Look, I’ll be honest - I’ve spent too many nights wrestling with various AI frameworks, trying to make them play nice with TypeScript. Most of them feel like they were built for Python folks first, with TypeScript support tacked on as an afterthought. Then I stumbled across Mastra, and I think my search might finally be over.
A Sandbox That Actually Makes Sense
One of the biggest headaches when building AI applications has always been testing. You write some code, deploy it somewhere, and then discover your agent is hallucinating wildly or your workflow is stuck in an infinite loop.
Mastra’s local sandbox environment changes the game completely. With a simple mastra dev command, you get an interactive playground where you can chat with your agents, visualize your workflows, and test your tools - all while developing locally on your machine.
The local development server provides a complete environment with everything you need: chat interfaces for testing agents, workflow visualizations, prompt experimentation tools, and agent memory management. I’ve found this incredibly useful for quick iterations - no more waiting for deployments just to test a small prompt change.
Agent Architecture That Feels Natural to TypeScript Developers
If you’re coming from the TypeScript world, you’ll appreciate how naturally Mastra’s agent architecture fits with the language. Mastra is an opinionated TypeScript framework that gives you the primitives you need: workflows, agents, RAG, integrations and evals.
What I particularly love is that everything is strongly typed. Your agent tools have typed parameters and return values. Your workflow steps have defined input and output schemas. This catches so many bugs before they ever hit production, which is something Python-based frameworks struggle with.
Agents in Mastra aren’t just black boxes either. They provide LLM models with tools, workflows, and synced data. Agents can call your own functions or APIs of third-party integrations and access knowledge bases you build. The level of control you get is impressive, without sacrificing developer experience.
Workflows: More Than Just Chained Prompts
I think what really sets Mastra apart is its approach to workflows. Most AI frameworks treat workflows as simple chains of prompts, but real applications need more sophistication.
Workflows in Mastra are durable graph-based state machines with features like branching, parallel execution, and resource suspension. This means you can build complex applications that include conditional logic, error handling, and human-in-the-loop interactions.
Perhaps the most powerful thing is that workflows can be built in code or with a visual editor. When you’re trying to map out complex business logic, sometimes seeing it visually makes all the difference. And since each step has built-in OpenTelemetry tracing, you can actually see what’s happening as your workflow executes.
The Memory That Makes AI Actually Useful
Let’s talk about one of my pet peeves with many AI frameworks - memory management. Most either don’t handle it at all or do it in ways that feel like awkward workarounds.
Mastra takes a different approach. With Mastra, you can persist agent memory and retrieve it based on recency, semantic similarity, or conversation thread. This means your agents can maintain context across sessions, which is crucial for any kind of ongoing user interaction.
I’ve found this particularly valuable when building assistant-style applications where the user expects the agent to remember previous conversations. The ability to retrieve memories based on semantic similarity (not just time-based) means your agents can recall relevant information even if it was mentioned many interactions ago.
The SDK That Plays Well With Others
One thing that kept tripping me up with other frameworks was integration. I’d build something cool, then struggle to integrate it with my existing TypeScript codebase.
Mastra supports bundling your agents and workflows within an existing React, Next.js, or Node.js application, or into standalone endpoints. This flexibility means you can start by adding a simple agent to your existing application, then gradually expand as needed.
The framework also leverages the Vercel AI SDK, which means you get a unified interface to interact with any LLM provider including OpenAI, Anthropic, and Google Gemini. I’ve found this incredibly valuable when experimenting with different models - just change one line of code, and you’re using a completely different provider.
Quality Control Through Evals
Perhaps my favorite feature - and one that’s often overlooked - is Mastra’s evaluation framework. In production, you need more than just hope that your AI is working correctly.
Mastra provides automated evaluation metrics that use model-graded, rule-based, and statistical methods to assess LLM outputs, with built-in metrics for toxicity, bias, relevance, and factual accuracy. This means you can actually measure how well your AI is performing and catch regressions before they impact users.
I think this focus on quality control shows that Mastra was built by developers who understand the reality of maintaining AI applications in production, not just getting them working in a demo.
Is Mastra Right for You?
Look, I’m not saying Mastra is perfect for every use case. If you’re deeply embedded in the Python ecosystem, you might prefer to stick with frameworks designed for that language. And if you’re building extremely specialized AI applications, you might need more custom solutions.
But if you’re a TypeScript developer looking to add AI capabilities to your applications, Mastra offers a compelling package. The combination of strongly typed APIs, excellent developer tooling, and production-ready features addresses many of the pain points I’ve experienced with other frameworks.
So maybe give it a try? A simple npm create mastra is all it takes to get started. You might find that AI development feels a lot more natural when you’re working with tools designed for the way you already code.
I’m curious to hear what other TypeScript developers think about this framework. Have you tried Mastra? What’s been your experience?