Awesome MCPKnowledge & Memory

Olow304/memvid

⭐ 16538 Python added to this list on 2025-07-05 repository created 2025-05-27

Memvid is an innovative video-based AI memory library designed to store millions of text chunks within MP4 video files, enabling lightning-fast semantic search without the need for traditional databases. It compresses entire knowledge bases into compact video files by encoding text as QR codes embedded in video frames, leveraging modern video codecs to achieve 50 to 100 times smaller storage compared to conventional vector databases. This approach allows for sub-100 millisecond retrieval times by directly seeking video frames, decoding QR codes, and returning text results with no server round-trips, making it highly efficient and portable. The project is currently in its experimental v1 phase, with a promising v2 version planned that introduces a Living-Memory Engine to maintain and update knowledge across sessions, Capsule Contexts for shareable memory capsules with rules and expiry, Time-Travel Debugging to review or branch chat histories, Smart Recall for ultra-fast local caching, Codec Intelligence for automatic codec tuning, and user-friendly CLI and dashboard tools for management and cloud publishing. Memvid's design emphasizes zero infrastructure requirements, running purely on Python and MP4 files without the need for database clusters or complex operations. It supports offline-first usage, making it suitable for environments without internet access. The library also offers advanced features such as scale optimization for huge datasets, custom embedding models, parallel processing for large-scale chunk addition, and compatibility with various codecs including AV1 and H.265. Real-world applications include documentation assistants, PDF library semantic search, and interactive web UIs for knowledge querying. The project provides a simple API for encoding text chunks into video memory and chatting with the stored knowledge, along with CLI tools for document processing and codec management. Performance benchmarks highlight its ability to index 10,000 chunks per second, search one million chunks in under 100 milliseconds, and compress 100MB of text into just 1-2MB of video storage. Overall, Memvid represents a novel approach to AI memory storage and retrieval, combining video compression technology with semantic search to create a portable, efficient, and scalable memory solution for AI applications.

https://github.com/Olow304/memvid

aiai-context-managementai-memory-storageav1capsule-contextcli-toolscodec-optimizationcontextembeddedembeddingsfaissh.265knowledge-baseknowledge-graphliving-memory-enginellmmachine-learningmemorymemvidmp4-filesnlpoffline-firstopencvparallel-processingpythonpython-libraryqr-codesragretrieval-augmented-generationscalable-memorysemantic-searchsmart-recalltime-travel-debuggingvector-databasevideo-based-ai-memoryvideo-compressionvideo-processing

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