Memgraph’s AI ecosystem
AI drives a wide range of innovations, from machine learning (ML) models to natural language processing (NLP) systems and beyond. These technologies frequently intersect, enabling the creation of powerful applications with Generative AI (GenAI), including advanced chatbots and agents.
Memgraph offers a robust ecosystem designed to support AI applications, featuring built-in processing capabilities and integrations with popular Agentic Frameworks. This ecosystem empowers developers to build sophisticated GenAI solutions that leverage the strengths of graph databases.
What you’ll find here
This section of Memgraph’s documentation is your guide to using Memgraph for AI:
- Building GenAI apps with GraphRAG: See how knowledge graphs enable more efficient and scalable RAG systems.
- AI integrations with Memgraph: We have several integrations with popular AI frameworks to help you customize and build your own GenAI application from scratch. Some of the libraries that support Memgraph include Model Context Protocol (MCP), LangChain, LlamaIndex, Cognee, Mem0 and LightRAG.
- Machine learning with Memgraph: Learn how Memgraph powers ML workflows with graph-powered insights.
- GraphChat in Memgraph Lab: Explore how natural language querying (GraphChat) ties into the GraphRAG ecosystem, making complex graphs accessible to everyone.
- Agents in Memgraph: Discover how you can leverage AI agents to automate graph modeling and migration tasks.
- Unstructured2Graph: Learn how to use Unstructured2Graph to transform unstructured data into structured graph data within Memgraph.