RAGFlow — Open-source RAG engine for building reliable AI agent context layers. Best for Enterprises needing a self-hosted RAG engine with high accuracy and security, Teams building AI agents for document-heavy industries like legal and finance, Organizations requiring visual workflow orchestration for complex retrieval pipelines. Free to start; paid plans from $2/mo.
Enterprises needing a self-hosted RAG engine with high accuracy and securityTeams building AI agents for document-heavy industries like legal and financeOrganizations requiring visual workflow orchestration for complex retrieval pipelinesDevelopers looking for an open-source RAG platform with production-grade ETL and searchCompliance-sensitive sectors needing on-premises or BYOC deployment
Not ideal for
Simple Q&A use cases where a hosted LLM with retrieval is sufficientTeams wanting a lightweight, credit-card-free free tier (storage is only 0.1 GB)Users seeking extensive pre-built integrations with SaaS tools (none listed)Budget-constrained individuals as paid plans start at $29/month for 5 GB storageCustom pipeline builders who prefer library-level flexibility over a visual platform
A strong choice for enterprises needing a production-ready, open-source RAG platform with advanced agent orchestration. However, the free tier is limited (0.1 GB storage, 500 credits/month), and pricing can scale quickly for large deployments.
RAGFlow stands out as an open-source RAG engine with a focus on enterprise reliability and agent integration. Its built-in ETL pipeline handles diverse data formats (images, documents, datasets) and structures them into rich semantic representations, which is a clear advantage over simpler RAG solutions. The hybrid search combining vector, full-text, and BM25 with re-ranking delivers high answer accuracy, crucial for domains like legal or finance. The visual agent orchestration platform, supporting MCP and multi-agent workflows, allows teams to build complex agent pipelines without extensive coding. We recommend RAGFlow when you need a self-hosted RAG system with strong data preprocessing and agent capabilities. However, the free tier is very restrictive (only 0.1 GB dataset storage and 500 credits/month), so serious use requires a paid plan starting at $29/month. The Pro plan offers unlimited apps and 20,000 credits for $129/month, which is reasonable for mid-size teams. The lack of explicit integrations list on the site (only mentions GitHub, Discord, etc.) means you may need to custom-integrate external tools. It's not ideal for simple Q&A bots where a hosted solution like ChatGPT with retrieval would be simpler. Compared to competing open-source RAG frameworks like LangChain or Haystack, RAGFlow provides a more packaged, visual approach but is less flexible for custom pipelines. Overall, RAGFlow is a solid choice for enterprises that value data security and agent orchestration, but budget-conscious teams should evaluate the cost vs. storage needs.
Skip RAGFlow if Skip RAGFlow if you need a plug-and-play SaaS RAG with minimal setup, or if you're a solo developer on a low budget with simple document Q&A needs.
Latest from RAGFlow
Updated 5 days ago
Across the latest 7 updates: 2 changelog entries and 5 news mentions.
How likely is RAGFlow to still be operational in 12 months? Based on 6 signals including funding, development activity, and platform risk.
funding runway
80
website health
90
github activity
50
category mortality
70
wrapper dependency
100
hyperscaler overlap
80
About RAGFlow
RAGFlow is a leading open-source RAG (Retrieval-Augmented Generation) engine that provides a superior context layer for AI agents. Built for enterprise, it combines a built-in ETL pipeline for multi-format data ingestion with high-precision hybrid search (vector, BM25, custom scoring, and re-ranking). The platform includes an all-in-one agent orchestration environment that integrates RAG, tools, and Model Context Protocol (MCP) within visual workflows. RAGFlow offers industry-specific solutions for financial services, legal, manufacturing, and education. Compared to generic RAG tools, RAGFlow emphasizes production reliability and enterprise-grade deployment options.
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Key Features
Built-in ETL ingestion pipeline for multi-format data
Concrete scenarios for the personas RAGFlow actually fits — and what changes day-one when you adopt it.
Financial analyst at a mid-size investment firm
You receive a new 100-page quarterly report with financial tables and want to extract key metrics and compare with previous quarters.
Outcome: Upload the report to RAGFlow, use DeepDoc parsing to extract tables, then run the equity investment research workflow—it automatically identifies stock tickers, cross-references external financial data, and generates a structured report with citations in under 5 minutes.
Legal researcher at a law firm
You need to find precedents similar to a current case, across public and internal databases.
Outcome: Input case attributes into RAGFlow's legal precedent analysis workflow. It formulates search queries, retrieves comparable cases from both internal datasets and external sources, and produces a structured analysis of how similar cases were resolved.
IT administrator at a manufacturing company
You need to provide maintenance staff with step-by-step instructions from internal manuals when a machine breaks down.
Outcome: Use RAGFlow's manufacturing maintenance support workflow: it validates the task description, extracts standard protocols from internal manuals, supplements with external technical data, and outputs clear execution instructions.
Infrastructure footprint is substantial (requires 16 GB RAM, 4+ cores, 50 GB disk, Docker). Initial ingestion of large document sets is slow because DeepDoc is compute-heavy. The agent builder is newer than the retrieval stack and less polished. Some documentation is translated from Chinese and occasionally rough. The free tier is very limited (5 apps, 500 credits/month, 0.1 GB storage). Community support is strongest in the Chinese-speaking ecosystem. Minimum Python is now 3.13 (as of v0.25.5).
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Annual total
Free
Over 12 months
Effective monthly
Free
Billed monthly
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published RAGFlow tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Open Source
$0/mo (Apache 2.0)
Ideal for
Developers and small teams who want full control and can self-host on their own infrastructure.
What this tier adds
Free entry point with Apache 2.0 license; you self-host and manage everything.
Hosted
$99/mo
Ideal for
Growing teams that need managed infrastructure with team workspaces and priority support.
What this tier adds
Adds managed hosting, team workspaces, and priority support; no need to manage servers.
Enterprise
Custom
Ideal for
Large organizations requiring BYOC/on-premises deployment, dedicated support, and custom SLAs.
What this tier adds
BYOC and on-premises deployment, dedicated support, custom SLA, and unlimited scalability.
Hidden costs & gotchas
What the public pricing page doesn't put in bold. Captured from pricing-page footnotes, contract terms, and recurring complaints.
•Overage credits cost extra beyond included monthly credits.
•Self-hosted deployment requires at least 16 GB RAM and 50 GB disk.
•Hosted plan starts at $99/month with 50 apps and 5 GB storage.
Where the pricing makes sense
The company stage and team size where RAGFlow's pricing actually pencils out — and where peers do it cheaper.
RAGFlow's Hosted plan at $99/month is competitive for teams needing managed RAG with visual agent workflows. It's cheaper than commercial alternatives like Relevance AI (starts at $199/mo) but more expensive than Basic I/O's hosted open-source offering. The free Open Source tier is genuinely free (Apache 2.0) but requires self-hosting and has limited support.
Setup time & first value
How long it actually takes to get something useful out of RAGFlow — broken out by persona, not the marketing-page minute.
For a technical user familiar with Docker, self-hosting takes about 30 minutes to 1 hour (clone repo, set vm.max_map_count, run docker-compose). The hosted version skips this entirely. Building a dataset and creating a first chatbot takes another 15-30 minutes. Configuring a multi-step agent workflow can take 1-2 hours.
Switching to or from RAGFlow
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
Migrating in
→From LangChain: Rebuild RAG pipeline as RAGFlow workflows using visual canvas.
→From LlamaIndex: Export indexes as documents and re-ingest into RAGFlow datasets.
→From Elasticsearch: Use RAGFlow's built-in hybrid search with Elasticsearch as backend (supported).
→From a custom Python RAG stack: Migrate by uploading documents and configuring retrieval settings.
→From Danswer: Export your document indexes and import into RAGFlow datasets.
Migrating out
↗To Danswer: Export datasets as JSON and re-upload into Danswer.
↗To Qdrant: Use RAGFlow's API to retrieve chunk embeddings and re-index in Qdrant.
↗To LangChain: Export RAGFlow workflows as JSON and manually convert to LangChain code.
Recent material changes
Pricing, brand, ownership, or deprecation changes worth knowing before you commit. Most-recent first.
