m_flow (M-Flow)

Cognitive memory engine modelled on biology that presents itself as a new paradigm for graph RAG for agents. #13 on GitHub agent-memory topic

Active · Verified 2026-10-23 · fresh as of 2026-10-23 · 16 cited facts, 0 with a third-party source · 1 recorded change · Suggest a correction

At a glance

Type: Framework repo fix
Tier: T3
Section: Retrieval-as-memory hybrids
Created: 2026-03-31 repo fix
Latest release: last commit 2026-08-03 repo fix
License: Apache-2.0 repo fix
GitHub: https://github.com/FlowElement-xinliuyuansu/m_flow (4512★) repo fix
Pricing: searched not found fix
Funding: searched not found fix
Freshness: 2026-10-23 fix
Layer: agent-infra repo fix
Licensing: open-source repo fix
Deployment locus: local repo fix
Platform vs product: platform repo fix
SDK languages: python repo fix
Protocols: mcp-server repo fix
Maintainer health: 0 issues in 90 days · 1 release in 12 months (latest 2026-04-12) · bus factor 2 — as of 2026-10-31 repo

Optimised for: searched not found

Anti-fit: searched not found

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A bio-inspired cognitive memory engine — a new paradigm for Graph RAG.

Alternatives to m_flow (M-Flow) → live options ranked by shared design, licence, hosting and MCP support

Other retrieval-as-memory hybrids in the catalog, ranked by inbound references.

GraphRAG (Microsoft) T1
Modular graph-based RAG. Leiden community detection over knowledge graph; community summaries at multiple levels. DRIFT Search hybrid (Oct 2024). The GraphRAG repo is now largely in maintenance mode: no new features or PRs, only bug fixes and dependency/CVE updates.
RETRO T3
DeepMind. Retrieval-Enhanced Transformer with chunked cross-attention over a 2T-token retrieval database. Frozen BERT retriever + differentiable encoder.
Atlas T3
Meta AI. Jointly fine-tunes retriever + LM for few-shot tasks. The facebookresearch/atlas repo is no longer maintained; the code is provided as is.
RAPTOR T3
Stanford recursive-summarisation tree over the corpus — clusters and summarises documents into a hierarchical index; retrieves at multiple levels. ICLR 2024.
Self-RAG T4
Trains an LLM end-to-end to emit reflection tokens that decide whether to retrieve, assess passage relevance, and critique output quality — all within a single model.
HippoRAG / HippoRAG2 T3
Hippocampus-inspired RAG. LLMs + knowledge graphs + Personalized PageRank to mimic neocortex/hippocampus roles. NeurIPS 2024.

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