As the founder of GraphGeeks, few things bring me more joy than watching our community come together to share knowledge, push boundaries, and genuinely enjoy each other's company. I’m happy to say that this spring has seen many new members join us to discover and explore the graph space.

From frenetic conference halls to intimate rooftop mixers, the spring events felt like a turning point for graph adoption and community building. But more than that, it was a reminder of why I started GraphGeeks in the first place: the incredible things that happen when passionate people gather to solve hard problems together.

The Knowledge Graph Conference

The Knowledge Graph Conference (KGC) kicked things off in spectacular fashion, and witnessing its evolution has been remarkable. What struck me most wasn't just the conference's growth—substantial enough to require organizers to seek additional partnerships for next year's tracks—but the fundamental shift in who was attending and why.

Paco Nathan captured this perfectly: "This is no longer about N graph database vendors and their coterie of adherents. Now the main narratives shift more toward enterprise use cases in production. This is a very good thing."

The presence of AbbVie, a major pharmaceutical company, as the top sponsor symbolized this sea change. The conference has matured beyond its origins as a vendor-driven gathering to become a destination for enterprises seeking real-world solutions. As someone who's watched this community grow from its early days, seeing this level of enterprise validation was deeply satisfying. Recorded sessions are now available!

The Lure of Agents and Good Visualization

If there was one theme that dominated conversations at all events, it was agents and their increasingly sophisticated relationships with graph technologies. The convergence of Graph RAG with agentic AI systems created a palpable buzz throughout the conference halls.

And this wasn't just theoretical excitement—financial firms openly discussed ROI-bearing knowledge graphs in production environments. Energy and transportation companies shared their implementations, expanding the conversation beyond typical tech sector use cases. We are finally seeing production use cases across multiple industries being discussed openly, which feels like a significant maturation.

Arthur Bigeard, founder of G.V() and a first-time KGC attendee, observed the magnetic pull of graph visualizations: "People love to see a large graph visualization. Our demonstration table at KGC was seeing a constant influx of visitors wanting to get a closer look at a graph visualization we showed on screen with a few tens of thousands of elements on display."

This visual magnetism serves as more than just eye candy—it's a powerful communication tool that makes complex relationships immediately comprehensible. As someone who thinks a lot about making graph technologies more accessible, I'm constantly reminded that sometimes the best way to explain the power of graphs is simply to show them in action.

Serendipitous Collaboration

Some of the most memorable moments at KGC happened away from the main stage. I'll never forget one particular lunch break where Prashanth Rao, Paco Nathan, Weidong Yang, me, Bogdan Arsintescu, David Hughes, and Adriano Vlad-Starrabar were all sharing things on our laptops at a couple of adjacent tables. Suddenly, we all just merged and started collaborating—and yes, we got a photo! I think two engineering blog posts and several business meetings have already come out of that spontaneous moment.

This kind of organic convergence is what makes our community special—the combination of high-level strategic thinking with hands-on technical innovation happening through real human connections.

Boston: Data Science Meets Graph Tech

Open Data Science Conference (ODSC)

The Open Data Science Conference in Boston provided another touchpoint and validation for our community. While KGC focused intensively on knowledge graphs, ODSC allowed graph practitioners to engage with the wider data science ecosystem—and the interest from the broader data science community was electric.

What really excited me was seeing how many graph-focused talks made it onto the ODSC program, featuring familiar faces from our GraphGeeks community. David Hughes and I presented "Advancing GraphRAG: Text, Images, and Audio for Multimodal Intelligence," diving deep into expanding GraphRAG beyond text to integrate different types of data for more associative intelligence that mimics human conceptual connections.

William Lyon delivered a characteristically insightful and dry-witted presentation on "Building Agentic APIs With LLM Tool Use & Knowledge Graphs," tackling the fundamentals of adding LLM-backed agentic features to applications. Meanwhile, Dr. Clair Sullivan shared her extensive industry experience in "Entity-Resolved Graphs: Taking Your Retrieval-Augmented Generation To The Next Level," showing data science teams how to clean up knowledge representation and enhance LLM-driven responses.

The fact that multiple graph-focused sessions drew practitioners at a general data science conference tells me we're hitting a sweet spot—the broader data science community is hungry for graph solutions, even if they’re just figuring out how to apply them. This crossover, bridging a specialized graph community with the wider data science world, feels like a crucial connection we are building.

GraphGeeks-Glasswings Mixer

Also in Boston was the GraphGeeks-Glasswings Mixer, which holds a special place in my heart as our first GraphGeeks in-person event! These kinds of smaller gatherings represent what I hope to achieve within this community—creating spaces where organic collaborations flourish naturally.

I was also jazzed by the diversity of perspectives. We had startup founders talking directly with enterprise architects and business leaders, and everyone was learning from each other's viewpoints. It felt like being part of an inner circle where everyone speaks the same language. No need to explain what a knowledge graph is—we could jump straight to the interesting problems.

"Every conversation was with someone who truly gets graph tech, so we really dove into details," shared Erik Rottsolk of Rocketgraph. "It's refreshing to network with graph experts and compare ideas on where the broader ecosystem is headed."

Glasswing Ventures provided an amazing rooftop venue and were lovely hosts, keeping the mixer moving and making introductions. James Massaquoi, an investor at Glasswing Ventures, captured another crucial dimension: "One thing that really stood out was how clearly knowledge graphs are becoming foundational to the AI stack. Whether powering long-term memory for agents, grounding LLMs with structured context, or enabling dynamic reasoning over complex systems, graphs are no longer a niche data layer; they're a necessity for any exciting AI company. You could feel the momentum on the roof: everyone's building, integrating, or rethinking graphs as part of their core architecture."

Enterprise Maturation and Startup Energy

Another significant development I witnessed this spring was the clear evidence of enterprise maturation in graph technology adoption. Not only are we seeing more production deployments of graph tech but also the recognition of semantics as core to successful AI solutions.

This spring, the buzz surrounding graph startups was particularly exciting. I saw several demos showcasing emerging solutions that are pushing boundaries in ways that combine graph technologies with the latest AI developments. This momentum, combined with enterprise adoption and academic interest, creates the kind of virtuous cycle that drives continued innovation.

Yet this evolution exists alongside a paradox that Weidong Yang, CEO of Kineviz, observed at recent conferences like Snowflake and Databricks: "The vast majority of analytics and BI vendors are doing traditional dashboards. However, when I talk to people, many have problems that need a graph solution. What struck me was this disconnect—you'd have these incredibly sophisticated data teams struggling with relationship problems that graphs solve elegantly, but many are still unfamiliar with graph technology.”

This reminds me that while we're celebrating genuine progress, there's still work to be done and enormous untapped potential in the broader data ecosystem.

A Personal Reflection

What made this spring particularly special wasn't just the technological progress or business milestones—it was the human connections that made it all possible. From impromptu laptop demonstrations during lunch breaks to evening dinners and rooftop conversations, our community's growth has been fundamentally driven by personal relationships and shared enthusiasm. I’m lucky to be accepted by a group of people who, like me, geek out on and appreciate the beautiful complexity of life. If that sounds like you too, we’re here and ready to share ideas.

Why This Spring Reinforced My Belief in Our Graph Future