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SmoLAgents

Hugging Face's lightweight agent framework

smolagents is Hugging Face's lightweight agent framework for building AI agents that can use tools, write and execute code, and collaborate in multi-agent setups. Designed for simplicity with minimal abstractions — agents are just LLMs that write Python code to orchestrate tool calls rather than using JSON-based function calling. Supports any LLM provider, integrates with Hugging Face Hub for sharing tools and agents, and runs with as few as 1,000 lines of core library code.

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About SmoLAgents

smolagents is Hugging Face's take on agent frameworks, prioritizing simplicity and minimal abstractions. The core library is remarkably small — around 1,000 lines — making it easy to understand and modify.

The key design choice is code-based agents: instead of JSON function calling, agents write Python code to orchestrate tools. This provides more flexibility for complex logic, loops, and data manipulation within a single agent step.

Supports any LLM provider through a simple interface. Integrates with Hugging Face Hub for discovering, sharing, and loading pre-built tools and agents. Multi-agent setups allow specialized agents to collaborate.

Ideal for developers who want a lightweight, understandable agent framework without the complexity of larger libraries. The code-first approach is particularly natural for Python developers.

Pricing & Platform Specs

Pricing Summary

smolagents is Hugging Face's lightweight, open-source library for building code-centric AI agents. It is 100% free under the Apache 2.0 license, using standard Hugging Face Inference or local model endpoints.

Supported Platforms

Python, Hugging Face Hub

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All SmoLAgents alternatives →
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fast-agent is an Apache-licensed Python framework for building and running LLM agents with full MCP (Model Context Protocol) and ACP support. It ships with an interactive shell mode, Skills management, and multi-model routing — making it a practical platform for coding agents, workflow automation, and agent evaluation across Claude, Codex, HuggingFace, and local models.

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Side-by-Side Comparisons

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SmoLAgents
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Pydantic AI logo
Pydantic AI

SmoLAgents vs Pydantic AI: Code-First Agents or Typed Production Systems?

SmoLAgents and Pydantic AI are modern Python agent frameworks with different definitions of leverage. SmoLAgents lets an agent express multi-step actions as Python code and can move that execution into Docker or remote sandboxes when stronger isolation is required. Pydantic AI centers typed dependencies, validated outputs, model portability, evals, observability, human approval, graphs, and durable-execution integrations. Pydantic AI serves as the more practical daily standard for testable production services; SmoLAgents is the sharper choice when code generation and composition are the agent's primary working method.

SmoLAgentsPydantic AI
smolagents official documentation mascot illustration
SmoLAgents
vs
LangGraph logo
LangGraph

smolagents vs LangGraph — Dynamic Code Agents or Stateful Graph Orchestration

smolagents and LangGraph both help teams build agentic applications, but they optimize for different stages. smolagents is best for compact Python-first experiments and code-agent loops. LangGraph is stronger when the workflow needs durable state, branching, human checkpoints, retries, and production orchestration. Choose smolagents for speed and simplicity; choose LangGraph when reliability and stateful control matter more.

SmoLAgentsLangGraph
genericagent logo
GenericAgent
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smolagents official documentation mascot illustration
SmoLAgents

GenericAgent vs SmoLAgents — Minimal Python Agent Frameworks in 2026

Both projects bet that you do not need a 50K-line framework to ship useful agents. GenericAgent is a ~3K-line self-evolving local computer agent; SmoLAgents is Hugging Face's equally compact but tool-centric agent library. Which minimal agent actually fits your workflow depends on whether you want a skill-accumulating local worker or a portable tool-using agent you can drop into any pipeline.

GenericAgentSmoLAgents
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SmoLAgents
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CrewAI

smolagents vs crewAI — Code-First Agent Execution vs Role-Based Multi-Agent Teams

smolagents by Hugging Face advocates for 'CodeAgents' where the LLM writes and executes Python code directly to call tools — achieving 30% fewer steps on complex benchmarks. crewAI organizes agents as role-based teams with structured collaboration workflows used by 100K+ certified developers. This comparison pits Hugging Face's minimalist code-first approach against crewAI's structured multi-agent orchestration.

SmoLAgentsCrewAI

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Verification dates are editorial checks. Routine CMS saves and automatic updatedAt timestamps do not advance them.

FAQ

What is SmoLAgents?

smolagents is Hugging Face's lightweight agent framework for building AI agents that can use tools, write and execute code, and collaborate in multi-agent setups. Designed for simplicity with minimal abstractions — agents are just LLMs that write Python code to orchestrate tool calls rather than using JSON-based function calling. Supports any LLM provider, integrates with Hugging Face Hub for sharing tools and agents, and runs with as few as 1,000 lines of core library code.

Is SmoLAgents free?

Yes — SmoLAgents is open source and free to use. smolagents is Hugging Face's lightweight, open-source library for building code-centric AI agents. It is 100% free under the Apache 2.0 license, using standard Hugging Face Inference or local model endpoints.

Is SmoLAgents open source?

Yes — SmoLAgents is open source.

Is SmoLAgents still maintained?

Yes — SmoLAgents is active. Its listing was last verified on August 26, 2026.

What are the best SmoLAgents alternatives?

The first editor-selected SmoLAgents alternatives are Browser Use, Agno, Claude-Flow, and more.