LangChain vs Mastra: TypeScript Agent Frameworks in 2026

Two structural facts fall out of that table.

LangChain's TypeScript story is a port, and ports lag. LangGraph.js has feature parity with the Python implementation on the core primitives (state graphs, conditional edges, checkpointing, streaming, human-in-the-loop) and a real user base. But the ecosystem's centre of gravity is Python: new integrations, new patterns, and most of the documentation and community examples land there first. For a team that will never write Python, that lag is a recurring tax with no offsetting benefit.

Mastra's bundling is the whole product decision. Memory, evals, and a local playground being in the box is not a convenience feature, it is the thesis. You get working behaviour without assembling four libraries, and you inherit opinions you did not choose. Whether that is an advantage depends entirely on whether the opinions match your constraints, which is exactly what the performance result exposes.

A public head-to-head built the same agent pipeline in both frameworks and ran them in parallel. LangChain was 25-45% faster on every run. Mastra used 1.5-2.5x the tokens.

That result is real and it is worth taking seriously. It is also not evidence that one framework is inherently more efficient, and reading it that way leads to the wrong decision. The dominant cause is what each framework does when you have not told it to do anything.

Mastra maintains conversational memory by default and compacts it automatically at around 30,000 tokens. Every turn therefore carries context the framework decided you wanted, and periodically pays for a compaction pass. That is tokens and that is latency, on every request, whether or not this particular agent benefited from it.

LangChain does nothing until you pick a memory class. No automatic context, no compaction, no surprise spend. Also no long-session coherence until you build it, and the version you build will cost roughly what Mastra's costs once it does the same job.

So the honest reading of the benchmark: it measures the price of Mastra's defaults, not a ceiling on Mastra's performance. Configure both frameworks to the same memory behaviour and the gap narrows sharply. What the number genuinely tells you is that Mastra's defaults are tuned for good behaviour rather than cheap behaviour, which is the correct default for a product team and the wrong one for a high-volume classification endpoint where you are counting tokens. The general point about defaults and token spend applies well beyond these two frameworks, and we went into the mechanics in why your agentic AI token cost tripled.

Framework choice stops mattering for prototypes and starts mattering the first time an agent has to survive a process restart in the middle of a multi-step workflow.

LangGraph models the agent as a state graph and persists progress through a checkpointer you configure. The checkpointer is an explicit component with an explicit backend, and durable execution, interrupts, time travel, and human-in-the-loop all derive from it. You have to think about it on day one, which is annoying on day one and correct on day two hundred, because the persistence layer is a thing you chose, sized, and can reason about during an incident.

Mastra persists through a storage adapter, with suspend and resume for long-running workflows, and defaults that work immediately. The workflow serialises its state, waits, and resumes. This gets you to a working durable workflow considerably faster and it means the storage decision is one you may not make consciously until you are already in production on it.

Neither model is better in the abstract. The question to ask is which failure you would rather have: a slower start because you had to configure persistence before writing an agent, or a faster start followed by a migration when the default storage does not meet a retention, residency, or scale requirement you did not think about in week one. For regulated work the answer is usually the first, because the storage backend is a compliance artifact rather than an implementation detail. For a product team racing a competitor, it is usually the second, and that is a legitimate choice as long as it is a choice.

Nothing communicates the difference faster than the same trivial agent written twice: one tool, one model, one durable turn.

// Mastra: the framework owns memory, storage, and the turn loop
import { Agent } from "@mastra/core/agent";
import { Memory } from "@mastra/memory";
import { createTool } from "@mastra/core/tools";
import { z } from "zod";

const lookupPolicy = createTool({
  id: "lookup-policy",
  description: "Fetch the current text of an internal policy by its code.",
  inputSchema: z.object({ code: z.string() }),
  execute: async ({ context }) => policyStore.get(context.code),
});

export const support = new Agent({
  name: "support",
  instructions: "Answer only from retrieved policy text. Cite the policy code.",
  model: provider("your-model"),
  tools: { lookupPolicy },
  memory: new Memory(),          // storage, recall and compaction, all defaulted
});

// One durable turn. Thread state is persisted for you.
const res = await support.generate("What is the refund window?", {
  resourceId: userId,
  threadId: conversationId,
});

// LangGraph.js: you declare the state machine and own the persistence
import { StateGraph, MessagesAnnotation } from "@langchain/langgraph";
import { PostgresSaver } from "@langchain/langgraph-checkpoint-postgres";
import { ToolNode } from "@langchain/langgraph/prebuilt";
import { tool } from "@langchain/core/tools";
import { z } from "zod";

const lookupPolicy = tool(
  async ({ code }) => policyStore.get(code),
  {
    name: "lookup_policy",
    description: "Fetch the current text of an internal policy by its code.",
    schema: z.object({ code: z.string() }),
  },
);

const model = provider("your-model").bindTools([lookupPolicy]);

const graph = new StateGraph(MessagesAnnotation)
  .addNode("agent", async (s) => ({ messages: [await model.invoke(s.messages)] }))
  .addNode("tools", new ToolNode([lookupPolicy]))
  .addEdge("__start__", "agent")
  .addConditionalEdges("agent", (s) =>
    lastMessage(s).tool_calls?.length ? "tools" : "__end__")
  .addEdge("tools", "agent")
  // The persistence backend is a decision you make here, explicitly.
  .compile({ checkpointer: PostgresSaver.fromConnString(process.env.PG_URL!) });

const res = await graph.invoke(
  { messages: [{ role: "user", content: "What is the refund window?" }] },
  { configurable: { thread_id: conversationId } },
);

The Mastra version is shorter and it is shorter for a reason worth naming precisely: the loop (call model, run tools, feed results back, decide when to stop) and the memory policy are inside the framework. The LangGraph version makes both visible, which is why it is longer and why you can change either without fighting a default.

That difference stops being cosmetic the first time you need something the loop did not anticipate. A compliance step between tool call and tool execution, a hard cap on iterations per turn, a branch that routes to a human at a specific state, a retry policy that differs by tool: all of those are edges you add in the LangGraph version and framework internals you work around in the Mastra one. Conversely, if you never need those, the second file is 40 lines of scaffolding you maintain forever to express what the first expressed in a constructor argument.

Both bind MCP servers as tool sources, so tool portability across the two is better than it used to be and is not a strong differentiator either way. What is a differentiator is what happens when you have too many tools, which is a problem neither framework solves and both make easy to create, discussed in agent tool selection at scale.

Most framework comparisons are written for teams deploying to a public cloud with a hosted observability product. Most of the work we do is not that, and the deciding factors invert.

Three notes on reading that table.

The certification row is the one procurement will fixate on and it is frequently misapplied. If you run open source packages on your own infrastructure, the certification question attaches to your infrastructure and your operating practices, not to the library, and a competent security review will treat the framework as a dependency. The moment any vendor-hosted control plane, storage, or trace collector enters the design, that vendor becomes a subprocessor and the paperwork question becomes real. Decide which of those two deployments you are actually doing before letting a certification row decide the framework.

The telemetry row deserves an actual test rather than a documentation read. Run the framework in a network namespace with egress denied, exercise a full agent turn, and look at what fails and what it tried to reach. This takes an afternoon and it is the only version of the answer that survives a version bump.

The air-gapped row is where both frameworks are workable and both need an audit pass. The failure mode is never the framework's core; it is a default callback handler, an evaluation helper, or a model provider client with a telemetry ping that nobody noticed in development because the machine had internet.

Choose Mastra if your team is TypeScript end to end, you are building a product surface rather than a data pipeline, and you want memory, evals, and a dev playground without assembling them. Accept that you are buying opinions, that the defaults cost tokens, and that the compliance paperwork is yours to produce because you are self-hosting. This is the right default for a product engineering team shipping user-facing agents.

Choose LangGraph.js if Python exists anywhere in your organisation and you need one orchestration model across both languages, if you want explicit control over exactly what enters the context window, or if procurement will ask for a compliance posture and an enterprise support contract. Accept the Python-first release cadence and the fact that you will assemble more of the stack yourself. This is the right default for a platform team and for regulated deployments.

Run both if the boundary is natural. Durable multi-step orchestration and anything that needs the Python ecosystem on one side, the TypeScript application layer and the interactive agent on the other, a plain HTTP or queue interface between them. This costs one interface and buys independence on each side, and it is a considerably better answer than a migration for most teams that already have working Python orchestration.

Whichever you pick, the framework is not the part that determines whether the agent works. Scaffolding, tool design, and evaluation dominate outcomes far more than the orchestration library does, which is the argument in agent scaffolding beats model upgrades, and memory architecture is its own decision that outlives the framework, covered in agent memory frameworks tested. If you want the wider comparison set including the Python-side options, see LangGraph vs CrewAI vs OpenAI Agents SDK and the AI development tools pillar.

At Particula Tech we do this selection as a short engagement before the build, because the cost of getting it wrong is not the framework, it is the state layer underneath it and the six months of production behaviour that depend on where you put it.