llama_index vs ragflow
The two are close in size: 52.6k stars for llama_index, 92.5k for ragflow. Over the days we have tracked them llama_index moved +3% and ragflow +7.9%, so ragflow is growing faster right now.
llama_index leads on setup ease. ragflow does not take any axis by a clear margin.
Stars and commit dates come from our own daily tracking. The six axes are read off each project's documentation by our review pipeline, so they describe what a project says about itself, not what we measured in its code.
Where they stand today
A data-first RAG framework focused on document agents and agentic OCR with extensive indexing, retrieval, and 300+ integrations.
- Stars
- 52.6k
- Tracked growth
- +3%
- Maturity
- ●●●●●
- Last commit
- 4h ago
- Language
- Python
- License
- MIT
- Cost to run
- Your API key (or optional LlamaParse cloud)
Combines a production-grade RAG engine with agent orchestration and template-based document understanding to provide an agentic context layer for LLMs.
- Stars
- 92.5k
- Tracked growth
- +7.9%
- Maturity
- ●●●●●
- Last commit
- 13m ago
- Language
- Go
- License
- Apache-2.0
- Cost to run
- Your API key (paid LLMs); self-host or use cloud
Six axes, head to head
Each axis runs 0 to 5. The label under a score is what that project's own docs claim, not a category average.
| Axis | llama_index | ragflow |
|---|---|---|
Context depth How much of your codebase it sees before it answers: the open diff, the diff plus related files, or the whole repository. | ●●●●● Whole-data ingestion | ●●●●● Whole-repo analysis |
Noise control How it keeps output volume down — severity thresholds, deduplication, incremental runs over new commits only. | ●●●●● Rerankers + filters | ●●●●● Re-ranking & citations |
Customization How far it bends to your team: custom rules, prompts, style guides, per-path config. | ●●●●● Highly extensible | ●●●●● Config files & templates |
Privacy Whether your code stays on your own infrastructure: fully local, self-hostable, or cloud API only. | ●●●●● Local model support | ●●●●● Self-hostable |
Model freedom Whether you can point it at any provider, or it is wired to one. | ●●●●● BYO models & keys | ●●●●● Bring-your-own key |
Setup ease What it takes to get a first useful run out of it. | ●●●●● Config + API key | ●●●●● Multi-service deploy |
Which one to pick
Pick llama_index if…
Document-agent focused — pick LlamaIndex when you need a flexible, extensible RAG/document-agent/OCR framework with strong local model and integration support.
- Setup ease: Config + API key (4/5 against 1/5)
Pick ragflow if…
Self-hostable — choose RAGFlow when you need a production-ready, agentic RAG platform with template-based ingestion, grounded citations, and configurable LLM/embedding providers.
What people want from each one
run-llama/llama_index
infiniflow/ragflow
Hacker News: RAGFlow is an open-source RAG engine based on OCR and document parsing drew 230 points and 53 comments.
Questions people ask
Is llama_index better than ragflow?
llama_index leads on setup ease. ragflow does not take any axis by a clear margin. llama_index is worth picking when document-agent focused — pick LlamaIndex when you need a flexible, extensible RAG/document-agent/OCR framework with strong local model and integration support.
Which of llama_index and ragflow keeps my code private?
llama_index: Local model support (5/5). ragflow: Self-hostable (4/5).
What does each one cost to run?
llama_index: Your API key (or optional LlamaParse cloud). ragflow: Your API key (paid LLMs); self-host or use cloud.
Full profiles: run-llama/llama_index and infiniflow/ragflow. Everything else in RAG & retrieval.