<!-- StarterPick AI-readable guide source -->
<!-- Canonical: https://starterpick.com/guides/mastra-vs-vercel-ai-sdk-vs-langgraph-js-agent-boilerplate-2026 -->
<!-- Raw Markdown: https://starterpick.com/guides/mastra-vs-vercel-ai-sdk-vs-langgraph-js-agent-boilerplate-2026/raw.md -->
<!-- Source path: content/guides/mastra-vs-vercel-ai-sdk-vs-langgraph-js-agent-boilerplate-2026.mdx -->

---
title: "Mastra vs Vercel AI SDK vs LangGraph.js: AI Agent SaaS Boilerplate Stack 2026"
description: "Mastra, Vercel AI SDK, and LangGraph.js compared for AI agent SaaS boilerplates: programming model, observability, deployment, and use cases."
date: "2026-04-26"
tier: 2
authors: ["team"]
tags: ["ai-agents", "mastra", "vercel-ai-sdk", "langgraph", "saas-boilerplate", "2026"]
---

## Quick Verdict

For an AI-native SaaS in 2026:

- **Vercel AI SDK** — fastest path to "send messages to an LLM with tool use and streaming UI." If your AI feature is a chat or single-step agent, this is enough.
- **Mastra** — opinionated TypeScript agent framework with workflows, memory, evals, and a dev UI. The 2026 default for "I'm building a real agent product, not a chatbot."
- **LangGraph.js** — graph-based stateful agents with the most expressive control flow. Best when your agent has explicit branching, retries, and human-in-the-loop steps.

If unsure, start on Vercel AI SDK and graduate to Mastra when you need workflows, memory, and evals. Reach for LangGraph when the agent's control flow is genuinely a graph.

## Key Takeaways

- All three are TypeScript-first and run on Node, Bun, and Edge runtimes.
- The trade is **simplicity (Vercel AI SDK)** vs **opinionation (Mastra)** vs **expressiveness (LangGraph.js)**.
- Most SaaS boilerplates with AI features bundle the Vercel AI SDK. Mastra and LangGraph adoption is growing among AI-first products.

## Decision Table

| Product | Pick |
|---------|------|
| Customer-facing chatbot with tool use | **Vercel AI SDK** |
| Multi-step research agent that searches, summarizes, writes | **Mastra** |
| Agent with explicit human approval gates and retry policies | **LangGraph.js** |
| AI feature inside an existing CRUD SaaS | **Vercel AI SDK** |
| New AI-native SaaS (verticalized agent) | **Mastra** |
| Workflow with parallel tool calls and conditional edges | **LangGraph.js** |

## What an Agent Framework Buys You

A "naive" AI feature is `streamText({ model, messages })`. You quickly outgrow it:

- **Memory** — the agent should remember a user across sessions.
- **Tools** — the agent should call your APIs (search, database, third-party).
- **Workflow** — multi-step plans with branching and retries.
- **Evals** — repeatable tests that catch regressions when you change the prompt or model.
- **Observability** — traces of every step, every prompt, every tool result.

Vercel AI SDK gives you tools and streaming. Mastra adds workflows, memory, evals, and a dev UI. LangGraph adds first-class graph-shaped control flow.

## Vercel AI SDK

**Pricing:** Free, MIT.

**Fit:** Any chat or single-step agent feature inside a Next.js, SvelteKit, Nuxt, or Astro boilerplate. Pairs naturally with the [Vercel AI Gateway](/guides/vercel-ai-gateway-vs-openrouter-vs-litellm-vs-portkey-saas-2026).

```ts
import { streamText, tool } from 'ai';
import { gateway } from '@ai-sdk/gateway';
import { z } from 'zod';

const result = await streamText({
  model: gateway('anthropic/claude-sonnet-4-6'),
  messages,
  tools: {
    searchProducts: tool({
      description: 'Search the product catalog',
      parameters: z.object({ query: z.string() }),
      execute: async ({ query }) => db.products.search(query),
    }),
  },
  maxSteps: 5,
});
return result.toDataStreamResponse();
```

What you get:

- `streamText`, `generateText`, `streamObject`, `generateObject` covering 90% of API patterns.
- Tool use with Zod schemas — typed end to end.
- Automatic UI streaming (`useChat`, `useCompletion`) for React/Vue/Svelte.
- Multi-step tool loops with `maxSteps`.
- Provider abstraction — swap Anthropic, OpenAI, Google, Mistral with one line.

Where it bites:

- No first-party memory or persistence — you wire it.
- No first-party workflow / branching — you write `if` statements.
- No first-party evals.

## Mastra

**Pricing:** Free, Apache 2.0. Mastra Cloud is the optional managed plane.

**Fit:** Anything past a single chat surface. Multi-agent products. Apps where you'll add memory, RAG, evals, and workflows over time.

```ts
import { Mastra } from '@mastra/core';
import { createAgent } from '@mastra/core/agent';

const supportAgent = createAgent({
  name: 'support',
  instructions: 'Help customers with billing and product questions.',
  model: openai('gpt-4o'),
  tools: { lookupCustomer, refund },
  memory: new Memory({ storage: postgresStorage }),
});

const mastra = new Mastra({ agents: { supportAgent } });
```

What you get:

- Agents with first-class **memory** (vector + key-value).
- **Workflows** — typed step graphs with retries, branching, parallel execution.
- **Evals** — answer relevance, hallucination, faithfulness baked in.
- **RAG** primitives — chunkers, embedders, retrievers, rerankers.
- A **local dev UI** for tracing every agent run, viewing memory, replaying conversations.
- Built on top of the Vercel AI SDK — no rewrite of provider code.

Where it bites:

- Newer ecosystem; fewer Stack Overflow answers than LangChain.
- Conventions to learn — agents, workflows, tools, memory have specific shapes.
- Smaller battle-test surface than LangChain in 2026.

## LangGraph.js

**Pricing:** Free, MIT. LangSmith (observability) and LangGraph Cloud are the paid managed pieces.

**Fit:** Agents whose flow is genuinely a graph: research → critique → revise loops, human-in-the-loop approvals, complex retry / fallback policies.

```ts
import { StateGraph } from '@langchain/langgraph';

const graph = new StateGraph<{ query: string; results: string[] }>()
  .addNode('search', async (s) => ({ ...s, results: await search(s.query) }))
  .addNode('summarize', async (s) => ({ ...s, summary: await llm.summarize(s.results) }))
  .addNode('critique', async (s) => ({ ...s, ok: await llm.critique(s.summary) }))
  .addEdge('search', 'summarize')
  .addEdge('summarize', 'critique')
  .addConditionalEdges('critique', s => s.ok ? '__end__' : 'summarize')
  .compile();
```

What you get:

- Explicit graph control flow — nodes, edges, conditional edges, cycles.
- Persistent state (checkpointer) — pause and resume runs across sessions.
- Human-in-the-loop interruption built in.
- Multi-agent supervisor patterns documented and supported.
- Tight integration with LangSmith for tracing.

Where it bites:

- Steepest learning curve of the three.
- Heavier code surface for simple agents — overkill if your "agent" is a single LLM call with one tool.
- LangChain.js dependency surface is famously broad (improving in 2026).

## Programming Model Comparison

| Concept | Vercel AI SDK | Mastra | LangGraph.js |
|---------|---------------|--------|--------------|
| Unit | Generation call | Agent / Workflow | Graph node |
| Memory | DIY | First-class (vector + KV) | Checkpointer + DIY |
| Tools | `tool({...})` | `tool({...})` | `Tool` class |
| Multi-step | `maxSteps` | Workflow steps | Graph edges |
| Evals | DIY | First-class | DIY (LangSmith add-on) |
| Streaming UI | `useChat`, `useCompletion` | Stream from agent | `streamEvents` |
| Local dev UI | None | Mastra Studio | LangGraph Studio |
| Hosted ops | Vercel | Mastra Cloud | LangGraph Cloud |

## What Boilerplates Bundle

| Boilerplate | AI Layer |
|-------------|----------|
| Vercel AI Chatbot template | Vercel AI SDK |
| Open SaaS (Wasp) | Vercel AI SDK |
| Mastra Starter | Mastra |
| LangGraph.js Starter | LangGraph + LangSmith |
| Most premium SaaS boilerplates (ShipFast, Makerkit) | Vercel AI SDK |
| AI agent kits | Mastra or LangGraph |

For a deeper look at the AI-feature-bundling boilerplates, see [the best AI SaaS boilerplates ranking](/guides/best-ai-saas-boilerplates-build-ai-products-2026).

## Cost Profile

All three frameworks are free; the cost is your model spend. The framework choice does affect cost indirectly:

- **Vercel AI SDK** — minimal overhead. You pay for the tokens you send.
- **Mastra** — adds memory and embeddings; vector storage cost is real for high-volume agents.
- **LangGraph** — checkpointing adds storage cost; nodes can fan out and double-spend if you don't budget step counts.

A well-instrumented agent always costs more than the same logic in a single LLM call — that's the price of being correct, retryable, and inspectable. Pair with [an AI gateway](/guides/vercel-ai-gateway-vs-openrouter-vs-litellm-vs-portkey-saas-2026) for cost attribution and caching.

## Choosing in 60 Seconds

- **One chat surface, no memory needed** → Vercel AI SDK.
- **Multi-agent product, want memory and evals** → Mastra.
- **Complex graph control flow, human approvals** → LangGraph.js.
- **Greenfield AI-native SaaS** → Mastra unless you have a specific reason to want LangGraph's graph semantics.

## What This Replaces

If you've been writing custom agent loops with `while (toolCalls.length)` over OpenAI's responses, all three replace that boilerplate. Mastra and LangGraph also replace your homegrown memory store, your homegrown evals, and your homegrown workflow engine — see [durable workflow comparison](/guides/dbos-vs-trigger-dev-vs-inngest-durable-workflows-saas-2026) for when Mastra workflows are enough and when you need a separate durable execution layer for the non-AI parts.

## FAQ

**Can I mix them?** Yes. A common pattern: Mastra for the agent, Vercel AI SDK's `streamText` inside Mastra tools for ad-hoc generation, and LangGraph for one specific complex workflow.

**Do they support local models?** All three. Vercel AI SDK via OpenAI-compatible providers; Mastra via the same; LangGraph via Ollama and provider integrations.

**Edge runtime?** Vercel AI SDK is the most edge-friendly. Mastra runs on Edge for many features but memory storage usually pins to Node. LangGraph.js depends on the checkpointer.

**Do I need LangSmith?** Useful for LangGraph users; not required. Mastra has its own dev UI; Vercel AI SDK has provider-side dashboards plus your gateway's observability.

---

*If you're starting from a generic SaaS foundation rather than an AI-native one, the [best AI SaaS boilerplates with Claude/OpenAI integration](/guides/best-ai-saas-boilerplates-claude-openai-integration-2026) covers what to bolt these onto.*