RAGFlow

RAGFlow is the open-source Retrieval-Augmented Generation engine built by InfiniFlow Inc. It combines deep document understanding (DeepDoc parsing of PDFs, images, tables and scanned files) with hybrid retrieval — dense vector search, BM25 full-text and tensor/multi-vector re-ranking — and an integrated visual agent platform, to serve as a context layer for LLM applications. The platform ships as an Apache-2.0 self-hosted Docker deployment and as RAGFlow Cloud, a hosted multi-tenant service. Its RESTful HTTP API covers dataset (knowledge base) management, document ingestion and parsing, chunk management, retrieval, chat assistants, sessions, agents, memory, workspace file versioning and search apps, plus an OpenAI-compatible chat-completions surface. A first-party Python SDK and an optional self-hosted Model Context Protocol server expose the same retrieval core to agents.

RAGFlow publishes 1 API on the APIs.io network. Tagged areas include Company, Artificial Intelligence, Retrieval Augmented Generation, Search, and Vector Database.

RAGFlow’s developer surface includes documentation, getting-started guide, API reference, engineering blog, support, pricing, signup flow, and 31 more developer resources.

🌐 Visit website 📡 Source on GitHub

45.6/100 developing ▬ flat Agent 15/100 agent aware Full breakdown ↓
scored 2026-09-15 · rubric v0.22.0

AccessFreemiumSelf serve⚡ Free to try

1 APIs 1 MCP Servers

Company Artificial Intelligence Retrieval Augmented Generation Search Vector Database Document Processing Knowledge-Management Agents Open-Source LLM MCP

Kin Score

Kin Score How this is scored →
scored 2026-09-15 · rubric v0.22.0

Composite quality — 45.6/100 · developing

Contract Quality 0.0 / 25

Developer Ergonomics 13.8 / 20

Access Clarity 13.2 / 20

Operational Transparency 9.6 / 13

Contract Governance 2.2 / 12

Discoverability 6.9 / 10

Agent readiness — 15/100 · agent aware

Machine-Readable Contract 0 / 18

Agentic Access Contract 0 / 10

Documented Reversibility 0 / 6

MCP Server 12 / 12

Machine-Readable Auth 10 / 10

Idempotency 0 / 9

Stable Error Semantics 8 / 8

Request/Response Examples 0 / 7

Rate-Limit Signaling 7 / 7

Typed Event Surface 0 / 6

Agent Skills 5 / 5

Well-Known Catalog 0 / 4

Consent & Bot Identity 0 / 3

A2A Agent Card 0 / 8

Dry-Run / Simulate Mode 0 / 4

Delegated User Identity 0 / 6

Protected Resource Metadata 0 / 5

Registration Without a Human 0 / 6

Agentic Commerce Well-Known Document 0 / 5

Create-or-Update Ergonomics could not be measured. We hold no machine-readable contract for this provider to read, so there is nothing to measure a write surface against. Excluded rather than scored zero: never-measured and measured-empty are different facts. Publishing an OpenAPI is what makes this facet — and several others — scorable at all.

Improve this rating by publishing the missing artifacts — every area above can be raised, and the full rubric is at apis.io/rating/. Every facet and dimension name above is a link: it opens that measurement's own page — what it means, the exact checks that feed it, how the whole catalog distributes on it, and the providers at the top of it. This rating is computed from github.com/api-evangelist/ragflow: open an issue to ask a question, or submit a pull request to add artifacts. Submit an artifact on GitHub — free → Manage your own listing — the Influence plan, $499/mo →

Standards implemented 1

Interfaces this provider implements that became standards by being copied rather than ratified. Each is profiled by the API Commons, and the evidence column says how the claim was established — not that it was made.

The OpenAI Interface

prose — states compatibility; nothing published to check it against

2 core of 338 operations graded · api-commons/models

APIs 1

Individual APIs this provider publishes, each with its own machine-readable definition.

RAGFlow HTTP API

The RAGFlow RESTful HTTP API — 95 documented operations under /api/v1, authenticated with a tenant API key carried as a bearer token. Covers dataset management, document upload ...

MCP Servers 1

Model Context Protocol servers that expose these APIs to AI agents.

RAGFlow MCP Server

A first-party Model Context Protocol server that InfiniFlow ships inside the RAGFlow repository at mcp/server/server.py. It is an OPTIONAL, DISABLED-BY-DEFAULT component of a RA...

MCP SERVER

Pricing Plans 1

Published pricing tiers and plan structures.

Ragflow Plans Pricing

5 plans

PLANS

Rate Limits 1

Documented rate limits and quota policies.

Ragflow Rate Limits

3 limits

RATE LIMITS

Security Posture 3

Authentication, domain security, vulnerability disclosure, and trust-center signals.

Ragflow Authentication

1 scheme

SECURITY

Ragflow Domain Security

TLSv1.3 · HSTS · DMARC

SECURITY

Ragflow Vulnerability Disclosure

Hackerone

SECURITY

Resources

Get Started 3

Portal, sign-up, and the first successful call

GettingStarted

GettingStarted

SignUp

SignUp

Sandbox

Sandbox

Documentation 2

Reference material describing how the API behaves

Documentation

Documentation

APIReference

APIReference

Agent Surfaces 3

MCP servers, agent skills, and machine-readable catalogs

MCPServer

MCPServer

AgentSkill

AgentSkill

LLMsTxt

LLMsTxt

Design & Contract 6

Pagination, idempotency, versioning, errors, and events

Conventions

ErrorCatalog

Conformance

DataModel

Lifecycle

Components

Build 5

SDKs, sample code, and the tooling you integrate with

GitHubOrganization

SourceCode

Packages

SDKs

ToolCrosswalk

Access & Security 5

Authentication, authorization, and security posture

Compliance

Compliance

Security

Security

VulnerabilityDisclosure

VulnerabilityDisclosure

DomainSecurity

DomainSecurity

Authentication

Authentication

Operate 7

Status, limits, changes, and where to get help

Roadmap

Support

Community

Deprecation

ChangeLog

ReleaseNotes

RateLimits

Scroll for all 7

Commercial 5

Pricing, plans, and the legal terms of use

License

Pricing

TermsOfService

PrivacyPolicy

Plans

Company 2

The organization behind the API

Website

Website

Blog

Blog

Source (apis.yml)

aid: ragflow
name: RAGFlow
description: RAGFlow is the open-source Retrieval-Augmented Generation engine built by InfiniFlow Inc. It combines deep document
  understanding (DeepDoc parsing of PDFs, images, tables and scanned files) with hybrid retrieval — dense vector search, BM25
  full-text and tensor/multi-vector re-ranking — and an integrated visual agent platform, to serve as a context layer for
  LLM applications. The platform ships as an Apache-2.0 self-hosted Docker deployment and as RAGFlow Cloud, a hosted multi-tenant
  service. Its RESTful HTTP API covers dataset (knowledge base) management, document ingestion and parsing, chunk management,
  retrieval, chat assistants, sessions, agents, memory, workspace file versioning and search apps, plus an OpenAI-compatible
  chat-completions surface. A first-party Python SDK and an optional self-hosted Model Context Protocol server expose the
  same retrieval core to agents.
url: https://raw.githubusercontent.com/api-evangelist/ragflow/refs/heads/main/apis.yml
deliveryModel:
  model: unknown
  open_source: unknown
  commercial: true
  callable_host: false
  label: Delivery model not determined — needs a product licence on record
  confidence: low
  source:
  - pricing
  - repository-unlicensed
  generated: '2026-08-28'
  method: derived
accessModel:
  pricing: freemium
  onboarding: self-serve
  trial: false
  try_now: true
  public: false
  label: Freemium · Self-serve signup
  confidence: high
  source:
  - plans
  - authentication
  - rate-limits
  - security
  - sandbox
  generated: '2026-09-03'
  method: derived
image: https://ragflow.io/img/logo.svg
x-type: company
x-source: harvest:self-hosted-agent-stack
specificationVersion: '0.20'
created: '2026-08-27'
modified: '2026-08-27'
tags:
- Company
- Artificial Intelligence
- Retrieval Augmented Generation
- Search
- Vector Database
- Document Processing
- Knowledge-Management
- Agents
- Open-Source
- LLM
- MCP
tags_raw:
- Company
- Artificial Intelligence
- Retrieval Augmented Generation
- Search
- Vector Database
- Document Processing
- Knowledge Management
- Agents
- Open Source
- LLM
- Model Context Protocol
apis:
- aid: ragflow:ragflow-http-api
  name: RAGFlow HTTP API
  description: The RAGFlow RESTful HTTP API — 95 documented operations under /api/v1, authenticated with a tenant API key
    carried as a bearer token. Covers dataset management, document upload / parse / ingest, chunk management, retrieval, chat
    assistants, sessions, agents, memory, file and workspace-commit management, search apps, and an OpenAI-compatible chat-completions
    endpoint. Documented as a human reference; no machine-readable OpenAPI is published.
  humanURL: https://ragflow.io/docs/http_api_reference
  baseURL: https://cloud.ragflow.io/api/v1
  tags:
  - Retrieval Augmented Generation
  - Search
  - Document Processing
  - Agents
  properties:
  - type: APIReference
    url: https://ragflow.io/docs/http_api_reference
  - type: Documentation
    url: https://ragflow.io/docs
  - type: Authentication
    url: authentication/ragflow-authentication.yml
  - type: ErrorCatalog
    url: errors/ragflow-error-codes.yml
  - type: Conventions
    url: conventions/ragflow-conventions.yml
  - type: DataModel
    url: data-model/ragflow-data-model.yml
  - type: RateLimits
    url: rate-limits/ragflow-rate-limits.yml
  - type: Lifecycle
    url: lifecycle/ragflow-lifecycle.yml
maintainers:
- FN: Kin Lane
  email: kin@apievangelist.com
- FN: APIs.json
  email: info@apis.io
common:
- type: License
  name: Apache-2.0
  url: https://github.com/infiniflow/ragflow/blob/main/LICENSE
- type: Website
  url: https://ragflow.io/
- type: Documentation
  url: https://ragflow.io/docs
- type: GettingStarted
  url: https://ragflow.io/docs
- type: APIReference
  url: https://ragflow.io/docs/http_api_reference
- type: Blog
  url: https://ragflow.io/blog
- type: GitHubOrganization
  url: https://github.com/infiniflow
- type: SourceCode
  url: https://github.com/infiniflow/ragflow
- type: Roadmap
  url: https://github.com/infiniflow/ragflow/issues/12241
- type: Support
  url: https://ragflow.io/contact-us
- type: Community
  url: https://discord.gg/NjYzJD3GM3
- type: Pricing
  url: https://ragflow.io/#pricing-plan
- type: SignUp
  url: https://cloud.ragflow.io/
- type: TermsOfService
  url: https://ragflow.io/policies/terms
- type: PrivacyPolicy
  url: https://ragflow.io/policies/privacy
- type: Compliance
  url: https://ragflow.io/policies/dpa
- type: Security
  url: security/ragflow-vulnerability-disclosure.yml
- type: VulnerabilityDisclosure
  url: security/ragflow-vulnerability-disclosure.yml
- type: DomainSecurity
  url: security/ragflow-domain-security.yml
- type: Packages
  url: packages/ragflow-packages.yml
- type: SDKs
  url: packages/ragflow-packages.yml
- type: MCPServer
  url: mcp/ragflow-mcp.yml
- type: ToolCrosswalk
  url: mcp/ragflow-tool-crosswalk.yml
- type: AgentSkill
  url: skills/_index.yml
- type: LLMsTxt
  url: llms/ragflow-llms.txt
- type: Conventions
  url: conventions/ragflow-conventions.yml
- type: ErrorCatalog
  url: errors/ragflow-error-codes.yml
- type: Authentication
  url: authentication/ragflow-authentication.yml
- type: Conformance
  url: conformance/ragflow-conformance.yml
- type: DataModel
  url: data-model/ragflow-data-model.yml
- type: Lifecycle
  url: lifecycle/ragflow-lifecycle.yml
- type: Deprecation
  url: lifecycle/ragflow-lifecycle.yml
- type: ChangeLog
  url: changelog/ragflow-changelog.yml
- type: ReleaseNotes
  url: https://ragflow.io/docs/release_notes
- type: Plans
  url: plans/ragflow-plans-pricing.yml
- type: RateLimits
  url: rate-limits/ragflow-rate-limits.yml
- type: Sandbox
  url: sandbox/ragflow-sandbox.yml
- type: Components
  url: components/ragflow-components.yml
x-enrichment:
  date: '2026-08-27'
  status: enriched
  artifacts_added: 21
  pass: local-v1

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