Awesome GitHub RepositoriesMemory-Optimized Storage

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

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

Vector similarity search extension for PostgreSQL.

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

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

jcode is a framework for developing autonomous AI coding agents that automate software development tasks. It functions as an agent orchestrator, tool runtime, and semantic memory engine, enabling the creation of agents that can modify code, run tests, and iterate on their own functionality. The project is distinguished by its use of recursive agent swarming, where a hierarchy of collaborating agents can spawn child agents to decompose complex tasks. It implements a semantic memory system that combines vector-based retrieval with graph-based relationship mapping to maintain context across sess

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

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

TinyPinyin चीनी अक्षरों को Pinyin ध्वन्यात्मक अभ्यावेदन (phonetic representations) में बदलने के लिए उपयोग की जाने वाली एक Java और Android संगत लाइब्रेरी है। यह यह पहचानने के लिए चीनी चरित्र पहचान यूटिलिटीज प्रदान करती है कि क्या टेक्स्ट चीनी लेखन प्रणाली का है। इस लाइब्रेरी में एक पॉलीफोनिक कैरेक्टर रिज़ॉल्वर शामिल है जो प्रासंगिक विश्लेषण के माध्यम से कई ध्वनियों वाले अक्षरों के लिए सही उच्चारण निर्धारित करता है। इसे मोबाइल डिवाइसेस पर कम मेमोरी उपयोग के लिए डिज़ाइन किया गया है।

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

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

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