Showing 4 open source projects for "free memory"

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    TypeAgent Python

    TypeAgent Python

    Structured RAG: ingest, index, query

    ...The project focuses on implementing structured Retrieval-Augmented Generation workflows that allow agents to ingest information, index it in structured form, and answer queries using language models. Instead of relying solely on free-form prompts, the architecture emphasizes converting natural language interactions into structured representations that can be processed by deterministic software components. This design allows the system to combine the flexibility of language models with the reliability of traditional programming logic. The repository is intended primarily as a research prototype and sample code rather than a production-ready framework, allowing developers to experiment with building AI agents that maintain structured memory and perform tasks through defined actions.
    Downloads: 2 This Week
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  • 2
    Flowise

    Flowise

    Drag & drop UI to build your customized LLM flow

    Open source UI visual tool to build your customized LLM flow using LangchainJS, written in Node Typescript/Javascript. Conversational agent for a chat model which utilizes chat-specific prompts and buffer memory. Open source is the core of Flowise, and it will always be free for commercial and personal usage. Flowise support different environment variables to configure your instance. You can specify the following variables in the .env file inside the packages/server folder.
    Downloads: 24 This Week
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  • 3
    OpenClaw

    OpenClaw

    Your own personal AI assistant. Any OS. Any Platform.

    OpenClaw (formerly Clawdbot/Moltbot) is an open-source, self-hosted autonomous AI assistant designed to run on user-controlled hardware and bridge conversational natural language with real-world task execution, effectively acting as a proactive digital assistant rather than a reactive chatbot. It lets you send instructions through familiar messaging platforms like WhatsApp, Telegram, Discord, Slack, Signal, iMessage, and more, and then interprets those instructions to carry out actions such...
    Downloads: 22 This Week
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  • 4
    In imitative learning, an agent will attempt to match what is observed to their memory. By visualizing the incorrectly matches "scenes", this project will allow algorithm developers to gain a better understanding of what causes their algorithms to fail.
    Downloads: 0 This Week
    Last Update:
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