Memvid

Memvid is an MCP server that encodes raw text chunks into compact video files and exposes them for semantic search. Built on top of the memvid library, this streamable-HTTP server allows AI clients to store information in an MP4 video format and query it later using semantic similarity. Software developers and AI system builders use Memvid when experimenting with video-based context storage and non-traditional vector storage formats. It connects an MCP-compatible host directly to a local Python server via HTTP endpoints, allowing models to index new text blocks and look up relevant context on demand. The server implements tools to ingest batches of text chunks, automatically rendering them into an indexed video representation named memory.mp4, and to execute queries that return top-matching text segments. This setup gives language models an alternative multimedia storage medium for contextual recall and document retrieval.

Category: AI Memory & Context

Tags: data storage, encoding, multimedia, semantic search, video

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How to install and configure Memvid

1. Clone the repository and set up a Python 3.11 environment: bash python3.11 -m venv my_env . ./my_env/bin/activate pip install -r requirements.txt 2. Start the server locally: bash python server.py (Optionally specify a custom port with PORT=3002 python server.py). 3. Register the server in your MCP client configuration file by adding the streamable HTTP endpoint: json { "mcpServers": { "memvid": { "type": "streamable-http", "url": "http://localhost:3000" } } }

What you can do with Memvid

• Encoding large text fragments into a single video file format for experiments with novel AI memory storage architectures. - Querying dense text documentation semantically through the search tool with configurable top_k results. - Resetting and generating an updated memory video index from fresh project notes using the add_chunks tool. - Integrating an alternative HTTP-based semantic vector retrieval mechanism into compatible MCP desktop and agent clients.

Key facts

• https://github.com/ferrants/memvid-mcp-server
• AI Memory & Context, Data & Analytics, Design, Media & Creative
• data storage, encoding, multimedia, semantic search, video

Part of MCP Servers

What is Memvid?

Memvid is a Streamable-HTTP Model Context Protocol server that uses the memvid Python package to encode text chunks into video files and perform semantic searches over that stored data.

What tools does Memvid provide?

The server provides two primary actions: add_chunks, which writes chunks to the memory video and rebuilds the memory.mp4 file, and search, which queries the video index and returns matching text chunks based on the top_k parameter.

How do I install Memvid?

You install Memvid by creating a Python 3.11 virtual environment, installing the dependencies from requirements.txt, running server.py, and pointing your MCP client to the HTTP endpoint at localhost.

Which MCP clients work with Memvid?

Any MCP client that supports streamable-HTTP server connections can use Memvid by defining the server URL in its configuration file, typically under the mcpServers configuration block.