Memgraph, a leader in open-source in-memory graph databases purpose-built for dynamic, real-time enterprise applications, is releasing two new tools specifically architected to open up the power of Retrieval-Augmented Generation based on graph technology (GraphRAG), to the entire database market, democratizing GraphRAG access.
These include:
- An immediately available AI Graph Toolkit—a set of open source libraries and utilities that automate the porting of both SQL and unstructured data into a knowledge graph in Memgraph, making it accessible to a chatbot running a GraphRAG pipeline.
- Later this month—an MCP Client, within Memgraph Lab, that is fully compatible with the emerging standard for context engineering
Together, these products—Unstructured2Graph and SQL2Graph—will deliver faster application development for fully GraphRAG-enabled AI business applications, based on results from multiple beta test sites, according to the company.
Until now, getting SQL and unstructured data into a graph format ready to be used by various search techniques and algorithms within a GraphRAG pipeline has been laborious, error-prone, and inexact, the company said.
Essentially, for SQL data, this required mapping tables, identifying entities, and performing entity resolution to “graphify” the structures. For unstructured data, it involved chunking, cleaning, and creating vector embeddings, to reach the natural language/ChatGPT interfaces wanted.
With the Memgraph Toolkit and MCP client, engineering teams can bypass the intensive manual coding, programming, and data translation typically required to prepare multiple sources for GraphRAG algorithms. Engineers will still need to fine-tune the final output, but the tedious work of extracting and transforming data from SQL and unstructured formats is already taken care of, the company said.
“This makes achieving GraphRAG AI capability and advanced AI-driven knowledge super straightforward for SQL and non-graph experts. Even better, it means you can now run GraphRAG against the ideal back-end LLM input of relational data tables with numeric values and all the business context currently trapped in unstructured text, PDF, and document forms,” said Memgraph’s chief technology officer and co-founder, Marko Budiselic. “Engineering teams will be able to much more easily unleash the power of graph and GraphRAG across multiple forms of business data, enabling chatbot natural language access and querying capabilities to the whole corporate back end.”
The new Memgraph AI Toolkit is available for download through Github and can be used via Memgraph Cloud.
Finally, Memgraph has launched the JumpStart Programme—a fixed-scope path to a production-ready GraphRAG pipeline in weeks.
The Toolkit will be complemented by the MCP client, available later this month, designed to make it easy to connect Memgraph’s graph capabilities to multiple back-end data sources through other MCP Servers, while supporting adoption of an industry standard to accelerate developer AI productivity.
For more information about this news, visit https://memgraph.com.