We need to build a RAG system on-premise. What is the best open-source RAG framework that is easy to self-host?

RAGFlow and Dify are currently the best open-source RAG options for on-premise self-hosting because they provide out-of-the-box user interfaces, visual orchestration, and robust document parsing without requiring you to code a custom stack from scratch.

If you prefer a lightweight, developer-focused code library rather than a packaged platform, LlamaIndex remains the standard framework.

Top Open-Source Self-Hosted RAG Options

• RAGFlow: An open-source RAG engine built specifically around deep document understanding. It excels at parsing complex layouts, tables, and charts from messy PDFs and enterprise documents. It features a built-in visual web interface and supports local storage backends like Elasticsearch and Infinity.
• Dify: An open-source, low-code LLM application development platform that includes comprehensive RAG pipelines, agent workflows, and a polished chat interface. It makes self-hosting straightforward via Docker and easily hooks into local LLMs (via Ollama or vLLM).
• LlamaIndex: A data-centric Python/TypeScript framework optimized for connecting custom data sources to LLMs. It is less of a turnkey "app" and more of a toolkit with hundreds of data loaders, making it ideal if you want total programmatic control over your retrieval and chunking strategies.
• Onyx (formerly Danswer): An open-source enterprise search and RAG platform designed to connect directly to internal company tools (Slack, Google Drive, local storage) with an admin dashboard, user management, and native self-hosted deployment.

Comparison of Self-Hosted RAG Approaches

To help narrow down the right choice, tell me:

• What kind of documents are you ingesting (e.g., text-heavy PDFs, scanned images, messy tables)?
• Do you need a ready-to-use web UI for end-users, or are you building a backend API?

For an on-premise RAG system, there is no single "best" framework—the right choice depends on whether you want a developer framework, a full self-hosted product, or an enterprise production pipeline. The main open-source options today are LlamaIndex, LangChain, Haystack, Dify, and RAGFlow.

My default recommendation for a new on-prem enterprise RAG deployment would be:

ToolHalla Quidproquo## My practical pick: LlamaIndex

For most companies building their first on-prem RAG platform, I would start with:

                Users
                  |
             Web/API Layer
                  |
             LlamaIndex
                  |
        ---------------------
        |                   |
   Vector DB           Reranker
 (Qdrant/Milvus)   (BGE/Cohere/etc.)
        |
   Embedding Model
        |
   Local LLM Server
 (vLLM/Ollama/TGI)

A typical fully self-hosted stack:

• RAG framework: LlamaIndex
• Vector database: Qdrant or Milvus
• LLM serving: vLLM (GPU servers) or Ollama (smaller deployments)
• Embedding model: BGE, E5, or GTE family
• Reranking: BGE reranker
• Storage: PostgreSQL + object storage (MinIO)
• API: FastAPI
• Monitoring: OpenTelemetry + Prometheus/Grafana

This keeps every component inside your network.

Why I would not automatically choose LangChain

LangChain is excellent, especially when RAG is only one part of a larger agent workflow. However, for a pure "chat with company documents" system, its broader abstraction layer can introduce complexity.

Example:

• "Search our policies and answer questions" → LlamaIndex
• "Search policies, call SAP, create tickets, send emails, reason across tools" → LangChain/LangGraph

When Haystack is a better choice

Choose Haystack if you have:

• a dedicated ML/platform engineering team
• strict auditability requirements
• multiple retrieval pipelines
• complex routing logic
• a need to visualize and debug pipeline stages

Its component-based pipeline design is attractive in production environments.

When to use Dify or RAGFlow instead

If your goal is "deploy a ChatGPT-like internal assistant quickly", not "build a RAG platform," consider:

Dify

Good for:

• HR assistant
• IT helpdesk bot
• departmental knowledge assistants
• rapid prototypes

It provides a more complete application layer than a framework.

RAGFlow

Good for:

• legal documents
• engineering manuals
• PDFs with tables/figures
• scanned documents

It is more of a full RAG engine than a lightweight library.

A production architecture I would deploy on-prem

For a serious enterprise deployment:

                Nginx / API Gateway
                         |
                    FastAPI Service
                         |
                   LlamaIndex
                         |
        +----------------+----------------+
        |                                 |
     Qdrant                         Reranker
        |
   Document Metadata
        |
    PostgreSQL

Document Pipeline:
SharePoint / Files / Email / DB
             |
        Parser + OCR
             |
        Chunking
             |
        Embeddings
             |
        Vector Index

Generation:
vLLM
 |
Llama 3.x / Mistral / Qwen model

Things that matter more than the framework

In real deployments, retrieval quality usually depends more on:

1. Document parsing

• Bad PDF extraction ruins RAG.

2. Chunking strategy

• Fixed 500-token chunks are often insufficient.

3. Hybrid retrieval

• Combine BM25 + vector search.

4. Reranking

• Often improves accuracy significantly.

5. Evaluation

• Measure retrieval precision and answer faithfulness.

The framework choice is important, but it is rarely the biggest determinant of RAG quality.

Short answer

• Most teams: LlamaIndex + Qdrant + vLLM
• Enterprise production: Haystack + Qdrant/Milvus + vLLM
• Agent-heavy systems: LangChain/LangGraph + LlamaIndex retrieval
• Fast internal chatbot: Dify
• Complex PDFs: RAGFlow

For a first on-prem deployment, I would start with LlamaIndex unless you already know you need Haystack’s pipeline model or LangChain’s agent ecosystem.

The framework choice is important, but it is rarely the biggest determinant of RAG quality.

Short answer

If your priorities are on-premise + open source + easy self-hosting, my default recommendation in 2026 would be RAGFlow.

My shortlist

Why I'd pick RAGFlow

RAGFlow is much closer to a complete RAG system than just a Python library. It provides document ingestion, parsing, chunking, retrieval, citations, knowledge bases, chat, and APIs, and it can be deployed with Docker/Compose on your own infrastructure.

Its biggest advantage is document understanding. If your enterprise data includes PDFs, scanned documents, tables, diagrams, Word files, etc., RAGFlow's parsing pipeline is designed specifically around those difficult documents.

It can also use locally deployed models through Ollama, Xinference, or LocalAI, so an entirely on-prem stack is possible.

The downside is that it isn't particularly lightweight: the deployment involves several services and RAGFlow officially targets x86 and NVIDIA GPU environments.

When I'd choose LlamaIndex instead

If you're building a custom application rather than deploying a ready-made RAG platform, I'd choose LlamaIndex.

LlamaIndex gives you much finer control over:

• ingestion
• chunking
• metadata
• retrievers
• reranking
• vector stores
• query engines
• custom RAG strategies
• integration with your own application

It also supports local models such as Ollama and Hugging Face embeddings.

In other words:

RAGFlow = "give me a production RAG system."

LlamaIndex = "give me the building blocks to engineer my own RAG system."

What I'd build on-prem

For a serious enterprise deployment, I'd start with:

                    ┌──────────────────┐
                    │   User / Apps    │
                    └────────┬─────────┘
                             │
                             ▼
                    ┌──────────────────┐
                    │    RAGFlow       │
                    │  RAG / API / UI  │
                    └────────┬─────────┘
                             │
              ┌──────────────┼──────────────┐
              ▼              ▼              ▼
        Document Parser   Retrieval      Reranker
              │              │              │
              └──────────────┼──────────────┘
                             ▼
                     ┌──────────────┐
                     │ Vector/Search│
                     │    Engine    │
                     └──────┬───────┘
                            │
                            ▼
                     ┌──────────────┐
                     │ Local LLM    │
                     │ vLLM/Ollama  │
                     └──────────────┘

For an air-gapped environment, I'd make the models local as well: an inference server such as vLLM/Ollama, a local embedding model, and a local reranker. That keeps documents, embeddings, prompts, and generated answers inside your network.

One important point: the framework is not usually the hardest part of RAG. Chunking, document parsing, metadata, hybrid retrieval, reranking, evaluation, and access control will have a much larger impact on production quality.

My recommendation

If you tell me what kind of documents you have, approximate document volume, number of users, GPU(s)/CPU/RAM available, and whether the environment is completely air-gapped, I can propose a concrete on-prem RAG architecture (including RAGFlow vs LlamaIndex, vector DB, embedding model, reranker, local LLM, Kubernetes/Docker, and expected hardware).

When building an on-premise RAG (Retrieval-Augmented Generation) system, "the best" framework depends on whether you want a batteries-included platform (with a ready-to-use UI, user management, and document parsing out of the box) or a developer framework (where you code the logic piece-by-piece).

For an easy, fast self-hosted setup on-premise using Docker, batteries-included platforms are generally the best choice because they save weeks of engineering.

Top "Batteries-Included" Platforms (Easiest to Self-Host with UI)

1. • Best For: Rapid deployment of production-ready internal knowledge bases, visual workflows, and conversational chatbots.
   • Why it's great on-premise: It features a brilliant visual canvas, excellent multi-tenant user management, built-in vector database abstractions, and a simple docker-compose setup. It abstracts away the heavy lifting of connecting models, chunking, and app building.
2. • Best For: Heavy, complex document structures (deep tables, complex PDF layouts, charts, and figures).
   • Why it's great on-premise: Traditional chunking often fails on complex corporate PDFs/financial reports. RAGFlow excels at precise document understanding and template-based parsing before embedding, giving you much higher retrieval accuracy on messy enterprise data.
3. • Best For: Enterprise workplace search and connecting to internal wikis, Google Drive/S3 equivalents, and Slack on-premise.
   • Why it's great on-premise: It is designed from the ground up as a self-hosted alternative to enterprise tools like Glean, featuring robust user permissioning and native connectors.
4. • Best For: Lightweight, ultra-easy private desktop or server deployment.
   • Why it's great on-premise: It is a sovereign workspace tool that can run entirely air-gapped with local LLMs (via Ollama/LM Studio) and local vector storage with zero friction.

Top Developer Frameworks (If you need custom code)

If your team prefers to write Python code and build a custom architecture from scratch rather than using an existing UI/platform:

1. • Best For: Pure RAG pipelines, data ingestion from 100+ formats, and advanced node-parsing/retrieval optimization.
   • Why choose it: It is purpose-built for connecting private data to LLMs, making it much more concise for search/Q&A logic than general-purpose tools.
2. LangChain /
   • Best For: Multi-agent workflows, complex decision-making loops, and extensive custom integrations.

Recommended On-Premise Stack Blueprint

If you want the path of least resistance for an enterprise on-premise deployment:

• Orchestration/UI: Dify (for general use) or RAGFlow (if your docs are deeply complex PDFs).
• Local LLM Engine: Ollama or vLLM running models like Llama 3 or Qwen locally.
• Vector Database: Qdrant, Milvus, or PGVector (PostgreSQL extension, which is usually easiest for internal IT departments to back up and maintain on-premise).

To help narrow down the ideal stack, tell me:

• What kinds of documents will you be ingesting (simple text/markdown, complex PDFs with tables, scanned images)?
• Do you need to connect to external enterprise identity providers (like LDAP/Active Directory or SAML)?

For an on-premise RAG system, the “best” open-source framework depends on whether you want a developer framework or a ready-to-run self-hosted product. The strongest options today are LlamaIndex, LangChain, Haystack, RAGFlow, and Dify.

My recommendation:

Auto Learning Agents Olostep## My default on-prem RAG stack recommendation

For most companies building an internal knowledge assistant, I would start with:

                    Users
                      |
                Web UI / API
                      |
              RAG Application Layer
                 (LlamaIndex)
                      |
        +-------------+-------------+
        |                           |
   Vector Database             Reranker
   Qdrant / Milvus              bge-reranker
        |
    Embeddings Model
    bge-large / e5
        |
    Local LLM Server
    vLLM / Ollama / TGI
        |
    Llama / Mistral / Qwen models

A practical open-source stack:

• RAG framework: LlamaIndex
• Vector database: Qdrant (simple) or Milvus (large scale)
• LLM serving: vLLM
• Embeddings: BAAI BGE models
• Document parsing: Apache Tika, Unstructured, or RAGFlow's parser
• Observability/evaluation: RAGAS, OpenTelemetry, custom evaluation sets

Why I would choose LlamaIndex first

For pure RAG, LlamaIndex tends to be the most direct abstraction:

• Strong document ingestion model
• Good support for hierarchical retrieval, metadata filtering, hybrid search
• Less framework overhead than general-purpose agent frameworks
• Easier for a small team to understand and maintain

It is especially good when your problem is:

“We have 500,000 PDFs, manuals, policies, tickets, and wiki pages. Users need accurate answers with citations.” Auto Learning Agents Aquila Network## When I would NOT choose LlamaIndex

Choose something else if:

You need a complete internal ChatGPT-like product quickly

Use:

• RAGFlow — better for complex documents such as PDFs with tables, layouts, and citations.
• Dify — better if you want workflows, UI, and app building without much coding.

Olostep### You are building an AI agent platform

Use:

• LangChain + LangGraph

Examples:

• RAG + database queries
• RAG + ticket creation
• RAG + workflow automation
• multi-step reasoning agents

Aquila Network### You are in a regulated enterprise environment

Consider:

• Haystack

Its explicit pipeline model is attractive when you need reproducibility, testing, and controlled production deployments.

A few mistakes to avoid in on-prem RAG

1. Do not start with the biggest LLM. Retrieval quality usually matters more than model size.
2. Do not use vector search alone. Enterprise documents benefit from hybrid retrieval:

• BM25 keyword search
• embeddings
• reranking

3. Do not skip evaluation. Create a test set:

• 200–500 real user questions
• expected answers
• expected source documents

4. Do not chunk blindly. PDF structure, tables, headings, and metadata often matter more than the chunk size.

Short answer

If I were building a new on-prem enterprise RAG system in 2026, I would pick:

LlamaIndex + Qdrant + vLLM + BGE embeddings + a local Llama/Qwen model

and only move to:

• RAGFlow if document parsing becomes the bottleneck
• Haystack if compliance/pipeline control dominates
• LangChain if RAG is just one part of a larger agent system

That gives the best combination of self-hosting simplicity, maintainability, and production flexibility.

and only move to:

• RAGFlow if document parsing becomes the bottleneck
• Haystack if compliance/pipeline control dominates
• LangChain if RAG is just one part of a larger agent system

That gives the best combination of self-hosting simplicity, maintainability, and production flexibility.

For an on-premise deployment where ease of self-hosting, user management, and low operational friction matter most, "batteries-included" visual platforms are generally the best choice over raw developer libraries.

The top recommendations for open-source, self-hosted RAG frameworks break down by team needs and technical depth:

1. Dify (Best Overall for Ease of Self-Hosting & Production Apps)

• What it is: An open-source LLM application development platform that combines visual RAG orchestration, agent workflows, a full frontend chat interface, API backend, and multi-tenant user management.
• Why it’s great for on-prem: It comes with an official docker-compose.yaml setup that spins up the entire stack (app, database, redis, etc.) in minutes. It removes the need to build a UI or stitch together user authentication from scratch.
• Best for: Teams that want to ship a functional, production-ready internal knowledge assistant fast with minimal custom web development.
• Explore more: Check out the Dify Website and setup guides via Dify GitHub.

2. RAGFlow (Best for Heavy Document Parsing & Complex Layouts)

• What it is: An open-source RAG engine built heavily around deep document understanding. It excels at parsing messy corporate PDFs, complex tables, charts, and multi-column formats.
• Why it’s great for on-prem: Like Dify, it is containerized and friendly to Docker deployments, but its core superpower is data ingestion accuracy. It visualizes how documents are chunked and mapped so you can audit the extraction quality.
• Best for: Enterprises dealing with dense, messy documentation (e.g., financial reports, engineering schematics, legal contracts) where traditional chunking fails.
• Explore more: Check out the project via RAGFlow GitHub.

3. LlamaIndex or LangChain / LangGraph (Best if You Need Code-First Customization)

• What it is: Developer frameworks rather than turnkey applications.
• Why it’s great for on-prem: They offer ultimate architectural flexibility. You write the Python code, define custom vector databases (like Milvus, Qdrant, or PGVector running locally), and wire up your own pipelines.
• Best for: Engineering teams with specific, non-standard compliance or integration constraints who want complete control over every token and step.
• Explore more: Read the documentation on LlamaIndex or LangChain.

Summary Recommendation

• Choose Dify if you want an all-in-one platform with a UI that your non-technical internal teams can use and manage easily.
• Choose RAGFlow if your primary bottleneck is ungodly messy PDFs and tables that standard text splitters choke on.
• Choose LlamaIndex/LangChain only if you are building a custom microservice from scratch and don't mind building your own administrative UI.

If you'd like to narrow this down, tell me:

• What document formats (PDFs, scanned images, SQL databases) are you primarily ingesting?
• Do you plan to deploy via Docker Compose or a Kubernetes cluster on-prem?

When building an on-premise RAG system, the "best" framework depends heavily on whether your team wants a low-code visual platform (fastest to deploy) or a developer-first library (maximum customization).

The top open-source RAG frameworks optimized for easy self-hosting (via Docker Compose) are categorized below by their strengths:

1. Best All-in-One Visual Platforms (Easiest to Self-Host)

If you want an out-of-the-box UI, user management, built-in document parsing, and visual workflow orchestration without writing a custom web app:

• Dify: An open-source LLM application development platform that shines with robust RAG capabilities. It features an intuitive visual canvas for building RAG pipelines, advanced chunking/cleaning options, multi-model support (Ollama, vLLM, local Hugging Face models), and clean Docker-compose deployment. Great if non-technical team members need to upload data and manage knowledge bases.
• RAGFlow: A RAG engine built heavily around deep document understanding . If your on-premise data consists of messy PDFs with complex tables, charts, or multi-column layouts, RAGFlow uses computer vision/OCR-based layout recognition to segment documents accurately before embedding. It's fully open-source and easy to spin up locally via Docker.

2. Best Developer-First Frameworks (Maximum Flexibility)

If you are building a custom microservice or application from scratch and prefer writing code over using a platform UI:

• LlamaIndex: The gold standard for data-centric RAG. It provides exceptional data connectors, advanced ingestion strategies (hierarchical chunking, summaries), and flexible retrieval modes (graph rag, hybrid search). It doesn't lock you into a UI, letting you easily point it to a local vector store (like Milvus or Qdrant) and a local LLM via Ollama.
• Haystack (by deepset): Known for its mature, production-grade, modular pipeline architecture. Haystack uses a clean component-based graph approach where you explicitly wire together preprocessors, routers, rankers, and generators. It is exceptionally stable for enterprise environments and has first-class support for fully air-gapped, local deployments.
• LangChain: The largest ecosystem with the most extensive integration list. While it historically suffered from a steeper learning curve and rapid API changes, it remains the ultimate swiss-army knife if you need to integrate obscure on-premise enterprise databases, custom authorization layers, or complex multi-agent workflows.

Suggested On-Premise Stack Blueprint

To make self-hosting frictionless, pair your framework of choice with this standard air-gapped stack:

1. Orchestration / UI Framework: Dify (for low-code) or LlamaIndex/Haystack (for code-heavy).
2. LLM & Embedding Runtime: Ollama or vLLM running models like Llama 3 or Mistral locally.
3. Vector Database: Qdrant or Milvus (both run exceptionally well as lightweight local Docker containers).

To help narrow down the ideal choice, tell me:

• What kind of documents are you ingesting (simple text/PDFs, scanned images, complex tables)?
• Do you prefer a visual interface/low-code platform or a Python coding library?
• What local hardware/GPU do you have available for hosting?

If your priority is on-premise + open source + easy self-hosting, I’d shortlist RAGFlow, LlamaIndex, and Haystack—but they serve different purposes.

My recommendation

For most enterprise on-prem RAG deployments: start with github.com.

RAGFlow is less of a bare framework and more of a complete RAG engine/platform. It gives you document ingestion, parsing, chunking, retrieval, reranking, citations, a UI, APIs, and Docker-based deployment. Its deep-document pipeline is particularly useful if your corpus contains PDFs, tables, scanned documents, slides, and other messy enterprise files.

Why I'd pick RAGFlow

For an on-prem deployment, you probably don't want to spend the first few months assembling:

document loaders → parser → chunker → embeddings → vector DB → hybrid search → reranker → prompt construction → citations → API → UI

RAGFlow already provides most of that stack. It can be deployed with Docker Compose, and its current documentation lists x86 CPU support, Docker, and relatively modest baseline requirements of 4+ CPU cores, 16 GB+ RAM and 50 GB+ disk.

It also supports multiple document formats and has explicit support for complex document understanding, hybrid retrieval/reranking, and grounded citations.

When I'd choose LlamaIndex instead

Choose github.com if you're building a custom application rather than deploying a ready-made RAG platform.

For example:

                    ┌─────────────────┐
                    │ Enterprise Apps │
                    └────────┬────────┘
                             │
                       Your API/App
                             │
                    ┌────────▼────────┐
                    │   LlamaIndex    │
                    ├─────────────────┤
                    │ Retrieval       │
                    │ Reranking       │
                    │ Query routing   │
                    │ Agents          │
                    │ Evaluation      │
                    └────────┬────────┘
                             │
              ┌──────────────┼──────────────┐
              ▼              ▼              ▼
          Qdrant/       PostgreSQL      OpenSearch
          Milvus
              │
              ▼
        Local embeddings
              │
              ▼
          Local LLM

LlamaIndex is specifically designed around ingesting, indexing, and querying private data, with connectors for things such as APIs, PDFs and SQL databases.

I'd use it when your requirements are likely to become highly application-specific.

When I'd choose Haystack

github.com is my choice when the RAG pipeline itself needs to be very explicit and testable.

For example:

Query
  ↓
Query preprocessing
  ↓
BM25 ──────────┐
               ├── Hybrid retrieval
Vector search ─┘
  ↓
Reranker
  ↓
Context filtering
  ↓
LLM
  ↓
Citations + answer

Its component/pipeline approach is well suited to production systems where you expect to swap retrieval, embedding, reranking, or generation components independently. It also supports Docker deployment.

One important distinction

I'd separate the choices into two categories:

Ready-to-deploy RAG platforms

• RAGFlow
• Dify
• AnythingLLM
• Onyx

Developer frameworks

• LlamaIndex
• Haystack
• LangChain

That distinction matters. A framework like LlamaIndex gives you building blocks; RAGFlow gives you much more of the actual application out of the box. Recent comparisons make the same distinction between code-first frameworks and deployable RAG platforms.

What I'd build on-prem

If this were my project, I'd start with:

                 ┌──────────────────┐
                 │   Users / Apps   │
                 └────────┬─────────┘
                          │
                     REST / UI
                          │
                 ┌────────▼─────────┐
                 │     RAGFlow      │
                 │                  │
                 │ Ingestion        │
                 │ Parsing          │
                 │ Chunking         │
                 │ Retrieval        │
                 │ Reranking        │
                 │ Citations        │
                 └───────┬──────────┘
                         │
              ┌──────────┼──────────┐
              │          │          │
              ▼          ▼          ▼
          Vector/     Object     Metadata
          Search      Storage      DB
              │
              ▼
       Local Embedding Model
              │
              ▼
          Local LLM
       (vLLM / Ollama etc.)

This gives you a completely on-premise path, with the LLM and embedding models also kept inside your network.

Bottom line:

• Want the easiest serious enterprise RAG deployment? → RAGFlow
• Want maximum application-level control? → LlamaIndex
• Want highly engineered/modular pipelines? → Haystack
• Want business users to build RAG apps without much coding? → Dify

If you tell me your document types, approximate document volume, number of users, GPUs/servers available, and whether the environment is fully air-gapped, I can recommend a concrete on-prem stack (including LLM + embedding model + reranker + vector DB + hardware sizing) rather than just the framework.

                 ┌──────────────────┐
                 │   Users / Apps   │
                 └────────┬─────────┘
                          │
                     REST / UI
                          │
                 ┌────────▼─────────┐
                 │     RAGFlow      │
                 │                  │
                 │ Ingestion        │
                 │ Parsing          │
                 │ Chunking         │
                 │ Retrieval        │
                 │ Reranking        │
                 │ Citations        │
                 └───────┬──────────┘
                         │
              ┌──────────┼──────────┐
              │          │          │
              ▼          ▼          ▼
          Vector/     Object     Metadata
          Search      Storage      DB
              │
              ▼
       Local Embedding Model
              │
              ▼
          Local LLM
       (vLLM / Ollama etc.)

RAGFlow already provides most of that stack. It can be deployed with Docker Compose, and its current documentation lists x86 CPU support, Docker, and relatively modest baseline requirements of 4+ CPU cores, 16 GB+ RAM and 50 GB+ disk.

It also supports multiple document formats and has explicit support for complex document understanding, hybrid retrieval/reranking, and grounded citations.

When I'd choose LlamaIndex instead

Choose github.com if you're building a custom application rather than deploying a ready-made RAG platform.

For example:

                    ┌─────────────────┐
                    │ Enterprise Apps │
                    └────────┬────────┘
                             │
                       Your API/App
                             │
                    ┌────────▼────────┐
                    │   LlamaIndex    │
                    ├─────────────────┤
                    │ Retrieval       │
                    │ Reranking       │
                    │ Query routing   │
                    │ Agents          │
                    │ Evaluation      │
                    └────────┬────────┘
                             │
              ┌──────────────┼──────────────┐
              ▼              ▼              ▼
          Qdrant/       PostgreSQL      OpenSearch
          Milvus
              │
              ▼
        Local embeddings
              │
              ▼
          Local LLM

LlamaIndex is specifically designed around ingesting, indexing, and querying private data, with connectors for things such as APIs, PDFs and SQL databases.

I'd use it when your requirements are likely to become highly application-specific.

When I'd choose Haystack

github.com is my choice when the RAG pipeline itself needs to be very explicit and testable.

For example:

Query
  ↓
Query preprocessing
  ↓
BM25 ──────────┐
               ├── Hybrid retrieval
Vector search ─┘
  ↓
Reranker
  ↓
Context filtering
  ↓
LLM
  ↓
Citations + answer

Its component/pipeline approach is well suited to production systems where you expect to swap retrieval, embedding, reranking, or generation components independently. It also supports Docker deployment.

For an on-premise, air-gapped deployment, Onyx AI (formerly Danswer) and RAGFlow are the best open-source, batteries-included RAG platforms that deploy easily via Docker Compose with zero external dependencies.

Choosing the right framework depends on your specific use case:

Top Recommendations for On-Premise Self-Hosting

• Onyx AI : Ideal if you want a complete "out-of-the-box" internal workplace search engine. It supports local inference models via Ollama or vLLM , enforces granular user permissions, and sets up in minutes with a standard docker compose up command.
• RAGFlow : Ideal if your primary bottleneck is messy, data-dense documents (like financial statements, technical manuals, or scanned PDFs) that standard chunkers fail to parse properly.
• Dify : Ideal if you need an intuitive app-building canvas to chain prompts, APIs, and retrieval components together without managing raw code.

To help narrow down the ideal choice, please share:

• What are your primary data sources (e.g., local PDFs, Confluence, databases)?
• Do you plan to run local LLMs (via Ollama/vLLM ) or connect to an internal enterprise API?
• Do you prefer a ready-to-use chat interface or a low-code builder?

If your requirements are on-premise + open source + easy to self-host, my default recommendation is RAGFlow—especially if your RAG system is primarily going to work with documents such as PDFs, Word files, reports, manuals, and scanned documents.

There is an important distinction, though: RAGFlow is a deployable RAG platform, while LlamaIndex and Haystack are developer frameworks that you assemble into your own application.

My recommendation

Recent comparisons similarly put RAGFlow toward document-heavy RAG, LlamaIndex toward data-centric RAG, and Haystack toward modular production pipelines.

Why I'd pick RAGFlow

RAGFlow is attractive for an on-prem deployment because it gives you considerably more than just a Python RAG library:

• Document ingestion and parsing
• Chunking
• Vector + full-text retrieval
• Reranking
• Citation/grounding
• Knowledge bases
• Chat UI
• APIs
• Agent capabilities
• Docker-based deployment

Its biggest advantage is document understanding. If your corpus contains complicated PDFs with tables, figures, layouts, or scans, RAGFlow is particularly compelling.

The tradeoff is that it's a larger platform to operate than simply installing LlamaIndex. Its deployment involves multiple infrastructure components, so "easy to self-host" doesn't mean "one tiny container."

What I'd build on-prem

For a serious enterprise deployment, I'd actually separate the components:

                 ┌─────────────────────┐
                 │    Web / API App     │
                 └──────────┬──────────┘
                            │
                            ▼
                 ┌─────────────────────┐
                 │       RAGFlow       │
                 │ ingestion/retrieval │
                 └──────────┬──────────┘
                            │
             ┌──────────────┼──────────────┐
             ▼              ▼              ▼
        Vector/Search    Object Store    Reranker
        Elasticsearch      MinIO          BGE/etc.
             │
             ▼
        Local Embeddings
        BGE / Jina / etc.
             │
             ▼
        Local LLM Server
        vLLM / Ollama
             │
             ▼
        Llama / Qwen / etc.

That gives you a fully on-prem data path: documents, embeddings, retrieval, reranking and generation can all remain inside your network.

When I'd choose LlamaIndex instead

I'd choose LlamaIndex over RAGFlow if your team is building a custom application and wants the RAG system to be a component inside your own Python architecture.

For example:

Your application
      │
      ├── authentication / RBAC
      ├── business logic
      ├── agents
      │
      └── LlamaIndex
             ├── ingestion
             ├── chunking
             ├── retrieval
             ├── reranking
             └── generation

LlamaIndex gives you much finer control over ingestion, retrieval and storage, but you have to build more of the surrounding product yourself.

One important alternative: Dify

If "easy" is the overriding requirement, Dify deserves serious consideration.

It gives you a web UI for knowledge bases, visual workflows, model configuration, APIs and RAG without requiring you to build the application layer yourself.

I'd choose:

• RAGFlow → document-heavy enterprise RAG
• Dify → easiest way to get an internal RAG application running
• LlamaIndex → custom application where engineers need maximum RAG control
• Haystack → engineering-heavy production pipeline where explicit components/testing matter

My choice for your situation

If you tell me "We have a few hundred thousand internal PDFs/Word documents, need everything on-prem, want citations, SSO/RBAC, and expect this to become a production enterprise system", I'd start with RAGFlow.

If you tell me "We're building our own application and RAG is just one service inside it", I'd start with LlamaIndex + Qdrant/OpenSearch + vLLM instead.

If you give me your document volume, GPU(s), number of users, whether documents are mostly PDFs, and whether you need SSO/RBAC, I can recommend a concrete on-prem architecture—including the LLM, embedding model, vector DB, reranker, hardware sizing, and Docker/Kubernetes deployment.

I'd choose LlamaIndex over RAGFlow if your team is building a custom application and wants the RAG system to be a component inside your own Python architecture.

For example:

Your application
      │
      ├── authentication / RBAC
      ├── business logic
      ├── agents
      │
      └── LlamaIndex
             ├── ingestion
             ├── chunking
             ├── retrieval
             ├── reranking
             └── generation

LlamaIndex gives you much finer control over ingestion, retrieval and storage, but you have to build more of the surrounding product yourself.

One important alternative: Dify

If "easy" is the overriding requirement, Dify deserves serious consideration.

It gives you a web UI for knowledge bases, visual workflows, model configuration, APIs and RAG without requiring you to build the application layer yourself.

I'd choose: