Use case
AI systems
Unify your data with a knowledge graph to ground AI systems and agents. Manage context to boost accuracy and explainability. Evolve and enrich agent knowledge over time.
Boost accuracy
Organize knowledge for accurate retrieval and memory recall.
Contextual, smarter AI
Unify data for multi-step retrieval to deliver complete answers
Increase explainability
Optimally model data for AI reasoning and explanation of its steps
Future-proof your AI
Continuously enrich context as requirements & data evolve.
Demo: How to manage context to ground agentic AI
Watch this five-minute demo about how to feed LLMs the right context for smarter AI.
Capabilities
Optimize context engineering to build smarter agents
Pair RAG with a knowledge graph. Then use AI agents to retrieve information from the knowledge graph by traversing its interconnected data for richer contextual insights.
Add new datasets with ease due to a flexible schema. Feed your LLM new context to keep agents intelligent as users interact, requirements change, and AI evolves.
Model your data with a knowledge graph that keeps relationships with their valuable context intact. Improve AI’s ability to understand, reason, and reliably execute tasks.
Boost query performance with index-free adjacency for memory and tool calling. Scale seamlessly and query 1,000x faster than a traditional database.
1. Get real-time data updates.
2. Gain deeper insights from graph analytics.
3. Connect and update agent memory across sessions.
Use cases: Build smarter AI with graph technology
Expert domain knowledge & automation
Query massive, specialized knowledge graphs using natural language. Combine vector search, automated query generation, and LLM summarization to help with domain-specific tasks and research.
Semantic knowledge graph layer for deep context
Give AI deep context across your business domains—an understanding of what data means, how it connects, and when to use it.
Improve enterprise search & knowledge assistants
Unify knowledge across data sources for applications in SaaS, Legal & Compliance, Network & Security, and more. Build agents to dynamically reason across all your enterprise knowledge.
Scalable long-term memory for agents
Optimize memory management that grows intelligently over time for immediate decision-making as well as storing and recalling information from across different sessions.
Analyst Report
Complimentary Gartner report on knowledge graphs for AI
Gartner, How to Build Knowledge Graphs That Enable AI-Driven Enterprise Applications, By Afraz Jaffri, 22 May 2025
GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved.
Loved by devs. Deployed worldwide.
1,700+ organizations build on Neo4j for data breakthroughs. Build with a comprehensive platform for modeling, managing, and retrieving context.
Real world AI innovations. Powered by graph.
“Agentic AI without a knowledge graph is like a self-driving car with no GPS map. Kind of brilliant, but dangerously unaware.”
Natalie Romanov, Associate Director, Knowledge Management & Data Strategy, Merck Group
“Graph databases are a natural way to express interconnected data. The simplicity extends from humans to LLMs — making our configurations more accessible and explainable.”
Vignesh Murugesan, Senior Staff Engineer, Uber
“Sales teams, collections specialists—they can all query the graph directly through natural language. What once took days of data team effort now happens in minutes.”
Jin Foo, Head of Data & Analytics, Prospa
Works seamlessly with your tech stack
Neo4j integrates with leading technology providers
Explore AI resources
Tools and Guides for Easily Building AI
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What is GraphRAG?
Discover how GraphRAG combines knowledge graphs to deliver accurate, reliable, and context-rich AI answers.
GraphRAG Python package: GenAI with knowledge graphs
Turn unstructured data into knowledge graphs to enhance GenAI retrieval
The GraphRAG manifesto: Add knowledge to GenAI
Boost AI accuracy and explainability by providing context to AI with GraphRAG.
Get started with GraphRAG: Neo4j’s ecosystem tools
Develop GenAI applications grounded by knowledge graphs
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AI real-world use cases
Learn how to build AI app for practical use in the real world.
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Devs guide: GraphRAG agent for customer & retail analytics
See how to improve data quality and retrieval with this end-to-end worked example.