Comparisons / AutoGen vs Smolagents
AutoGen vs Smolagents: Which Agent Framework to Use?
AutoGen autogen by microsoft models agents as conversableagents that chat with each other. Smolagents smolagents is huggingface's minimalist agent library. Here is how they compare — and what the same patterns look like in plain Python.
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 | Plain Python |
|---|---|---|---|
| Agent | `ConversableAgent` with `system_message`, `llm_config` | `CodeAgent` or `ToolCallingAgent` with model and tools list | A function with a system prompt that POSTs to the LLM API |
| Tools | `register_for_llm()` and `register_for_execution()` | `@tool` decorator or `Tool` class with name, description, and callable | A dict of callables + JSON schema descriptions |
| Conversation | Two-agent chat with `initiate_chat()`, message history | — | A `messages` array that grows with each turn |
| Multi-Agent | `GroupChat` with `GroupChatManager`, speaker selection | — | Multiple agent functions called in sequence on shared `messages` |
| Nested Chats | `register_nested_chats()` for sub-task handling | — | A task queue (BFS) — agent schedules follow-ups via a tool |
| Termination | `is_termination_msg` callback, `max_consecutive_auto_reply` | — | The `while` loop exits when no `tool_calls` or `max_turns` reached |
| Code Actions | — | `CodeAgent` writes Python code as its action, executed in sandbox | LLM returns code string, you run `exec(code, {"tools": tools})` |
| Sandbox | — | E2B, Docker, Modal, or Pyodide sandbox for safe code execution | `subprocess.run()` in a Docker container, or restricted `exec()` with limited globals |
| Agent Loop | — | Internal loop: think (LLM reasons), act (code/tool call), observe (result) | A `while` loop: call LLM, execute action, append observation, repeat |
| Model Support | — | HuggingFace Hub models, OpenAI, Anthropic, local via LiteLLM | An HTTP POST to whichever provider's API you choose |
What both do in plain Python
Every concept in the table above — agent, tools, loop, memory, state — maps to a handful of Python primitives: a function, a dict, a list, and a while loop. Both AutoGen and Smolagents wrap these primitives in their own class hierarchies and APIs. The underlying pattern is the same ~60 lines of code. The difference is how much ceremony each framework adds on top.
When to use AutoGen
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.
What AutoGen does
AutoGen's core abstraction is the `ConversableAgent` — an agent that can send and receive messages. Two agents chat by alternating turns on a shared message history. `GroupChat` extends this to N agents, with a `GroupChatManager` that selects the next speaker (round-robin, random, or LLM-based selection). **Nested chats** allow an agent to spin up a sub-conversation to handle a complex subtask before returning to the main thread. AutoGen also provides code execution sandboxes, letting agents write and run code as part of their conversation. The framework thinks in terms of **conversations, not chains or graphs**. This makes it natural for workflows where agents need to debate, critique, or iteratively refine outputs together.
The plain Python equivalent
A `ConversableAgent` is a function that takes a `messages` array, calls the LLM with a system prompt, and returns the assistant message. Two-agent chat is a `while` loop where you alternate between calling `agent_a(messages)` and `agent_b(messages)`, appending each response. `GroupChat` is the same loop but with a **speaker selection step** — either rotate through a list or ask the LLM "who should speak next?" and call that agent function. Nested chats are a function call within the loop: pause the main conversation, run a sub-loop with different agents, and inject the result back. Tool registration is adding functions to a `tools` dict with their JSON schemas. The conversation-as-primitive model is **just `messages` arrays passed between functions**.
When to use Smolagents
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.
What Smolagents does
Smolagents takes a **distinct approach**: instead of having the LLM emit structured JSON tool calls, the `CodeAgent` has the LLM write Python code that performs the action directly. The model reasons about the task, writes code that calls available tools, and the framework executes that code in a sandboxed environment. This reduces the number of LLM calls by **about 30%** compared to traditional tool-calling agents, because the model can chain multiple operations in a single code block. The framework provides two agent types: - `CodeAgent` for the code-writing approach - `ToolCallingAgent` for traditional structured tool calls Sandbox options include E2B, Docker, Modal, and Pyodide for secure execution. The core library is deliberately minimal — **about 1,000 lines of logic** with few abstractions over raw Python.
The plain Python equivalent
A code agent is an LLM call that returns a Python code string, which you execute with `exec()` in a restricted namespace. You pass available tools as the namespace globals, the LLM writes code that calls them, and you capture the output. The sandbox is a Docker container or a restricted `exec()` with limited builtins. The agent loop is **identical to every other framework**: call the LLM, execute the action (code or tool call), append the result as an observation, repeat until done. Tool definitions are functions in a dict. Model support is an HTTP POST to an API endpoint. The entire code-agent pattern — including sandbox setup — fits in about **70 lines**. Smolagents wraps this cleanly, but the underlying mechanic is **straightforward `exec()` with safety guards**.
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.
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