Memvid
Memory layer for AI agents in 1 file.
What it does
Memvid V2 is live! We just killed every RAG and Vector Database company (by storing AI memory inside video frames) - 5 minute setup - Zero infrastructure. - Hybrid search - Sub-5ms retrieval - Fully portable - Open Source Memvid V2 introduces the first portable, serverless AI memory layer that replaces traditional RAG pipelines and vector databases with a single file. No database. No preprocessing. Just a reliable, long-term memory layer that agents can carry anywhere.
Does the same job
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Hey HN! We're Taranjeet and Deshraj, the founders of Mem0 (https://mem0.ai). Mem0 adds a stateful memory layer to AI applications, allowing them to remember user interactions, preferences, and context over time. This enables AI apps to deliver increasingly personalized and intelligent experiences that evolve with every interaction. There’s a demo video at https://youtu.be/VtRuBCTZL1o and a playground to try out at https://app.mem0.ai/playground. You'll need to sign up to use the playground – this helps ensure responses are more tailored to you by…
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Hi HN, We’ve been building [memU](https://github.com/NevaMind-AI/memU), an open-source memory framework for AI agents that supports both classic RAG and LLM-based direct file reading. RAG has become the default in LLM systems, but many of its failures don’t come from the model — they come from the retrieval assumptions. Embedding-based retrieval is fundamentally an approximation over semantic similarity. It works well for fuzzy recall, but it often breaks when relevance ≠ correctness, which is common in real systems. From a retrieval perspective, RAG struggles with: -…
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