LlamaIndex vs RAGFlow
A side-by-side comparison of LlamaIndex and RAGFlow, drawn from Ignaite's continuously-verified listings.
Compared from listings verified as of
LlamaIndex
OrchestrationThe data framework for LLM apps — RAG, agents, and document workflows.
View LlamaIndexAt a glance
| Attribute | LlamaIndex | RAGFlow |
|---|---|---|
| Category (differs) | Orchestration | Search |
| Pricing | FREEMIUM | FREEMIUM |
| License | Open core | Open core |
| Deployment (differs) | — | Hybrid |
| Platforms (differs) | API, CLI | Web, API |
| Model support | Model-agnostic | Model-agnostic |
| Vendor (differs) | LlamaIndex | InfiniFlow Inc. |
| Capabilities (differs) |
|
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The honest brief
LlamaIndex
Retrieval-first where LangChain is orchestration-first — LlamaParse is the go-to for PDFs that defeat normal parsers.
- Best-in-class RAG primitives
- LlamaParse for hard documents
- Python + TypeScript
- Managed LlamaCloud option
- Narrower than full orchestration frameworks
- LlamaCloud parsing is paid
- API churn between versions
RAGFlow
DeepDoc parsing turns messy PDFs, tables, and scans into citation-backed chunks—grounding answers better than naive text-splitting RAG stacks.
- Apache-2.0, fully self-hostable
- Deep document, table, and scan parsing
- Hallucination-resistant grounded QA
- Hybrid vector + full-text search
- Built-in agent orchestration
- Heavier setup than hosted RAG APIs
- Cloud tiers cap apps and storage
- Resource-intensive to self-host
When to pick which
Both cover RAG pipeline, Vector search, Chat with documents, and Document parsing (structured).
Pick LlamaIndex if you need Agent framework and Structured extraction.
- Agent framework (secondary capability)
- Structured extraction (secondary capability)
Pick RAGFlow if you need Multi-agent orchestration and Cited answers.
- Multi-agent orchestration (secondary capability)
- Cited answers (secondary capability)
They also differ on:
- Platforms
- API, CLI · Web, API