langgraph vs smolagents
Side-by-side comparison of two AI agent tools
langgraphopen-source
Build resilient language agents as graphs.
smolagentsopen-source
🤗 smolagents: a barebones library for agents that think in code.
Metrics
| langgraph | smolagents | |
|---|---|---|
| Stars | 28.0k | 26.4k |
| Star velocity /mo | 2.5k | 427.5 |
| Commits (90d) | — | — |
| Releases (6m) | 10 | 2 |
| Overall score | 0.8081963872278098 | 0.7115452455171448 |
Pros
- +Durable execution ensures agents automatically resume from exactly where they left off after failures or interruptions
- +Comprehensive memory system with both short-term working memory for ongoing reasoning and long-term persistent memory across sessions
- +Seamless human-in-the-loop capabilities allow for inspection and modification of agent state at any point during execution
- +Code-first agent approach provides precise control over agent actions compared to natural language-based systems
- +Extremely lightweight architecture with core logic in ~1,000 lines of code, making it easy to understand and customize
- +Multiple sandboxed execution options ensure secure code execution in production environments
Cons
- -Low-level framework requires more technical expertise and setup compared to high-level agent builders
- -Graph-based agent design paradigm may have a steeper learning curve for developers new to agent orchestration
- -Production deployment complexity may be overkill for simple chatbot or single-turn use cases
- -Limited documentation in the provided source, potentially creating learning curve for new users
- -Code-based approach may require more programming knowledge compared to natural language agent frameworks
- -Dependency on external sandbox providers (Blaxel, E2B, Modal) for secure execution may add complexity
Use Cases
- •Long-running autonomous agents that need to persist through system failures and operate over days or weeks
- •Complex multi-step workflows requiring human oversight, approval, or intervention at specific decision points
- •Stateful agents that must maintain context and memory across multiple sessions and interactions
- •Building AI agents that need to perform precise code-based actions like data analysis, file manipulation, or API integrations
- •Developing secure agent systems where code execution must be isolated in sandboxed environments
- •Creating shareable agent tools and workflows that can be distributed through the Hugging Face Hub ecosystem