- AI Developers & Engineers: Rapidly prototype agentic systems that interact with real-world tools and data.
- ML Practitioners & Researchers: Compare JSON-based vs. code-based agent paradigms and build multi-step workflows.
- Open-Source Contributors: Extend tools, integrate new models, or enhance sandboxing via community-driven code.
- Educators & Learners: Understand agent design and workflows through clear, minimal code.
- Enterprise Teams: Deploy secure, sandboxed agents with Docker, E2B, or WebAssembly environments.
- Automation Engineers: Build agents capable of web browsing, data extraction, code evaluation, or multimodal tasks via tool integrations.
How to Use Smolagents?
- Install the Package:
CopyEdit
pip install smolagents[toolkit]
-Create Your Agent:
python
CopyEdit
from smolagents import CodeAgent, DuckDuckGoSearchTool, InferenceClientModel
agent = CodeAgent(tools=[DuckDuckGoSearchTool()], model=InferenceClientModel())
- Run a Task:
CopyEdit
agent.run("Search latest news on AI and summarize key points")
The framework supports both code agents (which emit Python code to execute) and traditional tool-calling agents (with JSON-based tool invocation).
- Sandbox Execution: Run code in secure environments via Docker, E2B, or WebAssembly to ensure safe operation.
- Use Any LLM: Plug in local models (Transformers, Ollama) or hosted services (OpenAI, Anthropic, HF Inference) through LiteLLM integration.
- Share & Reuse Tools: Import or export tools and agents via Hugging Face Hub as reusable modules.
- Minimalist Architecture: Entire agent logic resides in ~1,000 lines—simple to inspect and modify.
- Code-Based Agents: Rather than JSON, agents generate executable Python code, enabling richer logic and fewer LLM calls.
- Modality & Tool Agnostic: Handles text, vision, audio, and video inputs; integrates with LangChain, Spaces, and custom tools.
- Secure Execution: Built-in support for secure execution via sandboxing using Docker, E2B, or Pyodide.
- Model Flexibility: Works with any LLM—from open-source transformer models to proprietary APIs.
- Seamless Reusability: Push and pull agents and tools through Hugging Face Hub for modular, shareable agent design.
- Elegant minimal design—easy to understand and contribute.
- Code agent architecture leverages Python’s full power and readability.
- Secure, sandboxed execution protects against unsafe code.
- Plug-and-play support for multiple models and tool integrations.
- Lively open-source ecosystem with Hub sharing and extensibility.
- May require setup of Docker/E2B or WASM environments for sandboxing.
- Less beginner-friendly than higher-level agent frameworks.
- Limited out-of-the-box UI or workflow tooling compared to commercial alternatives.
Custom
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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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