripts/python.exe -c "from tests.test_integration import *; ..."Licencia
MIT
This server cannot be installed
Maintenance
Resources
Unclaimed servers have limited discoverability.
Looking for Admin?
If you are the server author, to access and configure the admin panel.
Related MCP Servers
FlicenseNot gradedqualityNot gradedmaintenanceProvides local-first memory storage and retrieval with automatic embedding, vector search, and knowledge graph capabilities. Enables agents to store memories locally and retrieve relevant context through hybrid search with optional Neo4j graph traversal.- AlicenseNot gradedqualityDmaintenanceA persistent memory server that implements a local knowledge graph using the Kuzu embedded database to store entities, relationships, and observations. It enables AI models to maintain structured long-term context through searchable nodes and comprehensive tag-based organization.8MIT
- AlicenseNot gradedqualityCmaintenanceA lightweight, powerful local memory server for AI agents supporting text, entities, and relations. Enables persistent codebase understanding and user preference management.5749MIT
- AlicenseNot gradedqualityDmaintenanceProvides persistent knowledge graph memory for AI agents, enabling them to store, recall, and query facts about people, projects, and relationships across sessions.MIT
Related MCP Connectors
Persistent memory and knowledge graphs for AI agents. Hybrid search, context checkpoints, and more.
Persistent memory and knowledge management for AI agents with semantic search and 50+ tools.
Shared, governed long-term memory for AI agents across tools and sessions via MCP and REST.
Latest Blog Posts
- Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)By Om-Shree-0709 on .mcpAgent IdentityOAuth 2.1
- Your AI Chatbot Just Exposed Your CEO's Salary to an InternBy Om-Shree-0709 on .Agent IdentityMCP SecurityOAuth Delegation
- Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)By Om-Shree-0709 on .Agentic AiPrompt InjectionWebAssembly
MCP directory API
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/DarylAndrian/Mnemosyne'
If you have feedback or need assistance with the MCP directory API, please join our Discord server