Smolagents is a minimalist, lightweight AI agent library developed by Hugging Face. It enables building AI agents that think and act by generating and executing Python code snippets for tasks, allowing more flexible and composable workflows compared to traditional action representations like JSON or text commands.

What are the main features of Smolagents?

The main features include code-based actions, where agents write executable Python code to perform tasks, enabling natural composition, state management, and complex logic. Simplicity is a key aspect, with the core library having a compact codebase designed for ease of understanding and debugging. It is model-agnostic, compatible with any large language model including those from Hugging Face Hub, OpenAI, Anthropic, and others via LiteLLM integration. Smolagents also offers secure execution by supporting code execution safely in sandboxed environments like E2B or Docker. It supports multi-agent and multi-step workflows, allowing chaining agents, multi-step reasoning, and parallel tool calls. Furthermore, it offers multiple input/output modalities, supporting text, images, video, and audio for diverse applications, and enables community sharing of tools and agents via the Hugging Face Hub.

Which language models work with Smolagents?

You can use any model, including Hugging Face models like Qwen, OpenAI models, Anthropic, and others supported through LiteLLM and Transformers.

What types of agents does Smolagents support?

Smolagents supports two main types of agents: CodeAgent, which is the default style where the agent generates Python code snippets to perform actions, and ToolCallingAgent, which uses structured tool calls expressed in JSON or text for invoking external tools.

What built-in tools come with Smolagents?

Smolagents includes a few essential tools to get started, such as DuckDuckGo Web Search, a Python Code Interpreter (built-in for CodeAgent), and a Transcriber (Speech-to-Text). Additionally, custom tools can be created with minimal effort to tailor agents for specific tasks.

How does Smolagents ensure safe code execution?

Code generated by agents runs in secure sandboxed environments such as E2B or Docker containers. This isolation safeguards the host environment from malicious or erroneous code.

What types of tasks can Smolagents perform?

Smolagents excels at web searching and retrieval, data analysis via Python execution, automating complex workflows with multi-step reasoning, orchestrating multi-agent systems (such as planners delegating to specialist agents), and handling multi-modal inputs like images, videos, and audio.

How do I get started with Smolagents?

To get started with Smolagents, you need to install it via pip using the command 'pip install smolagents huggingface_hub', then log in to Hugging Face with your API token. After that, define your agent by choosing an LLM and the necessary tools, and finally, run the agent with tasks such as web searches or code execution.

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Smolagents

4.6

• 169 views

Smolagents, from Hugging Face, helps you easily build smart AI agents. These agents are special because they write and run Python code to complete tasks, offering flexible automation. It works with many AI models and keeps code safe, making powerful AI solutions accessible.

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About Smolagents

Who It's For

Smolagents is for developers who want to build smart AI agents quickly. It's ideal if you need AI to solve problems by writing and running its own code, or if you seek a simple framework that works with many different AI models.

What You Get

You get a powerful, simple framework that enables AI agents to write and execute Python code. This offers flexible task automation and improved performance. It includes basic tools like web search. You can also share and use more tools from the Hugging Face Hub.

How It Works

Smolagents allows AI models to generate and run Python code to solve tasks. It integrates with many AI models, like those from Hugging Face and OpenAI. All code runs in secure, isolated environments. You install it, choose an AI model, add tools, and give your agent a task.

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Features & Capabilities

⚙️ Core Agent Framework

Minimalist Design

A compact codebase with minimal abstractions simplifies development of robust AI agents.

User-Friendly Development

Developers can swiftly define agents, provide tools, and run them immediately without intricate configurations.

Code-Centric Agents

Agents are designed to write and execute Python code snippets directly to perform actions.

High Efficiency

Code agents offer enhanced efficiency and accuracy, reducing steps and LLM calls by about 30%.

🔗 Ecosystem & Integration

Broad LLM Compatibility

Integrates effortlessly with any large language model, including those from Hugging Face, OpenAI, and Anthropic.

Secure Code Execution

Supports running code in sandboxed environments like E2B for protected and isolated execution.

Hugging Face Hub Integration

Allows easy sharing and loading of tools, fostering collaboration and ecosystem growth within the community.

Traditional Tool-Calling Support

Offers flexibility by also supporting conventional tool-calling agents where actions are defined as JSON or text.

✨ Advanced Agent Capabilities

Superior Code Composability

Code naturally supports function nesting and reuse, allowing for the expression of complex logic in agent workflows.

Ultimate Flexibility

Code can represent any operation a computer can perform, providing immense versatility for agents.

Efficient Object Handling

Simplifies the management and transfer of objects within agent workflows compared to JSON.

Performance Parity

Matches the performance of proprietary models using efficient open-source code agents in various benchmarks.

Use Cases

Automating Code-Driven Development and Task Execution

Developers often face challenges in automating complex, dynamic tasks that require code generation and execution. Smolagents empowers LLMs to write and run Python code snippets within secure sandboxed environments, drastically simplifying the creation of sophisticated automation, enhancing composability, and improving efficiency for development workflows.

Software DevelopmentFor: AI/ML Engineers, Software Developers, DevOps Teams

Empowering Intelligent Research and Data Analysis Workflows

Researchers and data analysts frequently struggle with manual data gathering, complex analysis, and orchestrating multi-step investigative processes. Smolagents facilitates the creation of agents that can perform intelligent web searches, execute data analysis code, and manage multi-agent research pipelines, streamlining the discovery of insights and automating complex data-driven tasks.

Research & Development, Data Science, Business IntelligenceFor: Data Scientists, Business Analysts, Researchers, Academics

Developing Specialized AI Assistants for Complex Tasks

Creating AI assistants that can handle multi-step reasoning, integrate custom tools, and operate across various domains can be complex. Smolagents provides a flexible framework for building highly customized AI agents, like travel planners or virtual assistants, by allowing seamless tool integration and orchestration of multi-step and multi-agent workflows, reducing development effort and increasing versatility.

Technology, Consulting, Customer ServiceFor: AI Solution Developers, Product Managers, System Integrators

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Smolagents

4.6

• 169 views

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Overview Features Use Cases FAQ Integrations