RAGFlow
Deep document understanding — tables, images, multi-language
The top alternatives to WhyHow in the RAG Frameworks space, compared on features, pricing, and what they're best at.
WhyHow.AI is an open-source graph tooling provider focused on RAG systems and multi-agent systems, offering a RAG-Native Knowledge Graph (KG) Platform that makes it easy to create and query performant graph structures over data. The platform has built workflows and infrastructure that natively support small graph creation specifically optimized for RAG applications, with the KG Studio Platform currently supporting PDF, CSV, JSON, and TXT file formats. WhyHow.AI is building native connectors with vector databases to enable Graph RAG capabilities from pre-existing vector chunks through an API, with an SDK coming soon for uploading pre-processed data. The platform focuses on enhancing RAG systems through knowledge graph technology, allowing for more structured and connected data representations that improve retrieval quality and context understanding in AI applications.
RAGFlow
Deep document understanding — tables, images, multi-language
Unstructured
Ingests 65+ file formats: PDFs, DOCX, PPTX, HTML, images, emails
LlamaIndex
Data framework for LLM applications
Haystack
Modular RAG framework
Reducto
Vision parsing
Pathway
Rust-powered streaming engine — millions of data points/sec
Carbon (Perplexity)
Data connectors
Vectara
R2R
RAG engine
Docling
Converts PDFs, DOCX, PPTX, HTML, images to structured JSON/markdown
Chunkr
Captain
Scalable knowledge search
Compresr
Context compression
One platform for routing, observability, tracing, and evals across every LLM provider.