Graph Memory RAG MCP Server

Graph Memory RAG MCP Server is a lightweight, in‑memory graph database that exposes its functionality through the Model Context Protocol. It lets an AI assistant persist knowledge as a network of entities and relationships, rather than as flat text or key/value pairs. By storing facts in a graph, the assistant can maintain explicit context about how pieces of information relate to one another—critical for tasks that require reasoning, chaining of events, or maintaining long‑term conversational state.

The server implements a full MCP interface with tools for creating and managing entities, linking them via typed relationships, querying by type or relation, and deleting nodes with automatic cascading cleanup of dependent edges. Each entity carries a unique ID, a human‑readable name, a type label (e.g., Person, Location), and an array of observation strings that capture descriptive or historical details. Relationships are first‑class objects with their own IDs and a type that describes the semantic link (e.g., FRIENDS_WITH, LOCATED_IN). Because the data lives in memory, read and write operations are extremely fast, making it ideal for prototyping or for agents that need low‑latency access to contextual knowledge.

For developers, this server solves the problem of “context leakage” in AI workflows. Traditional prompt‑based memory can become unwieldy as the conversation grows, and key/value stores lose the ability to express complex relationships. By exposing a graph API over MCP, developers can let an assistant automatically build a knowledge base as it converses—adding new entities when encountering unfamiliar terms, linking them to existing concepts, and querying the network to surface relevant connections. This leads to richer, more coherent responses that reflect an understanding of how facts interrelate.

Typical use cases include:

• Conversational agents that need to remember user preferences, past interactions, and related entities (e.g., a travel planner linking destinations, dates, and user interests).
• Knowledge‑base construction where the assistant incrementally curates a domain ontology from user input or external documents.
• Reasoning tasks such as causal inference, where the assistant follows paths through the graph to explain why one event led to another.
• Rapid prototyping of new AI assistants that require fast, in‑memory context without the overhead of a persistent database.

Integration is straightforward: an MCP client (such as Claude or another compliant assistant) simply calls the provided tools during a conversation. The server’s in‑memory nature ensures that each session can start fresh or restore from a snapshot, while the graph structure keeps context richly connected. The clear separation of entities and relationships also allows developers to expose only the tools they need, keeping the assistant’s action space manageable.

Overall, Graph Memory RAG MCP Server offers a unique combination of speed, expressiveness, and protocol compliance. It empowers developers to give AI assistants a structured, relational memory that scales with conversational depth and supports sophisticated reasoning—all without leaving the MCP ecosystem.