can you share from where you too python code fro xMemory (Stanford) → Identity pattern learning

[Sandy4321]

great code thanks
only can you share from where you too python code fro xMemory (Stanford) → Identity pattern learning

i can not find original rep for xMemory

[Sandy4321]

https://arxiv.org/pdf/2602.02007
original paper
Beyond RAG for Agent Memory: Retrieval by Decoupling and Aggregation

[varun369]

Thanks for the kind words and for checking out the project! 👍

To clarify — SuperLocalMemory V2 is not a direct port of any single paper's code. The pattern learning system (src/pattern_learner.py) was
"inspired by" the concepts from xMemory and similar identity-learning research, but it's an original implementation written from scratch.

Specifically:

• xMemory (referenced in our docs as Stanford research on identity pattern learning) describes the concept of extracting user preferences
  and identity patterns from interaction history. We implemented this idea using frequency analysis, confidence scoring, and multi-category pattern
  recognition — all original Python code.
• There is no official open-source xMemory repository that we're aware of. The paper describes the approach; we built our own version of the
  concept.

The same applies to our other research references:

• GraphRAG (Microsoft) → We implemented Leiden clustering + TF-IDF entity extraction ourselves in src/graph_engine.py
• PageIndex (Meta AI) → Inspired our hierarchical indexing in src/tree_manager.py
• A-RAG → Inspired our multi-level retrieval architecture

All code is original, MIT-licensed, and in this repo. The papers provided the architectural ideas, not the implementation.

The paper you linked (Beyond RAG for Agent Memory) is a great reference too — thanks for sharing it! It aligns well with the retrieval decoupling approach we use in hybrid search.

— Varun Bhardwaj