FrameworkPicker
AutoGen
SmolAgents

Comparison Preset

VerdictAutoGen vs SmolAgents Ā· For Enterprises

Neither framework is a suitable choice for an enterprise environment without significant due diligence. While AutoGen's adoption metrics and lack of known vulnerabilities are strong points, its CC-BY-4.0 license presents a significant compliance risk for most organizations. Furthermore, its development has been inactive for over 100 days, raising concerns about long-term support. SmolAgents offers a preferable Apache-2.0 license and is actively maintained, but a known critical vulnerability makes it a non-starter from a security standpoint. Both options present unacceptable levels of risk regarding licensing, long-term stability, or security.

Overview

The bottom line ā€” what this framework is, who it's for, and when to walk away.

Bottom Line Up Front

AutoGen is a Python framework for developing AI agents and applications, ranging from no-code prototyping to scalable multi-agent systems. It offers a modular architecture, enabling conversational agents, complex workflows, and integration with external services. The framework supports research and distributed applications.

SmolAgents is a lightweight Python library designed for building and running AI agents with minimal code. It offers first-class support for 'Code Agents' that execute Python, along with standard tool-calling agents. The framework is highly model, modality, and tool-agnostic, providing great flexibility for integration.

Best For

Building conversational multi-agent AI applications, research, and scalable business workflows.

Rapidly building and deploying highly flexible, code-centric AI agents with diverse LLMs and tools.

Avoid If

no data

Complex built-in state management or persistent conversational memory is a core requirement.

Strengths

  • +Supports no-code agent prototyping via AutoGen Studio.
  • +Provides a programming framework for building conversational single and multi-agent applications (AgentChat).
  • +Offers an event-driven core for building scalable multi-agent AI systems.
  • +Facilitates deterministic and dynamic agentic workflows for business processes.
  • +Enables research on multi-agent collaboration.
  • +Supports distributed agents for multi-language applications through extensions like GrpcWorkerAgentRuntime.
  • +Extensible with built-in and community components for external services, including OpenAI API and Docker for code execution.
  • +Extremely simple, with agent logic abstracted minimally, making it easy to learn and use.
  • +First-class support for Code Agents executing Python actions in sandboxed environments for secure, composable logic.
  • +Supports common JSON/text-based Tool-Calling Agents for alternative paradigms.
  • +Model-agnostic, integrating easily with Hugging Face Inference, OpenAI, Anthropic, LiteLLM, Transformers, and Ollama.
  • +Modality-agnostic, capable of handling vision, video, and audio inputs.
  • +Tool-agnostic, allowing integration of tools from MCP servers, LangChain, or Hugging Face Spaces.
  • +Includes CLI tools for quickly running agents without boilerplate code.

Weaknesses

  • āˆ’no data
  • āˆ’Lacks explicit built-in state management for persistent agent memory or conversational context.
  • āˆ’Relies on external services (Modal, Blaxel, E2B, Docker) for secure code execution sandboxing.

Project Health

Is this project alive, well-maintained, and safe to bet on long-term?

Bus Factor Score

9 / 10
9 / 10

Maintainers

100
100

Open Issues

969
723

Fit

Does it support the workflows, patterns, and capabilities your team actually needs?

State Management

The framework manages state through conversational message exchanges and event-driven interactions between agents.

The framework does not provide an explicit built-in state management system; state must be managed via agent code or external mechanisms.

Cost & Licensing

What does it actually cost? License type, pricing model, and hidden fees.

License

CC-BY-4.0
Apache-2.0
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Perspective

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