Build Smarter AI Systems With Context
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.
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.
Explore AI Resources
Tools and Guides for Easily Building AI
Neo4j & GenerativeAI Fundamentals
Learn the basics of Neo4j and the property graph model.
4 hours
Importing Data Fundamentals
Learn how to import data into Neo4j and create a graph data model.
2 hours
Build a Neo4j-Backed Chatbot Using Python
Build a chatbot using Neo4j, Langchain and Streamlit.
2 hours
Build a Neo4j-Backed Chatbot with Typescript
Build a chatbot using Neo4j, Langchain and Next.js.
6 hours
Introduction to Vector Indexes and Unstructured Data
Understand and search unstructured data using vector indexes
2 hours
Using Neo4j With Langchain
Learn how to integrate Neo4j in into Langchain applications for GenAI.
2 hours
Building Knowledge Graphs with LLMs
Learn how to use Gen AI, LLMs, and Python to turn unstructured data into graphs.
2 hours
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

Build GraphRAG Apps for Accurate AI on Google Cloud
Learn about the native integrations with Google Cloud and Vertex AI.

Implement a RAG App With a Knowledge Graph for Langchain
Learn how to improve information retrieval.

Unify LLMs & Knowledge Graphs for AI Accuracy
Learn how to supply context to a LLM for accurate, relevant, and explainable results.

Implementing RAG: Write a Graph Retrieval Query in LangChain
Learn how to write a retrieval query that supplements or grounds an LLM’s answer

How an AI Agent Works
Discover how agents use tools, memory, and reasoning to complete complex tasks.

Expand Your MCP Toolbox
Combine vector search with the Cypher query language to add MCP as another tool for agents.

Evaluate Graph Retrieval in MCP Agentic Systems
Learn how to measure and improve retrieval quality.

GraphRAG in Action: A Simple Agent for KYC Investigations
Learn how to equip a KYC agent to uncover fraud.
Build AI Faster With The GraphRAG Python Package
Learn to build knowledge graphs, implement advanced retrievers, and create GraphRAG workflows.

Kickstart AI App Dev With Neo4j’s GraphRAG Ecosystem
Learn how to quickly build a knowledge graph from unstructured data.

AI Real-World Use Cases
Learn how to build AI app for practical use in the real world.

Build Accurate AI Chatbots
Learn how to ground chatbots in enterprise data with a knowledge graph.

Improve Vector Search Results Using a Knowledge Graph
Learn the latest data analysis with AI technologies.

Devs Guide to GraphRAG for Accurate AI
Learn how to prepare a knowledge graph and work with the three main GraphRAG patterns.

Devs Guide: AI For CX – A Retail Example
Find out how to improve all the touchpoints in a customer journey using GraphRAG.

Devs Guide: GraphRAG Agent for Customer & Retail Analytics
See how to improve data quality and retrieval with this end-to-end worked example.