Strands Agents vs Mastra: Agent Runtime or Application Framework?

These two answer different questions.

Strands answers "how does an agent decide what to do, and how do I run it in production?" Mastra answers a wider question: how do I build the whole AI feature, meaning the agent, the workflows around it, the memory, the evals and the deployment?

Both are good at what they do. The choice comes down to how much of the surrounding application you want to own.

The short answer

Choose Strands when the agent is the deliverable and it lives inside something you have already built. A Python service, an AWS pipeline, a backend that already has its own opinions about persistence and deployment.

Choose Mastra when the AI feature *is* the product and you are building it in TypeScript alongside your web app. Agents, workflows, memory, retrieval, evals, and deployment arrive as one coherent set.

What each one hands you

Strands is an agent runtime. An agent is a model, a system prompt, and tools. Around that it adds the parts you need to run one safely: turn limits and token budgets, conversation managers that keep long sessions inside the context window, tracing that is on by default, and multi-agent patterns when one agent is not enough.

from strands import Agent, tool

@tool
def check_stock(sku: str) -> int:
    """Current warehouse count for a SKU."""
    return inventory.count(sku)

agent = Agent(
    system_prompt="You answer stock questions for the warehouse team.",
    tools=[check_stock],
)

That is the whole surface. Where the agent runs, how the conversation is stored between sessions, and what the user sees are yours to decide, which is the appeal when those decisions are already made.

Mastra is an application framework. The agent is one primitive among several, and the others are the ones you would otherwise assemble yourself:

import { Agent } from "@mastra/core/agent";
import { Memory } from "@mastra/memory";

export const stockAgent = new Agent({
  name: "stock",
  instructions: "You answer stock questions for the warehouse team.",
  model: openai("gpt-5.6"),
  tools: { checkStock },
  memory: new Memory(),
});

Then workflows to sequence the multi-step parts, evals to score behaviour as it changes, observability, and a deployment story that puts the whole thing next to your Next.js app. Mastra reached v1.0 in January 2026 and comes from the team behind Gatsby, which shows in how much attention the developer experience gets.

Head-to-head

Two situations that decide it

Your stack is Python and AWS. Strands is the shorter road. The deployment targets are documented, tracing lands in the tools you already read, and the Python SDK sits naturally beside the rest of your services. Mastra would mean introducing a TypeScript runtime to hold your agent, which is a real cost for a team that does not otherwise need one.

You are building a product feature in TypeScript. Mastra is the shorter road. A support assistant that remembers previous conversations, runs a multi-step refund workflow, gets scored by evals in CI, and deploys with the app is exactly the set of things Mastra treats as one job. Strands would hand you the agent and leave the memory store, the workflow runner, the eval harness, and the deployment to you.

Note the shared middle: both are Apache 2.0, both are model-agnostic, and both speak MCP. Neither locks you to a provider, so the model you choose stays a separate decision from the framework you choose. That matters more than it sounds, it means a team can run an open model behind either one and change its mind later.

Where they meet

Strands has a TypeScript SDK, so "Strands is the Python one" is a shortcut worth dropping. The clearer distinction is scope: Strands stays focused on running an agent well, while Mastra covers the application that surrounds it.

That also makes them combinable in one direction. A Mastra workflow can call a service that runs a Strands agent, which is a reasonable arrangement when a Python team owns the agent and a TypeScript team owns the product.

How to choose in an afternoon

Take one real workflow. A genuine one, with a tool call, a failure path, and a point where a person needs to approve something. Then build it twice, badly, in a couple of hours each.

Then read the two versions and ask what you had to supply yourself. If most of your afternoon went on plumbing that Mastra would have handed you, that is your answer. If most of it went on fighting conventions you did not need because your service already solves those problems, Strands is your answer.

For more of the landscape, see Strands Agents vs LangGraph, Mastra vs Flue, and what Mastra is.

Sources

• Strands Agents documentation
• Introducing Strands Agents, an open source AI agents SDK
• Mastra