Mastra in 2026: TypeScript-First Agent Workflows for Full-Stack Developers

By Deepak Bagada — AI Architect & Developer

By Deepak Bagada, CEO at SaaSNext & Principal AI Architect.

For two years, production AI development had a language problem: the best agent frameworks spoke Python, while most product teams lived in TypeScript. The result was an awkward two-stack architecture — Python services for 'the AI part,' Node for everything else — with context, state, and bugs leaking across the boundary. Mastra is the framework that finally takes the other side: TypeScript-native agents, workflows, RAG, and evaluation built for teams whose entire stack is Node.js and Next.js.

This guide covers Mastra's core primitives — agents, workflows, vector stores, and evals — with production code you can run today, and where it fits next to MCP for tool access.

The Architecture: Full-Stack Agent App

+---------------------------+
| Next.js Frontend          |
| chat UI / admin panels    |
+-------------+-------------+
              |
              v
+-------------+-------------+
| Mastra Runtime (Node)     |
| +------------------------+|
| | Agents (typed tools)   ||
| | Workflows (DAG steps)  ||
| | Vector stores (RAG)    ||
| | Eval harness (CI)      ||
| +------------------------+|
+-------------+-------------+
              |
      +-------+--------+
      |                |
      v                v
+-----+-----+    +-----+-----+
| MCP tools |    | Vector DB |
| (via MCP  |    | (built-in |
| client)   |    |  or PG)   |
+-----------+    +-----------+

Prerequisites and Setup

npm create mastra@latest my-app
cd my-app
npm i @mastra/core @mastra/pg

For the broader MCP ecosystem, see the Daily AI World MCP Directory.

1. Define an Agent (src/agents/support.ts)

import { Agent } from "@mastra/core";

export const supportAgent = new Agent({
  name: "support-agent",
  instructions: "You are a support engineer. Answer from the knowledge base, cite sources.",
  model: {
    provider: "OPEN_AI",
    name: "gpt-4o",
    apiKey: process.env.OPENAI_API_KEY!,
  },
  tools: {
    searchKnowledgeBase: {
      description: "Search internal docs for an answer",
      inputSchema: { query: "string" },
      execute: async ({ query }) => searchDocs(query),
    },
  },
});

2. Compose a Workflow (src/workflows/ticket.ts)

import { Workflow } from "@mastra/core";
import { z } from "zod";

const ticketWorkflow = new Workflow({
  name: "support-ticket",
  triggerSchema: z.object({ message: z.string() }),
});

ticketWorkflow
  .step("classify")
  .input(z.object({ message: z.string() }))
  .handler(async ({ context }) => classify(context.message))
  .step("resolve")
  .after("classify")
  .handler(async ({ context }) => {
    const { severity } = context.classify.output;
    if (severity === "high") return { escalated: true };
    return await supportAgent.generate(`Resolve: ${context.trigger.message}`);
  });

3. RAG with a Built-in Vector Store (src/rag.ts)

import { PgVector } from "@mastra/pg";
import { embed, query } from "@mastra/rag";

const vectorStore = new PgVector(process.env.DATABASE_URL!);
await vectorStore.createIndex("kb", 1536);

// Ingest
const chunks = chunkMarkdown(await loadDocs());
await vectorStore.upsert("kb", chunks.map((c, i) => ({
  id: `doc-${i}`, vector: await embed(c), metadata: { text: c },
})));

// Retrieve
const hits = await query(vectorStore, "kb", await embed(userQuestion), { topK: 5 });

4. Evaluation Harness in CI (src/evals/answer.eval.ts)

import { evalWith } from "@mastra/core";

const result = await evalWith({
  name: "answer-accuracy",
  metric: "llm-judge",
  case: {
    input: "How do I reset my password?",
    expected: "Explain the reset flow and link to /account/security",
  },
  run: () => supportAgent.generate("How do I reset my password?"),
});
console.log(`Score: ${result.score}`); // Gate deploys on >= 0.9

Connecting MCP Tools

Mastra agents can consume MCP servers directly, so your TypeScript app gets the same enterprise tools — Slack, Jira, internal APIs — that Python teams use:

import { McpClient } from "@mastra/mcp";

const mcp = await McpClient.connect({ url: "https://kong.example.com/mcp" });
supportAgent.addTool(await mcp.tool("orders_get"));

See gateway patterns for exposing REST APIs as MCP in the Daily AI World MCP Directory.

When Mastra Wins (and When It Doesn't)

Wins: full-stack product teams in TypeScript, apps embedding agents in Next.js, teams that want one language across frontend/backend/evals, and projects needing quick RAG with Postgres.

Consider Python alternatives: deep research agents needing the scientific/ML ecosystem, teams standardized on LangGraph/Temporal in Python, or jobs requiring heavy custom model training integration.

Production Checklist

1. Pin model versions in agent definitions for reproducible behavior.
2. Wire evals into CI — score every prompt change before merge.
3. Use MCP for external tools, keep internal tools typed and local.
4. Vectorize with production Postgres from day one, not an in-memory demo store.
5. Instrument with OpenTelemetry — Mastra emits spans for steps and tool calls.

ROI Math

For a 12-person TypeScript team, removing the dual-stack (Python + Node) architecture eliminates roughly $80K/year in glue-code maintenance and cross-stack debugging, while eval-in-CI prevents the recurring 'prompt change broke production' class of incidents. The framework is open source — the cost is adoption, not licensing.

Explore more agent frameworks and patterns on the Daily AI World Workflows hub and keep up with framework news on the AI news feed.

Frequently Asked Questions

Is Mastra production-ready? In 2026, Mastra is at a stable 1.x with a growing community, MCP support, and deployment guides for Vercel, Docker, and Node clusters — solid for production TypeScript AI apps.

Does Mastra lock me into Next.js? No — it runs anywhere Node runs; Next.js is just a natural fit for its frontend-first philosophy.

Can I self-host the vector store? Yes — Postgres (pgvector) and other adapters run fully self-hosted, keeping data in your control.

Final Summary & Key Takeaways

• Mastra brings production agents to the TypeScript/Next.js stack.
• Workflows, RAG, and evals live in one runtime.
• MCP integration gives TypeScript teams enterprise tool access.

Go deeper with our AI Workflows library and MCP tools.

Deploying Mastra to Production

Mastra apps deploy like any Node service: a Docker image with the agent runtime, a Postgres instance for vector storage and workflow state, and a process manager or orchestrator for workers. The framework's Next.js integration means you can serve the chat UI and agent runtime from the same deployment, with serverless functions for stateless request handling and long-running workers for evals and scheduled jobs. Configure structured logging and OpenTelemetry tracing at startup, and pin the framework version in CI just as you pin model versions.

Community & Ecosystem Snapshot

In 2026 Mastra's ecosystem is maturing fast: first-party integrations for Postgres, Redis, and major vector stores; MCP support out of the box; and a growing registry of prebuilt tools and evaluators. The framework's TypeScript-first stance attracts full-stack teams that previously bolted Python AI services onto Node apps, and the community's emphasis on evaluation-in-CI reflects the industry's shift toward reliability engineering for agents. If your team already speaks TypeScript, the learning curve is measured in days, not weeks.

Frequently Asked Questions

Is Mastra suitable for high-throughput production traffic? Yes — stateless request handling scales horizontally, while Postgres-backed state and vector stores handle concurrency with standard connection pooling.

Can I use Mastra with non-OpenAI models? Yes — it supports Anthropic, Google, and local models through provider adapters, plus any OpenAI-compatible endpoint.

How does Mastra compare to building agents with plain function calls? The framework provides the scaffolding — tool execution loops, state management, RAG, evals — that function calls leave to you, which is exactly the code that gets error-prone at production scale.

Additional Implementation Notes

For teams adopting this pattern, start with a small pilot: pick one workflow, instrument it with the observability described above, and run it for two weeks before expanding. Document every failure mode you observe and feed those notes back into the retry and checkpointing configuration. Production agent systems are never finished — they are continuously hardened against the specific failure modes of the environments where they run. Pair this dispatch with the other blueprints in the Daily AI World Workflows hub and the tooling catalog in the MCP Directory to complete your production stack.