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Mastra vs LangGraph: Batteries-Included TypeScript or an Explicit Graph Runtime?

GitLab, Grab and Qodo run production agents on LangGraph, and yeos.ai removed it after months of spending more time on the framework than on its own logic. Brex runs about half of its agent layer on Mastra, and TypeScript builders pick it to skip LangGraph's complexity.

Reading time
9 min read
Sources
36 cited
In this piece

GILD verdict

Choose Mastra for TypeScript agents with memory, evals and durable workflows supplied, and LangGraph for checkpointed graphs with interrupts.

The workflow
A product team building long-running agents with human review steps, choosing between a bundled TypeScript framework and an explicit graph runtime.1326
The decisive evidence
  • Reported4 of 6 named teams adopted one; What teams report below sets out each account.
  • Reportedyeos.ai removed LangGraph in July 2026 after months of reasoning more about the framework than its business logic, and TypeScript builders on Reddit chose Mastra over LangGraph to skip that complexity.468
  • DocumentedLangGraph persists state through checkpointers with exit, async, and sync durability modes and pauses runs with interrupts. Mastra workflows suspend and resume on its built-in engine or Inngest, and its Temporal runner is labelled experimental.13142829
  • DocumentedLangGraph traces only when LANGSMITH_TRACING is set. Mastra sends usage telemetry by default until MASTRA_TELEMETRY_DISABLED=1 is set, and exports traces to the team's own observability tools.171830
  • GILD analysisAt 10,000 runs a month and two developers, Mastra Platform Starter comes to about US$61 and LangSmith Plus to US$78 before model tokens. The difference is small; the sources leave open how many events a Mastra run emits and what larger LangSmith deployments cost, so price both on a real run.
The tradeoff
LangGraph works in Python or TypeScript. Mastra gives more building blocks and less explicit control over each transition. LangGraph gives explicit control and a choice of durability, and leaves memory and evals to LangSmith or your own code.1914151626272829
What would change the call
The call moves to LangGraph when the team works in Python or must choose how each step is persisted, and to Mastra when TypeScript ergonomics and bundled evals matter more than graph-level control.162833

Named teams split by language and by how much control they want over each step. GitLab built its Duo Agent Platform on LangGraph and says its Flow Registry cut code per agentic flow by 45%, Grab and Qodo run LangGraph agents in production, and yeos.ai removed LangGraph in July 2026 after months of reasoning more about the framework than its business logic. Brex runs about half of its agent-layer applications on Mastra, and TypeScript builders choose it for bundled memory, evals and durable workflows over LangGraph's explicit graph, checkpointers and interrupts in Python or TypeScript.

What teams report

Failures and incidents 1

  1. SudolabsOn Mastra

    Since @mastra/deployer 1.79.0, mastra build on Sudolabs' larger app ran out of heap; one option took 2,268 MB on 1.79.0 against 176 MB on 1.78.0 (GitHub issue, Oct 27, 2026).

    “Since @mastra/deployer 1.79.0 (@mastra/core@1.79.0 release, CLI mastra 1.35.0), mastra build on a larger app runs out of memory before "Analyzing dependencies...".”
    Tomas Varga, Developer, SudolabsGitHub, mastra-ai/mastra, , post

Left or turned down 1

  1. yeos.aiOn LangGraph

    yeos.ai built its enterprise AI platform on LangGraph, used it for months, and then removed it completely, because the team spent more time reasoning about the framework than about its own business logic (Jul 27, 2026).

    “Over time we found ourselves thinking more about LangGraph than about our own business logic.”
    Nicola Richli, Builder of yeos.ai, yeos.aiDEV Community, , write-up

Adopted 4

  1. GitLabOn LangGraph

    GitLab chose LangGraph as the foundation of GitLab Duo Agent Platform; its Flow Registry compiles YAML into LangGraph flows, and GitLab says that cut its own code per agentic flow by 45% (Sep 22, 2026).

    “We researched available AI frameworks and selected LangGraph, an agent runtime and low-level orchestration framework from LangChain, as the foundation for GitLab Duo Agent Platform.”
    GitLab Duo Agent Platform engineering, Platform engineering team, GitLabGitLab, , write-up

  2. ReplitOn Mastra

    Mastra's customer story says the agents Replit Agent 3 generates are Mastra agents, and that durable execution on Inngest raised their workflow success rate from 80% to 96%.

    “Using Mastra's durable execution with Inngest, Replit boosted success rates from 80% to 96%, ensuring workflows complete even if a servers fails mid-execution.”
    Mastra customer story, citing Replit's CTO, Agent 3 team, ReplitMastra, , write-upVendor customer story

  3. GrabOn LangGraph

    Grab's Analytics Data Warehouse team, serving more than 1,000 users a month, runs a multi-agent assistant on FastAPI and LangGraph that answers simpler data questions on its own, and says it reclaimed hundreds of hours a month (Mar 19, 2026).

    “FastAPI & LangGraph: We use FastAPI to handle requests and LangGraph to manage the complex state and cyclical logic required for multi-agent collaboration.”
    Sneh Agrawal, Rishi Raj, Ayan Chatterjee, Wen Zhong Tan and Sai Reddy Kakumanu, Analytics Data Warehouse team, GrabGrab Tech Blog, , write-up

  4. QodoOn LangGraph

    Qodo chose LangGraph for its coding agent's proof of concept around November 2024, and says the framework carried it to production, with graph nodes for context collection, planning, code generation and validation (Mar 21, 2025).

    “…we settled on LangGraph for our initial proof of concept. We were pleasantly surprised to see that the framework has proven flexible and mature enough to carry us all the way to production.”
    Sagi Medina, Qodo engineering, QodoQodo, , write-up

Choosing between Mastra and LangGraph

Choose Mastra if

  • You build in TypeScript and want memory, scorers, and a local Studio from the same framework, the reason builders on Reddit gave for picking Mastra over LangGraph in 2026.
  • Long runs must pause for human approval and resume after a deploy, as one team's document-analysis runs did on Mastra workflows (Reddit, Aug 26, 2026).
  • You want a hosted platform with us or eu residency and no per-seat price on Starter.

Choose LangGraph if

  • You want every step and transition written down in a graph with state checkpointed between steps, the base GitLab chose for its Duo Agent Platform (Sep 22, 2026).
  • Agents work in cycles over shared state, as in Grab's multi-agent data assistant (Mar 19, 2026), or your team works in Python as well as TypeScript.
  • Runs must pause for human input anywhere and resume with a durability mode you choose, traced in LangSmith.

Side by side

DimensionMastraLangGraph
What teams reportNamed accounts GILD found. Each team describes its own side; none published a head-to-head on one workload.Brex runs about half of its agent-layer applications on Mastra, which replaced its own framework (Latent Space, Jan 17, 2026). A team's document-analysis run paused for review, survived a deploy and resumed from its checkpoint (Reddit, Aug 26, 2026). Sudolabs' build ran out of memory after @mastra/deployer 1.79.0 (Oct 27, 2026).7910GitLab built Duo Agent Platform on LangGraph and cut its own code per agentic flow by 45% (Sep 22, 2026). Grab runs a multi-agent data assistant on FastAPI and LangGraph (Mar 19, 2026). yeos.ai removed LangGraph after months, finding it thought more about the framework than its business logic (Jul 27, 2026).124
DesignA framework: agents with tools and memory, typed workflows built with createWorkflow that suspend and resume, subagents, and scorers111213A low-level orchestration framework and runtime for building, managing, and deploying long-running, stateful agents26
License and languagesLatest releases on GitHub and npm, checked Oct 28, 2026.TypeScript; Apache 2.0 outside ee/ directories, with ee/ features under the Mastra Enterprise Edition License 2.0 (effective Sep 22, 2026); @mastra/core 1.80.0, Oct 28, 2026232425MIT; Python (langgraph 1.2.18, Oct 26, 2026) and TypeScript (@langchain/langgraph 1.5.3, Oct 26, 2026)33343536
State and durabilityWorkflows run on the built-in execution engine and suspend and resume; Inngest adds step memoization and retries, which Mastra's customer story credits with raising Replit Agent 3's success rate from 80% to 96%; memory requires a storage provider5131415Checkpointers and stores persist state; durability modes exit, async, and sync set when changes are saved; in-memory checkpoints are lost on restart2728
Human reviewA workflow step can suspend, leaving the run in the suspended status until it is resumed with input13Interrupts pause graph execution at chosen points, and the graph resumes when re-invoked with a Command29
Telemetry and tracingUsage telemetry on by default, off with MASTRA_TELEMETRY_DISABLED=1; traces export to Langfuse, Datadog, Arize, or any OpenTelemetry platform1718Traces go to LangSmith when LANGSMITH_TRACING=true is set with an API key; without it nothing is traced30
Hosting and platform priceList prices in US dollars, checked Oct 28, 2026.Your own Node.js host, or Mastra Platform: Starter US$0 a month with unlimited users, 100K events, and 24 CPU hours; Teams US$250 a month with 1M events and 250 CPU hours; Enterprise custom1920Your own infrastructure, or LangSmith Deployment on Cloud, BYOC, or self-hosted; LangSmith Plus is US$39 per seat a month with 10K base traces, then 0.005 LSU per base trace at US$1.00 per LSU, and one free Serverless (Small) deployment3132
Cost at a stated workloadGILD's calculation from published rates, Oct 28, 2026. Inputs are stated, not measured; Mastra does not say how many events one run emits, and LangSmith meters deployments beyond the free one by vCPU and memory hours.GILD's calculation for 10,000 agent runs a month, each emitting 50 observability events and using 30 CPU-seconds, so 500,000 events and about 83.3 CPU hours, two developers, and model tokens left out: Mastra Platform Starter comes to about US$61 a month, US$40 for 400K events past the 100K included and US$20.77 for 59.3 CPU hours past the 24 included, with users unlimited; Teams covers it within US$2502021GILD's calculation for the same 10,000 runs at one trace each, two seats, and model tokens left out: LangSmith Plus comes to US$78 a month for two seats at US$39, with the 10,000 traces inside the 10K included and one free Serverless (Small) deployment; larger deployments add metered compute32
Trace retention and trainingData retention of 15 days on Starter and 6 months on Teams; the terms say Mastra does not use customer inputs or outputs to train models2022Base traces kept 14 days and extended traces 180 days; LangSmith states it does not use your data to train models32
When to chooseChoose Mastra if your team builds in TypeScript and wants memory, evals, and an optional hosted platform supplied with its agents and workflows1120Choose LangGraph if you want an explicit, checkpointed graph with interrupts in Python or TypeScript, traced and hosted on LangSmith2628

What practitioners report, side by side

Each product's figures come from its own practitioners, for the group you pick.

MastraMastra−36 NegativeHow this figure is made
LangGraphLangChain+25 PositiveHow this figure is made
Whose sources to count
Compare Mastra with
  • LangGraph

Overall signal

All practitioners rate LangGraph +25, 39 points above Mastra at −14. Most of the gap is in complaints, with 27 of Mastra's 66 sources negative against 8 of LangGraph's 62.

The score counts the authors from all practitioners that take a side, one vote each with their latest view, and is how far positive votes lead negative ones, per 100 votes, so Mastra's −14 rests on votes split 18 positive, 20 mixed and 27 negative and LangGraph's +25 on 23, 30 and 8.

A lead of a fifth of the votes reads positive and a lead of half strong positive, with the same lines below zero. The dots show each source, positive, mixed, negative. Build one pilot agent to measure the glue code and debugging time the framework adds.

Footprint on the AI Application Platform stack

They differ on five of 9 layers. Coverage is each product's placement on this stack, as its product page shows it. Core is a job the product owns; Supports and Touches are jobs it helps with. Coverage says nothing about how well a product does them.

Layers where Mastra and LangGraph differ
LayerMastraLangGraph
Model
Model gatewaySupportsTouches
Data
RetrievalSupportsTouches
Evaluate
EvaluationSupportsTouches
Operate
ObservabilitySupportsTouches
GuardrailsSupportsTouches
The 4 layers where they match
Layers where Mastra and LangGraph match
LayerMastraLangGraph
Model
Model APIsTouchesTouches
Build
OrchestrationCoreCore
Tool useSupportsSupports
Operate
Cloud deploymentSupportsSupports

What practitioners praise

Among all practitioners, LangGraph alone has four praise themes that reach 5 sources from 3 authors. Explicit Control Flow and Checkpointing & Durable State lead, at 22 of 25 and 10 of 18. Mastra alone has three praise themes that reach 5 sources from 3 authors. Agents in Real Work and TypeScript-First Developer Experience lead, at 15 of 15 and 14 of 14.

What practitioners praise, All practitioners
Praised forMastraLangGraph
Agents in Real Work15 of 15Not a theme in its sources
TypeScript-First Developer Experience14 of 14Not a theme in its sources
Workflows, Memory & Evals Included8 of 8Not a theme in its sources
Explicit Control FlowNot a theme in its sources22 of 25
Checkpointing & Durable StateNot a theme in its sources10 of 18
Production ReadinessNot a theme in its sources9 of 15
Human-in-the-Loop & Time TravelNot a theme in its sources4 of 5

Each count is the sources that praise a theme, or raise it as a concern, out of those that discuss it. A row pairs the two products when both carry a theme under the same or an equivalent name.

A count ending in "so far" has under 5 sources from 3 authors among all practitioners. "Not a theme in its sources" means GILD found no theme of that name in that product's sources.

What practitioners raise as concerns

Among all practitioners, Mastra alone has five concern themes that reach 5 sources from 3 authors. Memory, Recovery & Loop Defects and Opinionated Patterns lead, at 17 of 17 and 8 of 8. LangGraph alone has three concern themes that reach 5 sources from 3 authors. Complexity & Overhead and Breaking Changes & Docs lead, at 10 of 13 and 9 of 10.

What practitioners raise as concerns, All practitioners
Raised as a concernMastraLangGraph
Memory, Recovery & Loop Defects17 of 17Not a theme in its sources
Opinionated Patterns8 of 8Not a theme in its sources
Builds, Bundles & Hosting Surprises7 of 7Not a theme in its sources
Upgrades, Dependencies & Supply Chain7 of 7Not a theme in its sources
License, Lock-In & Vendor Trust5 of 5Not a theme in its sources
Complexity & OverheadNot a theme in its sources10 of 13
Breaking Changes & DocsNot a theme in its sources9 of 10
Typing & Language SupportNot a theme in its sources4 of 5
Mastra SignalsLangGraph Signals

36 sources

  1. GitLab blog: How GitLab reduced code-per-agentic-flow ratio by 45% (Sep 22, 2026)Public sourceabout.gitlab.com
  2. Grab Tech Blog: From firefighting to building, how AI agents restored our team's core productivity (Mar 19, 2026)Public sourceengineering.grab.com
  3. Qodo blog: Why we chose LangGraph to build our coding agent (Mar 21, 2025)Public sourceqodo.ai
  4. Nicola Richli on dev.to: Why we removed LangGraph from our AI platform (Jul 27, 2026)Public sourcedev.to
  5. Mastra customer story: How Replit's Agent 3 spins up thousands of Mastra agent sandboxes weekly (Sep 24, 2025)Official documentationmastra.ai
  6. r/LangChain: LangChain or Mastra for a faster TypeScript based AI platform? (thread from Sep 18, 2025)Public sourcereddit.com
  7. r/AI_Agents comment: a Mastra document-analysis run survived a deploy and resumed from its checkpoint (Aug 26, 2026)Public sourcereddit.com
  8. r/AI_Agents comment: chose Mastra over LangGraph for a TypeScript project (Feb 3, 2026)Public sourcereddit.com
  9. Latent Space: Full Story of Brex's AI Hail Mary, with Brex CTO James Reggio (Jan 17, 2026)Public sourcelatent.space
  10. Mastra issue #28645: mastra build needs about 13 times more memory since @mastra/deployer 1.79.0 (Oct 27, 2026)Public sourcegithub.com
  11. Mastra docs: Agents overviewOfficial documentationmastra.ai
  12. Mastra docs: SubagentsOfficial documentationmastra.ai
  13. Mastra docs: Workflows overviewOfficial documentationmastra.ai
  14. Mastra docs: Workflow runners (Inngest, Temporal)Official documentationmastra.ai
  15. Mastra docs: Memory overviewOfficial documentationmastra.ai
  16. Mastra docs: Evals overview (scorers)Official documentationmastra.ai
  17. Mastra docs: Observability overview (exporters)Official documentationmastra.ai
  18. Mastra reference: CLI, including telemetry and MASTRA_TELEMETRY_DISABLEDOfficial documentationmastra.ai
  19. Mastra docs: Deployment overview (Node.js 22.13, Hono server, deployers)Official documentationmastra.ai
  20. Mastra pricing (Platform Starter, Teams, Enterprise, and Self-Hosted)Official documentationmastra.ai
  21. Mastra docs: Mastra Platform observability (what counts as an event)Official documentationmastra.ai
  22. Mastra terms of service (no training on customer data, updated Oct 26, 2026)Official documentationmastra.ai
  23. Mastra LICENSE.md on GitHub (Apache 2.0 outside ee/)Official documentationgithub.com
  24. Mastra Enterprise Edition License 2.0 on GitHub (effective Sep 22, 2026)Official documentationgithub.com
  25. @mastra/core on npm (release history)Official documentationregistry.npmjs.org
  26. LangGraph.js docs: OverviewOfficial documentationdocs.langchain.com
  27. LangGraph docs: Persistence (checkpointers and stores)Official documentationdocs.langchain.com
  28. LangGraph docs: Checkpointers and durability modesOfficial documentationdocs.langchain.com
  29. LangGraph.js docs: InterruptsOfficial documentationdocs.langchain.com
  30. LangGraph.js docs: Observability (LANGSMITH_TRACING)Official documentationdocs.langchain.com
  31. LangSmith docs: LangSmith DeploymentOfficial documentationdocs.langchain.com
  32. LangSmith plans and pricingOfficial documentationlangchain.com
  33. LangGraph on GitHub (MIT license, 44,059 stars on Oct 28, 2026)Official documentationgithub.com
  34. LangGraph releases on GitHub (1.2.18, Oct 26, 2026)Official documentationgithub.com
  35. LangGraph.js on GitHub (MIT license, 3,386 stars on Oct 28, 2026)Official documentationgithub.com
  36. @langchain/langgraph on npm (1.5.3, Oct 26, 2026)Official documentationregistry.npmjs.org
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