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smolagents vs LangGraph

smolagentsLangGraph

Bottom line: smolagents for developers wanting minimal frameworks; LangGraph for engineering teams building production agents.

smolagentssmolagents

A barebones open-source library for agents that think in code

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LangGraphLangGraph

Graph-based orchestration for stateful, controllable LLM agents.

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Votes00
PricingFreeFreemium
CategoryAgent FrameworksAgent Frameworks
Tags
ai-agentsopen-sourcecode-agentshugging-facepython
agentsorchestrationllm-frameworkopen-sourceworkflow
Best for
  • Developers wanting minimal frameworks
  • Researchers prototyping agents
  • Hugging Face ecosystem users
  • Engineering teams building production agents
  • Developers needing controllable, resumable workflows
  • Teams already invested in LangChain
Pros
  • Extremely small, readable codebase
  • Code-first agent actions
  • Model-agnostic via LiteLLM and Hub
  • Free and fully open source
  • Hugging Face Hub sharing
  • Explicit, debuggable control over agent state and flow
  • MIT-licensed core, free to self-host
  • Model-agnostic, not tied to one LLM vendor
  • Strong support for cycles, checkpointing, and human-in-the-loop
  • Backed by the widely used LangChain ecosystem
Cons
  • Minimalism means fewer built-in features
  • No managed hosting or dashboard
  • Limited enterprise tooling
  • Code execution requires sandboxing care
  • Smaller feature set than large frameworks
  • Steeper learning curve than high-level agent builders
  • Requires comfort with graph and state concepts
  • Paid platform and observability are separate products
  • Documentation and APIs have evolved quickly
  • Can be overkill for simple single-shot LLM tasks

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