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Memvid Alternatives
Similar projects and alternatives to memvid
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cognita
RAG (Retrieval Augmented Generation) Framework for building modular, open source applications for production by TrueFoundry
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InfluxDB
InfluxDB – Built for High-Performance Time Series Workloads. InfluxDB 3 OSS is now GA. Transform, enrich, and act on time series data directly in the database. Automate critical tasks and eliminate the need to move data externally. Download now.
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rag
🚀 Retrieval Augmented Generation (RAG) with txtai. Combine search and LLMs to find insights with your own data.
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txtchat
💭 Build autonomous agents, retrieval augmented generation (RAG) processes and language model powered chat applications
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txtai
💡 All-in-one open-source AI framework for semantic search, LLM orchestration and language model workflows
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haystack
AI orchestration framework to build customizable, production-ready LLM applications. Connect components (models, vector DBs, file converters) to pipelines or agents that can interact with your data. With advanced retrieval methods, it's best suited for building RAG, question answering, semantic search or conversational agent chatbots.
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SaaSHub
SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives
NOTE:
The number of mentions on this list indicates mentions on common posts plus user suggested alternatives.
Hence, a higher number means a better memvid alternative or higher similarity.
memvid discussion
memvid reviews and mentions
Posts with mentions or reviews of memvid.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2025-05-30.
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Memvid – Video-Based AI Memory
sadly have to agree. I asked a question about this on the issue tracker.
https://github.com/Olow304/memvid/issues/39 if anyone wants to follow along.
- Show HN: I compressed 10k PDFs into a 1.4GB video for LLM memory
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I accidentally built a vector database using video compression
While building a RAG system, I got frustrated watching my 8GB RAM disappear into a vector database just to search my own PDFs. After burning through $150 in cloud costs, I had a weird thought: what if I encoded my documents into video frames?
The idea sounds absurd - why would you store text in video? But modern video codecs have spent decades optimizing for compression. So I tried converting text into QR codes, then encoding those as video frames, letting H.264/H.265 handle the compression magic.
The results surprised me. 10,000 PDFs compressed down to a 1.4GB video file. Search latency came in around 900ms compared to Pinecone’s 820ms, so about 10% slower. But RAM usage dropped from 8GB+ to just 200MB, and it works completely offline with no API keys or monthly bills.
The technical approach is simple: each document chunk gets encoded into QR codes which become video frames. Video compression handles redundancy between similar documents remarkably well. Search works by decoding relevant frame ranges based on a lightweight index.
You get a vector database that’s just a video file you can copy anywhere.
https://github.com/Olow304/memvid
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www.saashub.com | 20 Jun 2025
Stats
Basic memvid repo stats
6
7,907
6.1
15 days ago
Olow304/memvid is an open source project licensed under MIT License which is an OSI approved license.
The primary programming language of memvid is Python.
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