smolagentsHugging Face smolagents - minimalistus Python biblioteka, kuria naudojant pusę eilučių sukuriama kodas pirmiems AI agentams

How strong are open models for agentic workflows?

We've created CodeAgent instances with some leading models, and compared them on this benchmark that gathers questions from a few different benchmarks to propose a varied blend of challenges. Find the benchmarking code here for more detail on the agentic setup used, and see a comparison of using LLMs code agents compared to vanilla (spoilers: code agents works better). This comparison shows that open-source models can now take on the best closed models!

Asked by Diego Fernández · Feb 16, 2026

How smol is this library?

We strived to keep abstractions to a strict minimum: the main code in agents.py has <1,000 lines of code. Still, we implement several types of agents: CodeAgent writes its actions as Python code snippets, and the more classic ToolCallingAgent leverages built-in tool calling methods. We also have multi-agent hierarchies, import from tool collections, remote code execution, vision models... By the way, why use a framework at all? Well, because a big part of this stuff is non-trivial. For instance, the code agent has to keep a consistent format for code throughout its system prompt, its parser, the execution. So our framework handles this complexity for you. But of course we still encourage you to hack into the source code and use only the bits that you need, to the exclusion of everything else!

Asked by Ludovic Girard · Nov 27, 2025

How do Code agents work?

Our CodeAgent works mostly like classical ReAct agents - the exception being that the LLM engine writes its actions as Python code snippets. Actions are now Python code snippets. Hence, tool calls will be performed as Python function calls. For instance, here is how the agent can perform web search over several websites in one single action: Writing actions as code snippets is demonstrated to work better than the current industry practice of letting the LLM output a dictionary of the tools it wants to call: uses 30% fewer steps (thus 30% fewer LLM calls) and reaches higher performance on difficult benchmarks. Head to our high-level intro to agents to learn more on that. Since code execution can be a serious security concern (arbitrary code execution!), you should run agent code in a sandbox. We support several options: E2B, Blaxel, Modal — managed cloud sandboxes, simplest to set up Docker — self-hosted container isolation The built-in LocalPythonExecutor is not a security sandbox. It applies some restrictions but can be bypassed and must not be used as a security boundary. Alongside CodeAgent, we also provide the standard ToolCallingAgent which writes actions as JSON/text blobs. You can pick whichever style best suits your use case.

Asked by Greta Nowak · Oct 31, 2025