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數據保留

  • Long term memory solution for AI applications and agents.
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    What is Llongterm?
    Llongterm is designed to enhance AI applications and agents by providing a robust long-term memory solution. It allows AI to remember and recall important interactions and details over long periods, thus improving the overall efficiency and accuracy of the AI. With its compatibility with various AI chatbots and agents, and features like human-readable memory, knowledge mapping, and structured timelines, Llongterm represents a significant advancement in AI memory technology.
    Llongterm Core Features
    • AI mind for user conversations
    • Knowledge Map
    • Self-structuring minds
    • Virtual timeline
    • Scalable and prunable minds
    • Sharable between applications
    • Human-readable
    Llongterm Pro & Cons

    The Cons

    No explicit information on open-source availability.
    No direct information on mobile app availability or extensions.
    Pricing details not explicitly detailed on the website.

    The Pros

    Supports long-term context retention for AI applications.
    Compatible with all AI chatbots and agents.
    Human-readable memory structure for transparency.
    Scalable memory optimization.
    Sharable memory across multiple services and applications.
    Llongterm Pricing
    Has free planNo
    Free trial details
    Pricing model
    Is credit card requiredNo
    Has lifetime planNo
    Billing frequency
    For the latest prices, please visit: https://www.llongterm.com
  • WanderMind is an open-source AI agent framework for autonomous brainstorming, tool integration, persistent memory, and customizable workflows.
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    What is WanderMind?
    WanderMind provides a modular architecture for building self-guided AI agents. It manages a persistent memory store to retain context across sessions, integrates with external tools and APIs for extended functionality, and orchestrates multi-step reasoning through customizable planners. Developers can plug in different LLM providers, define asynchronous tasks, and extend the system with new tool adapters. This framework accelerates experimentation with autonomous workflows, enabling applications from idea exploration to automated research assistants without heavy engineering overhead.
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