GraphQLConf 2026

In 2015, we promised GraphQL would be "easy to learn and use". Ten years, and hundreds of billions of daily API calls later, we've learned that not all our hopes and promises turned out to be true.

This keynote is an honest retrospective from inside Meta. We'll share which assumptions didn't survive contact with thousands of engineers, the complexity traps we fell into, and what’s driving the new wave of GraphQL adoption and popularity at Meta.

Whether you're GraphQL-curious or GraphQL-exhausted, this is a rare look behind the curtain and a preview of what re-inventing GraphQL at scale actually looks like.

GraphQL's precise specification gives us incredible interoperability and a rich ecosystem of tooling to be used with any compliant GraphQL service... And yet, that hasn't led to every adopter of GraphQL having a great experience. Some leave disillusioned with performance pitfalls, security concerns, and unforeseen complexity. This can be frustrating for successful GraphQL practitioners since in many cases the solutions to these problems have existed for most of the last decade.

The Golden Path Initiative aims to make it so avoiding common pitfalls becomes the path of least resistance. By encouraging off-the-shelf GraphQL-related software to implement the recommended default behaviours, we hope that GraphQL adopters will have the greatest chance of being successful even without ingesting the vast amount of information in the ecosystem. The Golden Path is not centred on building the most optimal experience, instead it is focused on minimizing downsides: making it so users are exploring around the "pit of success", and taking them far from the "pit of despair".

But to do this will take a huge, coordinated community effort! We need successful GraphQL practitioners; maintainers of key GraphQL libraries, frameworks and tooling; and documentation writers to join us over the next 6 months as we lay out the Golden Path, its recommendations and requirements; and then next year: time to start implementing it across the ecosystem!

A year ago, we forecast an important role for GraphQL in an AI future. That prediction has come true, with GraphQL now serving as the foundation of critical AI initiatives at household brands in retail, hospitality, health care and many more. Just as importantly, GraphQL's declarative entity-based architecture has proven to be an ideal match for modern agentic development.

In this talk, we'll share a view of where GraphQL now sits in the modern enterprise stack, recount lessons we've learned putting MCP workloads and agentic software in production with the graph, our roadmap for an AI-first world, and a vision of where GraphQL can and must go next.

GraphQL offers a popular way for developers to access and interact with data. However, integrating GraphQL with enterprise databases often requires custom middleware, complex resolvers, and maintenance overhead. With Oracle AI Database 26ai, this changes: developers can now use GraphQL queries natively in the database, leveraging automated schema inference and built-in parsing with no loss of performance or scalability.

In this session, we will demonstrate Oracle’s first-class GraphQL integration, including the new table function that lets you run GraphQL queries as native SQL. We will showcase:

- How to map and query relational data with GraphQL, with built-in features for joins, predicates, ordering, & calculations.
- How LLMs can generate valid GraphQL queries from natural language, making API access approachable, and why targeting GraphQL via LLMs often delivers safer, better experiences than translating NL to SQL.

Whether you’re an architect modernizing data APIs or a developer working with complex schemas, this session will help you take advantage of the best of both relational databases and the GraphQL ecosystem, with added automation from today’s AI advancements.

In high-stakes industries, a GraphQL schema is more than a technical contract—it is a live map of your enterprise’s risk surface. For security teams, schema modifications are often "black box" events that threaten data integrity. To scale safely, we must move beyond manual gatekeeping to a Zero-Trust Supergraph where security is an invisible, automated fortress.

We will present a framework for Embedded Governance to bridge engineering and enterprise risk. Learn how to transform your graph's technical "menu" into a transparent Data Marketplace with radical observability, ensuring built-in security and compliance.

Attendees will learn to:
- Navigate the Risk Primer: Translate GraphQL features (types, fields, directives) into risk language to build organizational trust.
- Shift Security Left: Automate security with secure frameworks & replacing manual reviews.
- Architect for Data Isolation: Use of fine grained access to manage entitlements and prevent unauthorized data exposure.
- Harden the Control Plane: Reduce attack surface using technical strategies like disabling introspection and enforcing persisted query ownership.

Left to its own devices, software companies ship its own team structure (Conway’s Law). Scale leads to data silos, unclear ownership, and an incoherent GraphQL schema. Zillow pushes back against this natural entropy.
Organizing data post hoc is untenable — trying to keep up with the legions of changing SaaS systems is a losing battle. The schema must be organized at the data producer end. This is the story of Zillow’s journey to bring order to a chaotic GraphQL schema. Starting with its most critical data domains, listings and customers, Zillow has built a canonical data schema in its federated graph, that aligns its multiple business units and streamlines data sharing.

Have you ever wished you could better understand how the entities in your graph behave over time? Are they cacheable? How often are they updated? How often are they accessed? What is the distribution of keys?

The primitives of GraphQL federation are simple: Entities with keys to uniquely identify them, distributed across multiple services.

The story they tell? It's a fascinating one, but nobody talks about it. Until now.
And it's not even that complicated, just a couple of hashes and we're able to learn more about your data than you ever thought possible.

Incrementally adopting GraphQL is hard. The shape of the data differs between REST and GraphQL. Components that were designed for one don't automatically work with the other. And migrating by making multiple network requests can worsen performance unacceptably. And big bang refactors? Well, if you believe those will be successful, I have some oceanfront real estate in Nebraska to sell you.

Is there a better way? Well, what if instead of contorting our frontends for multiple backends, we gave our non-GraphQL backend one crucial property: generated queries that fetch exactly the right data. Then, migrating from one backend to another is as simple (and stress-free) as running an experiment and ramping up a decider!

And Isograph makes that easy! Isograph is an opinionated, compiler-driven framework that makes it easy to build stable, performant data-driven apps, and it generates queries for just the data needed by a given screen. And crucially, it can generate multiple different versions of the same query: GraphQL, SQL, whatever your heart desires.

Finally, adopting GraphQL can be simple, stress-free, and incremental!