RAG-Anything vs KAG
Multimodal document retrieval compared with knowledge-grounded Q&A
RAG-Anything and KAG are Python frameworks for retrieval and question answering that combine language models with structured knowledge. RAG-Anything focuses on retrieving evidence across text, images, tables, and equations, while KAG focuses on domain-specific reasoning over knowledge graphs linked to source text.
RAG-Anything: Build Multimodal Document RAG
RAG-Anything extends LightRAG to ingest and query documents containing text, images, tables, and equations in one retrieval workflow. It suits teams building knowledge bases from mixed-format documents, provided they can configure parsers and language or vision models.
KAG: Build Knowledge-Grounded Reasoning and Q&A Systems
KAG combines knowledge graphs, text retrieval, and language models for factual and multi-step Q&A over professional-domain knowledge bases. It suits teams that can model domain knowledge and operate the OpenSPG engine alongside a Python toolkit.
| RAG-Anything | KAG | |
|---|---|---|
| Language | Python | Python |
| License | MIT | Apache-2.0 |
| Stars | 23.5k | 9.1k |
| Forks | 2.7k | 722 |
| Last analyzed | Oct 9, 2026 | Oct 9, 2026 |
Key differences
- RAG-Anything processes mixed-content documents, including images, tables, and equations; KAG builds domain knowledge bases for factual and multi-hop questions.
- RAG-Anything combines vector search with graph retrieval and supports several query modes; KAG combines text retrieval with graph, language, and numerical reasoning.
- RAG-Anything can accept pre-parsed content lists from external parsers; KAG supports schema-free and schema-constrained knowledge construction.
- RAG-Anything uses an MIT license; KAG uses an Apache-2.0 license.
- RAG-Anything depends on document parsers and may require LibreOffice for Office documents; KAG's developer setup depends on the OpenSPG engine and supporting services.
- RAG-Anything is suited to researchers and teams searching multimodal files; KAG is suited to domain teams building knowledge-backed Q&A and reasoning workflows.
Choose RAG-Anything if you…
- need retrieval across text, images, tables, and equations in documents.
- want to combine vector search and graph retrieval in a LightRAG-based workflow.
- already have a parser and want to feed its structured content into retrieval.
Choose KAG if you…
- need factual or multi-hop reasoning over domain knowledge and source text.
- want to construct knowledge with schema-free or schema-constrained extraction.
- need to combine graph reasoning, language reasoning, and numerical calculation in Q&A.
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This comparison is generated with AI from the OSRepos analyses of both projects. Always check each project's repository and documentation before choosing.