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RAGFlow

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Ingest documents, inspect chunks and build retrieval-based answers with traceable source references.

ragflow.ioSources reviewed
Starting price
$0 software licence
Free access
Self-managed edition
Billing unit
Compute / provider usage separate
Platform
Docker / self-hosted web
Verified
17 Oct 2026

Overview

What you provide

  • You provide documents, chunking settings, retrieval questions and model connections.

What you get

  • You receive inspectable chunks, references and generated answers.

Choosing this tool

Choose RAGFlow when document ingestion and retrieval inspection need a dedicated application. Its visible chunks help diagnose evidence problems before changing the model. Choose AnythingLLM for a lighter document workspace, or Langflow when custom workflow composition is the central requirement.

A decision worked through

The documented Docker starting point is 4 CPU cores, 16 GB RAM and 50 GB disk. Treat that as a starting configuration, not a capacity promise: measure parsing time and retrieval on your actual files before adding concurrent users.

RAGFlow alternatives: choose an implementation route

For RAGFlow, compare an ingestion-and-chunk inspection application with a lighter document workspace and a customizable workflow canvas.

ProductDeployment requirementsFree allowanceInput → outputKey limit
RAGFlowRead profileOfficial source ↗Docker starting configuration: 4 CPU cores, 16 GB RAM and 50 GB disk. Connected LLM, embeddings and parsing workloads may require additional resources.Self-managed editionDocuments, chunking settings, retrieval questions and model connections → Inspectable chunks, references and generated answersSee the product-specific review limits below.
AnythingLLMRead profileOfficial source ↗Desktop installer for one user; Docker for shared workspaces and the website widget. RAM and GPU needs depend on the chosen model and embeddings.Self-managed editionDocuments, workspace rules and model/embedding connections → Document-grounded answers and optional agent actionsLLM, embedding and vector-store choices determine where data goes and what costs accrue. Retrieved citations still need checking; multi-user capabilities differ by edition.
LangflowRead profileOfficial source ↗Python 3.10–3.14 package or Windows/macOS Desktop; connect a model provider. A local GPU is not a requirement for a cloud-model workflow.Self-managed editionWorkflow graph, documents, model connection and tool settings → Runnable flows, API responses and MCP toolsRunning a local canvas does not make a connected cloud model local. Configure authentication before exposing its API; hosted model and database fees are separate.

Setup & workflow

  1. Check Docker resources.
  2. Follow the selected release guide.
  3. Ingest a small set.
  4. Inspect chunks.
  5. Test references and concurrency.
Requirements & installation
  • Docker / self-hosted web

Limits & review

  • Model, embedding, storage and parsing resources are separate cost drivers. Current documentation includes release-candidate setup; choose and pin a suitable release before production.

Your part

  • Check whether the retrieved chunks actually contain the evidence needed to answer the question.
Sources & verification6

This profile is based on official sources, not a hands-on product test.

Vendor descriptions and demos document advertised features. Editorial guidance is based on these sources.

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