docling vs langgraph
Side-by-side comparison of two AI agent tools
doclingopen-source
Get your documents ready for gen AI
langgraphopen-source
Build resilient language agents as graphs.
Metrics
| docling | langgraph | |
|---|---|---|
| Stars | 56.8k | 28.0k |
| Star velocity /mo | 1.3k | 2.5k |
| Commits (90d) | — | — |
| Releases (6m) | 10 | 10 |
| Overall score | 0.792451018042513 | 0.8081963872278098 |
Pros
- +Advanced PDF understanding with layout analysis, table structure recognition, and reading order detection
- +Supports wide variety of document formats including office documents, images, audio, and markup languages
- +Unified DoclingDocument representation simplifies integration with AI workflows and downstream processing
- +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
Cons
- -Processing complex documents with advanced features may require significant computational resources
- -Limited information available about performance benchmarks and processing speed for large document batches
- -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
Use Cases
- •Converting research papers and technical documents into AI-ready formats for RAG applications
- •Extracting structured data from business documents like invoices, contracts, and reports for automation
- •Preparing diverse document collections for training or fine-tuning language models
- •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