| --- |
| license: mit |
| tags: |
| - memory |
| - llm |
| - rag |
| - semantic-search |
| --- |
| |
| # 🧠 Mnemo v4.1 - SLM-Inspired Memory System |
|
|
| Memory augmentation library for LLMs based on the Semantic-Loop Memory architecture. |
|
|
| ## Installation |
|
|
| ```python |
| # Download mnemo.py from this repo |
| from mnemo import Mnemo |
| |
| mnemo = Mnemo() |
| mnemo.add("User prefers Python for data analysis") |
| results = mnemo.search("programming language") |
| ``` |
|
|
| ## Features |
|
|
| ### Core |
| - **Three-Tier Memory**: Working (50 items) → Token Loops → Semantic (persistent) |
| - **Neural Links**: 8 link types with different creation thresholds and decay rates |
| - **Memory Utility Predictor**: Decides WHEN to inject memory (90% accuracy) |
| - **Self-Tuning**: Auto-adjusts thresholds based on feedback |
|
|
| ### v4.1 New |
| - **Memory Decay**: Unused memories lose 1% quality per day |
| - **Auto-Pruning**: Removes stale memories (quality < 0.15, unused > 30 days) |
| - **Link Cleanup**: Orphaned links removed when memories are pruned |
|
|
| ## Benchmark Results |
|
|
| | Test | Without Memory | With Mnemo | Improvement | |
| |------|----------------|------------|-------------| |
| | Novel retrieval | 5% | 85% | +80% | |
| | Code retrieval | 60% | 88% | +28% | |
| | ROS continuous learning | 25% | 75% | +50% | |
|
|
| ## API |
|
|
| ```python |
| mnemo = Mnemo() |
| mnemo.add(content, namespace="default") |
| results = mnemo.search(query, top_k=5) |
| context = mnemo.get_context(query, top_k=3) |
| should_inject = mnemo.should_inject(query) |
| mnemo.maintenance_cycle() |
| ``` |
|
|
| ## Links |
|
|
| - [Demo](https://huggingface.co/spaces/AthelaPerk/mnemo) |
| - [MCP Server](https://huggingface.co/spaces/AthelaPerk/mnemo-mcp) |
|
|
