The OpenAI Agents SDK is a lightweight, Python-first framework for building production-ready agentic AI applications with minimal abstractions. It provides core primitives like agents, handoffs, and guardrails, along with built-in tracing and session management. The SDK prioritizes ease of learning while offering customization, making it suitable for rapid development and debugging of AI-driven workflows.

SmolAgents is a lightweight Python library designed for building AI agents with minimal code and abstractions. It provides first-class support for `CodeAgent` execution in sandboxed environments and `ToolCallingAgent` for traditional tool use. The framework is highly agnostic, allowing integration with various LLMs, input modalities, and tool sources.

• +Offers a lightweight, easy-to-use package with few abstractions for rapid development.
• +Provides a built-in agent loop that handles tool invocation and LLM interaction until task completion.
• +Enables Python-first orchestration and chaining of agents using native language features.
• +Features 'Agents as tools' (Handoffs) for powerful coordination and delegation across multiple agents.
• +Includes Guardrails for parallel input validation, safety checks, and fail-fast execution.
• +Automatically generates schemas and provides Pydantic-powered validation for Python function tools.
• +Offers a persistent memory layer via Sessions for maintaining working context across agent turns.
• +Supports built-in mechanisms for involving human users across agent runs (Human in the loop).
• +Comes with built-in tracing for visualizing, debugging, and monitoring workflows, supporting OpenAI evaluation and fine-tuning.
• +Allows building real-time voice agents with features like interruption detection and context management using `gpt-realtime-1.5`.

• +Extremely easy to build and run agents with minimal lines of code.
• +Supports `CodeAgent` for actions written in code, enabling natural composability.
• +Secure code execution is supported via sandboxed environments (Modal, Blaxel, E2B, Docker).
• +Offers `ToolCallingAgent` for standard JSON/text-based tool-calling paradigms.
• +Provides seamless integration with Hugging Face Hub for sharing and loading agents and tools.
• +Model-agnostic, allowing use of any LLM from Hugging Face Inference providers, APIs (OpenAI, Anthropic via LiteLLM), or local models.
• +Modality-agnostic, capable of handling vision, video, and audio inputs.
• +Tool-agnostic, supporting tools from MCP servers, LangChain, or Hugging Face Spaces.
• +Includes CLI tools (`smolagent`, `webagent`) for running agents without boilerplate.

• −Optimal functionality, especially for tracing, evaluation, and real-time features, is tied to the OpenAI ecosystem.
• −Limited to Python applications, making it unsuitable for polyglot environments or teams focused on other languages.
• −The 'very few abstractions' design may limit flexibility for highly complex or specialized agent architectures beyond its core primitives.

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

State management for agent execution is primarily handled through the underlying LLM's context window for single interactions or requires custom implementation within the agent's code for persistent or conversational state.