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

The payload: the semantic layer, memory, and the loop that fills it

28 min read
From Agent Memory to Portable Skills

From Agent Memory to Portable Skills

11 min read
Illustration representing a context graph for AI agent memory

What is a context graph?

13 min read

Constant-cost semantic memory for multi-agent systems

24 min read
a recipe knowledge graph and agent

One starter repo, three winners: Neo4j at Hack the Video Agent Context Graph

18 min read

AgentMemory for .NET: A native sibling to Neo4j Agent Memory

8 min read

Neo4j Virtual Graph is now in public preview

4 min read

Independent study: GraphRAG makes AI agents 80% more truthful

4 min read

Why public sector AI needs a workforce knowledge layer

6 min read

Introducing MCP for Aura: Hosted MCP, built into every Aura instance

10 min read

How AI decision-making works and how to improve it

11 min read
Chart of the knowledge layer comprising ontologies, data, and memory

The knowledge layer for enterprise AI

19 min read