Catalog Systems Research / specialised systems MemoRAG

MemoRAG

https://github.com/qhjqhj00/MemoRAG

RAG framework on top of a long-context memory model. Builds global memory once, generates contextual clues at query time. TheWebConf 2025.

Active · Verified 2026-10-28 · fresh as of 2026-10-28 · 27 cited facts, 1 with a third-party source · Suggest a correction

At a glance

Type
Global memory-enhanced RAG repo fix
Tier
T3
Section
Research / specialised systems
Created
2024-09 repo fix
Latest release
v0.1.5 2024-09-24 repo fix
License
Apache-2.0 repo fix
GitHub
2.2k★ Python repo fix
Pricing
searched not found fix
Funding
not applicable — not commercial fix
Freshness
2026-10-28 fix
Layer
agent-infra repo fix
Licensing
open-source repo fix
Developer activity
OpenRank 0.33 (12-mo avg 3.14) · 1 participants — 2026-01 third-party fix

Memory taxonomy

storage
vector
retrieval
similarity
persistence
long-term
update
read-only
unit
chunk
governance
inspectable
conflict
n/a

When to use

Optimised for: research positioning (see memory_model)

Anti-fit: not applicable - research paper

Pros & cons

Pros

Extends RAG with explicit memory layer — bridges retrieval and stored facts.

Cons

Position between RAG and memory layer is muddled — neither owns the use case decisively.

Claims & capabilities

Up to 1M-token contexts (with beacon_ratio=16). 30× pre-fill speedup via caching. Encoding+chunking+indexing 200K-token context: ~35s cold, 1.5s from cache.

Technical surface

API surface
not applicable — research paper
Backend storage
not applicable — research paper
Deployment
searched not found
Embedding model
not applicable — research paper
Multi-tenancy
not applicable — research paper
MCP
not applicable — research paper, no deployed product
A2A
not applicable — research paper, no deployed product
OpenTelemetry
not applicable — research paper, no deployed product

Similar systems

Other research / specialised systems in the catalog, ranked by inbound references.

  • A-MEM T3

    Treats memories as atomic linkable notes — explicit nod to Zettelkasten knowledge management. Dynamic linking; retroactive memory revision.

  • BAI-LAB MemoryOS T3

    Hierarchical "OS" with Storage / Updating / Retrieval / Generation modules. Short-term → mid-term via FIFO dialogue-chain; mid-term → long-term via segmented paging.

  • Titans (Google) T4

    Neural long-term memory module that learns to memorise at test time. Uses gradient-of-loss as "surprise" signal; adaptive weight-decay forgetting. Three variants: MAC (memory-as-context), MAG (memory-as-gate), MAL (memory-as-layer).

  • EverMemOS T4

    Self-organizing memory OS for structured long-horizon reasoning. Three-phase model: episodic, semantic, reconstructive.

  • EVOLVE-MEM T3

    Dynamic Memory Network + Hierarchical Memory Manager + Self-Improvement Engine. L0 raw embeddings, L1 contextual summaries, L2 high-level principles. NeurIPS 2025 (Scaling Environments for Agents workshop).

  • LiCoMemory T4

    Lightweight hierarchical graph (CogniGraph) with entities and relations as semantic indexing layers. Incremental graph construction, fast updates, low-latency inference. Nov 2025.

Sources (26 cited facts, 1 third-party)

Each fact links to the page it came from; the badge says who published it (how to read sources).