ragflow
★ 92.7k
vs
kglite
★ 73
ragflow vs kglite
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.; kglite: KGLite is an embedded, Cypher-queryable knowledge graph engine for Python and Rust, designed to serve applications, analysts, and LLM agents without an external database. It offers advanced features like schema-aware descriptions for LLMs, temporal querying over historical data, and a declared ontology for data quality.
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.
kgliteRAG / Knowledge Base · ★ 73
Providing domain knowledge (legal precedents, regulatory rules, medical ontologies) for LLM agents.
02
Side-by-Side Comparison
Field
ragflowkgliteCategory
RAG / Knowledge Base
RAG / Knowledge Base
Stars
★ 92.7k
★ 73
License
Apache-2.0
MIT
Install Command
docker compose -f docker-compose.yml up -d
pip install kglite
Updated
1d ago
1d ago
Open Source
Yes
Yes
Tags
RAG, AI Agent, LLM
Knowledge Graph, LLM Agents, Cypher Query Language
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.
kglite01Progressive-disclosure schema (`describe()`) optimized for LLM context windows.
02As-of queries over historical data using `valid_at()` and `valid_during()` functions.
03Declared ontology with data-quality gates and audit scorecards for semantic integrity.
04Hybrid retrieval combining vector similarity (`text_score()`) with Cypher traversal.
05Embedded, in-process graph engine with various storage modes (in-memory, mmap, disk).
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.
kglite↳Providing domain knowledge (legal precedents, regulatory rules, medical ontologies) for LLM agents.
↳Transforming business data from tabular sources (SQL, CSV, DataFrames) into a queryable graph.
↳Structuring RAG corpora with documents, chunks, entities, and their relationships for hybrid retrieval.
↳Codebase analysis by parsing code into a graph of functions, classes, and modules.
↳Ingesting Markdown knowledge bases (Obsidian vaults, Claude memory) into a queryable graph for analysis and agent memory.
05
Best For
ragflowMost PopularEssentialTrending
kglite—
FAQ
FAQ
What is the difference between ragflow and kglite?
Both ragflow and kglite are in the RAG / Knowledge Base category. ragflow has 92.7k stars, while kglite has 73 stars.
Which is better, ragflow or kglite?
The best choice depends on your use case. Choose ragflow if Building high-fidelity, production-ready AI systems with complex data., and kglite if Providing domain knowledge (legal precedents, regulatory rules, medical ontologies) for LLM agents..
Is ragflow free or open source?
Yes, ragflow is open source on GitHub (Apache-2.0).
Is kglite free or open source?
Yes, kglite is open source on GitHub (MIT).
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