ragflow
★ 92.7k
vs
floop
★ 26
ragflow vs floop
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.; floop: floop provides persistent memory for AI coding agents, allowing them to learn from corrections and maintain consistency across sessions. It extracts reusable behaviors from your feedback and activates them contextually using spreading activation, much like how the brain retrieves memories.
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.
floopRAG / Knowledge Base · ★ 26
Teach an AI agent new behaviors or corrections based on its mistakes.
02
Side-by-Side Comparison
Field
ragflowfloopCategory
RAG / Knowledge Base
RAG / Knowledge Base
Stars
★ 92.7k
★ 26
License
Apache-2.0
Apache-2.0
Install Command
docker compose -f docker-compose.yml up -d
go install github.com/nvandessel/feedback-loop/cmd/floop@latest
Updated
1d ago
2mo ago
Open Source
Yes
Yes
Tags
RAG, AI Agent, LLM
AI Agents, Persistent Memory, Feedback Loop
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.
floop01Learns from user corrections to build durable behaviors.
02Activates behaviors based on file type, task, and semantic relevance.
03Uses graph-based memory retrieval inspired by cognitive science (spreading activation).
04Optimizes context injection to stay within token limits.
05Offers a comprehensive CLI with JSON output for agent consumption.
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.
floop↳Teach an AI agent new behaviors or corrections based on its mistakes.
↳Manage and inspect the stored behaviors and their relationships.
↳Integrate with AI coding tools (e.g., Claude Code, Cursor) to provide dynamic context.
↳Generate context-aware prompts for AI agents based on the current project and task.
05
Best For
ragflowMost PopularEssentialTrending
floop—
FAQ
FAQ
What is the difference between ragflow and floop?
Both ragflow and floop are in the RAG / Knowledge Base category. ragflow has 92.7k stars, while floop has 26 stars.
Which is better, ragflow or floop?
The best choice depends on your use case. Choose ragflow if Building high-fidelity, production-ready AI systems with complex data., and floop if Teach an AI agent new behaviors or corrections based on its mistakes..
Is ragflow free or open source?
Yes, ragflow is open source on GitHub (Apache-2.0).
Is floop free or open source?
Yes, floop is open source on GitHub (Apache-2.0).
Cloud Infrastructure
AdDeploy on DigitalOcean
Host your MCP servers, Docker containers, and AI agents with $200 free credit over 60 days.
→