It is a barebones library called **smolagents** designed to enable the creation and orchestration of agents that write Python code to call tools and manage other agents.
It is a barebones library called **smolagents** designed to enable the creation and orchestration of agents that write Python code to call tools and manage other agents. The library emphasizes simplicity, with its core logic fitting into approximately 1,000 lines of code, and provides minimal abstractions to keep the system lightweight and efficient.
**smolagents** offers first-class support for **Code Agents**, which write their actions as Python code snippets rather than using traditional methods like JSON or text blobs. This approach has been shown to reduce the number of steps required by 30%, leading to fewer LLM calls and improved performance on complex benchmarks. To ensure security, the library supports executing code in sandboxed environments via **E2B**.
The library is **model-agnostic**, meaning it can work with any large language model (LLM), including local models like **transformers** or **ollama**, models from providers on the **Hugging Face Hub**, or models from **OpenAI**, **Anthropic**, and others via **LiteLLM** integration. It is also **modality-agnostic**, supporting text, vision, video, and audio inputs, and **tool-agnostic**, allowing the use of tools from **LangChain**, **Anthropic’s MCP**, or even a **Hub Space** as a tool.
**smolagents** includes integrations with the **Hugging Face Hub**, enabling users to share and pull tools. It also provides a **CLI** with commands like `smolagent` for running multi-step Code Agents and `webagent` for web-browsing tasks. The library supports both **CodeAgent** and the more traditional **ToolCallingAgent**, though the former is recommended for its efficiency.
The library is designed to handle complex tasks, such as maintaining consistent code formats across system prompts, parsers, and execution, while encouraging users to customize and use only the components they need. Benchmarks show that open-source models using **smolagents** can compete with closed models in agentic workflows. Contributions are welcome, and users are encouraged to cite the library in publications using the provided BibTeX entry.
It is an agent designed to use its own browser to perform tasks on your behalf. This operator functions as an automated assistant capable of navigating the internet, accessing websites, and executing specific actions as instructed.
It is a platform designed for building, deploying, and managing AI Agents with a focus on reliability, accuracy, and seamless integration across systems.
It is a minimalist AI agent framework developed by Hugging Face, designed to enable developers to create and deploy powerful AI agents with minimal effort and code.
It is an AI-powered platform designed to streamline the software development lifecycle (SDLC) by automating repetitive tasks and enhancing engineering team productivity.
It is a full-stack web scraping and data extraction platform designed for developers and businesses to build, deploy, and publish web scrapers, AI agents, and automation tools.
It is a platform that uses Autonomous AI agents to simplify and automate API integrations, enabling developers to connect software systems seamlessly, securely, and efficiently.
It is an AI-powered platform designed to enhance engineering and DevOps workflows by automating repetitive tasks, enabling self-service operations, and improving overall efficiency.
It is a lightweight framework designed for building LLM-based (Large Language Model-based) agents, enabling users to create multi-agent applications with a focus on simplicity and flexibility.
It is an AI super assistant that provides access to state-of-the-art (SOTA) large language models (LLMs) and enables users to build, automate, and optimize AI-driven solutions for a wide range of applications.
It is a minimalist, 100-line framework designed to enable large language models (LLMs) to program themselves, focusing on simplifying the development of LLM-based applications such as multi-agent systems, prompt chaining, and retrieval-augmented generation (RAG).
It is a platform that enables users to create advanced, customized AI agents without requiring technical expertise, coding knowledge, or lengthy development processes.
It is an AI-powered tool called GoodGist that automates the process of converting unstructured emails and their attachments into organized records and actionable tasks.
It is an AI super assistant that provides access to state-of-the-art (SOTA) large language models (LLMs) and enables users to build, automate, and optimize AI-driven solutions for a wide range of applications.
It is an advanced AI agent designed to enhance customer support by delivering natural conversations, improving resolution rates, and reducing costs across all interactions.
It is the world's first text-to-website builder that creates fully functional, multipage websites from a single prompt, eliminating the need for hiring expensive designers, copywriters, web developers, or SEO agencies.
It is a Python-based project called Teenage-AGI that enhances an AI agent's capabilities by giving it memory and the ability to "think" before generating responses.
It is a platform that demonstrates how AI can enhance and transform customer conversations in real-time by allowing users to create custom scenarios and receive live, personalized calls from an AI phone agent.
