LangGraph is a low-level orchestration framework for constructing stateful, long-running AI agents. It offers fine-grained control to combine deterministic and LLM-driven steps, providing durable execution, persistence, and human-in-the-loop capabilities. This framework is ideal for bespoke agent workflows, but it is not a high-level abstraction.

Mastra is a TypeScript framework designed for building AI agents and applications, providing a structured approach to integrating large language models and defining tools with Zod schemas. It offers a `Studio` UI and simplifies LLM access across multiple providers. The framework is suitable for a wide range of AI-driven use cases, from customer-facing assistants to DevOps automation.

• +Provides fine-grained control to mix deterministic, hand-coded steps with LLM-driven agentic steps in a single graph.
• +Enables durable execution, allowing agents to persist through failures and resume from where they left off.
• +Supports human-in-the-loop functionality for inspecting and modifying agent state at any point.
• +Offers comprehensive memory capabilities for both short-term working memory and long-term memory across sessions.
• +Integrates with LangSmith for deep visibility into complex agent behavior, tracing execution paths, and capturing state transitions.
• +Provides low-level infrastructure supporting production-ready deployment of stateful workflows.

• +TypeScript-first development, promoting type safety and developer experience with explicit schemas.
• +Structured AI agent and tool definition using `Agent` and `createTool` primitives with Zod schemas for predictable interactions.
• +Simplified LLM integration, abstracting provider specifics via a `provider/model` string format and environment variables for API keys.
• +Developer tooling includes Mastra Studio, an interactive UI for managing agents, workflows, and tools, alongside quick project creation commands.
• +Extensive framework integration with pre-built support for popular web frameworks like Next.js, React + Vite, Astro, Express, SvelteKit, and Hono.

• −Operates at a very low level of abstraction, requiring more manual configuration than higher-level frameworks.
• −Does not abstract prompts or architectural patterns, placing responsibility on the developer.
• −Requires familiarity with underlying components like models and tools before effective use.
• −Higher-level abstractions, such as LangChain's agents, are recommended for beginners or simpler use cases.

• −Strict tool definition enforcement, where plain object tool definitions silently fail, requiring `createTool()`.
• −Mandates `{ "type": "module" }` in `package.json`, which may require adjustments for existing CommonJS projects.
• −Requires Zod for defining input and output schemas, introducing a specific dependency and validation paradigm.
• −Documentation does not explicitly detail a framework-provided strategy for persistent or application-wide state management beyond per-execution context.

LangGraph manages state through its `StateGraph` mechanism, enabling durable execution, persistence, and comprehensive memory for long-running, stateful agents.

The framework provides an execution context object containing request, tracing, and abort signals to tool functions, but does not detail a general strategy for application-level or persistent state.