Self-hosted open-source RAG framework

If your priority is on-premise + open source + easy self-hosting, my top recommendation is RAGFlow.

My ranking

Framework	Self-hosting	Ease	RAG capability	Best fit
RAGFlow	⭐⭐⭐⭐⭐	⭐⭐⭐⭐⭐	⭐⭐⭐⭐⭐	Enterprise document RAG
Dify	⭐⭐⭐⭐⭐	⭐⭐⭐⭐⭐	⭐⭐⭐⭐	RAG + workflows/apps
LlamaIndex	⭐⭐⭐⭐⭐	⭐⭐⭐	⭐⭐⭐⭐⭐	Developer-built RAG
Haystack	⭐⭐⭐⭐⭐	⭐⭐⭐	⭐⭐⭐⭐⭐	Production/custom pipelines
LangChain	⭐⭐⭐⭐⭐	⭐⭐	⭐⭐⭐⭐	Complex agents/orchestration

🥇 RAGFlow — my choice for your use case

RAGFlow documentation

RAGFlow is particularly attractive if you're building an internal enterprise knowledge system rather than merely embedding a RAG library into an application.

It gives you:

• Docker-based deployment
• Web UI for uploading/managing knowledge bases
• Document parsing and chunking
• Hybrid retrieval
• Reranking
• Citations/grounding
• Support for complex documents
• APIs for integrating with your applications
• Ability to use local LLMs through Ollama, Xinference, or LocalAIragflow.cc

The important distinction is that RAGFlow is closer to a complete RAG platform, whereas LlamaIndex and Haystack are primarily developer frameworks.

The current RAGFlow quickstart lists roughly 4 CPU cores, 16 GB RAM and 50 GB disk as a starting point, with Docker Compose deployment.ragflow.cc

🥈 Dify — easiest if you want an internal AI platform

Dify GitHub repository

I'd choose Dify if your requirements are broader than RAG:

"We want an internal ChatGPT-like platform where employees can build/use RAG applications."

Dify combines RAG, workflows, agents, model management, APIs and observability. Its Docker Compose deployment is straightforward, and its documented minimum is only 2 CPU cores / 4 GiB RAM for the basic server.github.com

The tradeoff is that Dify is an LLM application platform, not a RAG engine specialized around document retrieval.

🥉 LlamaIndex — best if your engineering team wants control

LlamaIndex documentation

I'd pick LlamaIndex if you're building your own backend/API and want to control the architecture.

It gives you building blocks for:

Documents
   ↓
Parsing / chunking
   ↓
Embeddings
   ↓
Vector / hybrid index
   ↓
Retriever
   ↓
Reranker
   ↓
LLM
   ↓
Your application/API

LlamaIndex has a particularly strong data/RAG ecosystem and supports local-model workflows.github.com

The downside: you have to assemble and operate more of the system yourself.

What about Haystack?

Haystack documentation

Haystack is my choice over LlamaIndex when the team values explicit, testable production pipelines. Its component/pipeline architecture gives you granular control over retrieval, routing, generation and evaluation.docs.haystack.deepset.ai

What I'd deploy on-prem

For a typical enterprise deployment, I'd seriously consider:

                   ┌─────────────────┐
                   │  Internal Users │
                   └────────┬────────┘
                            │
                     ┌──────▼──────┐
                     │ RAGFlow     │
                     │ Web / API   │
                     └──────┬──────┘
                            │
             ┌──────────────┼──────────────┐
             │              │              │
        ┌────▼────┐    ┌────▼────┐    ┌────▼────┐
        │ Parser  │    │ Search  │    │ Reranker│
        └─────────┘    └─────────┘    └─────────┘
                            │
                     ┌──────▼──────┐
                     │ Vector/Full │
                     │ Text Index  │
                     └──────┬──────┘
                            │
                     ┌──────▼──────┐
                     │ Local LLM   │
                     │ Ollama/vLLM │
                     └─────────────┘

For strictly air-gapped/on-prem, I'd keep all four layers local:

• RAGFlow
• embedding model
• reranker
• LLM

That way documents, embeddings and prompts never leave your network.

My recommendation by scenario

"We have PDFs, Word docs, policies, manuals, SharePoint exports, etc. and want an internal chatbot."
→ RAGFlow

"We want a self-hosted platform for employees to build AI apps and workflows."
→ Dify

"We're engineers building a custom RAG product/API."
→ LlamaIndex

"We need highly controlled, observable production pipelines."
→ Haystack

One important caveat: the framework isn't usually the biggest determinant of RAG quality. Document parsing, chunking, metadata, hybrid retrieval, reranking and evaluation tend to matter more.github.com+2

If you tell me your approximate document volume (e.g. 100K PDFs / 10 TB), GPU(s), number of users, and whether the environment is fully air-gapped, I can propose a concrete on-prem architecture—including RAGFlow vs LlamaIndex, vector DB, embedding model, reranker, local LLM, Kubernetes/Docker, and sizing.