MemVid LogoMemVid
Early Cloud Access
Memvid
8K+ stars in 3 weeks

The Long-term MemoryLayer for AI

Store millions of context chunks as compressed, rewindable video frames.Stop spending thousands querying your entire dataset on every prompt.

8K+
GitHub Stars
in 3 weeks
6000+
Active Developers
building with MemVid
.mv2
Memory File
entire AI history
terminal
memvid-demo.sh
$pip install memvid
# Store text as video • Search in milliseconds
$python -c "from memvid import MemvidEncoder"
$memvid-cli build --input docs/ --output knowledge.mp4
🎬 Building video memory from 1,247 documents...
âś“ Created knowledge.mp4 & knowledge_index.json
$memvid-cli chat knowledge.mp4_

Join 600+ developers building the future of AI memory

Your data never leaves your infrastructure

Life With & Without MemVid

Without MemVid your vector DB balloons and slows. With MemVid every agent gets fast, low-cost memory it can search, recall, and carry forward—no re-indexing, just share a 50 KB capsule.

Without MemVid

Cold-start cost

Re-embed every record

Disk / RAM ratio

3-5Ă— data size

Live branching

Not supported

Portability

Complex infrastructure

Multi-modal

Mostly text only

With MemVid

Cold-start cost

Zero — pre-encoded

Disk / RAM ratio

0.4Ă— thanks to video codec

Live branching

Create branches in milliseconds

Portability

One .mv2 file

Multi-modal

Text • Images • Model Weights

Built for real-world AI workflows

Everything you need to manage AI memory at scale

Video-Based Storage

Store millions of text chunks as frames in MP4 files. 10x more efficient than traditional databases.

Single portable MP4 file

Semantic Search

Find relevant information instantly using natural language queries. No SQL, no complex queries.

Sub-second retrieval

Self-Adapting & Time Travel

Rewind any branch to past states or fast-forward with new knowledge. Models keep learning—your memory evolves without re-encoding.

Branch, replay & evolve

Interactive Chat Interface

Built-in Q&A interface to chat with your stored knowledge. Supports multiple LLM providers.

Start chatting in seconds

What People Are Building With MemVid 1.0

Discover how developers are transforming AI memory with MemVid across different domains

The Living Word Project
Video-to-Text Memory

The Living Word

A real video representation of The Book of Genesis built with MemVid. This project demonstrates how sacred texts can be stored and accessed through video memory formats, making ancient texts searchable and accessible.

Multi-modal text storage
RAG Without Pipelines
RAG Simplified

No Complex RAG Pipelines

Store text as video frames and instantly extract information for answers. Works with markdown and HuggingFace datasets, enabling easy domain-specific knowledge databases for Q&A systems without complex RAG pipelines.

HuggingFace integration ready
Ohio State Football AI
AI Sports Assistant

Ohio State Football AI

Chat with data embedded in video—without computer vision! Ohio State's 2024-25 season data stored as video frames in MP4, searchable via natural language with Osmosis-0.6B running locally.

Video as a database

Why Developers Choose MemVid

  • Turn any data into searchable video memory—sports stats, PDFs, even sacred texts
  • No complex infrastructure—just MP4 files that work anywhere
  • From sports stats to research papers—one memory format for all
10x

More efficient than

traditional vector DBs

Drop-in CLI & SDK

Get started in seconds with our intuitive command-line interface

Create Video Memory

from memvid import MemvidEncoder

# Create encoder and add text chunks
encoder = MemvidEncoder()
encoder.add_chunks([
  "The Earth orbits the Sun.",
  "Water boils at 100°C.",
  "Python is a programming language."
])

# Build video memory
encoder.build_video("knowledge.mp4", "index.json")

Chat with Your Memory

from memvid import MemvidChat

# Initialize chat with video memory
chat = MemvidChat("knowledge.mp4", "index.json")
chat.start_session()

# Ask questions
response = chat.chat("What temperature does water boil?")
print(response)

# Output: Water boils at 100 degrees Celsius.

PDF Document Support

from memvid import MemvidEncoder, MemvidChat

# Index PDF documents
encoder = MemvidEncoder()
encoder.add_pdf("research_paper.pdf")
encoder.add_pdf("documentation.pdf")
encoder.build_video("docs.mp4", "docs_index.json")

# Search across all PDFs
chat = MemvidChat("docs.mp4", "docs_index.json")
answer = chat.chat("What are the key findings?")

# Instant semantic search across all documents
Coming Soon

MemVid Cloud

Manage your .mv2 memory files in the cloud. Version control, collaboration, and seamless sync.

Version Control

Git-like version control for your memory files. Branch, merge, and track changes.

  • Track memory evolution
  • Collaborative editing

Cloud Storage

Store and manage your MP4 memory files in the cloud. Access from anywhere.

  • Automatic backups
  • Global CDN

Team Collaboration

Share memory files with your team. Control access and permissions.

  • Role-based access
  • Real-time sync

Be the first to know when MemVid Cloud launches

Get early access to cloud storage, version control, and team collaboration features.

MemVid 2.0

Ready for the Future ofAI Memory?

Join 6000+ developers using MemVid to build the next generation of AI applications.Store as video. Branch like Git. Carry anywhere in 50KB.

MemVid 2.0

Living AI Memory

First memory engine that grows forever with your AI

Branch & Rewind

Git for AI Memory - branch and rewind anywhere on timeline

.mv2 Files

Portable memory files carry entire history to any AI agent

Open source • Apache 2.0 licensed • Your data never leaves your infrastructure