Memvid is a single-file memory layer for AI agents that replaces complex RAG pipelines and server-based vector databases with one portable file. It packages your data, embeddings, search structure and metadata together, giving agents instant retrieval and long-term memory that is persistent, versioned and easy to move around, with no database to run. The project is offline-first and reports strong results on the LoCoMo long-conversation benchmark, including best-in-class multi-hop and temporal reasoning. Memvid has more than 16,000 stars on GitHub and remained under active development in 2026.
Classic retrieval stacks mean chunking pipelines, an embedding service and a hosted vector database before your agent can remember anything. Memvid collapses that into a single file you can copy, version and ship with your application: write memories in, query them back with semantic search, and keep everything working offline. That design suits long-running agents, enterprise knowledge bases, codebase understanding and customer support, anywhere memory needs to be fast, private and portable. The project publishes fully reproducible, open-source evaluations (LLM-as-judge on LoCoMo conversations of roughly 26K tokens each) so its accuracy and latency claims can be checked rather than taken on faith.
Memvid is free and open source under the Apache-2.0 license. There is no paid tier; you run it entirely on your own infrastructure.
Memvid is for developers who want to give agents long-term memory without operating a vector database, especially in offline, embedded or privacy-sensitive applications where a single portable file is a feature, not a limitation.