ragflow vs m3-memory
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.; m3-memory: M3 Memory provides persistent, local memory for MCP agents using SQLite, hybrid retrieval, and optional cross-device sync. It stores facts, decisions, and preferences across sessions, with contradiction handling and knowledge graph features. It works offline and integrates with Claude Code, Gemini CLI, and other MCP-compatible agents.
TL;DR
- 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.
- 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.
Coding agents like Claude Code or Gemini CLI need persistent memory across sessions
Side-by-Side Comparison
ragflowm3-memoryFeatures
ragflowm3-memoryUse Cases
ragflowm3-memoryBest For
ragflowm3-memoryFAQ
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