Mastra AI vs Vercel Eve: which TypeScript agent framework to use

If you need an ai agent framework in TypeScript in 2026, two names dominate architecture reviews: Mastra AI and Vercel Eve.

They overlap on tools, models, sessions, and observability — but they encode different bets. Mastra AI is a batteries-included TypeScript agent framework you compose in code and run on any Node-compatible host. The Eve agent framework is filesystem-first: it compiles an agent/ directory into an app, with the deepest leverage on Vercel Workflows, Sandbox, and AI Gateway.

This post is a practical Mastra AI vs Vercel Eve decision guide for senior engineers — close to documented primitives, not hype. (If you are comparing ecosystems more broadly, teams often also ask Mastra vs LangChain; that is a different axis — Python-heavy orchestration vs TypeScript product agents — covered briefly at the end.)

TL;DR: Mastra AI or Eve agent framework?

• Prefer Mastra AI when… — Prefer Eve when…
• The agent is a product backend you must keep cloud-portable — You are Vercel-native and want agents as platform primitives
• You need explicit graph workflows (.then / .branch / .parallel) with typed I/O — You want durable sessions that survive cold starts/deploys via Workflows
• You want memory + RAG + evals + Studio in one TypeScript story — You want directory conventions (instructions.md, tools/, skills/, channels/)
• You embed agents inside an existing Node/Next service you own — You want sandbox-isolated model-driven bash/file work on Vercel Sandbox
• Maturity matters more than beta DX — Fastest path scaffold → Slack/HTTP on Vercel matters more than portability

Both are TypeScript. Both use Zod-shaped tools. Neither replaces eval/ops discipline.

What Mastra AI is

Mastra AI is an open-source TypeScript framework for AI applications and agents: agents, workflows, memory, workspaces, and observability in one modular stack. Embed in React/Next/Node or deploy a standalone server. Core is Apache 2.0.

Canonical agent shape (from docs):

import { Agent } from '@mastra/core/agent'
import { createTool } from '@mastra/core/tools'
import { z } from 'zod'

export const weatherTool = createTool({
id: 'get-weather',
description: 'Get current weather for a location',
inputSchema: z.object({ location: z.string() }),
execute: async ({ location }) => {
return { location, temperatureCelsius: 21, conditions: 'sunny' }
},
})

export const weatherAgent = new Agent({
id: 'weather-agent',
name: 'Weather Agent',
instructions: 'You are a helpful weather assistant.',
model: 'openai/gpt-5.6-sol', // provider/model string
tools: { weatherTool },
})

Register into a Mastra instance, then generate() / stream(). Docs are explicit: use agents for open-ended work; use workflows when steps are known and you want structured control flow. That split is why Mastra AI works well as a TypeScript AI agent framework inside product codebases.

What the Eve agent framework is (Vercel)

Eve is an open-source, filesystem-first framework for durable backend agents. Official Vercel docs mark it beta. You author under agent/; Eve discovers files, validates them, compiles a manifest, and serves a runnable app — locally, on Vercel, or on a long-running Node host.

agent/
instructions.md # always-on system prompt
agent.ts # defineAgent({ model })
tools/*.ts # one tool per file; filename = tool name
skills/* # on-demand procedures
subagents/* # delegated child agents
channels/* # HTTP, Slack, …
connections/* # typed integrations
sandbox/* # isolated compute
instrumentation.ts # optional OTel

import { defineAgent } from 'eve'

export default defineAgent({
model: 'openai/gpt-5.4-mini',
})

On Vercel, model strings resolve through AI Gateway. Sessions are durable on Vercel Workflows; sandboxed execution can use Vercel Sandbox. Dashboard Agent Runs shows sessions, turns, tools, timing, tokens. The public repo is often referenced as vercel/eve on GitHub.

Authoring model: Mastra AI code vs Eve filesystem

This is the sharpest split between Mastra AI and the Eve agent framework.

Mastra AI expects you to write a TypeScript application. Agents, tools, and workflows are modules you import, register, and call — familiar if you already ship services.

Eve expects you to grow a directory. Adding a tool is adding agent/tools/get_weather.ts. Skills stay out of the always-on prompt until loaded. If your team likes Next.js file conventions, this transfers cleanly.

Reviewer questions:

• Mastra AI: “Where is the workflow graph and how do we type the step contracts?”
• Eve: “What is the agent’s surface area on disk, and which channel owns ingress?”

Orchestration: Mastra AI workflows vs Eve durable sessions

Mastra AI workflows

Graph-shaped and developer-owned: createStep + createWorkflow, then .then(), with branching/parallel primitives. Suspend/resume, streaming, shared state, agents callable from steps. Studio visualises the graph.

Use when the business process is mostly known: intake → enrich → approve → write-back.

Eve durable sessions

Primary durability unit is the session: started by a channel or HTTP request, persisted via Workflows through cold starts, redeploys, and long pauses. Clients reattach with a session id.

Use when the unit of work is a long-lived agent interaction and the platform should own resume semantics.

Review heuristic: process death mid-task on Vercel → Eve sessions. Deterministic multi-step business logic with human gates → Mastra AI workflows (optionally plus Inngest/Temporal later).

Tools, skills, subagents

• Concern — Mastra AI — Eve agent framework
• Tool definition — createTool + Zod; register on Agent — defineTool per file under tools/; filename = name
• Skills / procedures — Workspaces / filesystem skills — First-class skills/ loaded on demand
• Subagents / multi-agent — Supervisor patterns, agent networks — subagents/ with fresh history; built-in agent tool
• Channels — Slack/Discord/Telegram paths documented — channels/ as ingress (HTTP, Slack, …)

Eve pushes harder on skills vs always-on instructions (context budget). Mastra AI pushes harder on workflows as a separate primitive from the agent loop.

Memory, RAG, evals, observability

Mastra AI ships a full memory stack (recent messages, semantic recall, observational memory), RAG primitives, scorers/evals, traces/metrics, and local Studio — the “ship a product agent without five libraries” pitch.

Eve ships strong run observability (Agent Runs; optional OTel) and eval suites in the launch story, but memory/RAG are less of a single opinionated product layer. Durability and sandboxing are the headline infrastructure.

Sandbox and deployment (Mastra AI vs Vercel Eve)

Eve treats sandbox as first-class (sandbox/; Vercel Sandbox microVMs). Mastra AI’s classic default is tools in your process unless you build isolation — though workspace/filesystem features are expanding.

• Mastra AI: Node anywhere; optional Vercel/Netlify/Cloudflare deployers; standalone Hono-style server. Portability is a feature.
• Eve: Runs locally and can self-host, but production value peaks on Vercel. Beta terms apply.

If you might leave Vercel in 18 months, Mastra AI is the safer default. If you are all-in on Vercel Functions + Workflows, Eve is coherent.

Where Mastra vs LangChain fits

Search interest in Mastra vs LangChain is real, but it is usually the wrong primary question for a TypeScript product team. LangChain/LangGraph dominate Python-centric orchestration; Mastra AI is purpose-built as a TypeScript agent framework with workflows, memory, and Studio in-process. Eve competes with Mastra AI on the TypeScript/Vercel axis, not as a LangChain replacement. Pick the language and deploy gravity first; then pick the framework.

Architecture review checklist

1. Control flow known? → Mastra AI workflow. Durable conversation/job? → Eve session (or Mastra AI + external durability).
2. Must run off Vercel unchanged? → Mastra AI. Vercel as runtime of record? → Eve is in play.
3. Need memory/RAG/evals packaged? → Mastra AI.
4. Model needs isolated shell/filesystem day one? → Eve.

Prototype the hard path — not a weather demo.

Same weather tool, two shapes

Mastra AI — module passed into the agent (see earlier).

Eve — file the runtime discovers:

// agent/tools/get_weather.ts
import { defineTool } from 'eve/tools'
import { z } from 'zod'

export default defineTool({
description: 'Get the current weather for a city.',
inputSchema: z.object({ city: z.string() }),
async execute(input) {
return { city: input.city, condition: 'Sunny', temperatureF: 72 }
},
})

Same Zod instinct. Different packaging — and that packaging scales into PR review and onboarding.

When not to use either

• Chat UI + tool calling in a Next route → Vercel AI SDK / provider SDKs first.
• Python-first research stacks → LangGraph/Crew-class may fit better than forcing TypeScript.
• Need a vendor to own a fixed deliverable → wrong layer; frameworks are not delivery ownership.

How Devspace applies Mastra AI vs Eve

We treat framework choice as an architecture constraint:

• Greenfield TypeScript product agents with portability → Mastra AI is usually the default.
• Internal ops agents on an existing Vercel footprint → Eve agent framework is a strong candidate (eyes open on beta churn).
• Either way: senior engineers embed in your repo, enforce eval gates, keep tool blast radius explicit — AI consulting · AI-enabled engineers.

Bottom line

Mastra AI is the TypeScript agent application framework: code-first agents, first-class workflows, memory/RAG/evals, deploy-anywhere Node.

Eve is the TypeScript platform convention for an eve agent framework: filesystem-first authoring, durable sessions, sandbox + channels, deepest on Vercel, still beta.

Pick the gravity you will inherit — then invest in tool permissions, evals, and ownership before the demo looks too good.