M-flow
What it is
M-flow is a bio-inspired cognitive memory engine for Graph RAG that uses a four-level 'Cone Graph' (Episode, Facet, FacetPoint, Entity) to structure knowledge. It differentiates between simple vector similarity and semantic relevance by propagating evidence paths through these nested layers to retrieve coherent context bundles.
The problem it solves
Traditional RAG often suffers from 'similarity vs. relevance' issues where systems retrieve context based on keyword overlap rather than the actual causal or logical path of the query.
What you could use it for
- Retrieving complex multi-step reasoning paths (e.g., finding the specific cause of an event in a meeting transcript)
- Building AI agents with long-term memory that can associate new information with existing organizational context
- Querying complex datasets where the answer requires connecting disparate facts across different categories
Read the source ↗Back to the library →
Summary written by an AI model from the source; check the source before you rely on it.