Mnemosyne

Shared GraphRAG memory MCP server for 3 Hermes agents. Fully local -- no data leaves the network.

Architecture

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              |    +-------------+    +-------------+
+--------------------------+

• Machine 1 runs everything. Machines 2 & 3 are pure MCP clients.

• Neo4j handles graph + vector + full-text in one container.

• Ollama runs natively (not Docker) for simplicity.

• MCP is Hermes's native protocol -- agents get memory tools as first-class capabilities.

Related MCP server: Knowledge Graph Memory Server

Quick Start

# 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.main

The server starts on http://0.0.0.0:8080/mcp. Health check at /health.

Memory Graph Frontend

An Obsidian-style graph viewer is served at http://<HOST_IP>:8080/ (same port as MCP).

• Force-directed graph of entities, colored by type

• Click a node: aliases, facts, episodes, 1-3 hop neighborhood

• Double-click: expand neighborhood

• Recall box: hybrid RAG search (same pipeline as the recall tool)

• Filters by entity type, node limit, include episodes

• Paste MCP_API_KEY or an agent token in the top-right to unlock API calls

No build step and no internet needed -- plain HTML/JS with vis-network vendored in frontend/vendor/. The API endpoints (/api/graph, /api/entity, /api/recall, /api/episodes, /api/stats) require the same Bearer token as MCP; static files are public.

Environment Variables

MCP Tools

remember(content, agent_id, session_id?, tags?)

Store a memory. Server extracts entities, relationships, and facts into the knowledge graph. Detects conflicts with existing facts.

recall(query, top_k?, agent_id?)

Hybrid RAG search: vector similarity + keyword + graph neighborhood expansion. Fused with recency and access-count boosts.

context(entity_name, depth_limit?)

Graph neighborhood traversal. Returns all edges connected to an entity within 1-3 hops.

resolve(entity_a, entity_b)

Merge duplicate entities. Re-points all edges, keeps both names as aliases.

forget(memory_id)

Soft-delete an episode. Preserves provenance.

Agent Configuration

Add to your Hermes config:

{
  "mcpServers": {
    "mnemosyne": {
      "url": "http://<HOST_IP>:8080/mcp",
      "headers": {
        "Authorization": "Bearer <MCP_API_KEY>"
      }
    }
  }
}

Infrastructure

• Neo4j 5.26 (Community) -- graph + vector + full-text in one container

• Ollama 0.32+ -- qwen2.5:3b (extraction) + nomic-embed-text (embeddings)

• Python 3.11+ -- FastMCP server with Streamable HTTP transport

• Docker Compose -- Neo4j only (Ollama stays native)

Development

# Run integration tests (requires live stack)
.venv/Scripts/python.exe -c "from tests.test_integration import *; ..."

License

MIT