Head to head
LangGraph vs Mastra
Both are open-source agent frameworks with a real TypeScript story on one side and a real Python story on the other. Mastra is TypeScript-native from day one with batteries-included agents, workflows, and memory; LangGraph is a Python-first graph runtime that also ships a JavaScript port. The decision is which language your team is already standardized on, not which framework is more capable.
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LangGraph and Mastra are both open-source frameworks for building AI agents, and they sit at the same addressable intersection: a team that wants to write its own agent code rather than rent a hosted agent platform. What separates them is the stack they were built for and the shape they expect you to write.
LangGraph is a Python-first graph runtime. An agent is a cyclical graph of nodes and edges, with conditional branches and loops, and state checkpointed after every node transition under a thread_id. That makes it strong where a production agent has to survive a process restart, replay from any earlier checkpoint, or pause mid-run for human review. The same engine runs underneath LangChain's own quick-start `create_agent` harness, and production deploys sit on LangSmith, which adds tracing, evaluation, deployment, an LLM Gateway, and a no-code builder called Fleet. Pricing is $0/seat/month on Developer (5,000 traces), $39/seat/month on Plus (10,000 traces plus one free Serverless Small deployment), and custom on Enterprise, with usage-based LCU ($1.50 each) and LSU ($1.00 each) metering on top. Independent reviewers cite a steep learning curve, and the docs are split across the LangGraph, LangChain and LangSmith sites. A JavaScript port exists, but the canonical design and the production case studies — Klarna, LinkedIn, Uber and Elastic on the vendor's trusted-by strip — are Python.
Mastra is TypeScript-native. It was founded in 2024 by Sam Bhagwat, co-founder of Gatsby, raised a $22M Series A led by Spark Capital on April 9, 2026 ($35M total raised), and is Apache 2.0-licensed at the core. Where LangGraph asks you to compose a graph by hand, Mastra ships agents, durable typed workflows with retries and branching, observational and semantic memory, evals, metrics, traces and a server layer as one package. A model router abstracts OpenAI, Anthropic, Gemini and 180 other providers (6,772 models) behind one interface, with automatic fallback across providers. Built-in deployers target Vercel, Netlify and Cloudflare out of the box, and a separate Mastra Cloud platform adds a hosted Studio, observability and deployment. Self-hosted Apache 2.0 is free; Mastra Cloud Starter is free with 100K observability events and 24 CPU hours included; Teams is $250/month with 1M events, 250 CPU hours, SSO and SOC 2 docs; Enterprise and Enterprise Self-Hosted are custom. There is no Python variant at all, the Enterprise RBAC, SSO and IAM controls are gated behind a source-available Enterprise license rather than the Apache 2.0 core, and the ecosystem is younger — Mastra's 27.4K GitHub stars against LangChain's 142K+ reflect a multi-year gap.
The decision is rarely about raw capability. It is about which language your team is already standardized on. A Python shop building a long-running, stateful production agent with checkpointed replay and human review leans LangGraph, accepts the learning curve, and gets the LangSmith production layer with it. A TypeScript shop — Next.js, Node, serverless on Vercel or Cloudflare — leans Mastra, gets batteries-included without a separate Python service, and ships a working agent in a single framework. There is no third option: LangGraph's own not-for calls out "building a lightweight TypeScript app on serverless infrastructure where a framework like Mastra fits the deploy target better", and Mastra's own not-for calls out "Python-first teams and anyone needing a mature, multi-year-proven ecosystem".
How LangGraph and Mastra compare
| Feature | LangGraphVisit LangGraph ↗ | MastraVisit Mastra ↗ |
|---|---|---|
| Description | Open-source, MIT-licensed framework for building stateful, multi-agent AI workflows on cyclical graphs. | The TypeScript-native agent framework. Agents, durable workflows, memory and observability in one Apache 2.0 package, with an optional managed cloud platform. |
| Pricing | $0/seat/month | Free |
| Tags | ||
| Pricing model | freemium | freemium |
| Free tier | Yes | Yes |
| Model / LLM | OpenAI, Anthropic, Google, AWS Bedrock, Groq, Ollama and more | Model-agnostic (180 providers, 6,772 models) |
| Bring your own model | Yes | - |
| Interface | Library | Library |
| Public API | Yes | Yes |
| Open source | Yes | Yes |
| Deployment | Both | Both |
| Limitations |
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Who each one is for
LangGraph
Engineering teams building production agents who need cyclical graphs, durable state and replayable runs
From $0/seat/month
Mastra
Full-stack JavaScript and TypeScript teams, especially those already building in Next.js or Node, who want agents, durable workflows and memory unified in one framework without adopting a Python-first stack.
From Free
Verdict: LangGraph or Mastra?
Choose LangGraph if your team is standardized on Python, you are building a long-running production agent that has to survive restarts and replay from prior checkpoints, and you want the LangSmith deployment and tracing layer behind it. Choose Mastra if you are a full-stack TypeScript or JavaScript team shipping on Next.js, Node or serverless, and you want agents, durable workflows, memory and observability unified in one framework without a separate Python service.
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Frequently asked questions
- Is LangGraph the same as LangChain?
- No — LangGraph is a separate open-source, MIT-licensed library, but it is built and maintained by LangChain Inc. and is the runtime underneath LangChain's higher-level `create_agent` harness. LangSmith is the production platform that sits next to both for tracing, evaluation, deployment, an LLM Gateway and a no-code builder called Fleet.
- Can Mastra replace LangGraph in a Python project?
- No. Mastra is TypeScript-only, with no Python variant at all. A Python team that wants this kind of unified agent framework surface has to pick LangGraph or another framework that ships both languages — LangChain itself, LlamaIndex or the OpenAI Agents SDK all ship a Python variant alongside their JavaScript bindings.
- Which framework has lower total cost for a small team?
- Self-hosted Mastra is free on Apache 2.0; self-hosted LangGraph is free on MIT. Once you add production: LangGraph Plus is $39/seat/month with usage-based LCU ($1.50 each) and LSU ($1.00 each) metering on top; Mastra Cloud Starter is free up to 100K observability events and 24 CPU hours, with Teams at $250/month. A small TypeScript team usually spends less on Mastra Cloud than a small Python team spends on LangGraph Plus once trace and deployment metering starts.
- Does Mastra run on Vercel or Cloudflare Workers?
- Yes. Mastra ships built-in deployers for Vercel, Netlify and Cloudflare, and integrates directly into an existing Next.js app. LangGraph's managed LangSmith deployment is not designed for those serverless targets — which is one of the reasons LangGraph itself lists Mastra as the right fit in its not-for.
- Which has the larger ecosystem today?
- LangGraph, by a wide margin — it ships on top of LangChain's mature 142K+ GitHub-star ecosystem and a multi-year track record of named production users (Klarna, LinkedIn, Uber and Elastic on the vendor's trusted-by strip). Mastra is younger (founded 2024) with 27.4K GitHub stars and a shorter production reference list (Salesforce, Replit, Sanity, SoftBank, MongoDB, Brex and Factorial), but it has commercial backing from a $22M Series A led by Spark Capital.
Related comparisons and alternatives
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At a glance
Both are open-source agent frameworks with a real TypeScript story on one side and a real Python story on the other. Mastra is TypeScript-native from day one with batteries-included agents, workflows, and memory; LangGraph is a Python-first graph runtime that also ships a JavaScript port. The decision is which language your team is already standardized on, not which framework is more capable.
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Reach buyers mid-decision. Put your brand in front of them — sponsor slots are open to any advertiser (see the content policy); listed agents can go Featured at the top of their category.
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