Batteries-Included RAG Platforms: Dify vs. RAGFlow vs. Onyx

In our previous posts, we've learned how to build agents from the "Build-it-Yourself" kits: LangChain and LlamaIndex (see our RAG framework comparison and agent framework comparison). These are fantastic for giving us low-level control. They are like a box of Lego bricks—powerful, but you are responsible for building the entire car, including the engine, the chassis, and the UI.

This post is for you if you've ever felt that pain. You've spent a week writing Python scripts, Dockerfiles, and React code, only to have a simple RAG demo that isn't even close to a "product".

What if you could skip the assembly and start with a fully-built car?

Today, we're exploring the "Batteries-Included" RAG platforms. These are open-source projects that bundle the entire pipeline—from data upload to a production-ready API and even a UI—into one integrated application.

The problem: the "Week-Long" demo

With a "Build-it-Yourself" framework like LangChain, the RAG logic is only 10% of the work. The real work for a production app is everything else.

graph TD
subgraph APP["Your Simple RAG App Build-it-Yourself"]
direction LR
A[1. Your Python RAG Logic LangChain/LlamaIndex]
B[2. A FastAPI Server]
C[3. A React Frontend]
D[4. Dockerfile and CI/CD]
E[5. File Upload Logic]
F[6. API Key Management]
G[7. Chat History Database]
A --> B --> C --> D --> E --> F --> G
end

style A fill:#e3f2fd,stroke:#0d47a1

This is a ton of engineering just to get a chatbot online. The "Batteries-Included" platforms aim to solve this by giving you most of this out-of-the-box. Let's compare the top players.

1. Dify: the "All-in-One AI App Studio"

Dify is a full-featured, open-source LLMOps platform. Think of it as a visual, all-in-one studio for building and managing AI applications.

Philosophy: "You shouldn't need a backend team and a frontend team to launch a RAG app. Let's put it all in one visual interface."

Developer Experience (DX): UI-first. You click buttons and connect nodes in a visual editor. It feels like "Bubble" or "Webflow" for AI.

Best For: Teams that need to build and deploy a complete RAG app (with a UI, API, and analytics) in hours, not weeks.

The "How": a visual workflow

In Dify, you don't write Python chains. You visually configure a "prompt studio" that combines data, model choice, and even agentic tools.

graph TD
A[1. Upload Docs] --> B[2. Dify auto-chunks and indexes]
B --> C[3. Design Prompt in UI]
C --> D{Add Tools? Web Search}
D -->|Yes| E[4. Run Agentic Logic]
D -->|No| E
E --> F[5. Get a Deployable API and Web App]

style F fill:#e8f5e9,stroke:#388e3c

Observation: The "product" you are building in Dify is the entire application. It auto-generates a front-end (a chat widget you can embed) and a production API. It's incredibly fast for prototyping and building internal tools. Its weakness is that you are limited to the nodes and logic that Dify provides.

Think About It: Dify's visual builder is fast, but what happens when you need custom logic that a pre-built node doesn't support? This is the classic "low-code" trade-off: speed vs. flexibility.

2. RAGFlow: the "Data-First" RAG engine

RAGFlow is a different beast. It is not a full app builder. It's a highly specialized, "data-first" RAG engine that's obsessed with the retrieval part of the problem.

Philosophy: "Simple vector search is stupid. It just finds 'similar' chunks, not the right answer. We can do better by understanding the structure of the data."

Developer Experience (DX): API-first, with a UI for data management. You treat it as an external RAG "brain" that you call from your own application.

Best For: Engineers who are failing with simple RAG and need a more powerful, "deep-context" retrieval engine for complex documents.

The "How": Knowledge Graph Retrieval

RAGFlow's killer feature is its deep-parsing. When you upload a PDF, it doesn't just split it into 1000-character chunks. It creates a Knowledge Graph, understanding that a "title" is connected to a "paragraph", which contains a "table".

When you ask a question, it retrieves a structured set of information, not just a list of 10 random blue links.

graph TD
A[Upload 10-K PDF] --> B[RAGFlow Deep-Parser]
B --> C[Chunk 1 Summary]
B --> D[Chunk 2 Table 1.1]
B --> E[Chunk 3 Table 1.2]
B --> F[Chunk 4 Conclusion]

subgraph GRAPH["Knowledge Graph Built by RAGFlow"]
C -->|references| D
C -->|references| E
D -->|is part of| G[Section 1 Finances]
E -->|is part of| G
end

style B fill:#e3f2fd,stroke:#0d47a1

Observation: Let's say your query is "What was the revenue in Q4?"

A simple vector search might retrieve Chunk 1: "Summary" because it's semantically similar. This is useless.

RAGFlow understands the query is looking for a number mentioned in the summary, and it knows the summary references Table 1.1 and Table 1.2. It will retrieve the summary and the tables, giving the LLM the exact context it needs to find the answer.

Think About It: RAGFlow is a specialized component, not a full app builder. You would still need to build your own FastAPI and React app, but you would replace your simple ChromaDB retriever with a call to the RAGFlow API.

3. Onyx: the "Self-Hostable" enterprise platform

Onyx is a different approach entirely. It's an open-source, self-hostable AI platform that you deploy on your own infrastructure. Think of it as a complete LLMOps platform that you control.

Philosophy: "Your data, your infrastructure, your control. Deploy Onyx on-premises and maintain full transparency on usage and company data."

Developer Experience (DX): Self-hosted and enterprise-ready. You deploy it on your own servers, connect it to 40+ data sources (Slack, Notion, GitHub, etc.), and get a complete AI platform with chat, search, agents, and integrations.

Best For: Enterprise teams that need a production-ready AI platform with security, compliance (SOC 2, GDPR), and full control over their data and infrastructure.

The "How": the self-hosted stack

Onyx bundles the entire RAG pipeline, agent framework, and integrations into a single, self-hostable platform.

graph TD
subgraph INFRA["Your Infrastructure"]
A[User Query] --> B[Onyx Platform Web Interface]
B --> C[Onyx Backend API]
C --> D[Vector Store and RAG Engine]
C --> E[Agent Framework]
C --> F[40+ Connectors Slack, Notion, GitHub]
D --> G[Your Company Data]
E --> B
F --> D
G --> D
end

H[Cloud Services Optional]

style B fill:#e3f2fd,stroke:#0d47a1
style H fill:#fff8e1,stroke:#f57f17

Observation: Onyx solves the "production" problem by giving you a complete platform that you can deploy on your own infrastructure. It's not just RAG—it includes chat, search, agents, workflows, and integrations with your existing tools. For enterprise teams, this provides the security and control of self-hosting with the convenience of a batteries-included platform. See Onyx's website for more details.

The Engineer's Choice: Head-to-Head

Framework | Philosophy | Best For | Developer Experience (DX)
Dify | "All-in-One Studio" | Rapid Prototyping & Internal Tools | UI-First. Click, drag, and deploy.
RAGFlow | "Data-First Engine" | Complex Documents (PDFs/Tables) | API-First. A specialized "RAG brain" you call.
Onyx | "Self-Hostable Platform" | Enterprise & On-Premises | Deploy-First. A self-hostable, enterprise-ready platform.
LangChain/LlamaIndex | "Build-it-Yourself" | Total Control & Custom Logic | Code-First. You are the system architect.

How to choose: scenarios and recommendations

Scenario 1: "I need a simple chatbot for my 10 product PDFs, and I need it online by tomorrow for the sales team to use."

Choice: Dify.

Reason: This is a speed and convenience problem. Dify is the only tool on this list that gives you data ingestion, RAG, an API, and a polished, shareable web UI in one click. You'll be done by lunch.

Scenario 2: "My simple RAG bot (built with LangChain) is failing on our 100-page financial reports. It can't answer 'compare-and-contrast' questions."

Choice: RAGFlow.

Reason: Your problem is retrieval quality. Your simple vector search isn't smart enough. You need RAGFlow's "Knowledge Graph" approach to understand the structure of your complex documents and retrieve the right, related chunks.

Scenario 3: "I'm an enterprise team and need a complete AI platform that we can deploy on our own infrastructure. We need SOC 2 compliance and integrations with Slack, Notion, and GitHub."

Choice: Onyx.

Reason: This is an enterprise deployment and security problem. Onyx is designed to be self-hosted on your infrastructure, giving you full control over data, compliance requirements, and integrations with your existing tools. It's a complete platform, not just a RAG tool.

Scenario 4: "I need to build an agent that also connects to our SQL database, calls a weather API, and posts to Slack."

Choice: LangChain / LangGraph.

Reason: Your problem is agentic logic, not just RAG. These "batteries-included" platforms are too simple. You need the "build-it-yourself" kit to create custom tools and complex, multi-step chains. See our agent framework comparison for more.

Challenge for you

Use Case: You need to build a Q&A bot for your company's entire Slack history (exported as JSON files).

Your Task: Which of these tools would you not use, and why?

Analysis: Which tool's features (e.g., Dify's quick UI, RAGFlow's graph retrieval, Onyx's privacy) would be most valuable for this specific challenge?

Key takeaways

Batteries-included platforms solve the "week-long demo" problem: They bundle data ingestion, RAG logic, API, and UI into one integrated application

Dify excels at rapid prototyping: Use it when you need a complete RAG app with UI and API deployed in hours, not weeks

RAGFlow excels at complex document retrieval: Use it when simple vector search fails and you need knowledge graph-based retrieval for structured documents

Onyx excels at enterprise self-hosting: Use it when you need a complete, self-hostable AI platform with enterprise security, compliance, and integrations with your existing tools

Choose based on your primary constraint: Dify for speed, RAGFlow for retrieval quality, Onyx for privacy, and build-it-yourself frameworks for total control

These platforms complement build-it-yourself frameworks: Use Dify/RAGFlow/Onyx for rapid deployment, and LangChain/LlamaIndex when you need custom logic and total control