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MemVid

Memory layer for AI Agents — replace complex RAG pipelines with a serverless, single-file memory layer

★ 16,035 GitHub Apache-2.0 aimemoryragvector-databaseknowledge-graphsemantic-searchrustfaiss RAG & Knowledge

Overview

MemVid is a memory layer for AI agents that replaces complex RAG pipelines with a single portable file. Built in Rust, it provides instant retrieval, long-term memory, and semantic search capabilities. It uses FAISS for vector search and supports knowledge graphs, offline-first architecture, and a lightweight serverless design. With over 16,000 GitHub stars, MemVid is the go-to solution for giving AI agents photographic memory.

Requirements

Min vCPU
2
Min RAM
4096 MB
Min Disk
10 GB
Rec vCPU
4
Rec RAM
8192 MB
Rec Disk
20 GB

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Docker Compose

# Generated by Run This Ai — docker-compose.yml
services:
  memvid:
    image: tuxed/memvid-mcp:latest
    restart: unless-stopped
    ports:
      - 8080:8080
    volumes:
      - ./data/memvid:/data

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Guides & articles

Getting Started with MemVid: Installation, SDKs, and Performance Benchmarks

Learn how to install and use MemVid — from CLI quick start to Python SDK and Docker deployment. Includes full performance benchmarks showing 0.025ms latency and 1,372× throughput improvement.

Jul 24, 2026

MemVid: A Portable Single-File Memory Layer for AI Agents

Discover MemVid — a groundbreaking portable AI memory system that replaces complex RAG pipelines with a single file. Built in Rust, it delivers sub-millisecond retrieval and +35% SOTA on LoCoMo benchmarks.

Jul 24, 2026