Mastra positions itself as 'eval-first' versus Vercel AI SDK's intentional thinness. Translate that marketing line into the concrete primitives Mastra ships that Vercel AI SDK leaves to you, and say which kind of team should pick which.
Free · 2 AI evals / day
**Vercel AI SDK** is intentionally thin. It ships `streamText`, `generateText`, `generateObject`, and the React hooks (`useChat`, `useCompletion`, `experimental_useObject`). Anything beyond those, evals, traces, workflow orchestration, multi-step state, is the application's job. The SDK does not have an opinion about how you do evaluation; you wire in Langfuse, Phoenix, Helicone, or your own harness.
**Mastra** takes the opposite stance. It bundles **workflows** (typed DAGs of named steps you can inspect and re-run), **agents** with eval-runner integrations, **tools** as a first-class concept, **evals** as part of the framework (not a separate library you wire in), and **OpenTelemetry tracing** that lights up the Mastra playground automatically. "Eval-first" means Mastra ships an evaluation loop in the box rather than treating it as someone else's concern.
**Both are TypeScript-first.** The axis is not language; it's opinionation. Vercel says: here are sharp primitives, you assemble. Mastra says: here is an opinionated frame, plug in your model and prompts.
**When to pick which.** Pick **Vercel AI SDK** if the team wants minimum lock-in, will assemble their own evaluation and observability, and primarily needs streaming UX for a Next.js app. Pick **Mastra** if the team wants opinions baked in, workflow structure, eval gates, tracing, and is willing to take on a heavier framework.
**Decoded.** Mastra's "eval-first" claim maps to four concrete primitives bundled into the framework that Vercel AI SDK leaves to you.
1. **Workflows.** A Mastra workflow is a typed DAG of named steps with input/output schemas, inspection hooks, and a runtime that threads data through. Vercel AI SDK has no workflow concept; multi-step composition is plain TypeScript glue.
2. **Agents with eval-runner integration.** A Mastra `Agent` is a class with tools and a built-in `.eval()` path. Vercel AI SDK has `streamText` with tools but no agent class and no eval hooks.
3. **Evals as a framework primitive.** Mastra ships eval runners (LLM-judge, deterministic checks, custom metrics) wired into workflow and agent lifecycles. Vercel AI SDK does no evaluation; wire in Phoenix, Ragas, or roll your own.
4. **OTel tracing into a playground.** Mastra emits structured spans by default; the playground UI consumes them. Vercel's `experimental_telemetry` is opt-in and unopinionated about the consumer.
**Vercel AI SDK's surface.** `streamText`, `generateText`, `generateObject`, React hooks (`useChat`, `useCompletion`, `experimental_useObject`), tools via Zod. Everything else is your code.
**Both are TypeScript-first.** The axis is opinionation, not language.
**When to pick which.** Pick **Mastra** when the team wants opinions, evaluation matters from day one, and the workflow shape is non-trivial. Pick **Vercel AI SDK** when the team wants thin primitives, plans to assemble eval/obs from best of breed pieces (Langfuse, Phoenix), and the product is a Next.js chat UI where the React hooks earn their keep.
Quick reference
TL;DR
Mastra ships workflows, agents, evals, and OTel tracing as first-class primitives; Vercel AI SDK ships thin generate/stream primitives and React hooks. Both TypeScript-first; the axis is opinionation.
Picture two TypeScript bakeries. Vercel AI SDK is the one that sells you flour, eggs, and a great oven, and trusts you to know how to bake. Mastra sells you a kitchen: opinionated recipes, a built-in scale that weighs each step, a thermometer wired into the oven, and a tasting harness that scores every loaf. If you already have your own recipe and tools and you want maximum control, the bakery-supply store fits. If you want the kitchen turnkey so you can spend your time on what to bake rather than how to measure it, the kitchen fits.
Everything you need to truly understand this topic: intuition, mechanics, step by step explanation, code, formulas, and worked example. Click to expand.
Everything you need to truly understand this topic: intuition, mechanics, step by step explanation, code, formulas, and worked example.
Mastra: a single-screen Next.js chat where the only real need is streaming UX and a React hook. The workflow/agent/eval machinery is overhead for a one-call surface. Vercel AI SDK is shorter and fits the deployment target better.
Vercel AI SDK: a multi-step pipeline that needs typed orchestration, durable state, and eval gating in CI. You'll end up writing a workflow runner, an eval harness, and tracing config by hand. Mastra bundles the stack, taking on the lock-in is cheaper than reimplementing it.
Either one: a team standardized on Python (DSPy, LlamaIndex, FastAPI). Both Mastra and Vercel AI SDK are TypeScript-first. Forcing them into a Python stack adds polyglot complexity for no real win; pick a Python-native framework instead.
Quick scan · 2–4 min
Everything important, quickly.
Decode the eval-first claim into four concrete Mastra primitives (workflows, agents, evals, tracing), contrast with Vercel AI SDK's thin surface (`streamText`, `generateText`, `generateObject`, React hooks), note both are TypeScript-first, articulate the opinionation axis, give a production fit for each, and close with the pair-them scenario.
Real-world examples
Real products, models, and research that use this idea.
1Vercel's own chat templates (Next.js AI Chatbot) use Vercel AI SDK with `useChat` and a Postgres-backed message store, observability is added by the team via Langfuse or LangSmith.
2Mastra's example apps demonstrate a research-agent workflow with eval gates that fail the workflow if the judge metric drops below a threshold.
3Production TS shops commonly pair Vercel AI SDK in the Next.js layer with a backend evaluation harness (Phoenix or a homemade Ragas equivalent), reproducing parts of Mastra's bundled approach by hand.
4Cursor and Vercel both use thin streaming primitives over their respective UIs; their internal evaluation harnesses are built separately rather than bundled into the UI SDK.
Interviewer follow-ups
What an interviewer would ask next. Try answering before peeking at the approach.
QSketch how you'd reproduce Mastra's eval-first loop on top of Vercel AI SDK.
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QWhen does it make sense to use both Vercel AI SDK and Mastra in the same product?
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QWhat lock-in risk does Mastra carry that Vercel AI SDK does not?
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Don't say this
Red flags and common mistakes that signal junior thinking. Click to expand.
Most common mistake
Calling one framework "better" than the other. They sit at different points on the opinionation axis; the right pick depends on whether the team wants assembly or thin primitives.