AI SDK Multi-Step Preview
https://github.com/vercel-labs/ai-sdk-preview-multi-steps- Category
- AI Agents
- Rank
- No. 1462Tools index
- Pricing
- Open Source
- Type
- TOOL
- Builder
- vercel-labs
- GitHub
- 118 stars
- Date
About
Vercel AI SDK preview demo for building multi-step agent workflows.
What it does
This interactive chat example presents an assistant’s intermediate reasoning as titled cards before a final response. A structured schema represents each step with content and a continue-or-finish decision. The React interface renders tool results, streams formatted text, animates new content, offers a starter prompt, and keeps the conversation scrolled to the latest update.
Why it's ranked here
The concept is easy to grasp because the schema, chat surface, and reasoning-card renderer stay narrowly focused. It also offers direct local setup and Vercel deployment. The main reservation is serious: the supplied code does not include the server handler that generates the steps, making this more useful as a presentation pattern than as a complete runnable reference.
What's good
Structured output is the strongest teaching detail. Each reasoning step has a title, content, and explicit continue-or-finish state, validated with Zod and reflected into TypeScript. Tool results become readable cards, streamed Markdown uses a dedicated renderer, and motion plus automatic scrolling keeps progressive output legible. The package also provides standard development, build, start, and lint scripts.
Tradeoffs
The interface names one specific OpenAI model and offers only one suggested prompt. Several included commerce components and data helpers are not connected to the shown chat page. Setup instructions point to a differently named repository, while the package metadata uses another internal name. Most importantly, the supplied text omits the server route behind chat requests, preventing inspection of step orchestration, error handling, and API key protection.
How to use it well
Use it when teaching or prototyping how structured intermediate results can appear inside a streamed chat interface. Replace the small reasoning schema and card presentation with fields suited to your domain. It fits frontend engineers already comfortable with Next.js and provider keys. It does not demonstrate the adjacent production work: server orchestration, retries, observability, evaluation, access control, or tests.
Technical notes+
app/(preview)/page.tsx uses useChat from ai/react, maps returned messages into the chat UI, and provides one starter action. components/message.tsx renders Markdown with Streamdown and recognizes addAReasoningStep tool results, while components/reasoning-step.tsx displays those results as cards. lib/schema.ts defines the Zod-backed ReasoningStep shape with title, content, and nextStep. package.json declares a private Next.js application with AI SDK, OpenAI provider, React, Framer Motion, Tailwind CSS, and Zod dependencies. README.md documents local installation and Vercel deployment, but references ai-sdk-preview-steps-reasoning, while package.json names the project ai-sdk-preview-roundtrips. No server route is included in the supplied repository text.
Observed
- Primary language
- TypeScript and TSX
- Packaging
- Private npm package with development, build, start, and lint scripts
- Interface
- Browser-based chat application built with Next.js and React
- AI integration
- Uses the AI SDK and its OpenAI provider package
- Structured output
- Zod schema defines titled reasoning steps with continue or final-answer states
- Deployment surface
- Documents local npm installation and a Vercel deployment button
Read from README.md, package.json, lib/schema.ts, next.config.mjs, postcss.config.mjs, tailwind.config.ts, components/data.ts, components/icons.tsx, components/orders.tsx, components/message.tsx, components/tracker.tsx, components/reasoning-step.tsx, components/use-scroll-to-bottom.ts, app/(preview)/page.tsx, app/(preview)/layout.tsx.
What it can do
Create multi-step agent workflows
Workflow definition and steps → Executable agent workflow
Chain AI agent tasks sequentially
Multiple AI tasks and dependencies → Coordinated task execution flow
Process complex requests through multiple AI steps
Complex user request → Step-by-step AI processing results
Demo AI SDK workflow capabilities
User interaction with preview interface → Visual demonstration of multi-step AI processes
Execute conditional workflow branching
Workflow conditions and branch logic → Dynamically routed workflow execution
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