# aiming-lab/simplemem

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2,972 stars · 288 forks · Python · mit

## Links

- GitHub: https://github.com/aiming-lab/SimpleMem
- awesome-repositories: https://awesome-repositories.com/repository/aiming-lab-simplemem.md

## Description

SimpleMem is a persistent memory system for AI assistants designed to maintain context across different user chat sessions. It functions as a memory server and multimodal vector database that stores and retrieves information from text, images, audio, and video.

The project features a context compression engine that distills interaction histories into compact units to reduce token consumption. It utilizes a distributed memory orchestrator and worker-thread parallel processing to reduce latency when querying large-scale dialogue datasets.

The system implements a hybrid indexing approach combining semantic and keyword search for multimodal retrieval. It also includes a diagnostic framework for retrieval optimization that identifies failures and adjusts configurations to improve search precision.

## Tags

### Artificial Intelligence & ML

- [Long-term Memory Stores](https://awesome-repositories.com/f/artificial-intelligence-ml/agent-architectures/memory-management-systems/long-term-memory-stores.md) — Provides a persistent memory store that allows AI assistants to maintain context across different user chat sessions.
- [Multimodal Context Providers](https://awesome-repositories.com/f/artificial-intelligence-ml/agent-context-providers/multimodal-context-providers.md) — Retrieves and assembles context from text, images, audio, and video to provide comprehensive situational awareness.
- [Context Compression](https://awesome-repositories.com/f/artificial-intelligence-ml/context-compression.md) — Provides an engine for summarizing long interaction histories into compact units to minimize LLM token usage.
- [Interaction Compression](https://awesome-repositories.com/f/artificial-intelligence-ml/multimodal-interaction-engines/interaction-compression.md) — Distills complex multimodal dialogue and media interactions into compact units to minimize LLM token consumption.
- [Retrieval Optimization](https://awesome-repositories.com/f/artificial-intelligence-ml/retrieval-optimization.md) — Provides an iterative loop to diagnose retrieval failures and automatically tune configurations for better search precision.
- [MCP Servers](https://awesome-repositories.com/f/artificial-intelligence-ml/mcp-servers.md) — Implements a Model Context Protocol server to expose persistent memory stores to AI assistants. ([source](https://cdn.jsdelivr.net/gh/aiming-lab/simplemem@main/README.md))
- [Model Context Protocol Servers](https://awesome-repositories.com/f/artificial-intelligence-ml/model-context-protocol-servers.md) — Exposes a standardized Model Context Protocol interface for AI assistants to maintain persistent session context.
- [Diagnostic Tuning](https://awesome-repositories.com/f/artificial-intelligence-ml/rag-performance-metrics/diagnostic-tuning.md) — Diagnoses retrieval failures and automatically adjusts search configurations to improve memory recovery precision.

### Part of an Awesome List

- [Multi-Modal Memory Stores](https://awesome-repositories.com/f/awesome-lists/ai/memory-and-context/multi-modal-memory-stores.md) — Operates as a persistent store managing text, images, and dialogue history across sessions using a standardized protocol.

### Data & Databases

- [LLM Token Compression](https://awesome-repositories.com/f/data-databases/data-compression-algorithms/visual-token-compression/llm-token-compression.md) — Reduces token consumption by compressing interaction histories into compact, non-redundant units.
- [Hybrid Vector-Keyword Indexing](https://awesome-repositories.com/f/data-databases/hybrid-vector-keyword-indexing.md) — Combines dense vector embeddings with inverted keyword indices to retrieve precise multimodal context.
- [History Distillation](https://awesome-repositories.com/f/data-databases/response-caching/interaction-history-caching/history-distillation.md) — Compresses multimodal interaction histories into compact memory units to reduce token usage and eliminate redundancy. ([source](https://cdn.jsdelivr.net/gh/aiming-lab/simplemem@main/README.md))
- [Multimodal Search](https://awesome-repositories.com/f/data-databases/semantic-search/multimodal-search.md) — Indexes and retrieves information across text, image, audio, and video using a multimodal semantic search system.
- [RAG Optimizations](https://awesome-repositories.com/f/data-databases/vector-memory-stores/rag-optimizations.md) — Includes a diagnostic framework to optimize the precision of memories recovered in RAG pipelines.

### Operating Systems & Systems Programming

- [Memory Processing Processors](https://awesome-repositories.com/f/operating-systems-systems-programming/kernel-core-internals/process-and-memory-management/memory-processing-processors.md) — Implements a memory processing pipeline that distributes retrieval and storage across multiple worker threads. ([source](https://cdn.jsdelivr.net/gh/aiming-lab/simplemem@main/README.md))

### Software Engineering & Architecture

- [Distributed Memory Orchestration](https://awesome-repositories.com/f/software-engineering-architecture/distributed-memory-orchestration.md) — Coordinates multiple workers to reduce latency when querying large-scale dialogue datasets.
- [High-Throughput Task Processing](https://awesome-repositories.com/f/software-engineering-architecture/high-throughput-task-processing.md) — Implements high-throughput task processing using distributed workers to accelerate memory building and retrieval.
- [Parallel Retrieval Processing](https://awesome-repositories.com/f/software-engineering-architecture/parallel-retrieval-processing.md) — Uses multiple background worker threads to parallelize memory building and query retrieval, reducing latency for large datasets.