Keywords AI
Docling vs LlamaIndex
Compare Docling and LlamaIndex side by side. Both are tools in the RAG Frameworks category.
Quick Comparison
| Category | RAG Frameworks | RAG Frameworks |
| Pricing | Open Source | Open Source |
| Best For | Developers and researchers who need accurate document parsing with layout and table understanding | Developers building data-intensive LLM applications who need flexible ingestion and retrieval |
| Website | github.com | llamaindex.ai |
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When to Choose Docling vs LlamaIndex
- PDF to structured data conversion
- Academic paper processing
- Financial report extraction
- Building RAG pipelines from any data source
- Enterprise knowledge base creation
- Multi-source data integration for AI
About Docling
Docling is IBM's open-source document conversion toolkit that transforms PDFs, DOCX, PPTX, and other document formats into structured JSON or markdown. It uses advanced layout analysis and table structure recognition to preserve document structure, making it ideal for preparing documents for RAG and LLM applications. Docling integrates with LlamaIndex and LangChain for seamless pipeline construction.
About LlamaIndex
LlamaIndex (formerly GPT Index) is a data framework for connecting LLMs with external data sources. It provides connectors for 160+ data sources, document parsers, indexing strategies, and query engines that make it easy to build RAG applications. LlamaIndex supports advanced retrieval patterns including recursive retrieval, knowledge graphs, and multi-document agents. The LlamaCloud managed service handles document ingestion and parsing at scale.
What is RAG Frameworks?
Frameworks and tools for building retrieval-augmented generation pipelines—document parsing, chunking, indexing, and query engines that connect LLMs to your data.
Browse all RAG Frameworks tools →