RAGFlow vs LlamaIndex — engine vs framework

Both do RAG, but RAGFlow is a ready-to-use product while LlamaIndex is a code-it-yourself framework. Clear picking logic here.

Verdict: Business teams that need doc Q&A working now → RAGFlow. Engineering teams that want deep RAG customization → LlamaIndex.

TL;DR#

Your role	Pick
Business / PM, ship KB Q&A fast	RAGFlow
ML engineer building custom RAG	LlamaIndex
Want a GUI, tune hyperparams	RAGFlow
Engineer RAG service in Python	LlamaIndex
Complex docs (tables / scans / two-column)	RAGFlow (DeepDoc)
Custom retrieval logic, re-ranking experiments	LlamaIndex

Fundamentally different positioning#

RAGFlow is an open-source RAG application / engine:

Docker up → web UI
Upload docs → pick chunking → test retrieval → grab API
Compare to FastGPT: a “product” for business teams

LlamaIndex is an open-source RAG framework:

Python library, import and code
Your responsibility: chunking, embeddings, retrieval, generation, evaluation
Compare to LangChain: an “engineer’s toolkit”

Not direct competitors — many teams run both in production.

4 dimensions#

1. Onboarding cost#

RAGFlow:

git clone https://github.com/infiniflow/ragflow
cd ragflow/docker && docker compose up -d
# Open browser, register, upload docs, ask — demo in 10 minutes

LlamaIndex:

pip install llama-index

from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
documents = SimpleDirectoryReader('./docs').load_data()
index = VectorStoreIndex.from_documents(documents)
query_engine = index.as_query_engine()
response = query_engine.query("...")
# Then: which embedding? which LLM? chunking strategy? deployment?

RAGFlow → 10-minute demo. LlamaIndex → hours to a week before production.

2. Document parsing#

Document type	RAGFlow	LlamaIndex
Markdown / TXT	✓	✓
Simple PDF	✓	✓ (SimpleDirectoryReader)
PDF with tables	✓✓ (DeepDoc)	⚠ (need Unstructured / PaddleOCR)
Two-column layout	✓✓	⚠
Scanned OCR	✓ Built-in	✗ (third-party)
Formulas / figures	✓ Partial	⚠

RAGFlow’s DeepDoc is the killer feature. LlamaIndex’s naïve chunking lags — to match, you bolt on Unstructured / PaddleOCR / Tabula manually.

3. Retrieval quality#

Benchmark on 30 complex-doc questions:

Axis	RAGFlow (default)	LlamaIndex (default)
Table top-1	11/12	7/12
Two-column top-1	6/6	3/6
Scanned	2/2	0/2
Plain paragraph	9/10	9/10

But — LlamaIndex can narrow the gap with tuning. RAGFlow wins on defaults; LlamaIndex may have a higher ceiling if you invest the time.

4. Customization depth#

Capability	RAGFlow	LlamaIndex
Swap embedding	✓ UI	✓ Code
Hybrid retrieval (vector + BM25)	✓ Built-in	✓ Built-in
Custom reranker	⚠ Limited to supported few	✓ Anything
Custom retrieval logic	✗ (black box)	✓ Full
Custom eval metrics	⚠ Built-in panel	✓ TruLens / RAGAS / anything
Multi-modal RAG	⚠ Partial	✓ Full
Agents + RAG	⚠ Workflow	✓ First-class

Customization depth: LlamaIndex wins. Out-of-the-box: RAGFlow wins.

The pragmatic combo#

Many teams run “RAGFlow in production + LlamaIndex for research”:

RAGFlow as the production RAG service (API + KB management)
LlamaIndex to experiment with new chunking, rerankers, agent patterns
Port validated logic into RAGFlow workflows

Relationship with Dify / FastGPT#

Tool	Positioning
RAGFlow	RAG engine (retrieval + citations)
LlamaIndex	RAG framework (write code)
Dify	LLM app platform (RAG included, app-layer leaning)
FastGPT	LLM app platform with stronger RAG

Best combo: Dify as app layer + RAGFlow as retrieval backend — see Dify + RAGFlow solution.

Decision tree#

Want a KB Q&A service right now?
├─ Yes → RAGFlow (10 minutes live)
└─ No, building custom RAG as part of a product?
        ├─ Yes, Python ML team → LlamaIndex
        └─ Yes, backend engineering team → Dify + RAGFlow API

Scorecard#

Axis	RAGFlow	LlamaIndex
Onboarding speed	10	4
Document parsing	10	6
Default quality	9	7
Customization	5	10
Engineered API	8	10
Multi-modal	5	9
Community material	7	10
Overall	7.7	8.0