RAGFlow is an open-source retrieval-augmented generation (RAG) engine, positioned as "building an excellent context layer for AI agents." It is dedicated to providing reliable context for AI agents through a leading open-source RAG engine, and integrates an agent platform, specifically built for enterprises.
In terms of purpose, RAGFlow mainly addresses the problem that AI agents need high-quality, reliable context in complex tasks. Through its built-in ingestion pipeline, it can clean and process multi-format data and structure it into rich semantic representations, thereby providing a high-quality foundation for subsequent retrieval.
Main capabilities include:
- Data ingestion and processing: The built-in pipeline can clean and process multi-format data and convert it into semantic representations.
- Hybrid retrieval and ranking: Combines vector search, BM25, and custom scoring, and applies advanced reranking to improve answer accuracy and context relevance.
- Integrated agent platform: Seamlessly integrates RAG, tools, and MCP in a visual workflow, supporting the construction of powerful agents.
- Multi-agent orchestration: Supports autonomous planning and multi-agent collaboration, and can be applied to complex workflows.
The materials present three typical workflow examples:
- Stock analysis workflow: Automatically collects company data, integrates financial metrics and research insights, and achieves advanced stock analysis through autonomous planning and multi-agent orchestration. It identifies stock tickers from user queries, aggregates information from external authoritative sources and internal records, and ultimately combines qualitative insights with financial metrics to generate a complete investment report.
- Legal case analysis workflow: Conducts structured precedent analysis of binding authority and persuasive authority in public case law and internal case records. After a case is input, it automatically extracts key attributes such as jurisdiction, court level, and legal issues, forms search queries and retrieves relevant precedents and their rationes decidendi, and ultimately integrates them into a structured analysis that reflects the court's reasoning and rulings on similar legal issues, with citations and distinguishing factors attached, ready for argumentation.
- Maintenance guidance workflow: Accurately obtains content from internal manuals, with external references used only as supplementary support, to provide structured maintenance guidance. When a task is input, the workflow first verifies input sufficiency to ensure it contains the necessary details, then extracts standard protocols from internal maintenance manuals, integrates external technical data as supplements, and ultimately generates clear execution instructions.
In terms of usage, RAGFlow offers a free starting plan to explore basic features. Paid plans include: the free plan provides 0.1 GB dataset storage, 500 credits per month, and does not provide an API key, making it suitable for individuals and small teams; another plan provides 5 GB dataset storage, 5,000 credits per month, and provides an API key; the Pro plan (most popular) is suitable for growing enterprises and provides 20 team members, 50 GB dataset storage, and 20,000 credits per month; the enterprise-level plan is aimed at large-scale production workloads and supports BYOC deployment, on-premises deployment, and dedicated support.
Applicable scenarios include stock analysis, legal case research, maintenance guidance, and other enterprise-level tasks that require reliable context and multi-agent collaboration.
In terms of limitations, the materials do not provide specific technical limitations, a list of supported models, pricing details, or detailed deployment requirements; this information is not currently provided.key; the Pro plan (most popular) is suitable for growing enterprises and provides 20 team members, 50 GB dataset storage, and 20,000 credits per month; the enterprise-level plan is aimed at large-scale production workloads and supports BYOC deployment, on-premises deployment, and dedicated support.
Applicable scenarios include stock analysis, legal case research, maintenance guidance, and other enterprise-level tasks that require reliable context and multi-agent collaboration.
In terms of limitations, the materials do not provide specific technical limitations, a list of supported models, pricing details, or detailed deployment requirements; this information is not currently provided.
