AI Integrations
Easily integrate with the AI stack you already have
Give every AI system you build a knowledge layer to reason from, connecting MCP, the frameworks your team already uses, and the agent platforms running your business into one connected source of context.
Faster time to production
Pre-built connectors compatible with the frameworks and platforms you run, cut integration time from months to weeks.
More accurate, explainable answers
Answers reflect the real relationships in your data, reducing hallucinations.
Agents that act with confidence
Grounded in real context, agents act smarter and more reliably.
Integrations
The tools you need, however you build
Call OpenAI, Azure OpenAI, Google Vertex AI, or Amazon Bedrock directly from Cypher, generating embeddings and text in the same query that reads your graph, with no separate service to stand up.
Give any MCP-compatible AI assistant direct, secure access to your graph data, whether you run Neo4j yourself or on Aura.
Create and manage Aura instances and run Cypher without leaving the command line, built for workflows that already run end-to-end without a browser.
Bring Neo4j into a framework your team already uses, from LangChain and LlamaIndex to several others across Python, JavaScript, and Java, as a vector store, graph store, or query-generating component.
Give autonomous agents a knowledge source they can reason over and act through, whether built with a framework like LangGraph or deployed inside a platform like AWS AgentCore, and pair it with Neo4j’s Agent Memory library so agents remember context across sessions.
Install ready-made skills for Cypher generation, GraphRAG, and more directly into Claude Code, Cursor, or Cline, giving coding agents Neo4j expertise without you having to write that context yourself.
Use cases
Top AI integration use cases
Give research agents the full competitive picture
Investment research usually means piecing together news, ownership, and competitor data from a dozen scattered sources by hand. Built with LangChain, Google’s MCP Toolbox, and a Neo4j knowledge graph, an agent surfaces investors, competitors, and partners in one connected answer.
Turn contract review into direct answers
Reviewing contracts by hand means rereading the same clauses every time a new question comes up, and standard document search doesn’t preserve how obligations and parties relate to each other. Built with Neo4j and Microsoft’s Semantic Kernel, a knowledge graph turns contracts into direct, structured answers instead of another document to search.
Give enterprise agents a map to the right data
Financial services teams on Google Cloud connect BigQuery, a data catalog, and Looker dashboards through a single Neo4j graph. Before running a query, a Gemini powered agent traverses that graph first to confirm meaning and lineage, built with Google’s ADK and the Neo4j MCP server.
Detect fraud and compliance risk in connected data
Know-Your-Customer (KYC) investigations are a connected-data problem, a web of customers, accounts, transactions, and devices that document search cannot untangle. Built with the OpenAI Agents SDK and Neo4j’s MCP server, an agent queries that web directly to surface circular transactions and other fraud signals.
Loved by devs. Deployed worldwide.
1,700+ organizations build on Neo4j for data breakthroughs.
