Most AI agent frameworks are backend-focused and written in Python, which introduces complexity when building full-stack AI applications with JavaScript or TypeScript frontends. This gap makes it harder for frontend developers to prototype, integrate, and iterate on AI-powered features.
Mastra is an open-source TypeScript framework focused on building AI agents and has primitives such as agents, tools, workflows, and RAG.
Sam Bhagwat and Abhi Aiyer are co-founders at Mastra. They join the podcast with Nick Nisi to talk about this state of frontend tooling for AI agents, AI agent primitives, MCP integration, and more.
Nick Nisi is a conference organizer, speaker, and developer focused on tools across the web ecosystem. He has organized and emceed several conferences and has led NebraskaJS for more than a decade. Nick currently works as a developer experience engineer at WorkOS.
Please click here to see the transcript of this episode.
Sponsors
This episode is brought to you by Augment Code.
You’re a professional software engineer—vibes won’t cut it.
Augment Code is the only AI assistant built for real engineering teams. It ingests your entire repo—millions of lines, tens of thousands of files—so every suggestion lands in context and keeps you in flow.
Where other tools stall, Augment Code sprints. Unlike vibe coding tools, Augment Code is built for shipping to production. And you don’t have to switch tooling: keep using VS Code, JetBrains, Android Studio, or even Vim.
Don’t hire an AI for vibes—get the agent that knows you and your codebase best.
Start your free trial at AugmentCode.com
Building agentic AI apps isn’t just about choosing the best LLM.
Agents need short‑term memory, long‑term recall, and lightning‑fast retrieval. Without it, you’re left with clunky prototypes that never scale.
You know, Redis? The world’s fastest caching solution?
It turns out fast data is the key to good context. And good context is essential for fast, accurate memory. It’s what makes AI agents actually work with your data.
Redis for AI. The right infrastructure. The right tools. The only way to scale.
Learn more at redis.io/genai
Have you tried building a text-to-SQL chatbot?
If your AI agents don’t understand your data – its definitions, queries, and lineage – they’re forced to guess. And bad guesses mean risky assumptions.
That’s where Select Star comes in.
Select Star automatically builds an always-up-to-date knowledge graph of your data – capturing metadata like lineage, usage, and example queries. So whether you’re training an AI model or deploying an agent, your AI can answer with facts, not assumptions.
Stop the wrong SQL queries before they happen. Learn more at selectstar.com.
