Neo4j blog

Neo4j Context Engine: Rapidly build dependable enterprise agents

Neo4j Context Engine, now in private preview, enables AI agents to accurately answer questions about your business and reliably execute its processes.

5 min read
MIT Technology Review Insights report cover, Connecting AI agents to enterprise knowledge

MIT Technology Review Insights research explains why 13% of companies have a 61% AI success rate

6 min read
Agentic GraphRAG illustration showing an agent reasoning loop connected to a knowledge graph

Agentic GraphRAG: How does an AI agent use a knowledge graph?

9 min read

Introducing Neo4j GraphAware Financial Crime Intelligence

8 min read

Emil Eifrem

CEO & Founder, Neo4j

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Sudhir Hasbe

Chief Product Officer, Neo4j

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Philip Rathle

Chief Technology Officer, Neo4j

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Most recent

Illustration showing how vector similarity, Cypher templates, and text-to-Cypher meet to create the Aura Agent

Neo4j Aura Agent: Create your own GraphRAG agent in minutes

14 min read

Reinventing retail: How Neo4j, Oneture, and AWS can power multi-agent store advisers

7 min read

Bolster your cybersecurity by visualizing attack graphs with Neo4j & G.V()

13 min read

Knowledge graph generation

27 min read

Introducing ToolsRetriever in the Neo4j GraphRAG Python package

6 min read

The Neo4j Startup Program: Tomorrow’s AI unicorns build on Neo4j

4 min read

Optimize weakly connected component projections

5 min read

Creating a Neo4j agentic memory multi-user dungeon

32 min read

What is an AI agent?

24 min read

Use weakly connected components to avoid Cypher query crashing

5 min read

Implementing Neo4j GraphRAG retrievers as MCP server

7 min read

Property sharding in Infinigraph: Smarter scaling for rich graph databases

4 min read