ragflow
★ 92.7k
vs
mykg
★ 78
ragflow vs mykg
ragflow: RAGFlow is a leading open-source Retrieval-Augmented Generation (RAG) engine that integrates RAG with Agent capabilities. It provides a superior context layer for LLMs and offers a streamlined RAG workflow adaptable to enterprises of any scale.; mykg: myKG automatically generates confidence-scored knowledge graphs from diverse document types, grounded in an induced RDFS/OWL ontology. It builds trustworthy knowledge graphs through a self-evolving ontology that adapts, maintains consistency, and assigns confidence scores to extracted knowledge.
01
TL;DR
ragflowRAG / Knowledge Base · ★ 92.7k
When to choose:
- Your documents have complex layouts (PDFs, scanned files, tables, slides) and you need layout analysis, OCR, and traceable citations.
- You want an Apache-2.0 all-in-one RAG platform where you can inspect and hand-tune template-based chunks in a web UI.
When to skip:
- Your server has less than 4 CPU cores and 16 GB RAM; RAGFlow's multi-service stack is heavy.
- You want an autonomous web research agent that writes 2,000-word reports from live search rather than indexing a local document corpus; GPT Researcher fits better.
mykgRAG / Knowledge Base · ★ 78
Converting websites, GitHub repositories, and personal notes into queryable knowledge graphs.
02
Side-by-Side Comparison
Field
ragflowmykgCategory
RAG / Knowledge Base
RAG / Knowledge Base
Stars
★ 92.7k
★ 78
License
Apache-2.0
MIT
Install Command
docker compose -f docker-compose.yml up -d
pip install mykg && mykg init
Updated
1d ago
2w ago
Open Source
Yes
Yes
Tags
RAG, AI Agent, LLM
Knowledge Graph, LLM, Ontology
03
Features
ragflow01Deep document understanding for knowledge extraction from unstructured data.
02Intelligent and template-based chunking with explainable options.
03Grounded citations with reduced hallucinations and traceable references.
04Compatibility with heterogeneous data sources including documents, images, and web pages.
05Automated and effortless RAG workflow orchestration with configurable models and fused re-ranking.
mykg01Schema-guided knowledge graph generation grounded in a formal RDFS/OWL schema.
02Supports mixed-format corpora including PDF, Word, HTML, and images, automatically converted to Markdown.
03Provides five output formats: JSONL, Turtle RDF, NetworkX, Obsidian vault, and Neo4j LOAD CSV.
04Enables incremental updates and schema growth by appending new or modified files to existing sessions.
05Integrates with AI coding assistants (e.g., Claude Code) by exporting an Obsidian vault as a 'second brain' for context.
04
Use Cases
ragflow↳Building high-fidelity, production-ready AI systems with complex data.
↳Developing enterprise-scale knowledge base and intelligent Q&A chatbots.
↳Facilitating intelligent document processing and advanced information retrieval.
mykg↳Converting websites, GitHub repositories, and personal notes into queryable knowledge graphs.
↳Building a 'second brain' for AI coding assistants to ground answers and trace relationships in documents.
↳Creating an LLM-powered wiki from diverse documents for AI agents to reason over.
↳Facilitating human-in-the-loop ontology design and refinement through schema review gates.
05
Best For
ragflowMost PopularEssentialTrending
mykg—
FAQ
FAQ
What is the difference between ragflow and mykg?
Both ragflow and mykg are in the RAG / Knowledge Base category. ragflow has 92.7k stars, while mykg has 78 stars.
Which is better, ragflow or mykg?
The best choice depends on your use case. Choose ragflow if Building high-fidelity, production-ready AI systems with complex data., and mykg if Converting websites, GitHub repositories, and personal notes into queryable knowledge graphs..
Is ragflow free or open source?
Yes, ragflow is open source on GitHub (Apache-2.0).
Is mykg free or open source?
Yes, mykg is open source on GitHub (MIT).
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