Mastra is a TypeScript framework for building AI agents and applications, abstracting away LLM complexities. It provides a structured approach for defining agents and tools, integrating with various LLM providers. Developers can quickly set up projects, leveraging templates and a Studio UI for management.

The OpenAI Agents SDK is a lightweight Python package for building production-ready agentic AI applications. It offers a small set of primitives for agents, tools, guardrails, and multi-agent coordination, abstracting away complex workflow management. Built-in tracing, state management, and specialized agent types like sandbox and realtime voice agents are included.

• +TypeScript-first framework for robust type checking and developer experience.
• +Structured agent and tool definition via dedicated classes and functions (Agent, createTool).
• +Integrated model router supports multiple LLM providers and thousands of models through a consistent API.
• +Automated environment variable handling for LLM API keys simplifies setup.
• +Includes an interactive UI, Mastra Studio, for building, testing, and managing agents and workflows.
• +Provides quickstart commands, framework integration guides, and pre-built templates for common use cases.
• +Tools enforce input and output schemas using Zod for data validation.

• +Lightweight and easy-to-use package with few abstractions for building agentic AI applications.
• +Production-ready, upgrading previous experimentation like Swarm.
• +Supports delegation between agents via 'Agents as tools' or Handoffs.
• +Includes Guardrails for validation of agent inputs and outputs, failing fast on invalid checks.
• +Built-in tracing for visualizing, debugging, evaluating, and fine-tuning agentic flows.
• +Provides Sandbox agents for isolated, resumable workspaces with manifest-defined files.
• +Supports Realtime agents for low-latency voice applications with features like interruption detection and context management.
• +Offers persistent memory through Sessions for maintaining working context across turns.
• +Integrates human-in-the-loop mechanisms during agent runs.
• +Automatically generates schemas for Python function tools with Pydantic-powered validation.

• −Requires a Node.js and TypeScript development environment.
• −Tools must be defined with 'createTool()' and specific properties; plain objects silently fail execution.
• −Specific 'provider/model' string format must be followed for LLM models.
• −Abstracts LLM provider SDKs, potentially limiting direct access to provider-specific advanced features.

• −Its higher-level runtime wraps model calls and manages the loop, tool execution, and state, reducing direct control for specific advanced use cases.
• −May introduce overhead for very short-lived workflows primarily focused on returning a single model response, where direct API calls might be more efficient.

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Sessions provide a persistent memory layer for maintaining working context within an agent loop, managing turns and execution state.