Vercel AI SDK v7 operator

Look up Vercel AI SDK installation, inspection, core calls, operational limits, and focused patterns across generation, streaming ui, structured output, tools and agents, providers and middleware. Use when the user wants a concise reference instead of a guided workflow.

Inspect Vercel AI SDK package versions, runtime and framework context, configuration, environment variable names, adapters or providers, persistence, scripts, and safe local checks without changing anything. Use before Vercel AI SDK work, when the target is uncertain, or when the user asks what is configured.

Operate AI SDK text, image, video, speech, transcription, and realtime generation with explicit models, inputs, abort signals, usage accounting, stop conditions, and error handling. Use when adding a model call or controlling modality, latency, cost, streaming, and cancellation.

Connect Vercel AI SDK to application runtimes, persistence, authentication, observability, CI, deployment, and adjacent external services while preserving credential, data, and action boundaries. Use when Vercel AI SDK must cross another system or operate consistently from local development through production.

Configure AI SDK providers, model registries, provider options, middleware, fallbacks, embeddings, telemetry, and shared model policy. Use when adding or switching providers, wrapping models, routing by task, enabling telemetry, or standardizing model configuration.

Install and configure Vercel AI SDK from zero with project-local packages, explicit runtime and external targets, server-only credentials, baseline policy, one minimal example, and bounded verification. Use when a project has no Vercel AI SDK wiring or the user explicitly asks to initialize it.

Build AI SDK streaming user interfaces with UI messages, useChat, server stream responses, data parts, tool states, cancellation, reconnection, and persisted history. Use when creating chat UI, fixing stream parsing, restoring messages, or rendering tool and error states.

Generate and stream structured AI SDK outputs with explicit schemas, descriptions, validation, partial object handling, error recovery, and compatibility checks. Use when a model must return typed data, object generation fails validation, or a schema must evolve.

Design and operate AI SDK 7 tools and agents with runtime context, input schemas, execution boundaries, result contracts, WorkflowAgent durability, timeouts, approvals, and observability. Use when adding model-callable tools, building an agent, requiring human approval, or preventing runaway loops.

Diagnose Vercel AI SDK failures involving versions, configuration, credentials, runtime boundaries, streaming or events, persistence, external APIs, rate limits, costs, deployments, and recovery without masking the original error. Use when Vercel AI SDK fails, behaves differently across environments, or causes unexpected external effects.