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Agents & Automation

Ragflow

RAGFlow is an open-source Retrieval-Augmented Generation (RAG) engine designed to enhance AI agents by providing truthful question-answering capabilities supported by citations from complex formatted data. It integrates with large language models (LLMs) and uses a converged context engine alongside pre-built agent templates to convert complex data into production-ready outputs. The platform includes built-in ingestion pipelines that cleanse and process multi-format data into semantic representations, enabling deep document understanding. RAGFlow supports multi-agent orchestration, combining RAG, tools, and visual workflows to build sophisticated AI agents. It also offers local model deployment options and RESTful API access for integration.

Updated Jan 23, 2026open-source
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Overview

RAGFlow is an open-source RAG engine that enables building production-ready AI agents with deep document understanding and multi-agent orchestration.

Pricing
open-source
Category
Agents & Automation
Company
Visual Guide
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Key Features
01
Parses complex formatted data with options like PaddleOCR integration to support accurate information extraction.
02
Includes templates such as Deep Research with lead agents for task planning and subagents for web search, content reading, and synthesis.
03
Supports vector search, BM25, custom scoring, and re-ranking to improve retrieval accuracy.
04
Allows users to build agents using graph-based task orchestration without coding.
05
Supports deployment of local models via Ollama, Xinference, IPEX-LLM, or Jina, reducing dependency on external services.
06
Provides API and client support for integration and interaction with RAGFlow functionalities.
Real-World Use Cases

Building Production-Ready AI Agents

Developers and enterprises can create AI agents that answer questions truthfully with citations from complex data sources.

Multi-Agent Research Workflows

Use pre-built multi-agent templates to orchestrate tasks such as web search, content reading, and synthesis for deep research applications.

Quick Start
1
Start RAGFlow Server
Launch a local RAGFlow server using Docker or by building from source.
2
Create Dataset
Upload files for parsing and configure intervention options as needed.
3
Configure Model Providers
Add LLM API keys and set system model settings via the UI under Model Providers.
4
Set Up AI Chat
Select datasets and chat models in the Chat tab to start interacting.
5
Use APIs or Python Client
Integrate or interact with RAGFlow functionalities using the RESTful API or Python client.
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Strategic Context for Ragflow

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Pricing
Model: open-source

RAGFlow is open-source under the Apache-2.0 license. No pricing details are available for plans or costs. A SaaS offering is previewed but not yet released.

Assessment
Strengths
  • Open-source with an active community of 448 contributors.
  • Supports parsing of complex data formats with citation-backed answers.
  • Includes pre-built agent templates for multi-agent workflows.
  • Enables local model deployment without relying on external services.
  • Offers no-code visual workflows for building agents.
Limitations
  • Enterprise features like advanced permissions are only available in the demo SaaS version, not in the open-source release.
  • Human intervention for agents is planned but not yet implemented in current versions.
  • Certain OCR functionalities require internet access unless self-hosted.