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Memory layer for AI Agents. Replace complex RAG pipelines with a serverless, single-file memory layer. Give your agents instant retrieval and long-term memory.

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Memvid is a single-file memory layer for AI agents with instant retrieval and long-term memory.
Persistent, versioned, and portable memory, without databases.

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⭐️ Leave a STAR to support the project ⭐️

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.

Why Video 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:

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.

SDKs & CLI

Use Memvid in your preferred language:

Installation (Rust)

Requirements

Add to Your Project

Feature Flags

Enable features as needed:

Quick Start

Build

Clone the repository:

Build in debug mode:

Build in release mode (optimized):

Build with specific features:

Run Tests

Run all tests:

Run tests with output:

Run a specific test:

Run integration tests only:

Examples

The examples/ directory contains working examples:

Basic Usage

Demonstrates create, put, search, and timeline operations:

PDF Ingestion

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

CLIP Visual Search

Image search using CLIP embeddings (requires clip feature):

Whisper Transcription

Audio transcription (requires whisper feature):

File Format

Everything lives in a single .mv2 file:

No .wal, .lock, .shm, or sidecar files. Ever.

See MV2_SPEC.md for the complete file format specification.

Support

Have questions or feedback?
Email: [email protected]

Drop a ⭐ to show support

License

Apache License 2.0 — see the LICENSE file for details.

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Memory layer for AI Agents. Replace complex RAG pipelines with a serverless, single-file memory layer. Give your agents instant retrieval and long-term memory.

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