Mastra vs LangGraph

Detailed side-by-side comparison to help you choose the right tool

Mastra

🔴Developer

AI Agents

TypeScript-native framework for building AI agents, workflows, and RAG pipelines — from the team behind Gatsby.js.

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Starting Price

Free

LangGraph

🔴Developer

AI agent framework

LangGraph is LangChain's open-source framework for building stateful, durable, multi-agent workflows in Python and JavaScript with graph-based control flow.

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Starting Price

Free

Feature Comparison

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FeatureMastraLangGraph
CategoryAI AgentsAI agent framework
Pricing Plans36 tiers8 tiers
Starting PriceFreeFree
Key Features
  • TypeScript-first agentic framework for agents, tools, memory, and instructions
  • Durable workflows and typed control flow
  • Observability with metrics, logs, and traces
  • Graph-based workflow orchestration
  • Deterministic state machine execution
  • Human-in-the-loop workflows

💡 Our Take

Choose Mastra if your team wants a TypeScript-first agent framework with hosted deployment and observability. Choose LangGraph if you prefer the LangChain ecosystem and need graph-based state machines with broad Python adoption.

Mastra - Pros & Cons

Pros

  • Best-in-class developer experience — the local playground is genuinely delightful
  • Type safety end-to-end via Zod schemas, rare in agent frameworks
  • MCP-native in both directions out of the box
  • Runs on Cloudflare Workers and Vercel Edge — not Node-only
  • Free and open source (MIT) with active backing from a credible founding team
  • Avoids the Python context switch for TypeScript-heavy teams

Cons

  • Younger ecosystem than CrewAI or LangChain — fewer community integrations
  • Mastra Cloud is still in preview with no public pricing yet
  • Smaller pool of example crews/templates compared to Python frameworks
  • Some advanced RAG features (multi-modal, hybrid search) still in beta

LangGraph - Pros & Cons

Pros

  • Open-source library is MIT-licensed and runs anywhere without platform lock-in
  • Native checkpointing makes durable, resumable, human-in-the-loop agents straightforward
  • First-class multi-agent patterns: supervisor, hierarchical, sequential, parallel branches
  • Tight integration with LangSmith for production observability, evaluations, and replays
  • Active maintenance from the LangChain team with frequent releases and strong community

Cons

  • More verbose than LangChain for simple agents — explicit state schemas and edge functions add overhead
  • LangSmith trace pricing ($2.50/1k base traces) is a real cost at production scale
  • LCU + deployment-minute billing makes pricing harder to predict than seat-only competitors
  • Steeper learning curve than role-based frameworks like CrewAI for newcomers
  • Best documented in Python; JavaScript SDK exists but lags in features

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🔒 Security & Compliance Comparison

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Security FeatureMastraLangGraph
SOC2✅ Yes
GDPR✅ Yes
HIPAA
SSO✅ Yes
Self-Hosted🔀 Hybrid
On-Prem✅ Yes
RBAC✅ Yes
Audit Log✅ Yes
Open Source✅ Yes
API Key Auth✅ Yes
Encryption at Rest✅ Yes
Encryption in Transit✅ Yes
Data Residency
Data Retentionconfigurable
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