RAGFlow
A self-hosted RAG engine focused on document understanding, retrieval, and traceable answers.
By InfiniFlow · reviewed 2026-08-07
What RAGFlow does
RAGFlow provides document ingestion, parsing, retrieval pipelines, chat applications, and agent workflows. It is aimed at teams building knowledge systems where document processing and source grounding matter more than a lightweight desktop footprint.
Pricing model
Free and open source
Position in the stack
Where RAGFlow fits
RAGFlow is a document processing and retrieval application. It sits above model providers and focuses on parsing files, building searchable knowledge, retrieving evidence, and producing answers that can be traced back to source material.
Typical deployment flow
A practical sequence for evaluating RAGFlow before making it part of a permanent stack.
- STEP 1
Prepare infrastructure
Provision the supported Linux and Docker environment with enough CPU, memory, storage, and dependent services.
- STEP 2
Connect model providers
Configure chat, embedding, and reranking models based on privacy, language, quality, and cost requirements.
- STEP 3
Tune document pipelines
Ingest a representative corpus, inspect parsing and chunks, evaluate retrieval, then publish chat or API access.
Best for
- Document-heavy knowledge systems
- Retrieval workflows requiring source traceability
- Team RAG applications
Not the right layer for
- A lightweight app for a low-memory laptop
- Serving foundation models directly without external providers
Capabilities
Capabilities refer to the tool's application layer. Hardware and model support can still depend on a connected inference engine.
Deployment and compatibility
- Deployment
- Self-hosted
- Platforms
- Linux · Docker
- Hardware backends
- CPU · Uses connected model runtime
- Install methods
- Docker Compose
Models and integrations
- Model formats
- Uses connected model runtime
- Common integrations
- OllamaOpenAI-compatible APIsElasticsearchInfinity
Strengths
- Document parsing and retrieval focus
- Visual application and agent tooling
- Self-hosted API and user interface
Limitations
- Heavier infrastructure requirements than desktop RAG tools
- Model inference usually depends on an external provider
Planning checklist
Before you choose RAGFlow
Answer these questions with the exact models, hardware, users, and data you expect to operate.
- Are the source document formats parsed accurately enough?
- Which embedding and reranking models perform well on the target language and domain?
- How will retrieval quality be measured before and after updates?
- Can the team operate the required storage and search dependencies?
RAGFlow FAQ
Does RAGFlow run the foundation model itself?
Its primary job is document and retrieval orchestration. It connects to supported model providers for chat, embeddings, reranking, and related inference.
Why does RAGFlow require more infrastructure than desktop RAG tools?
It includes document parsing, indexing, retrieval, application, and dependent data services intended for richer team workflows.
Is adding documents enough to ensure accurate answers?
No. Parsing, chunking, embeddings, reranking, prompts, and corpus quality all affect results and should be evaluated with representative questions.
Official sources
Use these links to confirm current compatibility and installation requirements.
Related tools
Comparisons featuring RAGFlow
Use a reviewed comparison when the choice is between two adjacent tools.
Execution evidence
Known working recipes using RAGFlow
Recipes connect hardware, a model artifact, tools, settings, verification, and a reportable result.
No verified recipe is linked to this record yet.
Compatibility estimates remain available in the planner. A recipe appears here only after its exact stack and verification protocol are documented.