Understand the project
Practical overview
When it fits
Use M Flow when a retrieval task depends on tracing relationships between facts, events and entities, rather than ranking isolated text chunks by similarity.
Who it is for
- Developers building AI agents
- Agent builders working with episodic or long-term memory
- Teams implementing Graph RAG or knowledge-graph-backed context retrieval
Documented uses
- Retrieve context through scored evidence paths instead of simple chunk matching
- Store and retrieve episodic and long-term agent memory
- Assemble context from Episodes, Facets, FacetPoints and Entities
- Build a knowledge-graph-backed memory component for an AI system
Before you choose
Requirements & limits
The repository is public and uses the Apache-2.0 license. The README identifies Python 3.10–3.13. It is presented as self-hosted, server and CLI software; no pricing or account requirement is documented.
- Its graph is used as the retrieval scoring engine, not only as an organizational layer.
- Queries can anchor at different granularities, from Entity and FacetPoint to Facet and Episode.
- Returned bundles are Episodes scored by their strongest connected evidence path.
- MCP is listed among the repository topics, but the exact interface and configuration are not documented.
- A specific-agent integration is not verified in the supplied material.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Evidence and transparency
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 1 October 2026
- Original website title
- FlowElement-xinliuyuansu/m_flow · Official homepage title · retained in its source language