RAGFlow
What it is
Overview
RAGFlow processes documents, PDFs, spreadsheets, and other file formats into searchable knowledge bases for AI agents. Built as a hybrid system that combines vector search, BM25 keyword matching, and custom retrieval methods. The tool is Apache-2.0 licensed open source, which draws AI engineers and enterprise developers who need local deployment control. Early users praise its document structure understanding — the system preserves layout context when chunking files, rather than treating everything as plain text.
At a glance
Usability & Quality overview
Best for
- document-heavy RAG knowledge bases
- self-hosted AI agents and workflow automation
- technical teams that want citation-backed retrieval
Watch out for
- technical setup and configuration effort
- insufficient independent user review volume in the supplied corpus
Real product, not a wrapperIndependent product
RAGFlow offers proprietary DeepDoc models for understanding document structure and layout, specialized fine-tuning for retrieval workflows, and automated processing chains. The Apache-2.0 licensed weights indicate genuine model development beyond simple API wrappers.
Strong evidenceQuality score
RAGFlow Open-source RAG and AI agent platform with citation-backed document parsing and hybrid retrieval.
This score is our editorial judgment, computed automatically from the sources, weights, and dates shown above. It reflects the data we could verify as of August 13, 2026, not a guarantee or statement of fact about RAGFlow. Third-party ratings and quotes belong to their original platforms and authors. Thin data lowers our confidence label, and we say so instead of guessing. Work on RAGFlow? Dispute any datapoint and we will review it, publish your response, and correct verified errors.
Plans
Pricing
How free is free?
Free with limits
500 credits/month, 0.1GB storage; Starter $59/mo for regular use
What you get for free
- 5 Apps maximum
- 1 team member only
- 0.1 GB dataset storage
- 500 credits per month
Behind the paywall
- API key accessStarter
- More than 5 AppsStarter
- Team collaboration (multiple members)Starter
- More than 0.1 GB storageStarter
- More than 500 credits/monthStarter
Community feedback
Aggregated reviews
Ratings and quoted comments below are aggregated from third-party sources and reflect those users' views, not SearchTools.ai's.
What reviewers talk about
themes inside the Sentiment pillar — not score ingredients
43Output Quality12 mentions
“Hi. I started using RAGFlow. I've built a knowledge base based on PDF documentation files, which works perfectly when using the chat. I want to give him a new context from code files (Terraform, Kotlin, Java, Python, etc.).Does RAGFlow support building a knowledge base from code files? How can I achieve this?”
“I've been using it for several months, trying to adapt it to the legal industry (my field) and my honest opinion is that it doesn't work as advertised... The biggest reason for this (I think) is because they've gone using deepdoc (their [infiniflow's] tool) to perform chunking of documents based upon the structure. It doesn't do a good job. I've had much better results using RecusiveCharacterTextSplitting (Langchain) + Faiss + ollama, than RAGflow + ollama.”
“Hey everyone,I'm currently working on upgrading our Retrieval-Augmented Generation (RAG) system at my company and could really use some input. I’m restricted to using RAGFlow, and my original hypothesis was that implementing a multi-agent architecture would yield better performance and more accurate results. However, what I’ve observed is that: Multi-agent workflows are significantly slower than the single-agent setup The quality of the results hasn’t improved noticeably I'm trying to figure out”
“Looks like a very ambitious “be-everything-to-everyone” mega system! I’m concerned that the architecture looks like it’s gonna struggle with real world enterprise workloads, both volume and performance wise. Ever had to run through tens of thousands of resumes or hundreds of gigabytes of product data from enterprise e-commerce dropship suppliers? My personal preference for document parsing esp MS Office formats is to use well battle tested components like Apache POI. Looks like we also need to h”
45Ease of Usethin data · 7 mentions
“I get the best results with Open WebUI (if using reranking), but I also like Msty. Kotaemon is very promising, but the file management is very rudimentary and clunky if you have a lot of files. It has the best presentation of the results. AnythingLLM is OK, but file management needs more work too. RAGFlow is pretty good too, and has probably the best file management out of the web-based tools. As always, it’s essential to set up chunking right and get larger context window.”
“RAGFlow gives a RAG pipeline with a simple docker image: https://ragflow.io/. I want to use it next to ollama (both LLM and embeddings) vast.ai and runpod, the 2 cheapest gpu providers, do not support me installing and running docker. Apparently there is a instance that works, but none are 48gb+: https://www.reddit.com/r/vastai/comments/1g0d2uq/comment/lw6jcta/ Max is 32gb which could work for my needs, but I have to go right now and will come back and test later. Is it possible to combine the r”
“You can run a secure, fully local RAG, but you need to self-host every piece (LLM, embeddings, vector DB), hard-block egress, and tune retrieval to get decent performance. For RAGflow: deploy via Docker on an isolated host, bind services to localhost, disable any telemetry/updates, and set models to Ollama-only. Use a firewall rule that blocks all outbound traffic for those containers; verify with tcpdump or netstat while ingesting and querying. Make sure embeddings and rerankers are local (e.g.”
“Hey r/OpenWebUI, I’ve been working on a robust integration between OpenWebUI and RAGFlow. If you aren't using RAGFlow yet, it’s great for parsing complex PDFs (tables, OCR) and handling DeepDoc understanding. I built a custom Tool that goes beyond simple retrieval. It exposes RAGFlow's advanced features directly into your OpenWebUI chat. Features: 🔌 Easy Setup: Configure your API Key and URL directly in the OpenWebUI interface (Valves). 🧠 Knowledge Graph Support: If you have graph data in RAGF”
Watch & learn
Video content
YouTube
YOUTUBE25 viewsRAGFlow入門｜社内文書を答えるAIに変えるオープンソースRAGエンジン（LLMアプリ開発者向け）
%E3%81%95%E3%81%A4%E3%81%8D-c6s23 days ago
YOUTUBE15 viewsragflow Practical Walkthrough: What to Try First, Fast
DoramagicAI1 month ago
YOUTUBE2 viewsRAGFlow Teardown + Daily AI News | Atlas Studios — Daily Agent Affairs
billywhited444223 days ago
Capabilities
Key features
Knowledge Base
Builds searchable knowledge bases that answer questions from your stored documents
Agent Builder
Builds autonomous AI agents that plan and execute multi-step tasks for you
Search Engine
Answers questions by searching the web and synthesizing results with sources
Documents Assistant
Helps you read, summarize, and edit documents through natural-language queries
The honest take
What users love & flag
Distinct themes surfaced across user reviews — each grounded in real review text, ranked by how often it comes up.
Questions
Frequently asked
What is RAGFlow?
RAGFlow is an open-source RAG (Retrieval-Augmented Generation) engine that helps enterprises build AI agents with superior context layers. It processes multi-format data through an ETL pipeline and delivers high-precision hybrid search combining vector search, BM25, and custom scoring mechanisms. The platform includes visual workflow orchestration capabilities for building AI agents that integrate RAG, tools, and Model Context Protocols.
Is RAGFlow free to use?
Yes, RAGFlow offers a free plan that includes 5 apps, 1 team member, 0.1 GB dataset storage, and 500 credits monthly. However, API access is not available on the free tier. Paid plans start at $29/month for the Starter plan, which includes 50 apps, 5 team members, 5 GB storage, and API access.
What types of data formats can RAGFlow process?
RAGFlow can process multi-format data through its comprehensive ETL pipeline, though the specific formats aren't detailed in the available information. The system cleanses and structures diverse data sources, transforming them into rich semantic representations optimized for retrieval by AI agents.
What makes RAGFlow's search different from basic RAG implementations?
RAGFlow uses a hybrid search approach that combines vector search, BM25, and custom scoring mechanisms with advanced re-ranking algorithms for high-precision results. This enterprise-grade approach, combined with visual workflow orchestration capabilities, distinguishes it from simpler RAG implementations that typically rely on vector search alone.
What industry-specific workflows does RAGFlow support?
RAGFlow offers specialized workflows for equity investment research (automating company data collection and financial analysis), legal precedent analysis (structuring case law research with automatic jurisdiction extraction), and manufacturing maintenance support (sourcing from internal manuals and external technical references). These industry-specific capabilities are built into the platform's workflow system.
Can I deploy RAGFlow on my own infrastructure?
Yes, RAGFlow is available as both a cloud service and open-source solution on GitHub. Enterprise customers can access BYOC (Bring Your Own Cloud) deployment and on-premises installation options, along with dedicated support and custom SLAs through the Enterprise plan.
What's included in RAGFlow's Pro plan?
The Pro plan costs $129 monthly (regularly $259) and includes unlimited apps, 20 team members, 50 GB dataset storage, and 20,000 credits per month. It also provides API access and is described as the most popular plan for growing businesses requiring advanced tools and higher limits.
How does RAGFlow's visual workflow builder work?
RAGFlow includes unified AI agent orchestration capabilities that allow users to build agents within visual workflows. These workflows integrate RAG functionality, tools, and Model Context Protocols (MCPs), enabling users to create sophisticated AI agent systems without complex coding.
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