Mastra vs LangGraph vs GenKit 2026

TL;DR

For most TypeScript product teams, start with Mastra. It gives you a more application-shaped framework out of the box: agents, workflows, memory, and a DX that feels closer to modern app development than to research tooling. Choose LangGraph when agent state, branching, approvals, and long-running graph logic are the hard part of your system. Choose GenKit when your team wants typed AI flows with strong Google and Firebase alignment without buying into LangChain-style abstractions.

Quick Comparison

Why this matters in 2026

The question changed. In 2024, most teams were still asking whether agent frameworks were mature enough to ship. In 2026, the real question is which framework creates the fewest production problems after the demo works.

Three things made this decision more important:

• Tool calling is reliable enough that orchestration choices now matter more than raw model capability.
• Teams are shipping AI inside existing TypeScript apps, so framework fit with routes, jobs, evals, and persistence matters.
• Tracing, evaluation, and replay are now table stakes. If your framework makes those awkward, you feel it quickly.

That is why Mastra, LangGraph, and GenKit end up in the same shortlist. They all help you build with LLMs, but they optimize for different failure modes.

What actually changes the decision

• Choose based on where state lives. If you need explicit graph state and resumable branching, LangGraph has the clearest model.
• Choose based on whether you want an agent framework or an app framework with agent features. Mastra leans toward the latter.
• Choose based on provider and platform strategy. GenKit is strongest when Firebase, Google Cloud, or multi-provider typed flows are already part of the plan.
• Ignore browser bundle size as a primary metric here. These are mostly server-side dependencies. Dependency surface and operational ergonomics matter more.

Package-by-package breakdown

Mastra

Package: mastra | Weekly downloads: ~287K | Latest: 1.6.3 | npm unpacked size: ~19.4 MB

Mastra is the most opinionated option in this comparison, and that is usually why people like it. It tries to give TypeScript teams a coherent AI application layer instead of a bag of primitives.

import { Agent } from "@mastra/core/agent";
import { openai } from "@ai-sdk/openai";

export const supportAgent = new Agent({
  name: "support-agent",
  model: openai("gpt-4o-mini"),
  instructions: "Answer using support policy and escalate when confidence is low.",
  tools: {
    lookupOrder,
    createRefundDraft,
  },
});

Why teams pick it:

• The mental model is close to how product teams already think: agents, memory, workflows, and app integrations.
• It fits naturally with modern TypeScript stacks that already use AI SDK style providers.
• It is easier to recommend to a Next.js or internal-tools team that wants one system to own agent behavior and operational glue.

Watch-outs:

• The package footprint is large enough that you should expect a more batteries-included framework, not a tiny library.
• If your actual problem is graph orchestration rather than app assembly, LangGraph can feel more precise.

LangGraph

Package: @langchain/langgraph | Weekly downloads: ~2.0M | Latest: 1.2.9 | Bundlephobia gzip: ~42 KB

LangGraph is the best fit when you need to model agent behavior as explicit nodes, edges, checkpoints, and human-in-the-loop transitions. It is the most exact tool here for complex control flow.

import { StateGraph } from "@langchain/langgraph";

const graph = new StateGraph(MessagesAnnotation)
  .addNode("plan", planStep)
  .addNode("research", researchStep)
  .addNode("answer", answerStep)
  .addEdge("__start__", "plan")
  .addConditionalEdges("plan", routePlan)
  .addEdge("research", "answer")
  .addEdge("answer", "__end__");

Why teams pick it:

• It makes agent state explicit instead of implicit.
• It works well for review steps, retries, approvals, and multi-agent routing.
• It pairs naturally with LangSmith if you want deep traces of how the graph executed.

Watch-outs:

• The ecosystem depth is real, but so is the cognitive overhead.
• Teams often adopt LangGraph and then realize they also adopted several surrounding LangChain conventions they did not intend to standardize on.

GenKit

Package: genkit | Weekly downloads: ~249K | Latest: 1.33.0 | Bundlephobia gzip: ~629 KB

GenKit is the cleanest choice for teams that want typed flows, structured generation, and strong provider plugins without stepping into full LangChain complexity.

import { genkit } from "genkit";
import { googleAI } from "@genkit-ai/googleai";

const ai = genkit({
  plugins: [googleAI()],
  model: "googleai/gemini-2.5-flash",
});

export const summarizeTicket = ai.defineFlow(
  {
    name: "summarizeTicket",
    inputSchema: ticketSchema,
    outputSchema: summarySchema,
  },
  async (ticket) => {
    const { output } = await ai.generate({
      prompt: `Summarize this ticket: ${ticket.body}`,
      output: { schema: summarySchema },
    });

    return output;
  }
);

Why teams pick it:

• Its flow model is easy to reason about for request-in, typed-output pipelines.
• Google and Firebase teams get a straighter path from local prototyping to hosted deployment.
• The APIs tend to stay closer to typed generation and app logic than to chain-building abstractions.

Watch-outs:

• If you need explicit long-running graph state, LangGraph is still stronger.
• If you want a broader batteries-included app framework, Mastra feels more complete.

Which one should you choose?

• Choose Mastra when you want the most product-team-friendly TypeScript framework and you expect memory, workflows, and app integration to matter immediately.
• Choose LangGraph when your hardest problem is orchestration logic itself: branching, checkpoints, supervisors, approvals, and replayable agent state.
• Choose GenKit when you want typed AI flows with good multi-provider support and especially when your stack already leans toward Firebase or Google Cloud.

Final recommendation

For a fresh TypeScript app in 2026, Mastra is the best default because it matches how most teams actually ship AI features: inside an existing product, with persistence, routes, tracing, and operational concerns from day one. LangGraph is the right upgrade when your agent behavior becomes graph-shaped and correctness of orchestration matters more than framework simplicity. GenKit is the best middle path for teams that want typed flows and provider flexibility without going full LangChain.

Data note: npm package versions and weekly download figures were checked against the npm registry on 2026-04-24. Bundle figures come from Bundlephobia where available.

Related reading

OpenAI Agents SDK vs Mastra vs GenKit · AI SDK vs LangChain · Langfuse vs LangSmith vs Helicone