LangGraph is a low-level Python framework designed for orchestrating complex, stateful LLM agents. It provides essential infrastructure for durable execution, streaming, persistence, and human-in-the-loop capabilities. Engineers gain fine-grained control over agent logic and state management.

Mastra is a TypeScript framework for building and shipping AI agents, focusing on rapid prototyping and production readiness. It provides a structured approach for defining agents, tools, and integrating with various LLM providers through a unified model router. The framework includes a Studio UI for managing development workflows and supports integration into existing web frameworks.

• +Provides durable execution, allowing agents to persist through failures and resume from where they left off.
• +Supports human-in-the-loop workflows, enabling inspection and modification of agent state at any point.
• +Offers comprehensive memory, supporting both short-term working memory and long-term memory across sessions.
• +Integrates with LangSmith for deep visibility, tracing execution paths, and debugging complex agent behavior.
• +Designed for production-ready deployment, handling challenges of stateful, long-running workflows.
• +Offers a low-level framework for maximum control over agent orchestration.

• +Provides a structured TypeScript framework for building AI agents, enabling fast prototyping and reliable deployment.
• +Offers an integrated model router that abstracts LLM providers (OpenAI, Anthropic, Google, xAI) using a `provider/model` string format and environment variables.
• +Enforces a clear structure for defining tools via `createTool()` including Zod schemas for input/output, preventing silent execution failures.
• +Includes Mastra Studio, an interactive UI for building, testing, and managing agents, workflows, and tools.
• +Supports integration with popular web frameworks like Next.js, React, Astro, Express, SvelteKit, and Hono.
• +Enables a wide range of use cases, including customer-facing assistants, internal copilots, data analysis, content automation, and DevOps.
• +Provides access to a large selection of models through its model router, beyond a short list of known IDs.

• −Requires familiarity with core agent components like models and tools before effective use.
• −Does not abstract prompts or architectural details, demanding manual management.
• −It is very low-level, which can increase complexity for beginners or those seeking higher abstractions.

• −Plain object tool definitions silently fail to execute; tools must be strictly defined via `createTool()` with specific properties.
• −Requires a `package.json` file with `"type": "module"` for project setup.
• −Requires Node.js 22.18.0 or later for direct TypeScript file execution.

LangGraph manages state by providing persistence, comprehensive memory, and durable execution for long-running agent workflows.

Mastra manages state through explicitly defined Agent and Tool objects, configured within a central Mastra instance, which then handles model routing and execution context via environment variables.