RAG-Anything vs attachments
Multimodal document retrieval compared with flexible file ingestion
RAG-Anything and attachments are Python projects that help make varied file content usable in LLM workflows. RAG-Anything focuses on indexing and querying text, images, tables, and equations with retrieval, while attachments focuses on turning many inputs into structured artifacts for prompts, chat APIs, and RAG pipelines.
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.
attachments: Turn Files Into LLM-Ready Context
attachments is a Python library and CLI that turns documents, images, audio, and other inputs into structured text and image artifacts for LLM workflows. It supports local processing, optional service fallback, and adapters for prompts, chat APIs, and RAG chunks.
| RAG-Anything | attachments | |
|---|---|---|
| Language | Python | Python |
| License | MIT | MIT |
| Stars | 23.5k | 368 |
| Forks | 2.7k | 31 |
| Last analyzed | Oct 9, 2026 | Oct 3, 2026 |
Key differences
- RAG-Anything builds a multimodal knowledge graph and combines vector search with graph retrieval; attachments produces structured text and image artifacts with metadata and segmentation.
- RAG-Anything emphasizes querying evidence across text, figures, tables, and formulas; attachments emphasizes a common ingestion interface for files, directories, URLs, archives, and GitHub repositories.
- RAG-Anything relies on configured language-model and embedding functions, with a vision-model function for visual analysis; attachments can use local processors, a configured service, or a self-hosted server.
- RAG-Anything is built on LightRAG and accepts pre-parsed content lists; attachments offers adapters for prompts, Claude or OpenAI messages, and segment-aware RAG chunks.
- Both projects use Python and MIT licenses; the supplied repository figures list 23.5k stars for RAG-Anything and 368 for attachments.
Choose RAG-Anything if you…
- need retrieval across text, images, tables, and equations in the same documents.
- want a multimodal knowledge graph with vector and graph retrieval modes.
- already have parsing pipelines and want to feed structured content into a LightRAG-based workflow.
Choose attachments if you…
- need one interface to ingest varied files, URLs, archives, or GitHub repositories.
- want structured artifacts that can be adapted into prompts, chat messages, or RAG chunks.
- prefer local processing with optional service or self-hosted processing paths.
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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.