New short course: Building Code Agents with Hugging Face smolagents!
Learn how to build code agents in this course, created in collaboration with @huggingface, and taught by @Thom_Wolf, its co-founder and CSO, and @AymericRoucher, Hugging Face’s Project Lead on Agents.
Tool-calling agents use LLMs to generate multiple function calls sequentially to complete a complex sequence of tasks. They generate one function call, execute it, observe, reason, and decide what to do next. Code agents take a different approach. They consolidate all these calls into a single block of code, letting the LLM lay out an entire action plan at once, which can be executed efficiently to provide more reliable results.
You’ll learn how to code agents using smolagents, a lightweight agentic framework from Hugging Face. Along the way, you’ll learn how to run LLM-generated code safely and develop an evaluation system to optimize your code agent for production.
In detail, you’ll learn:
- How agentic systems have evolved, gaining greater levels of agency over time—and why code agents are a next step.
- How code agents write their actions in code.
- When code agents outperform function-calling agents.
- How…
sandbox — An isolated environment where AI-generated code or agent actions run without being able to touch anything real.
Why it matters
Code-as-action is a real architectural alternative to function-calling loops, and the difficult piece — sandboxAn isolated environment where AI-generated code or agent actions run without being able to touch anything real.Full definition → execution of model-written code — is addressed directly.
Read the full post on X ↗
