CrewAI vs Mastra
A side-by-side comparison of CrewAI and Mastra, two Orchestration tools, drawn from Ignaite's continuously-verified listings.
Compared from listings verified as of
At a glance
| Attribute | CrewAI | Mastra |
|---|---|---|
| Category | Orchestration | Orchestration |
| Pricing | FREEMIUM | FREEMIUM |
| License | Open core | Open core |
| Deployment (differs) | — | Hybrid |
| Platforms | API, CLI | API, CLI |
| Model support (differs) | Model-agnostic | Multi-model |
| Vendor (differs) | crewAIInc | Mastra |
The honest brief
CrewAI
Models work as a crew of role-typed agents that delegate to each other, built standalone rather than on LangChain.
- Role-based multi-agent model
- Independent of LangChain
- Model-agnostic
- Good for research pipelines
- Opinionated structure
- Less flexible than graph frameworks
- Debugging multi-agent runs is hard
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