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.

Pydantic AI is a Python SDK for building typed, extensible AI agents that integrate with virtually any model and provider. It supports various interfaces and durable execution, enabling complex, long-running agent workflows with robust observability.

• +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.

• +Supports virtually every model and provider, swappable with a string or through Pydantic AI Gateway for failover and cost monitoring.
• +Provides structured outputs, typed dependency injection, and typed tools, moving errors from runtime to write-time, with Pydantic Graph for typed workflows.
• +OpenTelemetry-native instrumentation integrates with Pydantic Logfire for real-time debugging, tracing, and cost tracking, and uses Pydantic Evals for testing agent behavior.
• +Uses `capabilities` as reusable units for tools, instructions, and model settings, supporting composition of complex agents like Coder and Researcher.
• +Agents can run across various interfaces, including CLI, built-in web chat, realtime speech, UI event streams (AG-UI, Vercel AI), and as an editor agent.
• +Offers first-party, co-maintained durable execution on Temporal, DBOS, or Prefect, allowing agents to survive restarts and long waits with human-in-the-loop approval.
• +Built by the Pydantic team, leveraging its validation layer which is foundational for major AI SDKs and FastAPI, ensuring robust data handling.

• −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.

• −The ACP editor agent interface is marked as experimental, indicating it may not be fully stable or production-ready.
• −The `llms-full.txt` format, designed for LLMs, may be too large for some LLMs, limiting its utility for certain integrations.

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

The framework manages agent context and execution longevity using capabilities for memory and context management, and supports durable execution via integrations like Temporal to survive restarts and long waits.