Memgraph AI Toolkit

Memgraph AI Toolkit is a unified, mono‑repo solution that brings the power of graph databases into modern AI workflows. By exposing Memgraph’s rich graph capabilities through a lightweight Model Context Protocol (MCP) server, the toolkit allows AI assistants—such as Claude or GPT‑4—to query and manipulate graph data directly, without the need for custom adapters or complex integration layers. This removes a significant friction point in building AI‑powered applications that rely on relationships, traversals, and graph analytics.

The MCP server () implements a standard STDIO‑based protocol that maps Memgraph operations to AI tool calls. Developers can register graph queries, mutations, and analytical functions as tools that the LLM can invoke on demand. The server also exposes a schema‑aware prompt engine, enabling assistants to generate Cypher queries from natural language prompts or to suggest optimal graph models based on relational schemas. This abstraction lets developers focus on business logic while the LLM handles the intricacies of graph query formulation and execution.

Key capabilities include:

• Schema‑aware querying: The server introspects the Memgraph schema and offers autocomplete suggestions, reducing query errors.
• Real‑time analytics: Cypher queries can return aggregated metrics or visualizations that the AI can interpret and explain.
• Toolkits for LangChain: A companion package () turns Memgraph operations into LangChain tools, allowing seamless integration in existing LLM pipelines.
• Migration agent: An experimental component automates the migration from MySQL to Memgraph, leveraging LLMs for schema translation and data validation.

Typical use cases span from knowledge‑base construction, recommendation engines, fraud detection, to interactive chatbots that need to traverse complex relationships. For example, an e‑commerce assistant can ask the LLM to recommend products by querying a graph of customers, purchases, and product attributes—all handled transparently through the MCP server. In data migration scenarios, the agent can analyze a relational database, propose an optimal graph schema, and perform data transfer with validation checks, dramatically reducing manual effort.

Integrating the MCP server into AI workflows is straightforward: a client (e.g., an LLM wrapper) initiates a connection over STDIO, registers the available tools, and then delegates queries to the server. The server executes them against Memgraph, streams results back, and the LLM can use those results to refine its responses. This tight coupling ensures low latency, consistent data access, and a declarative way to expose complex graph operations as simple AI tool calls.