Unstructured2Graph and SQL2Graph will deliver more than 10× faster application development for fully GraphRAG-enabled AI business applications

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 more than 10x faster application development for fully GraphRAG-enabled AI business applications, based on results from multiple beta test sites.

Since its introduction by Microsoft in 2024 as a way to address the limitations of large language models, GraphRAG has emerged as a leading method for focusing LLM reasoning and delivering accurate, non-hallucinatory answers to natural language front-end or chatbot queries.

However, adoption beyond the graph community has been challenging, as many developers remain entrenched in the non-graph, SQL-based ecosystem. Compounding this, most of the world’s information is still in non-graph formats: relational databases made up over 62% of global DBMS revenue in 2023, while Gartner estimates 80–90% of new enterprise data is unstructured, locked in everything from text files to PDFs.

Marketing Technology News: MarTech Interview with Julian Highley, EVP, Global Data Science & Product @ MarketCast

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. 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.

Memgraph’s Chief Technology Officer and Co-founder, Marko Budiselić, says:

“ we’re announcing the introduction of two powerful new tools to help engineering teams get started on their GraphRAG journey—making it easier for your AI applications to deliver the answers you need with as little time wasted or hallucinations as possible.

“The AI Graph Toolkit does that by making it extremely easy for developers to transform SQL and unstructured data into knowledge graphs, and then produce GraphRAG-level data structures to superpower your AI chatbot capabilities.

“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.

“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.”

Marketing Technology News: Martech & the ‘Digital Unconscious’: Unearthing Hidden Consumer Motivations

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 package pairs a Memgraph Enterprise license with 20+ hours of hands-on help and implementation, powered by the Memgraph AI Toolkit. Enterprises can expect hybrid search (vector + graph reasoning), sub-second latency, and explainable answers with full provenance. Learn more and sign up here

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.