by FlowElement-xinliuyuansu · MCP Server · ★ 4.5k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
A bio-inspired cognitive memory engine — a new paradigm for Graph RAG.
| Stars | 4,469 |
| Forks | 257 |
| Language | Python |
| Category | MCP Server |
| License | Apache-2.0 |
| Quality Score | 58.7333919995158/100 |
| Open Issues | 18 |
| Last Updated | 2026-08-03 |
| Created | 2026-03-31 |
| Platforms | mcp, python |
| Est. Tokens | ~13k |
These tools work well together with m_flow for enhanced workflows:
Looking for a m_flow alternative? If you're comparing m_flow with other mcp server tools, these 6 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.
A bio-inspired cognitive memory engine — a new paradigm for Graph RAG.
Self-evolving memory OS for LLM & AI Agents: ultra-persistent memory, hybrid-retrieval, and cross-task skill r
A memory OS that makes your OpenClaw agents more personal while saving tokens.
The ultimate RAG for your monorepo. Query, understand, and edit multi-language codebases with the power of AI
Open-source persistent memory for AI agent pipelines (LangGraph, CrewAI, AutoGen) and Claude. REST API + knowl
A lightweight, rollbackable, and visual Long-Term Memory Server for MCP Agents. Say goodbye to Vector RAG and
Explore other popular mcp server tools:
m_flow is A bio-inspired cognitive memory engine — a new paradigm for Graph RAG.. It is categorized as a MCP Server with 4.5k GitHub stars.
m_flow is primarily written in Python. It covers topics such as agent-memory, agentic-ai, ai-reasoning.
You can find installation instructions and usage details in the m_flow GitHub repository at github.com/FlowElement-xinliuyuansu/m_flow. The project has 4.5k stars and 257 forks, indicating an active community.
m_flow is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to m_flow on Agent Skills Hub include m_flow, MemOS, EverMemOS. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.