Exploring Memvid: Video-Based Memory for AI Systems

Recently, I came across an open-source project called Memvid, which takes a very unconventional approach to long-term memory for AI systems. I wouldn’t call myself an expert on it by any means, but after skimming the docs, reading through the repository, and experimenting with a small example, I found the core idea interesting enough to keep thinking about.

Memvid isn’t positioned as a polished production tool; it feels more like an exploration of a design space. That’s exactly the kind of project that tends to stick with me.

What is Memvid?

At a high level, Memvid is a video-based memory system for AI applications.

Instead of storing embeddings in a hosted vector database, the system takes a different path:

documents → embeddings → QR codes → video file → semantic search

More concretely, Memvid:

• breaks text into chunks
• converts each chunk into an embedding
• encodes those embeddings as QR codes
• stitches the QR codes into a compressed video file (e.g., MP4)

That video file becomes the “memory store.” When a query is run, the video is decoded and searched to retrieve relevant chunks.

It’s a strange idea on first read, but also kind of compelling once you sit with it.

Why this caught my attention

Most modern AI memory systems converge on similar architectures:

• embeddings
• vector databases
• hosted services
• indexing pipelines

Memvid deliberately avoids that stack. Everything stays local, stored in a single artifact that can be copied, archived, or shared like any other media file.

What stood out to me immediately:

• No external database dependency
• Memory represented as a durable file
• A workflow that’s easy to reason about end-to-end
• An intentionally constrained, physical representation of memory

Even if this approach never becomes mainstream, it forces you to think differently about what “memory” actually means in AI systems.

A very small example

Here’s a simplified example based on what I experimented with after reading the docs.

from memvid import Memvid
 
mem = Memvid()
 
# Add some documents
mem.add_text("I studied databases, operating systems, and machine learning.")
mem.add_text("My senior project focused on time-series forecasting.")
mem.add_text("I enjoy systems design and performance optimization.")
 
# Build the video-based memory
mem.build("memory.mp4")
 
# Run a query
results = mem.find("What database topics did I study?", k=5)
print(results["hits"])

A use case I keep thinking about: accessibility and course materials

One use case that stood out to me isn’t strictly “AI agent” related, but student accessibility.

Course materials tend to be scattered across platforms:

• lecture slides
• PDFs
• recorded videos
• discussion posts
• personal notes

For students with cognitive or learning disabilities, navigating that fragmentation can be exhausting. Even for students without accessibility needs, remembering where something was covered is often harder than understanding the content itself.

I can imagine a workflow where:

• course materials are chunked and embedded
• stored locally as a Memvid video file
• paired with structured metadata (course, week, topic)
• queried conversationally or via assistive tools

Instead of asking “Which PDF was that in?”, a student could ask:

“What did we cover about database normalization before the midterm?”

And receive grounded, source-linked answers, without relying on cloud-hosted services, accounts, or complex infrastructure.

Even if Memvid itself isn’t the final solution, the idea of portable, local semantic memory feels especially relevant in accessibility-focused contexts.

Limitations and open questions

There are real tradeoffs here, and I don’t want to gloss over them:

• performance at scale is unclear
• video encoding and decoding introduces overhead
• observability and debugging look very different than traditional systems
• it’s not obvious how this fits into production pipelines

Memvid doesn’t pretend to solve all of these problems, and that’s okay.

Why I’m glad projects like this exist

What I appreciate most about Memvid is that it feels like a question more than an answer.

• What if memory didn’t live in a database?
• What if portability mattered more than raw throughput?
• What if constraints actually led to clearer systems?

Even if I never use Memvid directly, it’s influenced how I think about AI systems, storage, and architectural tradeoffs. And for me, that alone makes it worth paying attention to.

If nothing else, it’s a reminder that interesting engineering often starts by asking slightly uncomfortable questions.