RAGFlow

RAGFlow: Retrieval-Augmented Generation engine that combines data ingestion pipelines with agent orchestration tools to build context layers for LLMs.

RAGFlow functions as an open-source engine designed to create context layers for AI agents, specifically targeting enterprise needs. It includes a built-in ingestion pipeline that processes and cleanses data from multiple formats into semantic representations for retrieval. The system utilizes hybrid search, combining vector search, BM25, and custom scoring with re-ranking to improve answer accuracy. Users build agents within a visual workflow interface that integrates RAG, external tools, and Model Context Protocols. It supports various industry-specific workflows, such as financial research, legal precedent analysis, and manufacturing maintenance.

RAGFlow at a glance

Best for: Software engineer, Researcher
Runs on: self-hosted
Pricing: Free plan; Starter plan; Pro plan; Enterprise plan with BYOC and on-premises deployment options.
Released: December 2023
Category: AI research tool
Website: ragflow.io

Key features

Built-in ETL data ingestion pipeline
High-precision hybrid search with vector and BM25
Advanced re-ranking for retrieval relevance
Visual workflow editor for agent orchestration
Tool and Model Context Protocol integration
Autonomous planning capabilities for agents

Use it for

Automating equity investment research
Conducting legal precedent analysis
Generating manufacturing maintenance guidance

Alternatives to RAGFlow

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My RAG — Compare Python and Java implementations sharing identical data.
WeKnora — Combines RAG, multi-step agents, and automatic Wiki generation in one platform.
FastGPT — It uses hybrid retrieval and auto-cleansed data to prevent hallucinations.
llmware — Build private, local RAG pipelines with 300+ prepackaged models and integrated document parsing.
Langflow — Builds and deploys AI agents visually with Python and major LLMs.
Exa — Provides high-accuracy web search and deep research endpoints for AI agents.
Jonex — Provides traceable, agent-ready knowledge from diverse enterprise data sources.
RAG-Anything — It unifies text and multimodal document retrieval within a single framework.
Jina — It provides reliable reader and search APIs optimized for LLMs and RAG systems.
LightRAG — It uses a dual-layer architecture to reduce LLM calls and retrieval latency.
MLflow — Complete observability and evaluation for production AI apps.

All RAGFlow alternatives