Reverie MCP server

Reverie — graph memory that dreams

Reverie is KnowAll AI's Neo4j knowledge-graph memory for AI agents, served over MCP. Until @knowall-ai/reverie is published to npm, install the legacy package @knowall-ai/mcp-neo4j-agent-memory (0.2.x). The GitHub repository is now knowall-ai/mcp-reverie, and old URLs redirect.

Reverie turns an agent's memory from a pile of facts into a map of the entities in its world and how they relate, and keeps that map healthy. It is an MCP server, so any agent that speaks the Model Context Protocol (Claude Desktop, OpenClaw, Azure AI Foundry, Cursor…) gets the same graph; a Hermes Agent memory-provider flavour lives in hermes-reverie.

Why Reverie

• A typed entity graph, not a fact store. People, organisations, projects, places, concepts, meetings and decisions are nodes with typed relationships. "Who at the Irish FA have we talked to about Winnie?" is a graph walk, not a similarity search.
• Search that finds "Ben" when you say "Benjamin". Hybrid keyword + semantic search, with local embeddings by default (no API key) and OpenAI, Azure OpenAI, Ollama or Voyage a config switch away.
• It dreams. A dream tool merges duplicates safely, canonicalises labels, re-embeds, counts orphans and flags nodes that have become property dumps, so a nightly job can keep the graph clean.
• One graph, any agent. KnowAll runs Sallie (OpenClaw) and Poppie (Hermes) against the same conventions; Reverie is how they share what they know.
• LLM-driven, transparent tools. Simple atomic operations; the model does the entity recognition and conflict resolution, and every action is explicit.
• Yours to run. Neo4j on your own machine or VM. Nothing leaves it unless you choose a remote embedding provider.

Quick Start 🚀

You can run this MCP server directly using npx:

npx @knowall-ai/mcp-neo4j-agent-memory   # becomes `npx @knowall-ai/reverie` once 0.4.0 is published

Or add it to your Claude Desktop configuration:

{
  "mcpServers": {
    "neo4j-memory": {
      "command": "npx",
      "args": ["@knowall-ai/mcp-neo4j-agent-memory"],
      "env": {
        "NEO4J_URI": "bolt://localhost:7687",
        "NEO4J_USERNAME": "neo4j",
        "NEO4J_PASSWORD": "your-password",
        "NEO4J_DATABASE": "neo4j"
      }
    }
  }
}

Features

• 🧠 Persistent Memory Storage - Store and retrieve memories across conversations
• 🔗 Semantic Relationships - Create meaningful connections between memories (KNOWS, WORKS_AT, CREATED, etc.)
• 🔍 Hybrid Search - Keyword plus semantic search across all memory properties, with local embeddings by default
• 🏷️ Flexible Labeling - Use any label for memories (person, place, project, idea, etc.)
• ⏰ Temporal Tracking - Automatic timestamps and date-based queries
• 🌐 Graph Exploration - Traverse relationships to discover connected information
• 🎯 Context-Aware - Search with depth to include related memories
• 🔧 LLM-Optimized - Simple tools that let the AI handle the complexity
• 🏢 Enterprise Ready - Supports multiple Neo4j databases
• 📚 Built-in Guidance - Get help on best practices and usage patterns

Philosophy: LLM-Driven Intelligence

Unlike traditional approaches that embed complex logic in tools, this server provides simple, atomic operations and lets the LLM handle all the intelligence:

Shortened. The full README is on GitHub.