+ Where GraphQL pays off when AI agents talk to your API. +
+ {/* Mobile: compact stacked panels */} ++ {stat.label} +
++ {stat.explanation} +
+| + Metric + | ++ GraphQL + | ++ REST + | ++ Why + | +
|---|---|---|---|
| + {stat.label} + | ++ {stat.graphQL} + + {stat.graphQLDesc} + + | ++ {stat.rest} + + {stat.restDesc} + + | ++ {stat.explanation} + | +
+ Both protocols are typed; the gap is in traversal and discoverability, + not in whether types exist. +
+ + {/* Documentation is part of the schema, queryable via introspection */} +
+ Descriptions written with{" "}
+
+ """
+ {" "}
+ are stored on the type and every field. An agent reads them back with
+ the built-in{" "}
+
+ __type
+ {" "}
+ introspection query — no separate docs file or AGENT.md to point it
+ to.
+
+
+ + The GraphQL AI Working Group is open to everyone. Help define how + GraphQL powers the next generation of intelligent systems — + contribute to specs, share use cases, and collaborate with the + community. +
++ When AI agents need to interact with APIs, GraphQL's + self-describing schemas, strong typing, and composable queries make + it the natural choice. No hand-written tool definitions. No + token-wasting REST payloads. Just structured, predictable data. +
+ ++ Here's what happens when an AI agent uses a GraphQL API to answer + a real business question — from initial request to typed response. +
+ + ++ {step.description} +
+ + {/* Code snippet */} +
+
+ + Pick a prompt below and watch an AI agent introspect the schema, + compose a precise GraphQL query, and fetch structured results — all in + real time. +
+ ++ Select an example prompt to begin +
++ Watch the AI → GraphQL translation step by step +
+
+ {`# AI sends introspection query
+{
+ __schema {
+ queryType { name }
+ types {
+ name
+ kind
+ fields { name type { name kind } }
+ }
+ }
+}`}
+
+ + __schema → types discovered: +
++ ✓ Self-describing schema — no hand-written tool definitions required +
+
+
+
+
+
+
+ + What just happened +
++ {prompt.explanation} +
+
+
+