The OpenAI Agents SDK is a production-ready Python framework for building complex AI agent applications with minimal abstractions. It provides a managed runtime for orchestrating agents, tools, state, and sandboxed execution. Built-in tracing supports debugging, evaluation, and fine-tuning.

Pydantic AI is a Python agent framework for building robust Generative AI applications, leveraging Pydantic validation for type-safety and structured outputs. It offers deep observability integration and supports a wide array of models and providers. The framework emphasizes extensibility and durable execution for reliable agent operations.

• +Provides a lightweight, Python-first SDK with minimal abstractions for rapid development.
• +Includes a built-in agent loop, managing tool invocation and task completion.
• +Supports complex multi-agent orchestration through 'Agents as tools' and 'Handoffs'.
• +Offers sandbox agents for isolated, resumable execution environments.
• +Features built-in tracing for workflow visualization, debugging, evaluation, and fine-tuning.

• +Built by the Pydantic team, leveraging deep expertise in data validation and parsing.
• +Model-agnostic, supporting a wide range of LLMs and providers, with custom model implementation options.
• +Seamlessly integrates with Pydantic Logfire for real-time debugging, tracing, cost tracking, and performance monitoring.
• +Fully type-safe design, reducing runtime errors by catching issues at write-time with static type checking.
• +Provides powerful evaluation capabilities to systematically test agent performance and accuracy over time.
• +Extensible by design, allowing agents to be built from composable capabilities and defined via YAML/JSON.
• +Integrates Model Context Protocol (MCP) and UI event stream standards for external tools and interactive applications.
• +Supports human-in-the-loop tool approval, allowing specific tool calls to require user confirmation.
• +Offers durable execution, enabling agents to preserve progress across failures, errors, or restarts.
• +Provides streamed structured output with immediate validation, ensuring real-time data access.
• +Includes graph support for defining complex application flows using type hints, avoiding spaghetti code.
• +Features a type-safe dependency injection system for tools and instructions, enhancing testability and customization.

• −Adds runtime overhead and abstraction, which may be unnecessary for simple, short-lived model responses.
• −Reduces direct control over the agent execution loop, tool dispatch, and raw state management compared to direct API usage.

The framework manages state through 'Sessions', a persistent memory layer for maintaining working context across agent turns.

The framework enables durable agents to preserve their execution progress across failures and restarts, supporting long-running and asynchronous workflows.