Hitchhikethe internet
Directory/Vector databases/RAGFlow
Vector databases · Tool

RAGFlow

Researched

Build a superior context layer for AI agents - Empower your AI agents through the leading open-source RAG engine, delivering reliable context and an integrated agent platform, built for enterprise.

Online Checked Follow updates
Official websiteVisit website
At a glance

In one minute

Start here for the decision-making essentials: what RAGFlow does, who it is for, how it is accessed, and the first-party sources behind this profile.

PricingCurrent signal

See official pricing

Platforms
Web
API accessNot public
FoundedNot disclosed by source
AvailabilityWeb / remote
LicenseProprietary
Decision support

Common questions and adoption checks

3 sourced answers

Short answers to the questions buyers and builders commonly ask about RAGFlow. Each answer cites the shared ledger below, where every source is listed once.

01What does RAGFlow help with?

Vector databases

Ledger citation[1] ragflow.io
02What pricing information is available for RAGFlow?

See official pricing

Ledger citation[1] ragflow.io
03Does RAGFlow document API access?

No public API access is listed in the recorded profile sources.

Ledger citation[1] ragflow.io
Decision guide

Capabilities and operating fit

Vector databases

This profile connects the jobs RAGFlow is described as handling with its delivery model, access options and the subjects used to match it to related products in this directory.

Common use cases

  • Vector databases

Topics mapped

Vector databases

Access signals

Pricing model
See official pricing
API
Not publicly listed
Source links
1 recorded
Source-backed

Verified facts

Updated July 28, 2026

Each fact points to a recorded source, making it easy to distinguish verified product information from claims that need checking.

Official website

HTTP 200 verified

Official page title

RAGFlow

Practical capabilities

What it helps with

1 documented areas

A concise view of the jobs, capabilities and integrations described in the recorded product sources.

Focus area

Vector databases

Availability

Where it runs and where to get it

Source checked

Documented product formats, platforms and official distribution destinations. Availability can vary by region and plan.

Cost / license

See official pricing

Platforms

Web
Implementation details

Adoption notes

DeploymentWeb
LicenseProprietary
Model supportProvider-managed unless documented otherwise
Data controlReview the provider's current privacy, retention and subprocessors documentation before adoption
Learning curveBeginner
Primary use cases

What to verify before adopting

  • Confirm current pricing, quotas and feature availability with the provider.
  • Test output quality and reliability against your own workflow before committing.
  • Review privacy, security and contractual requirements for sensitive data.
Evolution and major updates

RAGFlow timeline

A concise history of software releases and material product changes. Events appear only when a dated source supports what changed.

4 dated updates
Latest first · exact dates

Showing the newest updates and meaningful milestones. Open an entry for its summary and source.

  1. Release note

    RAGFlow v0.26.4 released

    Open details

    Added a language-aware Snowball stemmer supporting 16 languages with dataset language integration across the tokenization pipeline, plus Dutch to the frontend. Includes fixes for crashes, parsing, and UI issues.

    View source [2]
  2. Release note

    RAGFlow v0.26.4

    Open details

    Adds a language-aware Snowball stemmer supporting 16 languages, integrates the dataset language parameter across the tokenization pipeline, and adds Dutch to the frontend; ships multiple bug fixes for MCP, parsers, and UI.

    View source [2]
  3. Release note

    RAGFlow v0.26.3 released

    Open details

    Introduced Google BigQuery as a data source connector for incremental sync, added MCP tools ragflow_list_datasets and ragflow_list_chats, integrated the SoMark OCR parser for layout-aware extraction, and exposed an Ingest documents API endpoint.

    View source [2]
  4. Release note

    RAGFlow v0.26.3

    Open details

    Introduces Google BigQuery as a data source connector with incremental syncing, integrates the SoMark OCR parser for tables and figures, exposes the Ingest documents API endpoint, and adds two MCP tools to the RAGFlow MCP server.

    View source [2]
Citation ledger

Recorded sources

2 unique pages

Facts, answers, structured details, milestones and primary resource links cite this shared ledger. Each external page appears once; release tags from the same GitHub project are grouped under one release history.

  1. 1ragflow.io 2 facts · 3 answers · Official site
  2. 2ragflow.io/changelog 4 milestones
Research status1 substantive facts · 1 source pages · quality score 32/100