GraphQLConf 2026

What if your GraphQL API could understand what developers need and generate valid operations from plain English? This talk introduces graphql-embedding, an open-source toolkit that parses GraphQL schemas into vector embeddings, stores them in a vector store, and uses a multi-agent LLM pipeline to generate validated GraphQL operations from natural language input.

The architecture is fully modular: swap vector stores between PGLite for local development and PostgreSQL for production, choose from Ollama, OpenAI, or Anthropic as LLM providers, and extend with your own. A key design decision was bundling a lightweight embedding model directly in the package, enabling local CPU inference with no external API calls, cloud dependencies, or GPU required. The entire pipeline to generate a operation works with small, efficient models like QWen 2.5 running locally via Ollama.

Everything ships as a VS Code extension called GraphQL Workbench, putting schema embedding and natural language operation generation directly in the developer's workflow. All packages, models, and the extension are fully open source under the MIT license.