LangGraph vs Mastra
A side-by-side comparison of LangGraph and Mastra, two Orchestration tools, drawn from Ignaite's continuously-verified listings.
Compared from listings verified as of
LangGraph
OrchestrationGraph-based agent orchestration. Stateful loops with checkpoints.
View LangGraphAt a glance
| Attribute | LangGraph | Mastra |
|---|---|---|
| Category | Orchestration | Orchestration |
| Pricing (differs) | FREE | FREEMIUM |
| License (differs) | Open source | Open core |
| Deployment (differs) | — | Hybrid |
| Platforms | API, CLI | API, CLI |
| Model support (differs) | Model-agnostic | Multi-model |
| Vendor (differs) | LangChain | Mastra |
The honest brief
LangGraph
Durable checkpointed state-graph with human-in-the-loop — long agent runs pause and resume, unlike one-shot chains.
- Durable checkpointed state
- Low-level graph control
- Debuggable long-running agents
- Runs in production at major firms
- Steeper learning curve
- More boilerplate than chains
- Tied to LangChain conventions
Mastra
Built TypeScript-first on the Vercel AI SDK — far less boilerplate and faster runtime than LangGraph's abstractions.
- TypeScript-native, low boilerplate
- Graph workflow engine plus memory and tools
- Self-hostable or deploy to Mastra Cloud
- Built-in observability
- Younger ecosystem, fewer examples
- Small plugin set (~50-60 integrations)
- Workflow chaining unintuitive for complex branching
- TypeScript-only; no Python path