| name | simplemem-lifelong-memory |
| title | SimpleMem: Efficient Lifelong Memory for LLM Agents |
| version | 0.0.2 |
| engine | skillxiv-v0.0.2-claude-opus-4.6 |
| license | MIT |
| url | https://arxiv.org/abs/2601.02553 |
| keywords | ["Memory Systems","LLM Agents","Semantic Compression","Context Management","Lifelong Learning"] |
| description | Implement efficient memory systems for long-term LLM agent interactions using semantic compression, achieving 30-fold inference token reduction while improving F1 scores by 26.4%—enabling agents to learn from extended interaction histories without prohibitive context costs. |
Overview
SimpleMem is an efficient memory framework for LLM agents engaged in long-term interactions. It solves a critical problem: managing interaction history in memory-constrained environments where full context retention causes exponential token bloat.
Core Challenge: Existing approaches either retain full histories (causing token explosion) or rely on iterative filtering (incurring high computational costs). SimpleMem achieves semantic lossless compression through a three-stage pipeline.
Three-Stage Memory Pipeline
Stage 1: Semantic Structured Compression
Distill unstructured agent interactions into compact, multi-view indexed memory units. This stage:
- Extracts semantic meaning from raw interaction sequences
- Creates structured representations with multiple indexing views (by task, entity, action type)
- Achieves information density maximization while eliminating redundancy
Stage 2: Online Semantic Synthesis
An intra-session process that instantly integrates related context into unified abstract representations.
Process:
- Detect related memory units within current session
- Synthesize unified representations combining related contexts
- Eliminate redundancy across retrieved memories
- Maintain temporal and causal relationships
This enables the agent to access coherent, consolidated context from fragmented experiences.
Stage 3: Intent-Aware Retrieval Planning
Infers search intent to dynamically determine retrieval scope and construct precise context.
Implementation approach:
- Analyze current task to infer what prior experiences are relevant
- Dynamically adjust retrieval scope based on task complexity
- Construct context that balances completeness with token efficiency
Performance Characteristics
Benchmark Results (LoCoMo dataset):
- F1 improvement: +26.4% vs. baseline approaches
- Inference-time token reduction: Up to 30-fold
- Maintains retrieval accuracy while dramatically reducing computational cost
- Superior balance between performance and efficiency
Token Efficiency Example:
- Full history retention: 10,000+ tokens per inference
- SimpleMem: ~300 tokens per inference
- Performance maintained or improved over baselines
Key Advantages Over Alternatives
vs. Passive Context Extension:
