Introducing smolagents: simple agents that write actions in code
Hugging Face has released smolagents, a lightweight agent framework where agents express actions as executable Python code rather than structured JSON tool calls. The library is designed for simplicity and composability, allowing agents to chain tool calls and manipulate outputs programmatically within a single code block. The release positions smolagents as a minimal alternative to heavier orchestration frameworks, with native integration into the Hugging Face ecosystem.
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Tiny Agents in Python: a MCP-powered agent in ~70 lines of code
Hugging Face published a tutorial demonstrating how to build a minimal AI agent in approximately 70 lines of Python using the Model Context Protocol (MCP). The post shows how MCP enables tool discovery and invocation for LLM-based agents with very little boilerplate. This is part of a broader trend of simplifying agent construction by standardizing tool interfaces.
Tiny Agents: an MCP-powered agent in 50 lines of code
Hugging Face published a blog post demonstrating how to build a minimal AI agent using the Model Context Protocol (MCP) in approximately 50 lines of code. The post showcases how MCP enables agents to discover and invoke tools dynamically, reducing the boilerplate required for agentic workflows. This serves as both a tutorial and a commentary on MCP's role in simplifying agent-tool integration in the current ecosystem.
DeepMath: A Lightweight Math Reasoning Agent with smolagents
Hugging Face published a blog post introducing DeepMath, a lightweight mathematical reasoning agent built on the smolagents framework. The post demonstrates how to construct a capable math reasoning agent using small models and tool-use patterns. This represents a practical application of the agent-tool ecosystem for specialized reasoning tasks.
CodeAgents + Structure: A Better Way to Execute Actions
Hugging Face published a blog post exploring the combination of code-based agents with structured outputs to improve action execution reliability. The post examines how enforcing structured generation can reduce errors and improve the robustness of agentic code execution pipelines. This represents a practical engineering approach to making code agents more dependable in production settings.
Introducing Agents.js: Give tools to your LLMs using JavaScript
Hugging Face released Agents.js, a JavaScript library that enables developers to equip large language models with tools and build agent workflows in a JS/TS environment. The library brings tool-use and agent orchestration capabilities—previously more common in Python ecosystems—to the JavaScript developer community. It integrates with Hugging Face's model hub and inference APIs.
smolagents Now Supports Vision-Language Models
Hugging Face has added vision-language model (VLM) support to its smolagents framework, enabling agents to process and reason over visual inputs alongside text. This update extends the agentic tooling ecosystem to multimodal workflows. The announcement comes from the Hugging Face blog, which serves as the primary communication channel for the smolagents project.
Agent-S: Open Agentic Framework for Human-Like Computer Use
Agent-S is an open-source Python framework by Simular AI designed to enable AI agents to interact with computers in a human-like manner. The project has accumulated 11,388 GitHub stars with modest daily growth of 29 stars. It represents an entry in the growing space of computer-use agent frameworks targeting GUI and desktop automation tasks.
Smol2Operator: Post-Training GUI Agents for Computer Use
Hugging Face published a blog post introducing Smol2Operator, a post-training approach for building GUI agents capable of computer use tasks. The work focuses on training small language models to operate graphical user interfaces, extending the SmolLM2 model family into the agent/computer-use domain. The post likely covers training methodology, datasets, and evaluation of the resulting GUI agent capabilities.