A Low Code Approach to Build Powerful AI Agents with Smolagents

Smolagents enable large language models (LLMs) to handle dynamic workflows with ease. Learn how its code-first, minimalistic design powers intelligent, flexible AI solutions for real-world tasks.

Smolagents is a Huggingface library designed to simplify the creation of agentic workflows for large language models (LLMs). By enabling LLMs to access external tools, manage dynamic workflows, and interact seamlessly with real-world data, Smolagents unlocks the potential of AI to handle tasks that require adaptability and decision-making. This article explores the concept of agents, the unique features of Smolagents, and how it bridges the gap between simplicity and functionality.

Table of Content

  1. What are Agents?
  2. When to Use Agents
  3. Key Features of Smolagents
  4. Building an Agent with Smolagents
  5. How Smolagents Compares

Let’s begin by understanding what agents are.

What Are Agents?

Agents enable LLMs to interact with the external world by accessing tools like APIs and search engines. They facilitate dynamic problem-solving through multi-step reasoning and workflows while controlling program execution based on observations and logic. This capability allows LLMs to handle complex tasks, retrieve information, and adapt intelligently to diverse scenarios in real-time.

When to Use Agents

Agents are ideal when workflows cannot be pre-determined or require flexibility. For example:

Deterministic Workflow (No Agent Needed): Pre-defined user paths like FAQs or booking systems.

Dynamic Workflow (Agent Required): Handling complex, multi-faceted queries, such as planning personalized travel itineraries based on variable user constraints.

In cases where deterministic systems fall short, agents provide the adaptability needed for real-world problem-solving.

Key Features of Smolagents

Smolagents focus on simplicity and flexibility while supporting advanced use cases.

Why Choose Smolagents?

Minimalistic Design: Built with ease of use in mind, it reduces abstraction complexity.

Code-First Agents: Supports writing actions in Python code for better composability, object management, and generality.

Secure Execution: Runs code in sandboxed environments, ensuring safe and isolated execution.

Multi-Model Compatibility: Works with open-source models from Hugging Face, as well as proprietary APIs like OpenAI and Anthropic.

Tool Hub Integration: Share and access tools seamlessly via Hugging Face Hub.

Image Source : HuggingFace

Building an Agent with Smolagents

Creating an agent with Smolagents requires:

Tools: Functions that interact with external systems.

Model: An LLM that powers the agent.

Step by Step Implementation

Step 1 : Install the Required Library

Step 2: Let’s create a custom tool to identify the most downloaded model on Hugging Face

Output

cross-encoder/ms-marco-MiniLM-L-6-v2

Step 3: Define a tool for model downloads

Now, let’s automate the process of fetching the most downloaded model for any given task.

Output

SmolAgents Output

Step 4: Run an agent to utilize the model download tool.

Next, we will create an agent to utilize this tool and provide results interactively.

Step 5: Create a web search tool.

Let’s now develop a more advanced system by integrating a web search tool into a managed agent architecture.

Output:

Smolagents Websearch tool output

How Smolagents Compares

Smolagents provide a streamlined alternative to traditional tool-calling agents by emphasizing code-based actions rather than relying on JSON or text-based instructions. This approach ensures better composability, allowing for reusable and nested structures that enhance workflow efficiency. 

It also offers improved object management, enabling seamless handling of outputs like images or files. Leveraging LLMs’ pretraining on code data, It ensures that Python instructions are more intuitive and effective for model interpretation, making them a powerful and practical choice.

Final Words

Smolagents enhance LLMs by enabling dynamic workflows, adaptability, and decision-making for real-world tasks. Its code-first design supports secure execution, multi-model compatibility, and tool integration via Hugging Face Hub, making it ideal for building flexible, powerful AI solutions for automation and complex queries.

References 

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Aniruddha Shrikhande

Aniruddha Shrikhande is an AI enthusiast and technical writer with a strong focus on Large Language Models (LLMs) and generative AI. Committed to demystifying complex AI concepts, he specializes in creating clear, accessible content that bridges the gap between technical innovation and practical application. Aniruddha's work explores cutting-edge AI solutions across various industries. Through his writing, Aniruddha aims to inspire and educate, contributing to the dynamic and rapidly expanding field of artificial intelligence.

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