Introducing smolagents: simple agents that write actions in code.

Today we are launching smolagents , a very simple library that unlocks agentic capabilities for language models. Here’s a glimpse:

Any efficient system using AI will need to provide LLMs some kind of access to the real world: for instance the possibility to call a search tool to get external information, or to act on certain programs in order to solve a task. In other words, LLMs should have agency . Agentic programs are the gateway to the outside world for LLMs.

AI Agents are programs where LLM outputs control the workflow .

Any system leveraging LLMs will integrate the LLM outputs into code. The influence of the LLM's input on the code workflow is the level of agency of LLMs in the system.

Note that with this definition, "agent" is not a discrete, 0 or 1 definition: instead, "agency" evolves on a continuous spectrum, as you give more or less power to the LLM on your workflow.

Agents are useful when you need an LLM to determine the workflow of an app. But they’re often overkill. The question is: do I really need flexibility in the workflow to efficiently solve the task at hand? If the pre-determined workflow falls short too often, that means you need more flexibility. Let's take an example: say you're making an app that handles customer requests on a surfing trip website.

In the above example, you could just make a multi-step agent that has access to a weather API for weather forecasts, Google Maps API to compute travel distance, an employee availability dashboard and a RAG system on your knowledge base.

In a multi-step agent, at each step, the LLM can write an action, in the form of some calls to external tools. A common format (used by Anthropic, OpenAI, and many others) for writing these actions is generally different shades of "writing actions as a JSON of tools names and arguments to use, which you then parse to know which tool to execute and with which arguments".

🧑‍💻 First-class support for Code Agents , i.e. agents that write their actions in code (as opposed to "agents being used to write code"). To make it secure, we support executing in sandboxed environments via E2B .

So this system runs in a loop, executing a new action at each step (the action can involve calling some pre-determined tools that are just functions), until its observations make it apparent that a satisfactory state has been reached to solve the given task. Here’s an example of how a multi-step agent can solve a simple math question:

For instance, a user wants to ask : "I can come on Monday, but I forgot my passport so risk being delayed to Wednesday, is it possible to take me and my stuff to surf on Tuesday morning, with a cancellation insurance?" This question hinges on many factors, and probably none of the predetermined criteria above will suffice for this request.

Multiple research papers have shown that having the tool calling LLMs in code is much better.