How Makers Build
Check my stackPlan my stack
Menu

LangChain vs Mastra

Two sides of the AI SDK & agent framework decision: broad framework and typeScript framework. When each fits, what it costs, who moves from one to the other, and what makers who chose it say.

LangChainBroad frameworkllms.txt
MastraTypeScript frameworkllms.txtCLI
Ask your AI about this, with this page as the source:ChatGPT ↗Claude ↗Perplexity ↗

Which fits you

Choose LangChain if
  • You need many prebuilt connectors for models, tools and data sources

Use it whenYou need many connectors and want LangGraph for stateful, multi-step agents.

Trade-offLayers of abstraction make debugging harder, and the API has changed often.

Choose Mastra if
  • Your app is TypeScript and you want agents without adding a Python service

Use it whenYou want a full agent framework — workflows, memory, evals — in the same TypeScript codebase as your app.

Trade-offYounger ecosystem, and some enterprise features sit under a separate source-available license.

At a glance

LangChainMastra
Used by65 makers' products · 205 open-source projects24 makers' products · 13 open-source projects
Downloads5.1M/wk−1% vs npm1.4M/wk3.6× vs npm
PricingOpen source and free to use; optional paid LangSmith platform for tracing, evals, and deployment. · paid from $39/seat/mo (LangSmith)Core framework free and open source; enterprise features under a separate source-available license. · paid from $250/mo
Free tierYesYes
Open sourceYes · self-hostableYes · self-hostable

What makers say

Makers on using it for AI SDK & agent framework, from Product Hunt and Starter Story interviews, each linked to the source. Products with a page of their own and fuller notes first.

On LangChain
We chose to integrate with LangChain as it is the leading LLM framework. We are happy that it was so straightforward to integrate with LangChain and build some use cases already.
Tilores entity resolution, the makerSep 2026 ↗
LangChain helped us orchestrate complex reasoning, memory, and planning steps behind our AI employee. It’s the brain behind turning a conversation into a functional website.
mysite.ai, the makerSep 2026 ↗
The whole ecosystem has been a great help till now. Even though we often have to develop our own. Langsmith is one of our go to tool for tracing and evaluating.
Kuration AI, the makerSep 2026 ↗
45 more on the LangChain page →
On Mastra
Really like the depth of product here, very hard to make a product of this scale high quality, impressed!
siift, the makerSep 2026 ↗
Mastra powers our chat infrastructure and makes developing a coding agent seamless
superset, the makerSep 2026 ↗
Shoutout to the team at Mastra for building an excellent agent orchestration layer. Mastra is a key part of Imagine.dev’s agentic architecture, helping us coordinate specialized agents and reliably turn high-level intent into structured execution. Their focus on developer experience and composability made it possible…
Imagine, the makerSep 2026 ↗
2 more on the Mastra page →

Loved and watch-outs

Themes that recur in makers' words and Hacker News comments, each linked to what it summarises.

LangChain
Most loved
  • One interface across many model providers, so switching models, prompts and chains is cheap. PHPH 2PH 3
  • A large ecosystem of document loaders, chunkers, retrievers and tool integrations for RAG and agents. PHPH 2PH 3
  • LangSmith tracing and evaluation show what an agent did and where it went wrong. PHPH 2PH 3
Watch-outs
  • Frequent breaking API changes force rewrites, and coding agents trained on older versions produce messy code for it. HNHN 2HN 3
  • Layers of abstraction make debugging slow, and many developers find calling model APIs or lighter libraries simpler. HNHN 2HN 3HN 4
  • Its built-in patterns for subagents and deep research lag current practice, such as handing off context through a file system. HNHN 2
On Product Hunt: 4.9★, 115 reviews · mentioned most: agentic workflow support, model integration, LangGraph framework
Mastra
Most loved
Watch-outs
  • Workflows with rule-based, non-LLM branching feel clunky, pushing some to write their own. HN
  • The Elastic v2 license bars offering it as a hosted service, so it is not open source in the usual sense. HN
  • Several Mastra npm packages were compromised in a supply-chain attack. HN
On Product Hunt: 5.0★, 9 reviews

Who uses each

LangChain65 makers' products
8baseAI Context FlowBrev.ioChirpz AgentClueoChatCoda by Conductor QuantumCodegenDataboxFabi.aifileAIGitLawgraph8+53 more
Mastra24 makers' products
ImagineonlookRevoltesiiftsupersetUpsolve AIArtifactCedarCounsel HealthDoculegisMedusaOrangecollective+12 more

What makers pair each with

With LangChain
LLM API
Frontend Framework
Transactional Email
Hosting
With Mastra
Transactional Email
Frontend Framework
Database
Payments

Neither quite right?

See the whole AI SDK & agent framework decision →
LlamaIndexBroad frameworkAgents that mostly retrieve and reason over your own documents.vs LangChain →
OpenAI Agents SDKThin SDKA small agent loop with tools, handoffs, guardrails and tracing built in, from the OpenAI team.
Pydantic AIThin SDKPython agents whose outputs must validate against typed schemas, across any model provider.
AI SDKThin SDKTypeScript agents with tool calling, multi-step loops and streaming to the UI, on any model provider.vs Mastra →
Claude Agent SDKAgent harnessAgents that read and edit files, run shell commands and manage long context, built on the same harness as Claude Code.
LangGraphMulti-agent orchestrationStateful agents built as an explicit graph of steps, with checkpoints, retries, streaming and pauses for human approval, in Python or JavaScript.