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.
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Open-source LLMs as LangChain Agents
This Hugging Face blog post explores using open-source LLMs as agents within the LangChain framework. It examines the capability of various open-weight models to perform tool use, reasoning, and multi-step task execution in agentic settings. The post likely benchmarks or compares several models on agent-relevant tasks, providing practical guidance for deploying open-source alternatives to proprietary models in agent pipelines.
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.
Jupyter Agents: Training LLMs to Reason with Notebooks
Hugging Face published a blog post on training LLMs to operate as Jupyter notebook agents, enabling models to reason and execute code iteratively within notebook environments. The work covers dataset construction, training methodology, and evaluation for notebook-native agentic behavior. This represents a step toward LLMs that can conduct multi-step data analysis and experimentation autonomously within a familiar scientific computing interface.
Microsoft agent-framework: open-source library for building and orchestrating AI agents
Microsoft has published an open-source framework on GitHub for building, orchestrating, and deploying AI agents and multi-agent workflows, with support for both Python and .NET. The repository has accumulated 11,061 stars. It represents Microsoft's entry into the agent harness tooling space alongside existing frameworks like LangChain and AutoGen.
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.
License to Call: Introducing Transformers Agents 2.0
Hugging Face announced Transformers Agents 2.0, a major update to their agent framework built on top of the Transformers library. The release introduces new abstractions for tool use, multi-step reasoning, and agent orchestration, positioning it as a production-ready framework for building AI agents. The update reflects growing ecosystem investment in standardized agent tooling patterns.
awesome-llm-apps: 100+ Runnable AI Agent & RAG Application Examples
A curated GitHub repository collecting over 100 deployable AI agent and RAG (Retrieval-Augmented Generation) applications built with LLMs. The collection is designed for practical use — clone, customize, and ship. With 110,915 total stars and 202 added today, it reflects strong community interest in applied LLM tooling.
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.