Which is more popular, Dolphin or LLM Sherpa?

Dolphin has more GitHub stars (9,059 vs 1,752).

Which is more actively developed, Dolphin or LLM Sherpa?

Dolphin had more commits in the last 90 days (0 vs 0).

Compare their capabilities, limitations and "best for" notes above. Trying each on a small task is the fastest way to decide.

Side-by-side comparison of two AI agent tools

Dolphin is growing faster: +28 GitHub stars in the last 30 days vs +0 for LLM Sherpa.
Pick Dolphin for: the official repo for “Dolphin: Document Image Parsing via Heterogeneous Anchor Prompting”, ACL, 2025. Pick LLM Sherpa for: developer APIs to Accelerate LLM Projects.

From GitHub data refreshed daily.

The official repo for “Dolphin: Document Image Parsing via Heterogeneous Anchor Prompting”, ACL, 2025.

LLM Sherpaopen-source

Developer APIs to Accelerate LLM Projects

+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

-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

•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

Which is more popular, Dolphin or LLM Sherpa?: Dolphin has more GitHub stars (9,059 vs 1,752).
Which is more actively developed, Dolphin or LLM Sherpa?: Dolphin had more commits in the last 90 days (0 vs 0).
Should I use Dolphin or LLM Sherpa?: Compare their capabilities, limitations and "best for" notes above. Trying each on a small task is the fastest way to decide.