AutoGen is a Python framework for building AI agents and multi-agent systems, providing components for no-code prototyping, conversational agent development, and scalable event-driven architectures. It supports complex workflows, research, and distributed applications through an extensible design.

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.

• +Supports multiple development entry points: no-code UI (Studio), Python for conversational agents (AgentChat), and a core event-driven framework (Core).
• +Facilitates scalable multi-agent AI systems, including deterministic/dynamic workflows and distributed agents.
• +Extensible through built-in and custom extensions for external services, like OpenAI Assistant API or Docker for code execution.
• +Provides specialized agents and tools, such as OpenAIAssistantAgent and DockerCommandLineCodeExecutor.

• +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.

• −Steep learning curve due to its layered architecture encompassing Core, AgentChat, Studio, and Extensions.
• −Potential overhead for extremely simple, single-turn LLM interaction tasks where multi-agent orchestration is not required.
• −Requires Python 3.10 or newer, which may conflict with older project environments.

Agents manage their state through conversational messages and reactions within an event-driven execution framework.

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.