Report: Mastra vs LangGraph

Executive summary

Mastra and LangGraph are capable frameworks for building agentic LLM applications, but they target slightly different audiences and trade-offs. Mastra (TypeScript-first) emphasizes developer ergonomics, RAG, streaming, and a batteries-included studio for JS/TS teams. LangGraph (LangChain ecosystem) focuses on graph-based, multi-agent orchestration, durable execution, and broad case studies at scale.

What supporters argue

• Mastra supporters point to documentation, tutorials, and workshops that show strong tooling for RAG, memory, workflows, and streaming (Mastra docs).
• LangGraph supporters highlight its graph model, first-class streaming, persistence, monitoring/observability, and published case studies (Klarna, Cisco, Elastic) showing production use (LangGraph overview).

What critics say

• Critics of Mastra raise concerns about real-time data processing limits, gaps in advanced error handling, and some reports of developer friction around debugging and state handling (critical review of Mastra).
• Critics of LangGraph note that its graph-first design can be rigid, requires manual workflow definitions, has state and memory management complexity (Redis checkpointing lifecycle), and community-reported issues including version compatibility, memory usage, and some security advisories in community posts (LangGraph issues discussion).

Side-by-side strengths

• Mastra: TypeScript/JS ecosystem fit, integrated RAG/memory recipes, streaming-focused UX, developer workshops and tutorials, strong community tutorials. (Mastra blog & tutorials, Couchbase tutorial).
• LangGraph: Graph-based orchestration, durable execution and persistence, built-in monitoring/analytics, multi-agent patterns (Supervisor, Swarm), and documented enterprise case studies (Cisco, Trellix, Elastic) (LangChain case studies, AWS blog on LangGraph+Bedrock).

Practical limitations to watch for

• Mastra: verify whether your app needs sub-100ms real-time guarantees; test observability and error-handling in your own infra; review community examples for edge cases.
• LangGraph: plan for state management (checkpointing, Redis lifecycle), expect to write workflow definitions (less out-of-the-box autonomy), and test memory/parallel execution for your load.

When to choose which

• Choose Mastra if you: prefer TypeScript, want quick RAG/memory recipes, need streaming-first UX, or are building primarily within the JS web stack.
• Choose LangGraph if you: need complex multi-agent orchestration, durable long-running workflows, strong observability for enterprise use cases, or integration into an existing LangChain-based stack.

Representative citations (selected)

• Mastra docs: https://mastra.ai/docs/workflows/overview
• Mastra blog: https://mastra.ai/blog/choosing-a-js-agent-framework
• Mastra tutorial (Couchbase): https://developer.couchbase.com/tutorial-rag-mastra-couchbase-nextjs/
• Mastra review / critical: https://blog.stackademic.com/mastra-ai-review-exploring-multi-agent-systems-with-house-m-d-example-4dcce446030c
• LangGraph docs: https://docs.langchain.com/oss/python/langgraph/overview
• LangGraph case studies: https://docs.langchain.com/oss/python/langgraph/case-studies
• LangChain customers / stories: https://www.langchain.com/customers
• Community issues and discussions: https://github.com/langchain-ai/langgraph/discussions/4210

Inline follow-up links

• does mastra support low-latency real-time?
• mastra observability & error handling
• langgraph state management & Redis checkpoints
• langgraph security & CVE discussion
• sample architectures: Mastra vs LangGraph

Conclusion

Both frameworks are mature enough for experimentation and production pilots, but they serve slightly different needs. Mastra is ideal for JS teams that want a batteries-included, streaming-first agent framework. LangGraph shines when you need durable, graph-mode orchestration for complex multi-agent systems with enterprise observability. Run a short pilot with representative workloads for each framework to validate performance, state handling, and operational overhead before committing.