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🚀 AI & Innovation
via Vercel News • 9 min read
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AI SDK 7 is a major release for building production agents in TypeScript. The SDK has grown from model calls and chat primitives into a broader agent platform for developing, running, integrating, and observing agents across text, audio, realtime, image, and video. Every major provider is supported out of the box.At a GlanceDevelop agents with reasoning control, tool and runtime context, provider files and skills support, MCP Apps, and a terminal UI.Run agents with tool approvals, durable WorkflowAgent execution, first-class timeouts, and sandbox support.Integrate any agent harness such as Codex, Claude Code, Deep Agents, OpenCode, or Pi.Observe agent behavior with redesigned telemetry, @ai-sdk/otel, Node.js tracing channel support, lifecycle callbacks, and step performance statistics.Build beyond text with stable speech/transcription APIs, richer file parts, image generation and editing, multimodal embeddings, reranking, realtime voice (experimental), and video generation (experimental).Upgrade with clearer APIs through the new migration skill, codemods, and migration paths for ESM, Node.js 22, instructions, telemetry, stream, finalStep, runtimeContext, and toolsContext.Before You UpgradeAI SDK 7 introduces two breaking requirements:Node.js 22 minimum: Node 22 is required because the SDK depends on APIs (including the native fetch implementation and improved AsyncLocalStorage semantics) that are not backported to earlier LTS lines.ESM imports required: AI SDK 7 requires ESM imports (import syntax or .mjs files). CommonJS require() is not supported. Update your package.json to include "type": "module" or migrate individual files to .mjs.Run the v7 codemods to automate most import and rename changes before manually reviewing semantic migration items. See the full v7 migration guide.Develop AgentsProvider-agnostic reasoning control: generateText and streamText now support a top-level reasoning option that maps to provider-native settings across OpenAI, Anthropic, Google, Groq, xAI, Bedrock, Fireworks, DeepSeek, Open Responses, and OpenAI-compatible providers. Note that exact behavior and available parameters vary by provider.Typed runtime context: Shared orchestration state lives in runtimeContext and flows through prepareStep, approval functions, lifecycle callbacks, telemetry, ToolLoopAgent, and WorkflowAgent.Scoped tool context: Tools can declare a contextSchema, and callers provide per-tool values through toolsContext, so third-party tools only receive the secrets or config they need.Provider file uploads: uploadFile uploads large inputs once and reuses provider references in later calls, reducing redundant uploads in repeated PDF, dataset, image, and multi-step workflows.Provider skill uploads: uploadSkill brings the same pattern to provider-managed skill environments.MCP Apps: MCP support now includes model-visible versus app-only tools, app metadata, sandboxed iframe rendering, and JSON-RPC communication for tools, resources, logs, and display updates. Add an MCP app to your agentic app.Terminal agent development: @ai-sdk/tui runs AI SDK agents in an interactive terminal UI, letting you test reasoning, tools, and markdown output before building a full app. Learn how to build your own terminal agent.Run Agents in ProductionAI SDK 7 adds the primitives agents need once they leave local demos.Tool approvals: generateText, streamText, and ToolLoopAgent can define approval policies at the call or agent level. Policies can require user approval, auto-approve, auto-deny, or delegate to typed approval functions.Hardened approval replay: Higher-risk approval flows can revalidate tool inputs and policies before continuation, use WorkflowAgent approval validation, and opt into HMAC signing. HMAC signing cryptographically binds the original tool inputs to the approval token, preventing tampering with tool arguments between the approval request and resumption.Durable execution: @ai-sdk/workflow introduces WorkflowAgent for long-running agents. Execution state is persisted to durable storage between steps, so agents survive deploys, process restarts, interruptions, and delayed approvals. To learn more, see the WorkflowAgent migration guide.Workflow-aware agent features: WorkflowAgent supports streaming, tools, approvals, typed runtime and tool context, lifecycle callbacks, stable telemetry, provider-executed approval resumption, and tool result conversion.First-class timeouts: Text generation and agent APIs can define total, per-step, per-chunk, default tool, and per-tool timeout budgets. Timeout aborts use TimeoutError, and abort reasons flow through stream and UI protocols.Sandboxed execution: The sandbox abstractions support command execution, streaming output, working directories, environment variables, abort signals, and step-level sandbox overrides.Integrate Agent HarnessesAI SDK 7 introduces a harness layer for bringing established agents into the AI SDK ecosystem. Wrap harnesses such as Claude Code, Codex, Deep Agents, OpenCode, and Pi behind the same agent interface used by the rest of the SDK.HarnessAgent: Run external agent harnesses through the AI SDK Agent interface, with standard generate and stream results.Adapters for agents: Claude Code, Codex, Deep Agents, OpenCode, and Pi harness adapters let teams plug existing agent runtimes into AI SDK apps.Configurable harness runs: Harness agents can receive sandboxes, instructions, custom skills, and tools, so the same runtime can be shaped for different products and workflows.Durable, resumable sessions: Workflow utilities, session bridging, and APIs for interrupted-turn continuation make harness runs suitable for longer tasks.Gateway-ready authentication: Harness adapters support Vercel OIDC for AI Gateway, simplifying hosted and sandboxed agent execution.Observe AgentsGlobal telemetry integrations: Register telemetry once and receive structured events across model calls, steps, tools, embeddings, reranking, and agent execution.Dedicated OpenTelemetry package: OpenTelemetry support now lives in @ai-sdk/otel, with GenAI-semantic convention spans and metrics, supplemental AI SDK attributes, and span-enrichment hooks. To learn more, see the @ai-sdk/otel docs.Node.js tracing channel: AI SDK 7 emits structured telemetry through the Node.js tracing channel, allowing observability providers to subscribe once while preserving async context across streaming and tool execution.Sensitive context controls: Runtime and tool context can be deliberately included in telemetry, with controls to prevent secrets from being exposed by default.Lifecycle callbacks: Callbacks are more consistent across core functions, agents, tools, embeddings, reranking, structured output, and UI streams. Callback payloads carry richer data for step, usage, content, file, source, warning, tool, model-call, and error events.Performance statistics: Step results expose timing and throughput metrics, including response time, total step time, tool execution time, time to first output, and output tokens per second.Build Beyond TextAI SDK 7 expands the SDK across realtime, video, speech, transcription, images, files, embeddings, and structured output.Realtime (experimental): Browser-to-provider WebSocket sessions for OpenAI, Google, and xAI, with audio/text conversations, client-driven tool calls, and normalized routing through AI Gateway.Video generation (experimental): Video generation works across AI Gateway, Google AI Studio, Google Vertex, fal, Replicate, ByteDance Seedance, Kling AI, Prodia, and xAI, with support for long-running SSE responses and safer bounded downloads.Stable speech and transcription: generateSpeech, transcribe, SpeechResult, and TranscriptionResult are stable exports.Images as files: Image parts move toward the same canonical file model as other media. Tool outputs can use a single file shape for inline data, URLs, provider references, and text-backed content.Richer media and model support: Providers add image generation, image editing, multimodal embeddings, speech, transcription, reranking, file references, reasoning files, and provider-specific media metadata.Structured output reliability: JSON Schema post-processing is stricter for Zod and Standard Schema inputs, malformed JSON extraction and repair is available for structured outputs and tool calls, and array output mode preserves transforms, coercions, defaults, and pipes.UI, Streams, and Message HandlingThe UI and streaming work in v7 focuses on making agent streams correct, composable, and reliable.Direct agent transport: DirectChatTransport can call an Agent directly from UI code.Tool approval in UI flows: UI messages support automatic approval responses and improved approval replay behavior.Framework improvements: React useChat callbacks update with current props/state; sendAutomaticallyWhen supports async conditions; Vue gains an idiomatic useChat composable; and Angular APIs are aligned with current AI SDK patterns.More reliable streams: Provider streams that end before a finish chunk are treated as errors, tool execution errors are emitted and cleaned up predictably, and streaming reasoning edge cases are handled more consistently.Provider metadata preservation: Provider metadata is retained across text generation, UI streams, tool invocations, and multi-turn provider ID mapping.Multi-step results: Top-level content, tool calls/results, files, sources, warnings, and usage now represent the full run. Final-step-only details are available through finalStep.Configure MCPThe MCP package grows from a tool transport layer into a richer integration surface for agent tools and app UIs.Protocol and metadata: MCP clients support protocol version 2025-11-25, server metadata, server instructions, ping responses, negotiated protocol headers, and public listTools().Typed tool outputs: MCP tools can expose outputSchema and structuredContent, and tool definitions can be separated from executable tools.Resource content: Tool results and prompt messages can include MCP resource_link content.App rendering: MCP Apps use tool metadata to render app-specific UI inside sandboxed iframes while keeping model-visible and app-only tools separate.Transport reliability: HTTP, SSE, and OAuth transports support custom fetch, redirect configuration, OAuth refresh deduplication, state validation, asynchronous client authentication, richer errors, and better SSE frame handling.Configure Runtime and PackagingNode.js 22 minimum: AI SDK packages require Node.js 22 or later.ESM imports required: AI SDK 7 requires ESM imports (import syntax or .mjs files). Update your package.json to include "type": "module" or migrate individual files to .mjs.Migration skill available: A dedicated migration skill is available that developers can install and ask their agent to use for AI SDK v6-to-v7 upgrades. Codemods available: The v7 codemods cover the majority of renames and cleanup migrations.Send this prompt to your AI coding agent to get started:Coming Out of ExperimentalThe following highlights cover the most impactful promotions:experimental_customProvider becomes customProviderexperimental_generateImage becomes generateImageexperimental_output becomes outputexperimental_prepareStep becomes prepareStepexperimental_telemetry becomes telemetryRenamed APIsThe following highlights cover the most impactful renames:system option becomes instructionsSystem messages inside prompt or messages require allowSystemInMessages: trueonFinish becomes onEndStreamTextResult.fullStream becomes streamCallSettings is split into model generation options and request/transport optionsDeprecated APIsThe following highlights cover the most impactful deprecations:Tool approval: needsApproval on tool() and dynamicTool() is deprecated. Move approval logic to toolApproval on generateText, streamText, or ToolLoopAgent.Stream response helpers: Result methods like result.toUIMessageStreamResponse() and result.toTextStreamResponse() are deprecated. Use top-level helpers like createUIMessageStreamResponse and createTextStreamResponse.Vue chat: The Vue Chat class is deprecated. Use the useChat composable instead.Other Migration ThemesReasoning configuration is centralized: The top-level reasoning option replaces overlapping provider-specific reasoning settings unless a provider-specific override is intentional.OpenTelemetry moved to @ai-sdk/otel: OpenTelemetry span collection is no longer built into the ai package. Telemetry is registered globally, and custom tracers move into the OpenTelemetry constructor.Request and response body retention is opt-in: Text generation results exclude request and response bodies by default.Multi-step results now represent the full run: Top-level usage, content, tool calls/results, files, sources, and warnings accumulate across all steps; final-step-only data lives under finalStep.Message parts are more canonical: Legacy media and image-specific parts move toward file parts with media types.Package-specific behavior changed: MCP HTTP/SSE redirects are treated as errors, OpenAI Responses reasoning summaries default to detailed, and Anthropic cache creation token metadata moves into standard usage fields.Upgrade PathFollow these steps to migrate an existing project to AI SDK 7:Update Node.js to 22+: Verify your runtime and CI environments meet the minimum requirement before upgrading packages.Update packages: Bump ai and all @ai-sdk/* packages to their v7 releases in your package.json.Run the v7 codemods: The codemods automate the majority of renames, import changes, and API moves. Review the diff before committing.Migrate OpenTelemetry: If you use tracing, move your setup to @ai-sdk/otel and register telemetry globally. Refer to the @ai-sdk/otel docs and the OpenTelemetry span schema for attribute details.Review semantic changes manually: Codemods cannot fully decide runtime requirements, ESM imports, instruction/message behavior, runtime/tool context separation, approval policy placement, stream helper usage, and multi-step result shapes. See the full v7 migration guide.For a guided migration, install the AI SDK v7 migration skill with npx skills add vercel/ai --skill migrate-ai-sdk-v6-to-v7. Read more
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💡 WHY THIS MATTERS FOR YOUR ROLE
TypeScript teams need a stable agent platform layer as they move AI features from demos to production systems.
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