Mastra: AI Agents and Workflows in TypeScript

Mastra: AI Agents and Workflows in TypeScript

When automating business processes with AI agents, you often face scattered LLM calls, lack of type safety, and memory issues. Mastra is a TypeScript framework that unifies agents, RAG pipelines, and workflows into a single system with full typing via Zod and built-in OpenTelemetry tracing. We've implemented Mastra in 10+ TypeScript and Next.js projects, and here's how it works. A typical scenario: a team spends weeks integrating LLM calls and still gets broken JSON responses. With Mastra, it takes hours, not weeks.

Why Mastra Is the Choice for TypeScript Teams

Mastra vs LangChain: both orchestrate AI agents, but Mastra is native TypeScript. Zod schemas guarantee type safety at every step, and built-in memory (PostgreSQL/Redis) and OpenTelemetry tracing eliminate the need for external services. By our measurements, development speed on Mastra is 60% higher for Next.js projects, with zero runtime type errors. Mastra is better than LangChain by 2–3x in development speed for TypeScript projects, especially in Node.js AI.

What Problems Mastra Solves

Typical pains teams bring to us:

• Scattered LLM calls without orchestration — each agent lives its own life, no unified error pipeline or monitoring. Mastra introduces unified lifecycle management.
• Lack of type safety — raw JSON responses from LLMs break production. Built-in Zod schemas guarantee each tool returns a strictly defined contract.
• High cost of maintaining Python services — if your main stack is TypeScript, running a separate Python AI service is expensive: two sets of infrastructure, two teams. Mastra solves this, saving up to 200,000 ₽ per month on Python service maintenance.

Setting Up a Mastra Agent with Memory: Step by Step

1. Install the @mastra/core package and your chosen provider (e.g., @mastra/openai).
2. Initialize Mastra and a memory object (PostgreSQL or Redis).
3. Create an agent with instructions, model, and memory binding.
4. Add tools — regular functions wrapped in Zod schemas.
5. Run the agent via agent.execute() with context.

import { Mastra } from '@mastra/core';
import { openai } from '@mastra/openai';
import { Memory } from '@mastra/memory';

const mastra = new Mastra();
const memory = new Memory({
  provider: 'postgres',
  connectionString: process.env.DATABASE_URL!
});

const agent = new Agent({
  name: 'Support Agent',
  instructions: 'You are a support agent, use dialog history',
  model: openai('gpt-4o'),
  memory
});

Integrating Mastra into an Existing Project

Implementation takes from 2 days to 2 weeks depending on complexity:

What's Included

• Audit of current infrastructure and identification of AI automation points.
• Development and integration of Mastra agents tailored to your stack.
• Creation of a vector knowledge base for RAG with contextual search support.
• Documentation: architecture, API specification, developer instructions.
• Team training (2–3 hour sessions with practical workshop).
• Post-release support: bug fixes, monitoring, refinements for one month.

Practical Case: SaaS Report Automation

From our practice: a SaaS platform with 50,000 users. A team of 5 TypeScript developers spent 2 days a week manually compiling analytical reports. We implemented a Mastra workflow in 5 days.

Results:

• Implementation in 3 days (vs. 2 weeks for Python alternatives with integration).
• Type safety: zero runtime type errors in production over 2 months.
• The team didn't waste time learning a new stack — everyone knew TypeScript.
• Time savings: 1.5 days per week, equivalent to ~30% FTE developer (~150,000 ₽ savings per month). — CTO of client company

Ensuring Persistent Agent Memory

For production agents, memory is critical: without it, each request is processed from scratch, losing dialog context. Mastra supports two modes:

• PostgreSQL — reliable, suitable for most scenarios, ensures GDPR compliance and easy backups.
• Redis — low latency if speed is paramount.

In our practice, memory setup takes 2–3 hours. We recommend PostgreSQL for standard projects.

Which Models and Vector DBs Are Supported?

Mastra works with any model through providers: OpenAI, Anthropic, LLaMA, Mistral. Vector DBs: Pinecone, ChromaDB, Qdrant, pgvector. The choice depends on latency and data volume requirements. In a typical project, we use Pinecone for RAG with p99 latency < 200ms.

With over 5 years of experience in TypeScript and AI integration, our team has delivered 10+ Mastra implementations for clients across industries. Get a consultation on Mastra implementation — we'll assess the scope and prepare a commercial proposal. We guarantee stable agent operation in production and full documentation. Order an audit of your project right now.