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A file-based agent memory framework that works like skill

Personal memory across agents. 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.

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  1. I compressed 10k PDFs into a 1.4GB video for LLM memory

    github.com

    Memory layer for AI Agents. Replace complex RAG pipelines with a serverless, single-file memory layer. Give your agents instant retrieval and long-term memory.

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  2. Continual Learning with .md

    github.com

    Turn your files into a memory filesystem for AI agents.

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  3. A local-first memory store for LLM agents (SQLite)

    github.com

    Local persistent memory store for LLM applications including claude desktop, github copilot, codex, antigravity, etc.

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  4. A lightweight

    polign.com

    stateless database for agent memory

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  5. YourMemory

    yourmemoryai.vercel.app

    agentic memory is a pruning problem, not a hoarding problem

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