smolagents

Agent Building

Use Hugging Face Smolagents framework for code-based agentic research with tool support. Supports multiple LLM providers and web search.

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Source SKILL.md: https://github.com/majiayu000/claude-skill-registry/blob/HEAD/skills/data/smolagents/SKILL.md

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Smolagents Skill

This skill leverages Hugging Face's Smolagents framework, a minimalist AI agent library where agents write Python code to accomplish tasks. It's highly efficient (30% token efficiency gain) and supports multiple LLM providers.

Setup

Dependencies: Requires smolagents with toolkit extensions.
```
pip install 'smolagents[toolkit]' python-dotenv
```

API Key Configuration: Supports multiple LLM providers. At minimum, set one:

# For Hugging Face Inference API (default, free tier available)
echo "HF_TOKEN=your_huggingface_token" >> .env

# OR for OpenAI
echo "OPENAI_API_KEY=your_openai_key" >> .env

# OR for Anthropic
echo "ANTHROPIC_API_KEY=your_anthropic_key" >> .env

if [ -f .gitignore ] && ! grep -q ".env" .gitignore; then echo ".env" >> .gitignore; fi

Usage

Use the scripts/agent.py script to run research tasks.

Command

python3 scripts/agent.py --task "<task_description>" [--model <model_type>] [--model-id <model_name>] [--web-search]

Parameters

--task (Required): The task or research question.
--model (Optional): Model type - hf (Hugging Face), openai, anthropic, or local (default: hf).
--model-id (Optional): Specific model ID to use.
--web-search (Optional): Enable web search tool (uses DuckDuckGo).
--verbose (Optional): Show detailed execution logs.

Example

# Using Hugging Face Inference API with web search
python3 scripts/agent.py --task "Research the latest developments in transformer architecture improvements" --web-search --verbose

# Using a specific model
python3 scripts/agent.py --task "Analyze the impact of RLHF on LLM performance" --model hf --model-id "Qwen/Qwen2.5-72B-Instruct" --web-search

Output

The script outputs:

Generated Python code (to stderr for visibility)
Task execution results
Final answer or research findings

Features

Code-as-Action: Agents write and execute Python code to solve tasks
Tool Support: Web search, file operations, and custom tools
Multi-Model: Supports HF Inference API, OpenAI, Anthropic, local models
Efficient: 30% token efficiency improvement over traditional approaches