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 | 2.9k |
| Commits (90d) | β | β |
| Releases (6m) | 10 | 10 |
| Overall score | 0.8081963872278098 | 0.8115414812824644 |
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 interface options (SDK, CLI, GUI) allowing developers to choose the best fit for their workflow and technical expertise
- +Highly scalable architecture that supports both local development and cloud deployment of thousands of agents simultaneously
- +Strong performance with 77.6 SWEBench score and active community support with nearly 70,000 GitHub stars
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
- -Complex setup process with multiple components and repositories that may overwhelm new users
- -Limited documentation clarity with information scattered across different repositories and interfaces
- -Requires significant technical knowledge to effectively configure and customize agents for specific development needs
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’Automating repetitive coding tasks and software development workflows across large development teams
- β’Building custom AI development assistants tailored to specific project requirements and coding standards
- β’Scaling AI-assisted development operations from individual developers to enterprise-level cloud deployments