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.

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

Website · Try Sandbox · Docs · Discussions

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.

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.

• 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.

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

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

• Rust 1.85.0+ — Install from rustup.rs

[dependencies]
memvid-core = "2.0"

Enable features as needed:

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

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(())
}

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 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

The examples/ directory contains working examples:

Demonstrates create, put, search, and timeline operations:

cargo run --example basic_usage

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

cargo run --example pdf_ingestion

Image search using CLIP embeddings (requires clip feature):

cargo run --example clip_visual_search --features clip

Audio transcription (requires whisper feature):

cargo run --example test_whisper --features whisper

Everything lives in a single .mv2 file:

┌────────────────────────────┐
│ Header (4KB)               │  Magic, version, capacity
├────────────────────────────┤
│ Embedded WAL (1-64MB)      │  Crash recovery
├────────────────────────────┤
│ Data Segments              │  Compressed frames
├────────────────────────────┤
│ Lex Index                  │  Tantivy full-text
├────────────────────────────┤
│ Vec Index                  │  HNSW vectors
├────────────────────────────┤
│ Time Index                 │  Chronological ordering
├────────────────────────────┤
│ TOC (Footer)               │  Segment offsets
└────────────────────────────┘

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

See MV2_SPEC.md for the complete file format specification.

Have questions or feedback? Email: contact@memvid.com

Drop a ⭐ to show support

Apache License 2.0 — see the LICENSE file for details.