1. [Home](https://www.g2.com/) 2. ... 3. [AI SDK Software](https://www.g2.com/categories/ai-sdk) 4. [Hugging Face smolagents Discussions](https://www.g2.com/products/hugging-face-smolagents/discuss) [ ![Product Avatar Image](https://images.g2crowd.com/uploads/product/image/large_detail/large_detail_567364cd027534fa57267991477dc8f3/hugging-face-smolagents.png "Product Avatar Image") ](/products/hugging-face-smolagents/reviews) [ Hugging Face smolagents ](/products/hugging-face-smolagents/reviews) (46)4.4/5 Smolagents is an open-source Python library developed by Hugging Face, designed to simplify the creation and execution of AI agents with minimal code. With a core logic comprising approximately 1,000 lines, smolagents emphasizes simplicity and efficiency, enabling developers to build powerful agents swiftly. The library is model-agnostic, allowing integration with various large language models (LLMs), including those from Hugging Face, OpenAI, Anthropic, and others via LiteLLM integration. It also supports multiple modalities, handling text, vision, video, and audio inputs, thereby broadening its application scope. Secure execution is ensured through sandboxed environments like E2B, Blaxel, Modal, and Docker. Additionally, smolagents offers deep integration with the Hugging Face Hub, facilitating seamless sharing and loading of agents and tools, and includes command-line utilities for quick agent deployment without extensive boilerplate code. Key Features: - Minimalist and Efficient Design: A compact codebase (~1,000 lines) with minimal abstractions enables quick agent development and easy understanding. - Code Agents for Direct Execution: Agents generate and run Python code snippets directly, reducing steps and LLM calls by approximately 30%, improving performance and handling complex logic. - Secure Sandboxed Execution: Supports running code in isolated environments like E2B to ensure safe and controlled execution of agent actions. - Wide LLM Compatibility: Compatible with any large language model, including Hugging Face Hub models, OpenAI, Anthropic, and others via LiteLLM integration. - Deep Hugging Face Hub Integration: Enables sharing and loading of tools and agents from the Hub, promoting community collaboration and ecosystem growth. - Support for Traditional Tool-Calling Agents: In addition to code agents, supports agents that generate actions as JSON or text blobs for flexible use cases. Primary Value and Problem Solved: Smolagents addresses the complexity and time-consuming nature of developing AI agents by providing a streamlined, efficient framework that requires minimal code. Its model-agnostic and modality-agnostic design ensures flexibility, allowing developers to integrate various LLMs and handle diverse input types. The secure execution environments mitigate risks associated with running agent-generated code, making it suitable for sensitive applications. By facilitating easy sharing and collaboration through the Hugging Face Hub, smolagents fosters a community-driven approach to AI agent development, accelerating innovation and deployment. Show More * * * ### 57.0 Nps Score Already have Hugging Face smolagents? ### All Hugging Face smolagents Discussions Search Most CommentedMost HelpfulCommon QuestionsNewest All DiscussionsDiscussions with CommentsCommon QuestionsDiscussions without Comments FilterFilter Filter byExpand/Collapse Sort by Most Commented Most Helpful Common Questions Newest Filter by All Discussions Discussions with Comments Common Questions Discussions without Comments ![Verified User in Computer Software](/assets/icons/anonymous-avatar-purple-4ae1032bdb50ee5682003170c8184aee790d25958bd397abbd384ba52c596a7b.svg "Verified User in Computer Software") G2 Verified User in Computer Software • about 1 month ago Software Developer Engineer Software Developer Engineer More Options - [ # How production-ready is Hugging Face smolagents for complex agentic AI apps? ](/discussions/how-production-ready-is-hugging-face-smolagents-for-complex-agentic-ai-apps) I like Hugging Face smolagents for its simplicity and functionality, and how it keeps agentic AI code flexible and easy to understand. It also helps reduce the complexity of building AI agents by connecting LLMs with tools and supporting multi-step task execution. My main concern is that it... 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