Mastra vs LangGraph
Detailed side-by-side comparison to help you choose the right tool
Mastra
🔴DeveloperAI agent framework
Mastra is a TypeScript-first AI agent framework and platform for building production agents with workflows, memory, MCP, evals, observability, and deployment.
Was this helpful?
Starting Price
FreeLangGraph
🔴DeveloperAI agent framework
LangGraph is LangChain’s framework for reliable agents with low-level control, deployment, observability, evaluation, sandboxes and enterprise LangSmith services.
Was this helpful?
Starting Price
FreeFeature Comparison
Scroll horizontally to compare details.
💡 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
- ✓Strong TypeScript fit for product teams already building in Next.js, Express, Hono, or similar JavaScript stacks
- ✓Combines framework, memory, workflows, evals, observability, and deployment instead of forcing teams to assemble every production feature separately
- ✓Apache 2.0 open-source framework gives teams a free self-hosted starting point before adopting the hosted platform
- ✓Public pricing includes useful operational limits such as observability events, CPU hours, retention, egress, and memory token usage
- ✓MCP support makes Mastra easier to connect with the growing ecosystem of agent tools and external capabilities
Cons
- ✗Developer-first framework; non-technical teams looking for a visual bot builder will likely move faster with Dify or a no-code platform
- ✗Usage-based overages for observability events, CPU time, egress, retrieval storage, and memory tokens require monitoring in production
- ✗Python-heavy teams may prefer OpenAI Agents SDK, Pydantic AI, or LangGraph rather than adding TypeScript to the agent stack
- ✗Production success still depends on careful eval design, tool permissions, security review, and rollback planning
- ✗Enterprise-grade controls such as RBAC, audit logs, dedicated SLAs, and VPC-style deployment are custom-priced rather than included in Starter
LangGraph - Pros & Cons
Pros
- ✓Excellent when you need deterministic agent control instead of one-shot prompt chains.
- ✓Pairs naturally with LangSmith for traces, evals, deployments, and production debugging.
- ✓The graph model makes approval steps, retries, routing, and long-running workflows easier to reason about.
Cons
- ✗More engineering-heavy than no-code builders; teams need Python/TypeScript skill and agent architecture discipline.
- ✗Pricing is split across framework and LangSmith services, so total cost depends on usage and deployment choices.
- ✗Overkill for simple chatbots or single API-call automations.
Not sure which to pick?
🎯 Take our quiz →🔒 Security & Compliance Comparison
Scroll horizontally to compare details.
Price Drop Alerts
Get notified when AI tools lower their prices
Get weekly AI agent tool insights
Comparisons, new tool launches, and expert recommendations delivered to your inbox.