Turn millions of text chunks into a single MP4 file. Search semantically in milliseconds.No databases. No servers. Just portable knowledge that works offline.
Join 600+ developers building the future of AI memory
No more re-indexing gigabytes. Just share a 50KB memory capsule.
Cold-start cost
Re-embed every record
Disk / RAM ratio
3-5× data size
Live branching
Not supported
Portability
Complex infrastructure
Multi-modal
Mostly text only
Cold-start cost
Zero — pre-encoded
Disk / RAM ratio
0.4× thanks to video codec
Live branching
Create branches in milliseconds
Portability
One .mv2 file
Multi-modal
Text • Images • Model Weights
Everything you need to manage AI memory at scale
Store millions of text chunks as frames in MP4 files. 10x more efficient than traditional databases.
Find relevant information instantly using natural language queries. No SQL, no complex queries.
No internet required. No API calls. Your knowledge stays on your device, always accessible.
Built-in Q&A interface to chat with your stored knowledge. Supports multiple LLM providers.
Get started in seconds with our intuitive command-line interface
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")
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.
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
Manage your MP4 memory files in the cloud. Version control, collaboration, and seamless sync.
Git-like version control for your memory files. Branch, merge, and track changes.
Store and manage your MP4 memory files in the cloud. Access from anywhere.
Share memory files with your team. Control access and permissions.
Join 600+ developers using MemVid to build the next generation of AI applications.Store as video. Branch like Git. Carry anywhere in 50KB.
First memory engine that grows forever with your AI
Git for AI Memory - branch and rewind anywhere on timeline
Portable capsule carries entire history to any AI agent
Open source • Apache 2.0 licensed • Your data never leaves your infrastructure