Can I build a production AI agent entirely in TypeScript?

Yes. Mastra covers agents, tool calling, workflows, memory and evals natively in TypeScript, and the model providers all ship first-class TypeScript SDKs. The gap that used to exist, with evaluation and observability tooling being Python-only, has largely closed. Langfuse, for instance, has a TypeScript SDK.

Mastra vs LangGraph for a Next.js app?

If the agent lives inside the Next.js app and your team is TypeScript-first, Mastra will cost you less in operations and context-switching. LangGraph has the larger ecosystem and more battle-tested durable execution, and a JavaScript port exists, but the centre of gravity is still Python. Pick Mastra for stack coherence, LangGraph for the deepest stateful-workflow support.

Why is pydantic-ai on a TypeScript page?

Because the reason people want TypeScript for agents is usually type safety and editor feedback, not the language itself. pydantic-ai delivers most of that inside Python, so if a Python service is unavoidable, it's the one that will annoy your TypeScript developers least.

Does running an agent in a Next.js route cause timeout problems?

It can, and it's the most common thing that bites. Serverless platforms cap execution time, and an agent doing five tool calls can run past it. Stream the response, move long runs to a background job or a queue, and never assume a happy-path latency you measured once on your laptop.

Agent Library · TypeScript

Building AI agents in TypeScript without standing up a Python service

Mastra is the leading open-source TypeScript framework for AI agents, offering agents, workflows, memory and evaluations that run inside an existing Next.js or Node application. Teams that must stay in Python but want comparable type safety should use pydantic-ai. Both are free and open source, and both remove the need to deploy a separate Python service purely to host an agent.

Free. 2 repositories, no payment. One email address opens the list.

Almost every agent tutorial assumes Python. Almost every mobile and web product I get called into is TypeScript. That gap costs real money.

It usually shows up as a second deployment target: a small Python service sitting next to the app, with its own container, its own secrets and its own on-call story, existing entirely because the agent library was written in the other language.

Sometimes that's the right call. If your team has Python depth and the agent is doing heavy retrieval or data work, a separate service is honest architecture. But if the agent is three tools and a conversation, splitting your stack to run it is a tax you're paying for nothing, and it slows down every change you make afterwards.

Mastra is the first TypeScript framework I've used that doesn't feel like a port. Agents, workflows, memory and an evaluation harness, designed to sit next to your Next.js app rather than behind it. Check the licence before you ship commercially. It isn't a plain MIT project, and for some buyers that matters more than anything technical on this page.

If you're stuck on the Python side by circumstance rather than choice, pydantic-ai is the closest thing to the TypeScript experience: typed end to end, failing at the boundary instead of three calls later. That is most of what type safety buys you in agent code anyway.

This week: find out whether your agent genuinely needs Python, or whether it needs Python because the first tutorial you read was in Python. Those are different answers.

Where to send the 2 repositories

Give me an email address and the full list opens on this page straight away, with a copy in your inbox you can search later, plus a note whenever we publish something new on AI and where mobile development is going. No payment, now or ever. That's the only trade.

If your product is TypeScript, start here

Most agent tutorials assume Python. Most mobile and web products do not. These two close that gap.

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The master list: all 100 repositories

The free list above is the short version, and every repository on it is in here too. The Gods Repo Library is the whole catalogue: 100 repositories across 12 categories, each with a written note on what it is for and when to reach for it, and it carries the AI optimisation half, inference, serving, quantisation and evaluation, that the free list barely touches. Every figure in it is read from the GitHub API rather than typed by hand, and nothing archived or idle survives the build. Verified 2026-09-23.

Orchestration: where the agent lives19
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Fine-tuning, quantisation and compression7
Getting the data in5
Courses, cookbooks and reference6

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