- Developers & Engineers: Quickly build custom agents using minimal code.
- AI Researchers: Experiment with code-based agent workflows and benchmarks.
- Data Scientists: Integrate AI agents into data processing and analysis pipelines.
- Product Teams: Deploy multi-modal agents for text, vision, and audio tasks.
- Open Source Enthusiasts: Share and reuse agent tools and models via the HF Hub.
How to Use Smolagents?
- Install the Package: Use pip to install with a toolkit of essential tools.
- Define Your Agent: Create a CodeAgent and assign tools and a model.
- Run Queries: Execute agent tasks by calling the run method with user input.
- Share Agents: Push agents to the Hugging Face Hub for reuse and collaboration.
- Code-Centric Actions: Agents write their steps as executable Python code.
- Model-Agnostic: Compatible with many LLMs and AI service providers.
- Sandboxed Execution: Securely run code with sandbox options to avoid risks.
- Multi-Modality Support: Handles inputs like text, images, video, and audio.
- Hub Integration: Easily share and load agents and tools with the HF Hub.
- Simple, concise codebase under 1,000 lines for easy customization.
- Supports numerous AI models and providers flexibly.
- Secure execution with sandbox environments to mitigate risks.
- Allows sharing and collaboration through Hugging Face’s Hub.
- Requires basic Python knowledge to fully leverage agent coding.
- Some advanced features need setup of external sandbox tools.
- Documentation may be challenging for absolute beginners.
- Limited GUI tools; mostly command-line and code-driven usage.
Custom
Pricing information is not directly provided.
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Editorial Note
This page was researched and written by the ATB Editorial Team. Our team researches each AI tool by reviewing its official website, testing features, exploring real use cases, and considering user feedback. Every page is fact-checked and regularly updated to ensure the information stays accurate, neutral, and useful for our readers.
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