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

Introducing concurrent writes to Cypher subqueries

9 min read

Create a Neo4j GraphRAG workflow using LangChain and LangGraph

15 min read

Enriching vector search with graph traversal using the GraphRAG Python package

7 min read

Ingesting documents simultaneously to Neo4j & Milvus

6 min read

Integrating Microsoft GraphRAG into Neo4j

22 min read

Entity linking and relationship extraction with Relik in LlamaIndex

8 min read

Easy local Neo4j cluster setup using k3d and K3s on Mac in 10 minutes

6 min read

Build a knowledge graph-based agent with Llama 3.1, NVIDIA NIM, and LangChain

7 min read

Using embeddings to represent string edit distance in Neo4j

8 min read

Graph databases offer a deeper understanding of organizational risk

7 min read

Visualizing graph data with Neo4j Bloom

14 min read

Loading data into Neo4j using pyneoinstance

12 min read