Hugging Face announced the launch of a new open source library called smolagents, which aims to simplify the process of building intelligent agents and give language models stronger execution capabilities. Smolagents has a simplified code structure and supports multiple language models, including Hugging Face's own free inference API and models from companies such as OpenAI and Anthropic. Users can easily define tools and models and create custom tools to meet specific needs. The library also supports safe code execution in a sandbox environment, keeping users safe. smolagents will gradually replace its predecessor transformers.agents and become a more popular intelligent agent building tool.
HuggingFace has launched a new open source library called "smolagents", which aims to give language models stronger intelligent agent capabilities. Through a simplified code structure, smolagents make it easier for users to build intelligent agents that can perform a variety of tasks.
In modern artificial intelligence systems, language models (LLM) need to interact with the real world, such as calling search tools to obtain external information, or executing specific programs to complete tasks. Therefore, it is particularly important to give language models “agent” capabilities. Intelligent agents allow LLM output to control workflows, driving the application of AI forward.
So, when should you use intelligent agents? If users need a flexible workflow to solve tasks efficiently, intelligent agents are crucial. Take a travel website that handles customer requests as an example. When the request is relatively clear, it is enough to use a preset workflow; when the request involves more uncertain factors, an intelligent agent can provide the necessary flexibility and help. Users find the most suitable solution.
smolagents supports various language models, including Hugging Face's free inference API and models from many companies such as OpenAI and Anthropic. Users can easily build their own intelligent agents by defining tools and models, and even create custom tools to meet specific needs. The sample code shows how to use the Google Maps API to obtain travel times and generate travel plans. After several calculations, the intelligent agent finally provides the user with a reasonable travel recommendation.
In addition to simplified code and diverse tool support, smolagents also supports safe code execution in a sandbox environment to ensure user security. In the future, smolagents will gradually replace its predecessor transformers.agents and become the more popular choice.
Research shows that using code to perform operations is more efficient than the traditional JSON format, with better composability, object management capabilities, and expressiveness. This means that smolagents will open a new door for developers to take a step further in the field of AI agents.
Entrance: https://huggingface.co/blog/smolagents
Highlights:
smolagents is a newly released open source library designed to simplify the process of building intelligent agents.
Users can quickly create intelligent agents to complete specific tasks by defining tools and models.
Using code to perform operations is more efficient than traditional methods and can improve the performance and flexibility of AI agents.
All in all, smolagents provides developers with a powerful and easy-to-use tool that simplifies the building process of intelligent agents and improves their performance and flexibility. Future development is worth looking forward to. New open source libraries are expected to drive further developments in the field of artificial intelligence.