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"description": "As developers build with AI agents, we face a challenge: how do we provide these agents with reliable, flexible access to our distributed data? GraphQL's graph-based approach makes it the ideal language for AI. Join Matt DeBergalis, CEO and Co-founder of Apollo GraphQL, to explore how \"thinking in graphs\" fundamentally transforms API orchestration from procedural code to declarative queries – creating the composable data layer that AI-driven applications require.",
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"description": "APIs give AI superpowers. The MCP protocol, introduced less than a year ago, has quickly become a popular method for connecting large language models (LLMs) with the outside world. But where does GraphQL fit in? It's structured, typed, and introspectable... so what's the catch?\n\nJoin our panel of experts as we delve into how to leverage GraphQL effectively and safely in AI applications. We'll discuss the trade-offs, potential pitfalls, and share insights into best practices and strategies. This interactive discussion will explore how combining GraphQL with MCP can unlock new superpowers for AI, offering attendees a chance to engage with thought leaders and gain valuable perspectives.",
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"description": "The Buyer Abuse Prevention team is responsible for preventing returns abuse on Amazon.com. We do this by leveraging multiple streams of data to help make accurate decisions that minimize friction to our good customers. Enrichment of entities, for example, orders, is often distributed across multiple APIs, which makes collecting and organizing large sets of data inefficient and inflexible.\n\nTo simplify development, we built a GraphQL API to consolidate the collection and storage of data that allowed us to break the dependency on API results and design our storage around entities in a way that was optimal for our business. Chaining API calls now only takes place within a single API without need for code replication. Swapping out the underlying API for specific fields no longer requires code refactoring as the shape of the entity remained the same. The schema is well-connected which allows for different entry points but ultimately arrives at the same data without needing to reinvent the wheel.\n\nWe can now focus on developing a schema and set of entities that match our business needs, without risk of major refactoring when a dependent API changes.",
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"description": "One of the great achievements of GraphQL is composable, full-stack type safety: a strongly-typed schema, against which one writes client components, and from which minimal yet sufficient queries are generated. This seamless flow from database to UI, with immediate feedback, compile-time guarantees and great performance, represents an unmatched DevEx breakthrough.\n \nBut what if we use a full-stack client (like Isograph aims to be)? Or use a rich client in combination with a Hasura, Prisma or PostGraphile, and effectively write components against an SQL schema? Have both the GraphQL schema and its operation language become mere implementation details?\n \nIn this talk, I'll explore how GraphQL's apparent disappearance into tooling actually represents its ultimate victory. Even as GraphQL-the-syntax fades from view, its architectural innovations—fragment composability, full-stack type safety, document merging, persisted operations—become the invisible foundation of modern development.\n \nThe best way to honor this legacy isn't to protect its syntax — it's to let its principles be reborn in new forms, evolving as our tools evolve, making app development better for years to come.",
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"name": "LLMs + GraphQL + MCP: A Blueprint for Scalable AI Tooling - Erik Wrede, Strawberry-GraphQL & Thore Koritzius, Independent",
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"description": "Plugging an LLM into GraphQL sounds simple—until it drowns in thousands of fields, types, and connections. Most models today can’t reason effectively over large APIs without brittle prompt hacks or hardcoded shortcuts.\n\nModel Context Protocol (MCP) is the cutting-edge solution for enabling seamless, dynamic interactions between LLMs and external tooling. It standardizes the way models interact with various tools, breaking down barriers between APIs and AI systems.\n\nIn this talk, you’ll discover how to turn any GraphQL endpoint into an MCP-compatible server with minimal overhead. Reuse your existing GraphQL infrastructure to avoid reinventing authorization, schema management, and validation enabling scalable, robust LLM integrations. We’ll compare existing tools and automated schema discovery against hand-crafted mappers based on benchmarks of public GraphQL APIs. Join us to learn about our experiences and recommendations for your next GenAI project, powered by GraphQL.",
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