Comparisons / AutoGen vs Smolagents
AutoGen vs Smolagents: Which Agent Framework to Use?
AutoGen by Microsoft models agents as ConversableAgents that chat with each other. Smolagents is HuggingFace's minimalist agent library. Here is how they compare — paradigm, ecosystem, and the use cases each one is actually built for.
By the numbers
AutoGen
56.7k
8.5k
Python
CC-BY-4.0
2023-08-18
Microsoft Research
Smolagents
26.4k
2.4k
Python
Apache-2.0
2024-12-05
Hugging Face
GitHub stats as of April 2026. Stars indicate community interest, not necessarily quality or fit for your use case.
| Concept | AutoGen | Smolagents |
|---|---|---|
| Agent | `ConversableAgent` with `system_message`, `llm_config` | `CodeAgent` or `ToolCallingAgent` with model and tools list |
| Tools | `register_for_llm()` and `register_for_execution()` | `@tool` decorator or `Tool` class with name, description, and callable |
| Conversation | Two-agent chat with `initiate_chat()`, message history | — |
| Multi-Agent | `GroupChat` with `GroupChatManager`, speaker selection | — |
| Nested Chats | `register_nested_chats()` for sub-task handling | — |
| Termination | `is_termination_msg` callback, `max_consecutive_auto_reply` | — |
| Code Actions | — | `CodeAgent` writes Python code as its action, executed in sandbox |
| Sandbox | — | E2B, Docker, Modal, or Pyodide sandbox for safe code execution |
| Agent Loop | — | Internal loop: think (LLM reasons), act (code/tool call), observe (result) |
| Model Support | — | HuggingFace Hub models, OpenAI, Anthropic, local via LiteLLM |
AutoGen vs Smolagents, head to head
AutoGen AutoGen by Microsoft models agents as ConversableAgents that chat with each other.
Smolagents Smolagents is HuggingFace's minimalist agent library.
Both wrap the same underlying agent pattern — an LLM call, a tool dispatch, a loop — in different abstractions. The choice between them is mostly about which mental model and ecosystem fits the team you have, not which one is technically more capable.
Pick AutoGen if
Pick AutoGen if autoGen excels at complex multi-agent workflows where agents need to debate or collaborate. For single-agent use cases or simple tool-calling agents, the plain Python version is significantly simpler. AutoGen is the right fit when the tradeoffs in its intro line up with how your team actually wants to work day-to-day; Smolagents would force you to translate.
Pick Smolagents if
Pick Smolagents if smolagents lives up to its name — it's genuinely minimal and the code-agent approach is a real innovation that reduces LLM calls by ~30%. If you want a lightweight agent library with HuggingFace ecosystem access, it's excellent. For understanding the fundamentals, the plain version is even simpler. Smolagents is the right fit when the tradeoffs in its intro line up with how your team actually wants to work day-to-day; AutoGen would force you to translate.
What both add
Both AutoGen and Smolagents pull in a class hierarchy and a dependency tree to wrap what is, at the core, an HTTP POST in a while loop. If your use case is straightforward — one provider, a handful of tools, a single agent — the framework cost may exceed the framework benefit. The lesson below shows the same pattern in ~60 lines without either dependency.
Or build your own in 60 lines
Both AutoGen and Smolagents implement the same 8 patterns. An agent is a function. Tools are a dict. The loop is a while loop. The whole thing composes in ~60 lines of Python.
No framework. No dependencies. No opinions. Just the code.
Build it from scratch →