The OpenAI Agents SDK provides a lightweight, Python-first framework for building agentic AI applications with built-in primitives for agents, tools, guardrails, and multi-agent coordination. It abstracts away common complexities like tool invocation, turn management, and state persistence, while offering customization. The SDK is designed for production-ready agent workflows that go beyond single LLM calls.

Pydantic AI is a Python agent framework for building production-grade Generative AI applications and workflows, emphasizing type safety, observability, and durable execution. It is model-agnostic, integrating with various LLM providers and offering extensive capabilities like web search, tool approval, and graph support. The framework aims to bring the ergonomic development experience of FastAPI to GenAI.

• +Provides a built-in agent loop handling tool invocation and result processing until task completion.
• +Offers a Python-first approach, using language features for orchestration without new abstractions.
• +Supports multi-agent coordination through 'Agents as tools' (Handoffs) for task delegation.
• +Includes 'Sandbox agents' for running specialists in isolated workspaces with resumable sessions.
• +Implements 'Guardrails' for parallel input validation and safety checks, enabling fail-fast behavior.
• +Generates automatic schemas for Python functions, enabling them as Pydantic-validated tools.
• +Features built-in tracing for visualizing, debugging, and monitoring workflows, with support for OpenAI evaluation and fine-tuning.
• +Enables building low-latency voice agents with `gpt-realtime-2` for real-time interactions and context management.

• +Built by the Pydantic Team, leveraging widely adopted Pydantic Validation.
• +Model-agnostic, supporting numerous LLM providers and custom model implementations.
• +Seamless observability through tight integration with Pydantic Logfire, compatible with other OpenTelemetry platforms.
• +Fully type-safe design enhances auto-completion and type checking, shifting errors to write-time.
• +Powerful evaluation capabilities allow systematic testing and performance monitoring of agentic systems.
• +Extensible by design, supporting composable capabilities, a Harness library, third-party packages, and YAML/JSON agent definitions.
• +Integrates with Model Context Protocol (MCP), Agent2Agent (A2A), and UI event stream standards for broad interoperability.
• +Provides human-in-the-loop tool approval for flagged tool calls based on context.
• +Supports durable execution to preserve agent progress across failures and manage long-running workflows.
• +Offers streamed structured outputs with immediate validation for real-time data access.
• +Includes graph support to define complex applications with type hints, preventing spaghetti code.
• +Utilizes dependency injection via `RunContext` for type-safe customization and simplified testing.
• +Guarantees structured outputs conform to Pydantic models, including schema generation and validation with self-correction.

• −Introduces a higher-level runtime that adds overhead for simple, short-lived API calls where direct model response is the only goal.
• −Less control over core model loop, tool dispatch, and state handling when compared to direct use of the OpenAI Responses API.

The SDK provides 'Sessions' as a persistent memory layer to maintain working context within an agent loop and across turns.

Agents preserve their progress across failures and restarts via durable execution, while `RunContext` manages runtime dependencies and contextual data passed into instructions and tool functions.