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loudiman/
memory-improvements

Name: memory-improvements
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ml-intern

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On-Device Memory Improvements for LLM Chat

Drop-in improvements for on-device chat apps using small language models with semantic memory (e.g., Llama-3.2-3B + EmbeddingGemma-300M + SQLite vector store).

📦 Files

File	Purpose
`src/types.ts`	Type definitions & configuration
`src/deduplication.ts`	Improvement 1: Mem0-style ADD/UPDATE/NOOP deduplication
`src/memoryDecay.ts`	Improvement 2: Heat-based decay & eviction (MemoryOS)
`src/typedMemory.ts`	Improvement 3: ENGRAM-style episodic/semantic/procedural router
`src/assistantMemory.ts`	Improvement 4: Store AI replies as memories
`src/smartFilter.ts`	Improvement 5: Only store declarative/factual content
`src/dynamicRetrieval.ts`	Improvement 6: Adaptive top-k, threshold, type filtering
`src/schema.ts`	SQLite schema migration
`src/index.ts`	`ImprovedMemoryService` class (combines all improvements)
`src/integrationGuide.ts`	Exact code changes for each file in your app
`src/beforeAfterWalkthrough.ts`	Side-by-side comparison of old vs new behavior
`tests/demo.py`	Validated Python demo showing all improvements

🚀 Quick Start

typescript

import { ImprovedMemoryService, DEFAULT_MEMORY_CONFIG } from './src';

const memoryService = new ImprovedMemoryService(db, embedFn, DEFAULT_MEMORY_CONFIG);

// Retrieve memories (replaces your current searchMemories)
const { context, memoryIds } = await memoryService.retrieve(userMessage, embedding);

// Store user message (with smart filtering + dedup)
await memoryService.storeUserMessage(userMessage, embedding);

// Store assistant reply (new!)
await memoryService.storeAssistantReply(assistantReply);

📊 Before vs After

Aspect	Before	After
Memories stored	200+ (noisy)	~80 (clean, deduplicated)
Context slots	5 (3 wasted on duplicates)	4-12 (all unique, relevant)
Token budget	Fixed	Dynamic (100-500 based on query)
Prompt structure	Flat list	Typed sections
Assistant recall	None	Tracks recommendations/promises
Stale memory handling	Never cleaned	Heat-based eviction
Duplicate handling	None	0.85 threshold auto-dedup

📚 Research Basis

ENGRAM (arxiv 2511.12960) — Typed memory stores with router
Mem0 (arxiv 2504.19413) — ADD/UPDATE/DELETE operations with deduplication
MemoryOS (arxiv 2506.06326) — Heat-based decay and hierarchical storage
MemLoRA (arxiv 2512.04763) — Distilled memory adapters for small models

Generated by ML Intern

This model repository was generated by ML Intern, an agent for machine learning research and development on the Hugging Face Hub.

Try ML Intern: https://smolagents-ml-intern.hf.space
Source code: https://github.com/huggingface/ml-intern

Usage

python

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "loudiman/memory-improvements"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

For non-causal architectures, replace AutoModelForCausalLM with the appropriate AutoModel class.