langgraph vs OpenHands
Side-by-side comparison of two AI agent tools
langgraphopen-source
Build resilient language agents as graphs.
OpenHandsfree
π OpenHands: AI-Driven Development
Metrics
| langgraph | OpenHands | |
|---|---|---|
| Stars | 28.0k | 70.3k |
| Star velocity /mo | 2.5k | 2.7k |
| Commits (90d) | β | β |
| Releases (6m) | 10 | 10 |
| Overall score | 0.8081963872278098 | 0.8100328600787193 |
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
- +Multiple flexible interfaces (SDK, CLI, GUI) allowing developers to choose their preferred interaction method
- +Strong performance with 77.6 SWE-Bench score demonstrating effective software engineering capabilities
- +Large open-source community with 69k+ GitHub stars and active development support
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
- -Multiple components may create complexity in setup and maintenance for users wanting simple solutions
- -Documentation appears fragmented across different interfaces, potentially creating learning curve challenges
Use Cases
- β’Long-running autonomous agents that need to persist through system failures and operate over days or weeks
- β’Complex multi-step workflows requiring human oversight, approval, or intervention at specific decision points
- β’Stateful agents that must maintain context and memory across multiple sessions and interactions
- β’Automated software development and code generation for complex programming tasks
- β’Local AI-powered coding assistance integrated into existing development workflows
- β’Large-scale agent deployment for organizations needing to automate development processes across multiple projects