Dolphin vs OpenHands
Side-by-side comparison of two AI agent tools
Dolphinfree
The official repo for “Dolphin: Document Image Parsing via Heterogeneous Anchor Prompting”, ACL, 2025.
OpenHandsfree
🙌 OpenHands: AI-Driven Development
Metrics
| Dolphin | OpenHands | |
|---|---|---|
| Stars | 8.9k | 70.3k |
| Star velocity /mo | 15 | 2.9k |
| Commits (90d) | — | — |
| Releases (6m) | 0 | 10 |
| Overall score | 0.5017123273298814 | 0.8115414812824644 |
Pros
- +Universal document parsing capability that handles both digital and photographed documents seamlessly
- +Advanced two-stage architecture with document-type-aware parsing strategies optimized for different document formats
- +Comprehensive 21-element detection including complex elements like formulas, code blocks, and tables with attribute field extraction
- +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
- -Research-focused tool that may require significant technical expertise to implement and integrate
- -Relatively new release with limited production use cases and community feedback
- -Large model size (3B parameters) may require substantial computational resources for deployment
- -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
- •Academic research document digitization and content extraction from PDFs and scanned papers
- •Enterprise document processing for complex reports, invoices, and forms with mixed content types
- •Automated parsing of technical documentation containing code snippets, mathematical formulas, and diagrams
- •Automating repetitive coding tasks and software development workflows across large development teams
- •Building custom AI development assistants tailored to specific project requirements and coding standards
- •Scaling AI-assisted development operations from individual developers to enterprise-level cloud deployments