Mastra vs LangGraph.js: TypeScript Agent Frameworks Head to Head

The TypeScript agent ecosystem has matured fast. Two years ago your choices were "use the Python library and suffer" or "roll your own." Now you have real options with real production deployments behind them. Mastra and LangGraph.js sit at the center of those options, and they attract very different kinds of builders.

Mastra pitches itself as a full-featured TypeScript framework - batteries included, studio UI included, model router included. LangGraph.js pitches itself as a low-level orchestration runtime - graph primitives, durable execution, and maximum control. Neither pitch is dishonest. The question is which one matches how you think about the problem you are solving.

Last updated: June 10, 2026

What Each Framework Actually Is

Before the comparison gets useful, it helps to be precise about the category each tool occupies.

Mastra is a framework for building AI-powered applications. You get an Agent class, a createWorkflow / createStep API, a built-in model router covering 3000+ models from OpenAI, Anthropic, Groq, Google, Cerebras, Mistral, and others, memory, voice, channels for Slack, Discord, and Telegram, guardrails, structured output, and a local dev studio at localhost:4111. You scaffold a project with npm create mastra@latest and you are running in minutes. The docs describe the goal as "everything you need to prototype fast and ship with confidence."

LangGraph.js is a low-level orchestration runtime. It gives you a directed graph - StateGraph, nodes, edges, START, END - and the infrastructure to run that graph durably: persistence across failures, human-in-the-loop interrupts, streaming at every layer, time-travel debugging, and comprehensive memory. The docs are explicit: "LangGraph is very low-level, and focused entirely on agent orchestration." It does not abstract prompts or architecture. It sits alongside LangChain (agent harness), LangSmith (observability and deployment), and Deep Agents (a higher-level harness built on LangGraph).

These are not competing at the same layer. Mastra is closer to a complete platform. LangGraph.js is closer to a runtime primitive you build a platform on top of.

Developer Experience

Getting started with Mastra takes one command:

npm create mastra@latest

You pick a project name and a model provider. You get a working project with a local dev server and Studio UI. Studio gives you a visual graph view of workflows, an auto-generated input form from your schemas, live step-by-step execution status, and time-travel replay for debugging completed runs. For teams that want to move fast and show stakeholders something real, this matters.

LangGraph.js setup is also straightforward:

npm install @langchain/langgraph @langchain/core

But the "hello world" reveals the abstraction level immediately. You define a StateSchema, write a node function, wire START to your node and your node to END, compile, invoke. There is no scaffold, no studio, no default model. You are working with graph primitives from line one. That is a feature if you want control; it is friction if you want to ship a customer-facing assistant in a sprint.

Mastra's agent definition is terse, taken directly from the docs:

import { Agent } from '@mastra/core/agent'

export const testAgent = new Agent({
  id: 'test-agent',
  name: 'Test Agent',
  instructions: 'You are a helpful assistant.',
  model: 'openai/gpt-5.5',
})

LangGraph.js has no equivalent Agent shorthand at the framework level. You compose one from graph nodes and edges, or you use LangChain's agent utilities that sit on top of LangGraph.

Workflows and Orchestration

This is where the comparison gets most interesting.

Mastra workflows are a first-class feature with a builder pattern. Each step carries typed inputSchema and outputSchema (Zod, Valibot, and ArkType all work). Steps share state through an explicit stateSchema. Workflows can suspend, resume, restart from the last active step, and be composed as nested child workflows. The execution result is a discriminated union on status - success, failed, suspended, tripwire, paused - which makes exhaustive handling in TypeScript natural. Workflow runners like Inngest handle managed infrastructure for longer-running jobs.

LangGraph.js builds orchestration out of graph topology. A workflow is a StateGraph with typed state, nodes that process state transitions, and edges (including conditional edges) that route between nodes. The Pregel-inspired execution model means you can express cycles naturally - something linear step chains handle awkwardly. Human-in-the-loop is a first-class interrupt primitive that pauses graph execution at any node and waits for external input. This is powerful for review workflows, approval chains, or any agentic loop where you need to hand control back to a human at a defined point.

Both handle the "agent as a workflow step" pattern. Both handle parallel branches. LangGraph's graph model has more expressive power for complex, non-linear flows - cycles, conditional routing, multi-agent handoffs through shared state. Mastra's step model is easier to read and reason about for linear and mostly-linear processes.

Memory and State

Mastra includes a memory system covering both short-term conversation context and long-term cross-session persistence. Agents configured with memory can recall prior context, maintain preferences, and accumulate knowledge across runs. Memory is opt-in and configurable at the agent level.

LangGraph.js treats memory as a core capability - short-term working memory for ongoing reasoning and long-term memory across sessions. The graph state is the primary persistence mechanism, and LangGraph's checkpointing infrastructure means state survives failures and can be resumed mid-graph. The time-travel capability (listed explicitly in the LangGraph.js docs sidebar) lets you replay from any checkpoint. For long-running, stateful workflows this checkpointing story is particularly strong.

Observability and Deployment

Mastra provides logging and telemetry through instance configuration. Studio handles local visibility. For production, Mastra supports deployment to workflow runners and integrates with standard observability tooling.

LangGraph.js has LangSmith as its native observability layer. Set LANGSMITH_TRACING=true and you get execution traces, state transition visualization, and runtime metrics. LangSmith Studio adds a visual agent builder. LangSmith Deployment handles hosting. For teams that need enterprise-grade observability with minimal custom plumbing, the LangSmith ecosystem is a real advantage.

Head-to-Head

Who Should Pick Which

Pick Mastra if:

• You are building a customer-facing product and want to ship fast
• Your team is comfortable with TypeScript but not necessarily with graph theory
• You want voice, Slack/Discord channels, and a Studio UI without stitching them together yourself
• You are embedding agents inside an existing Next.js, SvelteKit, or Hono app
• Your workflow patterns are mostly linear or lightly branching

Pick LangGraph.js if:

• You need maximum control over execution topology - cycles, conditional routing, complex multi-agent handoffs
• Human-in-the-loop interrupts are a core design requirement, not an afterthought
• Your team is comfortable thinking in graph terms and wants the flexibility that entails
• You need the full LangSmith observability and deployment stack, or are already invested in the LangChain ecosystem
• You are building for enterprise use cases where durable execution and auditability are non-negotiable

These are not mutually exclusive at the organization level. Some teams use Mastra for product-facing agents and LangGraph.js for complex internal automation pipelines where they need tighter control. The model router difference alone - Mastra gives you 3000+ models out of the box, LangGraph.js makes you configure providers yourself - is enough of a DX gap that greenfield projects often start with Mastra and only reach for LangGraph.js when they hit specific orchestration requirements.

The Honest Verdict

Mastra wins on developer experience, batteries included, and time to first working product. LangGraph.js wins on expressive power, the depth of its human-in-the-loop and checkpointing story, and the LangSmith observability ecosystem.

If you are a solo developer or small team shipping a product, Mastra is probably where you start. If you are a platform team building agent infrastructure that other engineers will build on top of, LangGraph.js gives you the primitives to design exactly what you need.

Both are legitimately production-ready in 2026, which is a sentence that could not have been written about the TypeScript agent ecosystem eighteen months ago.

FAQ

What is the difference between Mastra and LangGraph.js?

Mastra is a full-featured TypeScript application framework with a built-in model router, Studio UI, workflows, memory, and integrations for voice and chat channels. LangGraph.js is a low-level orchestration runtime that represents agent logic as a directed graph with durable execution, human-in-the-loop interrupts, and state persistence. Mastra prioritizes developer experience and fast time-to-ship; LangGraph.js prioritizes control and expressive power for complex agent topologies.

Is Mastra production-ready in 2026?

Yes. Mastra is used in production by Replit, Fireworks, Medusa, SoftBank, and others as of mid-2026. It supports workflow runners like Inngest for managed infrastructure, suspend and resume for long-running workflows, and full TypeScript type inference through its schema-first step and workflow APIs.

Can I use LangGraph.js without LangChain?

Yes, explicitly. The LangGraph.js documentation states it "can be used without LangChain" and that LangChain is included in examples for convenience, not as a requirement. You can bring any model provider via direct SDK integration and use LangGraph.js purely as the orchestration runtime.

Which TypeScript agent framework has better multi-agent support?

Both support multi-agent systems. Mastra offers supervisor agents, agents as tools, and multi-agent network patterns with a coordinator pattern. LangGraph.js supports multi-agent handoffs through shared graph state and conditional edge routing. Mastra's multi-agent patterns are more prescriptive and easier to get started with; LangGraph.js gives you more flexibility in how agents communicate and share state. For teams building multi-agent pipelines as a platform primitive, LangGraph.js is typically the stronger choice.

Sources

• Mastra documentation: https://mastra.ai/docs (fetched June 10, 2026)
• Mastra agents overview: https://mastra.ai/en/docs/agents/overview (fetched June 10, 2026)
• LangGraph.js overview: https://langchain-ai.github.io/langgraphjs/ (redirects to https://docs.langchain.com/oss/javascript/langgraph/overview, fetched June 10, 2026)
• LangGraph.js docs sidebar: https://docs.langchain.com/oss/javascript/langgraph/overview (fetched June 10, 2026)