# Memvid review

> Memvid is a single-file memory layer for AI agents that packages data, embeddings, search indexes, and metadata into one portable .mv2 file.

Devtoolwatch Board, the week of Oct 25–31, 2026. Canonical page: https://index.devtoolwatch.com/tools/memvid-memvid/

## TL;DR

Memvid is a portable memory system that stores data, embeddings, indexes, and metadata in one .mv2 file. Agents retrieve directly from that file, with no database or server. It is for developers building AI agents that need persistent, offline-capable memory. Its append-only Smart Frames design supports queries over past memory states.

## Memvid at a glance

- **Category:** [RAG, data and memory](https://index.devtoolwatch.com/categories/rag-data-and-memory/)
- **Maintained by:** Memvid (organization, [@memvid](https://github.com/memvid) on GitHub)
- **License:** Apache 2.0
- **Language:** Rust, Shell, Makefile
- **Repository:** [github.com/memvid/memvid](https://github.com/memvid/memvid)
- **Website:** [www.memvid.com](https://www.memvid.com)
- **Latest release:** [v2.0.140](https://github.com/memvid/memvid/releases/tag/v2.0.140) on GitHub, May 27, 2026
- **First published:** May 27, 2025

## What Memvid does

- Memvid stores content, embeddings, a full-text index, a vector index, and a time index in a single .mv2 file with no sidecar files.
- Memvid offers full-text search with BM25 ranking through Tantivy and vector similarity search with HNSW, enabled as feature flags.
- Memvid writes memory as append-only, immutable Smart Frames with timestamps and checksums, so you can query past memory states.
- Memvid includes an embedded write-ahead log for crash recovery.
- Memvid can extract PDF text, create CLIP image embeddings, and transcribe audio with Whisper through optional features.
- Memvid supports local ONNX text embeddings or OpenAI API embeddings, and can bind an index to one embedding model.
- Memvid supports password-based encryption capsules (.mv2e) and frame-level access control lists.

### How it works

Memvid runs as a library inside your application. A CLI and SDKs exist for Node.js, Python, and Rust. You create a .mv2 file, add documents with metadata, commit, and search it directly. The homepage says agents and models can connect through MCP, SDK, or direct API. The file can be stored locally, on-prem, or in a cloud.

## How to install Memvid

**CLI with npm**

```
npm install -g memvid-cli
```

**Python SDK with pip**

```
pip install memvid-sdk
```

**Rust crate with Cargo**

```
cargo add memvid-core
```

### Requirements

- Rust 1.85.0 or later for the Rust crate
- Local text embeddings need ONNX model and tokenizer files downloaded manually to ~/.cache/memvid/text-models
- OpenAI embeddings need the api_embed feature and an OPENAI_API_KEY environment variable

### First steps

1. Memvid's Rust crate can be added with features enabled, for example lex, vec, and temporal_track, in Cargo.toml.
2. Create a memory file with Memvid::create("knowledge.mv2"), add documents with put_bytes_with_options, then call commit.
3. Search the file with mem.search and a SearchRequest that sets the query, top_k, and snippet length.
4. Run the bundled example to try create, put, search, and timeline operations: cargo run --example basic_usage

## Who Memvid is for

- Developers building long-running AI agents that need persistent memory without running a vector database.
- Offline-first or air-gapped AI systems that must keep data local.
- Teams that need auditable AI workflows with replay and inspection of past memory states.
- Knowledge bases and codebase understanding tools that mix full-text and vector search.

### Limitations

- Memvid's local text embeddings do not download automatically. You fetch ONNX model files yourself before using the vec feature.
- Memvid binds an index to one embedding model once set, and switching models later fails with a ModelMismatch error.
- Memvid v1, the QR-code based version, is deprecated, so older tutorials referencing QR codes are outdated.
- Memvid recent releases fixed WAL corruption and time index crashes, so staying on current versions matters.

## Usage data (the week of Oct 25–31, 2026)

- Board rank: #29 overall, #2 in RAG, data and memory, momentum score 84
- New GitHub stars per week (last 12 weeks): Aug 9: 29; Aug 16: 218; Aug 23: 25; Aug 30: 39; Sep 6: 55; Sep 13: 18; Sep 20: 13; Sep 27: 29; Oct 4: 27; Oct 11: 25; Oct 18: 12; Oct 25: 731

## Memvid alternatives

| Rank | Tool | Stars | Downloads/wk |
|---|---|---|---|
| #1 | [XERJ](https://index.devtoolwatch.com/tools/xerj-org-xerj/) | 17.9k | – |
| #3 | [LEANN](https://index.devtoolwatch.com/tools/startrail-org-leann/) | 13.7k | – |
| #4 | [Graphify](https://index.devtoolwatch.com/tools/graphify-labs-graphify/) | 140k | 480.7k |
| #5 | [MarkItDown](https://index.devtoolwatch.com/tools/markitdown/) | 193.2k | 4.7M |
| #6 | [Firecrawl](https://index.devtoolwatch.com/tools/firecrawl/) | 197.4k | 2.9M |
| #7 | [Mem0](https://index.devtoolwatch.com/tools/mem0/) | 68.2k | 884.1k |

## Memvid FAQ

### Is Memvid free and open source?

Yes. Memvid's source code is on GitHub (memvid/memvid) under the Apache 2.0 license, so you can use it for free.

### How do I install Memvid?

Run `npm install -g memvid-cli`. The install section above has the other options from the project's README.

### Is Memvid still maintained?

Activity has slowed: the memvid/memvid repository was last updated on Jul 14, 2026, and the latest release, v2.0.140 on GitHub, came out on May 27, 2026.

### How popular is Memvid?

Memvid has 17.3k GitHub stars as of Nov 1, 2026. It gained 731 new stars in the week of Oct 25–31, 2026, against a 12-week average of 102 a week. That made it #2 among RAG, data and memory tools on the Devtoolwatch Board.

### Memvid vs Mem0: how do they compare?

Memvid and Mem0 are both tracked by Devtoolwatch as RAG, data and memory tools. Memvid has 17.3k GitHub stars and Mem0 has 68.2k. Mem0 is a memory layer for AI agents and apps that stores and retrieves user, session, and agent context across interactions. [Mem0 review](https://index.devtoolwatch.com/tools/mem0/)

### What are the best alternatives to Memvid?

Other RAG, data and memory tools on the Devtoolwatch Board in the week of Oct 25–31, 2026, by momentum: XERJ (#1), LEANN (#3), Graphify (#4), MarkItDown (#5), Firecrawl (#6). The alternatives table above has their numbers.

### What is Memvid?

Memvid is a memory layer for AI agents. It packages data, embeddings, search structures, and metadata into a single .mv2 file. Agents retrieve directly from that file instead of using a RAG pipeline or server-based vector database.

### How does Memvid work?

Memvid writes memory as an append-only sequence of immutable Smart Frames, with design ideas borrowed from video encoding. The .mv2 file holds a header, an embedded WAL, compressed data segments, a Tantivy full-text index, an HNSW vector index, a time index, and a footer.

### How is Memvid different from a typical RAG pipeline?

Memvid replaces the separate database and services of a typical RAG setup with one file. That file holds data, embeddings, and indexes. Memvid automatically chunks, embeds, and indexes imported content, and combines BM25 lexical search with vector search.

### Does Memvid store video or use QR codes?

No. Memvid borrows ideas from video encoding to organize memory, but it does not store video. The QR-based Memvid v1 is deprecated.

### Does Memvid work offline?

Yes. Memvid is described as working fully offline. It can use local ONNX embedding models, and the memory file can be deployed on-prem or air-gapped. OpenAI embeddings are optional.

### Which embedding models does Memvid support?

Memvid supports local ONNX models: bge-small-en-v1.5 (default), bge-base-en-v1.5, nomic-embed-text-v1.5, and gte-large. Through OpenAI, it supports text-embedding-3-small, text-embedding-3-large, and text-embedding-ada-002.

## Sources

1. [memvid/memvid on GitHub](https://github.com/memvid/memvid)
2. [Memvid README](https://github.com/memvid/memvid/blob/main/README.md)
3. [Memvid website](https://www.memvid.com)
4. [Release v2.0.140 (May 27, 2026)](https://github.com/memvid/memvid/releases/tag/v2.0.140)
5. [Release v2.0.139 (Mar 13, 2026)](https://github.com/memvid/memvid/releases/tag/v2.0.139)
6. [Release v2.0.138 (Mar 3, 2026)](https://github.com/memvid/memvid/releases/tag/v2.0.138)
7. [Devtoolwatch Board, the week of Oct 25–31, 2026](https://index.devtoolwatch.com/)