# LangGraph vs Mastra (2026): Agent Framework for Your Stack

> Source: https://theagentsindex.com/compare/langgraph-vs-mastra (structured, researched, re-verified)

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".

| Fact | LangGraph | Mastra |
| --- | --- | --- |
| What it is | 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 |
| API | Yes — LangSmith exposes an Assistants API with over 30 endpoints for state and memory. LangGraph itself is a Python and JavaScript library installed via pip or npm. | Yes |
| Tags | API · Claude · Enterprise · Free tier · GPT · Multi-agent · Open source · Self-hosted | API · Claude · Enterprise · Free tier · GPT · Open source · Self-hosted |
| Best for | Engineering teams building production agents who need cyclical graphs, durable state and replayable runs | 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. |
| Full facts | https://theagentsindex.com/langgraph.md | https://theagentsindex.com/mastra.md |

## 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.

## FAQ

**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.