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smolagents

Build and coordinate AI agents that write code, call tools, and manage workflows.

8|Updated Jan 9, 2026
Search Tags:#multi-agent#ai-agents#tool-calling#memory-management#secure-execution#code-agent#smolagents
One-click install
npx skills add https://github.com/svngoku/coding-agents-skills --skill smolagents
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: smolagents
Source: https://github.com/svngoku/coding-agents-skills/tree/main/skills/smolagents
Command: npx skills add https://github.com/svngoku/coding-agents-skills --skill smolagents

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

SmolAgents provides a lightweight framework to create and coordinate AI agents that can write and execute code, call tools, and orchestrate multi-agent workflows.

Core Features & Use Cases

  • CodeAgent: agents that write and run Python code to perform tasks.
  • ToolCallingAgent: agents that interact with tools via structured calls without direct code execution.
  • Multi-agent orchestration: hierarchical and collaborative agent setups, with memory management and planning.
  • Secure execution: supports E2B, Docker, Blaxel sandboxes, and configurable security.
  • Model backends: supports InferenceClientModel, LiteLLMModel, TransformersModel, OpenAIModel, and more for flexible deployments.

Quick Start

Install and run a simple CodeAgent with defaults to begin building your first agent.

Frequently Asked Questions about smolagents

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I build AI agents that can write and execute Python code securely?▼

Build AI agents that write and execute Python code securely by using the CodeAgent framework, which supports configurable E2B, Docker, and Blaxel sandboxes to isolate and protect multi-agent workflows.

What is the best way to orchestrate multi-agent workflows with tool calling?▼

Orchestrate multi-agent workflows with tool calling by using hierarchical and collaborative setups that combine ToolCallingAgent for structured tool interactions and memory management to coordinate planning and execution.

Can I use local models like Ollama or Transformers to run AI agents?▼

Run AI agents with local models by configuring flexible model backends, supporting LiteLLM, Transformers, Ollama, and OpenAI-compatible endpoints for rapid prototyping and production-grade scenarios.

Does this multi-agent framework support MCP server integration?▼

The multi-agent framework supports MCP server integration, allowing CodeAgent and ToolCallingAgent to connect with external tooling and manage workflows across different environments.

How do I manage memory and planning for collaborative AI agents?▼

Manage memory and planning for collaborative AI agents through built-in memory management features that track context and orchestrate hierarchical multi-agent workflows efficiently.

When should I use ToolCallingAgent instead of CodeAgent for task execution?▼

Use ToolCallingAgent for structured tool interactions without direct code execution, whereas CodeAgent is suited for tasks requiring agents to write and run custom Python code.

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