BANDF AD
Graph database supplier Neo4j is offering its Aura Agent to help customers build and deploy agents on their own data in minutes, an MCP Server to add graph-powered memory and reasoning into existing AI applications, and a $100 million investment in GenAI-native startups to increase its technology’s take-up.
Up until now, the main data source for GenAI large language models (LLMs) and agents has been unstructured file and, latterly object data. AI apps can get access to structured (block) data in relational databases using traditional standard SQL and other searches. But these don’t reveal the relationships between items in these databases. Graph databases encapsulate such relationships (see description here).
BANDF AD
We have understood graph database technology and suppliers, like Illumex, Memgraph, and Neo4j to be edging slowly into the GenAI LLM and agent world but that view may well need to be altered, as we shall see.
Agentic AI is going to involved agents co-operating in a context and needing to have a “memory” of that context as they execute. They will also need, Neo4j says, to be able to reason, and that requires awareness of relationships between the entities they are processing.
Neo4j cites an MIT research finding that 95 percent of AI pilots fail to deliver returns. It highlights “model quality fails without context” and points to a lack of memory and contextual learning as among the biggest reasons for failure. Neo4j reckons it can add context and memory to AI agents and wants to be “the default knowledge layer for agentic systems.”
Charles Betz, a Forrester VP and Principal Analyst, said in a 2025 blog. “The graph is essential. It is the skeleton to the LLM’s flesh.”
BANDF AD
Emil Eifrem, Neo4j Co-Founder and CEO, reckons: “Agentic systems are the future of software. They need contextual reasoning, persistent memory, and accurate, traceable outputs, all of which graph technology is uniquely designed to deliver. Neo4j transforms disconnected data into actionable knowledge, and this investment allows us to advance that vision faster.”
The company has built an MCP (Model Context Protocol) server so that external AI agents can link to and integrate with Neo4j’s graph database data. It supports natural language querying, auto-generated graph data models, memory persistence, and automated management of Neo4j AuraDB instances. AuraDB is Neo4j’s fully-managed graph database.
It has also developed its own Aura Agent, now in early access mode, with “end-to-end automated orchestration and AIOps for graph-based knowledge retrieval. This, it says: “that enables users to build, test, and deploy AI agents grounded directly on their enterprise data in minutes.”
Nitin Sood, SVP and Head of Product Portfolio and Innovation at multinational molecular biotechnology company QIAGEN, said: “Neo4j Aura Agent promises to improve healthcare by designing and deploying AI agents that create comprehensive knowledge graphs from our … biomedical knowledge. With new ways to interrogate these graphs, researchers can approach drug discovery in ways that were impossible before.”
BANDF AD
This means Neo4j has an agent-building facility with which its customers can interrogate their graph data, and an MCP server so that external agents can do the same. But how will it get AI-agent-building organizations to use them and its databases? That’s where the $100 million comes in.
The investment
Neo4j says it will invest $100 million over 12 months to support 1,000 worldwide GenAI-native startups to build and scale agentic AI with graph technology. Participants will receive access to cloud credits, technical enablement, and go-to-market support to help them build and scale agentic systems with graph technology. That means it will be dealing with an average rate of 83-84 companies a month.
We understand that these include cloud (Aura) credits, tech enablement (aka fully-managed AuraDB graph database and graph expert help) and go-to-market support (co-marketing and GTM partnerships) are valued in total at a notional $100 million. It doesn’t mean that each company will get $100,000 cash.
BANDF AD
David Klein, Co-Founder and Managing Partner at One Peak, and a Neo4j Board Director, said: “Eight out of ten GenAI-native startups I speak with are re-platforming on Neo4j. They tell me that it’s the natural choice when you’re serious about building intelligent systems with context and memory.”
We didn’t know how many GenAI-native AI startups there are, and what proportion of them get to speak with Klein, so we asked three chatbots about the startup number.
According to Grok, xAI’s clever chatbot, there are over 6,000 GenAI-native startups worldwide. This figure comes from ecosystem trackers that distinguish startups and scaleups innovating directly in GenAI (e.g., model development, content generation tools, and AI-native applications) from broader AI companies. It includes more than 16,500 total companies in the GenAI space, but the startup subset emphasizes early-stage innovators.
The Perplexity chatbot agreed with these numbers, as did ChatGPT: “According to StartUs Insights, there are ~6,020+ generative AI startups globally as of their 2025 report.” If Klein’s 8 out of 10 numbers are even remotely generally applicable then there are a large number of such startups thinking about graph technology. We shall see.
Neo4j’s Startup Program is now live at neo4j.com/startups. It has 208 members, including Firework, Garde-Robe, Hyperlinear, Mem0, OKII, Rivio, and Zep. Check out the Startup Program FAQ here. Apply to join the program here.
A fully supported version of the Neo4j MCP Server will be available by the end of the year. Aura Agent should also be generally available by then.
Bootnote 1
We might expect to see partnerships building up between AI LLM and agent-focussed unstructured data suppliers, both file and object, and the graph database companies. That would widen the latter’s go-to-market channel.
Bootnote 2
Neo4j was started up in 2007 and has raised approximately $580 million across 9 funding rounds and events, the most recent a $50 million grant in 2024. This is serious funding.
