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Ragflow
NewVerified
Open-source RAG engine with document processing and agent capabilities.
Overview
Ragflow is an open-source retrieval-augmented generation (RAG) platform designed for developers building AI applications. It combines document parsing, vector search, and agentic workflows to enable sophisticated question-answering and information retrieval systems. Built for teams needing self-hosted control and flexibility over their RAG infrastructure.
Pros
- Open-source with self-hosted deployment option for data privacy
- Integrated document parsing handles PDFs, images, and structured data
- Combines RAG with agent capabilities for complex workflows
- RESTful API for straightforward integration
- Active community development and transparent roadmap
✕ Cons
- Requires technical setup and infrastructure management skills
- Smaller ecosystem compared to commercial RAG platforms
- Limited enterprise support without paid services
Key Features
Document ingestion and parsing
Vector embedding and retrieval
Agent workflow orchestration
REST API
Self-hosted deployment
Multi-document QA
Use Cases
Developers building internal knowledge base chatbotsEnterprises requiring on-premise RAG solutionsTeams integrating document search into applicationsAI researchers prototyping retrieval-augmented agents
Best For
Backend DevelopersML & AI EngineersEnterprise Knowledge TeamsData Science TeamsDevOps & Infrastructure Teams
Frequently Asked Questions
What is the pricing model for Ragflow?▾
Ragflow is open-source and free to use. You can self-host it on your own infrastructure, though you'll need to cover hosting and maintenance costs depending on your deployment scale.
How steep is the learning curve for getting started?▾
Ragflow requires technical knowledge since it's designed for developers. Setup involves deploying the open-source codebase, configuring vector databases, and integrating with your document sources, so expect moderate complexity.
What integrations and APIs does Ragflow support?▾
Ragflow integrates with vector databases and supports API-based document ingestion and retrieval. As an open-source tool, it's customizable to connect with various data sources and third-party services via its extensible architecture.
What are the main limitations of Ragflow?▾
As an open-source project, Ragflow requires self-hosting and DevOps expertise. It lacks managed cloud deployment options and built-in compliance certifications, which may be concerns for enterprise users needing turnkey solutions.
What is Ragflow best used for?▾
Ragflow excels at building enterprise knowledge management systems, AI agents, and RAG pipelines where you need full control over document processing and retrieval logic. It's ideal for organizations processing large document volumes with custom knowledge requirements.
Pricing Plans
Free
Custom
- Up to 3 documents
- Basic text extraction
- Community support
- 1 GB storage
ProMost Popular
$99/monthly
- Unlimited documents
- Advanced OCR and extraction
- Priority support
- 100 GB storage
Business
$299/monthly
- Everything in Pro
- Custom integrations
- Dedicated account manager
- 1 TB storage
Enterprise
Custom
- Custom deployment options
- Unlimited storage
- SLA guarantees
- Custom integrations
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