12 个仓库
Storage formats designed to reduce the memory footprint of high-dimensional embeddings.
Distinct from Vector Memory Stores: Distinct from Vector Memory Stores: focuses on compact data representation formats rather than agentic context retention.
Explore 12 awesome GitHub repositories matching data & databases · Memory-Optimized Storage. Refine with filters or upvote what's useful.
Supermemory is an artificial intelligence memory management platform designed to provide autonomous agents with persistent, long-term knowledge bases. It functions as a centralized repository that synchronizes multimodal data, enabling agents to maintain context and historical information across complex, multi-session workflows. By serving as a knowledge graph engine and vector database orchestrator, the platform ensures that information remains accessible and relevant for automated tasks. The system distinguishes itself through its hybrid indexing approach, which combines vector similarity s
Decomposes documents into semantically meaningful chunks and resolves references to create high-signal, fact-based memory entries.
AgentMemory is a persistent knowledge store and memory server designed to provide AI coding agents with long-term memory. It functions as a knowledge graph engine and vector database store that saves and recalls project context, architectural decisions, and patterns across different sessions. The system distinguishes itself by using a tiered-memory consolidation pipeline that compresses raw observations into episodic, semantic, and procedural layers to optimize token usage. It employs a hybrid retrieval strategy combining keyword matching, vector embeddings, and graph traversal to surface rel
Records file operations, shell commands, and agent decisions to build a long-term history of observations.
Vector similarity search extension for PostgreSQL.
Utilizes compact binary and half-precision formats to reduce the memory footprint of stored embeddings.
TurboVec is a high-performance Rust vector database and quantized search index designed for storing and retrieving high-dimensional embeddings. It functions as a pluggable vector store for large language model orchestration frameworks, providing a memory-efficient alternative to standard in-memory storage. The project distinguishes itself through a high-dimensional vector compressor that utilizes random rotation and data-oblivious scalar quantization to reduce memory footprints. Retrieval is accelerated via SIMD kernels that process distance calculations and search operations for increased th
Reduces the memory footprint of vector indices by using low-bit representations of embeddings.
Redis is a high-performance in-memory key-value store that functions as a distributed cache, message broker, and NoSQL database. It provides sub-millisecond read and write access to data stored in RAM and can operate as a vector database for indexing high-dimensional embeddings. The system supports a wide range of data storage and synchronization primitives, including the management of strings, hashes, lists, sets, and JSON documents. It enables real-time data operations through atomic transactions, hybrid persistence using snapshots and append-only logs, and high-availability configurations
Consolidates duplicate or related information using semantic search to maintain a clean memory index.
jcode 是一个用于开发自主 AI 编码代理的框架,这些代理可自动化软件开发任务。它作为一个代理编排器、工具运行时和语义记忆引擎,支持创建能够修改代码、运行测试并迭代自身功能的代理。 该项目以其递归代理群集(Swarming)而著称,其中协作代理的层级结构可以生成子代理来分解复杂任务。它实现了一个语义记忆系统,结合了基于向量的检索和基于图的关系映射,以在会话间保持上下文。为了管理风险,该系统使用分级操作治理,要求人工批准敏感操作,并将代理活动隔离在单独的 Git 工作树中。 该框架包含一个全面的浏览器自动化工具包,用于与网页交互、提取 DOM 快照和捕获截图。它还实现了模型上下文协议(MCP)以集成外部工具和数据,并支持二进制热重载以在不丢失活动网络连接的情况下更新服务器。 该系统提供用于管理代理记忆的命令行界面,并包括用于跟踪计划进度和可视化代理群集拓扑的审计工具。
Implements confidence-based weighting for memories to prioritize more helpful information across sessions.
sqlite-vec is a C-based vector library and SQLite extension that adds virtual tables for storing and querying high-dimensional embeddings. It functions as a database plugin for performing nearest neighbor searches using distance metrics such as L2, cosine, and Hamming distance. The project provides a portable embedding store that supports deployment across Android, iOS, desktop environments, and web browsers via WebAssembly. It distinguishes itself by converting numerical arrays into compact binary formats and utilizing quantization to reduce the memory footprint and storage size of vector in
Reduces the memory footprint and storage size of embeddings using quantization and compact binary formats.
AIOS is an LLM agent operating system and orchestration kernel designed to manage memory, resource scheduling, and tool execution for multiple autonomous AI agents. It serves as a comprehensive framework for developing and deploying agents, featuring a dedicated resource manager that coordinates model backends, GPU memory, and isolated kernel instances. The system distinguishes itself through a semantic memory engine that uses vector search and autonomous clustering for long-term knowledge management, and a semantic file system that allows users to control computer files and system operations
Consolidates duplicate semantic information and updates anchors to maintain a clean knowledge index.
TinyPinyin 是一个兼容 Java 和 Android 的库,用于将汉字转换为拼音。它提供了汉字检测工具,可识别文本是否属于汉字书写系统。 该库包含一个多音字解析器,通过上下文分析确定多音字的正确读音。它专为移动设备上的低内存占用而设计。
Stores mapping data in a compact format to minimize memory usage on Android devices.
OpenMemory is an embeddable memory engine for LLM agents that stores, retrieves, and manages conversational context and agent state using semantic indexing and temporal facts. It functions as a semantic memory store backed by vector indexing, where memories are organized by meaning rather than by exact key, and includes a tiered decay engine that gradually reduces the salience of unused memories while compressing cold vectors and fingerprinting dormant entries to conserve storage. The system also maintains a temporal fact database that records factual statements with subject-predicate-object s
Stores new memory records with user-defined content, metadata, and salience settings for later retrieval.
OpenViking is a multi-tenant context server and knowledge base administration system designed to provide AI agents with persistent long-term memory. It enables the indexing of diverse documents and codebases to support retrieval-augmented generation, allowing agents to recall past interactions, user preferences, and learned experiences across sessions. The project is distinguished by its use of a URI-based virtual filesystem to organize memories, resources, and skills. It implements a tiered context loading system that balances retrieval precision with token budgets by structuring data into a
Asynchronously distills long-term memories and interaction patterns from chat sessions using LLMs.
pgvecto.rs is a database extension that integrates high-dimensional vector search capabilities directly into PostgreSQL. It functions as a specialized engine for storing and retrieving embeddings, allowing relational databases to perform similarity searches alongside traditional structured data queries. The extension distinguishes itself through hardware-aware execution strategies that maximize performance. It performs runtime analysis of the host machine to utilize specific processor instruction sets for accelerated mathematical operations. To manage memory efficiently, it employs quantizati
Reduces memory usage and improves processing speed by converting high-precision data into compact formats like half-precision floating-point numbers or integer-based representations.