AI Database

Integrated Graph Database

Graphs help you discover connections, uncover hidden patterns, and explore relationships in your data. Oracle Graph is an AI-ready, integrated feature of Oracle’s converged database that supports both property graphs and RDF graphs, eliminating the need for a separate graph database and data movement. Use property graphs for real-time operational graph analytics, and use RDF graphs with ontologies to build knowledge graphs, query semantic relationships across RDF and relational data, and apply inferencing. Analysts and developers can address use cases such as financial fraud detection, manufacturing traceability, and providing context for agentic applications while benefiting from enterprise-grade security, streamlined data ingestion, and support for operational and transactional workloads.

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Discover Graph user cases (PDF)

Oracle AI Database 26ai Powers the AI for Data Revolution

Oracle has architected AI into the core of Oracle AI Database 26ai, furthering Oracle’s commitment to helping customers securely bring AI to all their data, everywhere.

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Why use Oracle Graph?

Find the unexpected
Data is connected. Discover hidden patterns and find new insights easily and quickly with more than 80 prebuilt algorithms, automated analysis, visualization tools, and graph-powered AI using RDF or property graphs.
Real-time graph analytics
Make informed business decisions with graph analytics based on operational and transactional data in your Oracle AI Database.
Enterprise-grade
Benefit from scalability, high availability, security, AI capabilities, and other converged features of Oracle AI Database when running graph analytics.

AI and machine learning

GraphRAG

Take your generative AI–powered similarity searches to the next level with GraphRAG. By integrating relevant business data, retrieval-augmented generation (RAG) can enhance user queries through LLMs. GraphRAG can improve the accuracy and relevancy of the responses by leveraging graphs that capture and provide rich insights into relationships between data entities, such as social networks, financial flows, or supply chains. It also allows you to trace back the responses to their origins to prove the correctness for compliance or validation.

Feature engineering

Some feature engineering tasks are complicated to accomplish, and graphs can help simplify these tasks. For example, taking into account indirect relationships between entities or determining clusters of closely connected entities can be cumbersome without using graphs. Running graph algorithms on a data set creates enriched data which can then be used for machine learning models as features.

Graph neural networks

The use of graphs as a recommendation engine is well known, but graphs can also be used for predictive recommendations. For example, an online retail store wants to send recommendations to a customer, with timing determined by when the customer is predicted to run out of the item. Graph neural networks, which can capture the graph itself as an input of machine learning and neural networks, provide potentially better accuracy because the graph can hold more information than relational tables.

Oracle Graph pricing

Graph database and graph analytics are integrated into Oracle AI Database and included in on-premises database licenses and Autonomous AI Database.

Introducing Oracle's graph database

See how Oracle's graph database makes it easy to explore relationships and discover connections in data by providing support for different graph structures, powerful analytics, and intuitive visualization.

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The "AI for Data" revolution is here

AI is fundamentally reshaping how we think about data and apps. In this Oracle AI World main stage keynote, you'll hear from Juan Loaiza, Oracle’s EVP of database technologies, and T.K. Anand, Oracle’s EVP of healthcare and analytics, as they explore what the AI-driven future looks like.

Watch the keynote replay (1:21:28)

Announcing Oracle Autonomous AI Lakehouse

It combines Oracle Autonomous AI Database with vendor-independent Apache Iceberg, enabling customers to run AI and analytics securely on all their data—available on OCI, AWS, Azure, Google Cloud, and Exadata Cloud@Customer.

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Improve business insights by adding context to your data

Knowledge graphs, also known as ontologies, help applications query data with the associated context and enable users to make business decisions based on the context. Learn how Oracle Graph supports such ontologies with a utility use case.

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MARCH 23, 2026

Automate Graph Creation with Generative AI

Melliyal Annamalai, Distinguished Product Manager, Oracle

Generative AI—and especially large language models (LLMs)—are rapidly changing how organizations turn raw data into usable insight, lowering the barrier to tasks that once required deep specialist expertise. For example, LLMs have been simplifying and accelerating AppDev in areas such as natural-language-to-code generation and refactoring, automated test creation and edge-case discovery, documentation and API reference generation, and incident-response assistance through faster log summarization and guided troubleshooting.

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