Neo4j Aura Agent

Build knowledge graph grounded AI agents, from ontology to deployed endpoint

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What is Neo4j Aura Agent?

Neo4j Aura Agent is a developer tool that builds knowledge graph grounded AI agents, from ontology to deployed endpoint. A Neo4j product for generating AI agents grounded in an AuraDB knowledge graph, with auto-built retrieval tools, agentic GraphRAG, exposed reasoning steps and one-click deployment to an authenticated REST or MCP endpoint.

Neo4j Aura Agent is an agent-building environment layered on top of AuraDB, Neo4j's managed graph database service. Its premise is narrow and deliberate: agents are only as reliable as the data they retrieve, so every agent built here is grounded in a graph rather than a loose pile of documents. Users describe a use case, point the platform at a graph schema, and get a working agent with retrieval tools, prompts and descriptions already tailored to that data model. The build process leans on auto-generation. From an ontology, Aura Agent assembles a ready-to-deploy agent in minutes, then exposes the pieces for editing. Retrieval is handled through agentic GraphRAG: vector search, parameterized query templates and dynamic text-to-graph query generation sit alongside each other, so an agent can combine semantic similarity with explicit relationship traversal. A reasoning tab and the response payload both surface chain-of-thought and multi-hop steps, which matters in settings where someone has to justify an answer. Deployment is a single click to a secure, authenticated endpoint, callable over REST or wrapped in an MCP server so it can slot into Claude, Microsoft Copilot, Visual Studio, ChatGPT and similar clients. A playground handles testing and iterative debugging before anything goes live. The intended audience is technical: data engineers, graph developers and AI platform teams in pharma, legal, HR and financial services, where the vendor positions the product for drug discovery, regulatory compliance, case prep, knowledge management, portfolio analysis and fraud detection. An AuraDB instance is a hard prerequisite, and the runtime runs on Google Gemini 2.5 Flash, with a fine-tuned Gemini Flash variant behind the text2Cypher tool. Embeddings come from Google VertexAI or Azure OpenAI models. Against general-purpose agent builders, Aura Agent trades breadth for retrieval depth. Teams already holding connected enterprise data get accuracy and explainability; teams without a graph, or unwilling to model one, will find the entry cost steep.

How do you use Neo4j Aura Agent?

  1. 1Confirm you have an AuraDB instance
    Aura Agent builds agents against a graph database, so start by provisioning or locating an AuraDB Free, Professional or Business Critical instance and loading the data you want the agent to query.
  2. 2Generate an agent from your ontology
    Describe the use case and let the platform read your graph schema to auto-construct an agent, complete with retrieval tools, prompts and descriptions suited to that data model.
  3. 3Tune the retrieval tools
    Adjust the generated tools, combining vector search, parameterized query templates and text-to-graph generation so the agent retrieves the right subgraph for each question.
  4. 4Test and debug in the playground
    Run questions through the in-product playground, inspect the reasoning tab to follow chain-of-thought and multi-hop steps, then iterate on prompts and tools until answers hold up.
  5. 5Deploy to an endpoint
    Publish with a single click to a secure, authenticated endpoint and call it over REST, or wrap it in an MCP server to expose the agent as a tool in Claude, Copilot or other clients.
  6. 6Review use case patterns
    Compare your design against published examples spanning pharma, legal, HR and financial services to see how other teams structure graph-grounded agents.

Pros and cons

Pros

  • Ontology-driven generation produces a working agent with tailored retrieval tools, prompts and descriptions in minutesAI
  • GraphRAG retrieval mixes vector search, parameterized query templates and dynamic text-to-graph generationAI
  • Chain-of-thought and multi-hop reasoning are surfaced in a dedicated tab and in responses for auditabilityAI
  • Deployment is one click to a secure endpoint, usable via REST or wrapped as an MCP server for multi-agent systemsAI
  • Internal agents incur no charge; billing applies only to agents exposed on a public endpointAI

Cons

  • Requires an existing AuraDB graph database, so teams without graph data must model and load it firstAI
  • Runtime is tied to Google Gemini 2.5 Flash, with no indication of swapping in other model providersAI
  • Public endpoint agents bill at $0.35 per agent hour, a meter that can climb for always-on assistantsAI
  • Documentation and positioning assume graph literacy, which puts it out of reach for non-technical buildersAI
  • Feature depth is concentrated on retrieval and reasoning; broader agent orchestration is left to external systemsAI

How much does Neo4j Aura Agent cost?

Pricing

Charges accrue only for agents made accessible through a public endpoint, at $0.35 per agent hour. Internal agents are not charged. Aura Agent is available on AuraDB Free, Professional and Business Critical tiers, so database tier costs apply separately.

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Support

Support is handled through a general contact route on the Neo4j site; the product page does not detail plan-specific support terms or service levels.

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Integrations

Deployed agents can be wrapped as MCP servers for use in Claude, Microsoft Copilot, Visual Studio and ChatGPT. Neo4j also lists technology partnerships with Microsoft Azure, Google Cloud, AWS, Snowflake and Databricks.

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Features

Auto-generation of agents from an ontology, agentic GraphRAG retrieval through vector search, parameterized query templates and dynamic text-to-graph generation, a reasoning tab exposing chain-of-thought and multi-hop steps, a UI playground for testing and debugging, and single-click deployment to an authenticated REST endpoint or MCP server.

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Frequently asked questions about Neo4j Aura Agent

  • How much does Neo4j Aura Agent cost?
    Charges accrue only for agents made accessible through a public endpoint, at $0.35 per agent hour. Internal agents are not charged. Aura Agent is available on AuraDB Free, Professional and Business Critical tiers, so database tier costs apply separately.
  • Does Neo4j Aura Agent have an API?
    Yes, Neo4j Aura Agent offers an API.
  • How do you use Neo4j Aura Agent?
    The walkthrough on this page covers 6 steps: 1. Confirm you have an AuraDB instance 2. Generate an agent from your ontology 3. Tune the retrieval tools 4. Test and debug in the playground 5. Deploy to an endpoint 6. Review use case patterns.
  • What platforms does Neo4j Aura Agent support?
    Neo4j Aura Agent is available on Web App.
  • What does Neo4j Aura Agent integrate with?
    Deployed agents can be wrapped as MCP servers for use in Claude, Microsoft Copilot, Visual Studio and ChatGPT. Neo4j also lists technology partnerships with Microsoft Azure, Google Cloud, AWS, Snowflake and Databricks.
  • What are the limitations of Neo4j Aura Agent?
    Requires an existing AuraDB graph database, so teams without graph data must model and load it first. Runtime is tied to Google Gemini 2.5 Flash, with no indication of swapping in other model providers. Public endpoint agents bill at $0.35 per agent hour, a meter that can climb for always-on assistants.

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Added10/22/2026

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Web App

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