<div style="height: 16px;"></div> <a href="https://trendshift.io/repositories/17293" target="_blank"><img src="https://trendshift.io/api/badge/repositories/17293" alt="memvid%2Fmemvid | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a>  Memvid is a single-file memory layer for AI agents with instant retrieval and long-term memory. Persistent, versioned, and portable memory, without databases.  <a href="https://www.memvid.com">Website</a> · <a href="https://sandbox.memvid.com">Try Sandbox</a> · <a href="https://docs.memvid.com">Docs</a> · <a href="https://github.com/memvid/memvid/discussions">Discussions</a>   <a href="https://crates.io/crates/memvid-core"><img src="https://img.shields.io/crates/v/memvid-core?style=flat-square&logo=rust" alt="Crates.io" /></a> <a href="https://docs.rs/memvid-core"><img src="https://img.shields.io/docsrs/memvid-core?style=flat-square&logo=docs.rs" alt="docs.rs" /></a> <a href="https://github.com/memvid/memvid/blob/main/LICENSE"><img src="https://img.shields.io/badge/license-Apache%202.0-blue?style=flat-square" alt="License" /></a> <a href="https://github.com/memvid/memvid/stargazers"><img src="https://img.shields.io/github/stars/memvid/memvid?style=flat-square&logo=github" alt="Stars" /></a> <a href="https://github.com/memvid/memvid/network/members"><img src="https://img.shields.io/github/forks/memvid/memvid?style=flat-square&logo=github" alt="Forks" /></a> <a href="https://github.com/memvid/memvid/issues"><img src="https://img.shields.io/github/issues/memvid/memvid?style=flat-square&logo=github" alt="Issues" /></a> <a href="https://discord.gg/2mynS7fcK7"><img src="https://img.shields.io/discord/1442910055233224745?style=flat-square&logo=discord&label=discord" alt="Discord" /></a>

Benchmark Highlights

🚀 Higher accuracy than any other memory system : +35% SOTA on LoCoMo, best-in-class long-horizon conversational recall & reasoning

🧠 Superior multi-hop & temporal reasoning: +76% multi-hop, +56% temporal vs. the industry average

⚡ Ultra-low latency at scale 0.025ms P50 and 0.075ms P99, with 1,372× higher throughput than standard

🔬 Fully reproducible benchmarks: LoCoMo (10 × ~26K-token conversations), open-source eval, LLM-as-Judge

What is Memvid?

Memvid is a portable AI memory system that packages your data, embeddings, search structure, and metadata into a single file.

Instead of running complex RAG pipelines or server-based vector databases, Memvid enables fast retrieval directly from the file.

The result is a model-agnostic, infrastructure-free memory layer that gives AI agents persistent, long-term memory they can carry anywhere.

What are Smart Frames?

Memvid draws inspiration from video encoding, not to store video, but to organize AI memory as an append-only, ultra-efficient sequence of Smart Frames.

A Smart Frame is an immutable unit that stores content along with timestamps, checksums and basic metadata. Frames are grouped in a way that allows efficient compression, indexing, and parallel reads.

This frame-based design enables:

Append-only writes without modifying or corrupting existing data
Queries over past memory states
Timeline-style inspection of how knowledge evolves
Crash safety through committed, immutable frames
Efficient compression using techniques adapted from video encoding

The result is a single file that behaves like a rewindable memory timeline for AI systems.

Core Concepts

Living Memory Engine Continuously append, branch, and evolve memory across sessions.
Capsule Context (.mv2) Self-contained, shareable memory capsules with rules and expiry.
Time-Travel Debugging Rewind, replay, or branch any memory state.
Smart Recall Sub-5ms local memory access with predictive caching.
Codec Intelligence Auto-selects and upgrades compression over time.

Use Cases

Memvid is a portable, serverless memory layer that gives AI agents persistent memory and fast recall. Because it's model-agnostic, multi-modal, and works fully offline, developers are using Memvid across a wide range of real-world applications.

Long-Running AI Agents
Enterprise Knowledge Bases
Offline-First AI Systems
Codebase Understanding
Customer Support Agents
Workflow Automation
Sales and Marketing Copilots
Personal Knowledge Assistants
Medical, Legal, and Financial Agents
Auditable and Debuggable AI Workflows
Custom Applications

SDKs & CLI

Use Memvid in your preferred language:

| Package | Install | Links | | --------------- | --------------------------- | ------------------------------------------------------------------------------------------------------------------- | | CLI | npm install -g memvid-cli | | | Node.js SDK | npm install @memvid/sdk | | | Python SDK | pip install memvid-sdk | | | Rust | cargo add memvid-core | |

Installation (Rust)

Requirements

Rust 1.85.0+ — Install from rustup.rs

Add to Your Project

[dependencies]
memvid-core = "2.0"

Feature Flags

| Feature | Description | | ------------------- | ---------------------------------------------------------------- | | lex | Full-text search with BM25 ranking (Tantivy) | | pdf_extract | Pure Rust PDF text extraction | | vec | Vector similarity search (HNSW + local text embeddings via ONNX) | | clip | CLIP visual embeddings for image search | | whisper | Audio transcription with Whisper | | api_embed | Cloud API embeddings (OpenAI) | | temporal_track | Natural language date parsing ("last Tuesday") | | parallel_segments | Multi-threaded ingestion | | encryption | Password-based encryption capsules (.mv2e) | | symspell_cleanup | Robust PDF text repair (fixes "emp lo yee" -> "employee") |

Enable features as needed:

[dependencies]
memvid-core = { version = "2.0", features = ["lex", "vec", "temporal_track"] }

Quick Start

use memvid_core::{Memvid, PutOptions, SearchRequest};

fn main() -> memvid_core::Result<()> {
    // Create a new memory file
    let mut mem = Memvid::create("knowledge.mv2")?;

    // Add documents with metadata
    let opts = PutOptions::builder()
        .title("Meeting Notes")
        .uri("mv2://meetings/2024-01-15")
        .tag("project", "alpha")
        .build();
    mem.put_bytes_with_options(b"Q4 planning discussion...", opts)?;
    mem.commit()?;

    // Search
    let response = mem.search(SearchRequest {
        query: "planning".into(),
        top_k: 10,
        snippet_chars: 200,
        ..Default::default()
    })?;

    for hit in response.hits {
        println!("{}: {}", hit.title.unwrap_or_default(), hit.text);
    }

    Ok(())
}

Build

Clone the repository:

git clone https://github.com/memvid/memvid.git
cd memvid

Build in debug mode:

cargo build

Build in release mode (optimized):

cargo build --release

Build with specific features:

cargo build --release --features "lex,vec,temporal_track"

Run Tests

Run all tests:

cargo test

Run tests with output:

cargo test -- --nocapture

Run a specific test:

cargo test test_name

Run integration tests only:

cargo test --test lifecycle
cargo test --test search
cargo test --test mutation

Examples

The examples/ directory contains working examples:

Basic Usage

Demonstrates create, put, search, and timeline operations:

cargo run --example basic_usage

PDF Ingestion

Ingest and search PDF documents (uses the "Attention Is All You Need" paper):

cargo run --example pdf_ingestion

CLIP Visual Search

Image search using CLIP embeddings (requires clip feature):

cargo run --example clip_visual_search --features clip

Whisper Transcription

Audio transcription (requires whisper feature):

cargo run --example test_whisper --features whisper -- /path/to/audio.mp3

Available Models:

| Model | Size | Speed | Use Case | | --------------------- | ------ | ------- | ----------------------------------- | | whisper-small-en | 244 MB | Slowest | Best accuracy (default) | | whisper-tiny-en | 75 MB | Fast | Balanced | | whisper-tiny-en-q8k | 19 MB | Fastest | Quick testing, resource-const

Memvid

Install / Use

README