AgentIndex icon
AgentIndex
ragflow logo
ragflow
★ 92.7k
vs
pensyve logo
pensyve
★ 97

ragflow vs pensyve

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.; pensyve: Pensyve is a universal memory runtime for AI agents that eliminates amnesia across sessions. It offers three memory types (episodic, semantic, procedural), 8-signal fusion retrieval, and FSRS-based forgetting curve. Framework-agnostic with SDKs for Python, TypeScript, Go, and integrations with LangChain, CrewAI, AutoGen, plus native plugins for Claude Code, Cursor, and VS Code.

01

TL;DR

ragflow logo
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.
Read ragflow full review →
pensyve logo
pensyveRAG / Knowledge Base · ★ 97

Persist agent knowledge across sessions and avoid repetitive questions

Read pensyve full review →
02

Side-by-Side Comparison

Field
ragflow logoragflow
pensyve logopensyve
Category
RAG / Knowledge Base
RAG / Knowledge Base
Stars
★ 92.7k
★ 97
License
Apache-2.0
NOASSERTION
Install Command
docker compose -f docker-compose.yml up -d
pip install pensyve
Updated
1d ago
1d ago
Open Source
Yes
Yes
Website
↗ Visit
↗ Visit
GitHub
↗ GitHub
↗ GitHub
Tags
RAG, AI Agent, LLM
agent-memory, ai, ai-agents
03

Features

ragflow logoragflow
01Deep 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.
pensyve logopensyve
01Three memory types: episodic, semantic, procedural
028-signal fusion retrieval (vector, BM25, graph, intent, recency, frequency, confidence, type boost)
03FSRS forgetting curve with reinforcement for natural decay
04Offline-first with SQLite and ONNX embeddings, no cloud required
05Framework-agnostic with SDKs for Python, TypeScript, Go, and MCP
04

Use Cases

ragflow logoragflow
↳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.
pensyve logopensyve
↳Persist agent knowledge across sessions and avoid repetitive questions
↳Integrate memory into any AI framework (LangChain, CrewAI, AutoGen) via adapters
↳Enable agents to learn effective strategies through procedural memory
05

Best For

ragflow logoragflow
Most PopularEssentialTrending
pensyve logopensyve
—
FAQ

FAQ

What is the difference between ragflow and pensyve?
Both ragflow and pensyve are in the RAG / Knowledge Base category. ragflow has 92.7k stars, while pensyve has 97 stars.
Which is better, ragflow or pensyve?
The best choice depends on your use case. Choose ragflow if Building high-fidelity, production-ready AI systems with complex data., and pensyve if Persist agent knowledge across sessions and avoid repetitive questions.
Is ragflow free or open source?
Yes, ragflow is open source on GitHub (Apache-2.0).
Is pensyve free or open source?
Yes, pensyve is open source on GitHub (NOASSERTION).
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