Showing 3 open source projects for "plug"

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    MongoDB Atlas runs apps anywhere

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    MongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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    Build Agents and Models on One Platform

    Everything you need to build production-ready agents and models. Access 200+ Google and third-party AI models and tools.

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  • 1
    CopilotKit

    CopilotKit

    Build in-app AI chatbots, and AI-powered Text areas

    A bridge between your copilot and your app. A programmable 2-way bridge between your copilot, and your application state (client & cloud). Supports 3rd party integrations (e.g. Salesforce, Zendesk, etc.). Plug-and-play, fully customizable, copilot infrastructure. Build in-app AI chatbots that can "see" the current app state + take action inside your app. The AI chatbot can talk to your app frontend & backend, and to 3rd party services (Salesforce, Dropbox, etc.) via plugins. Autocompletion + AI editing + generate from scratch. Indexed on your users' content.
    Downloads: 3 This Week
    Last Update:
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  • 2
    NagaAgent

    NagaAgent

    A simple yet powerful agent framework for personal assistants

    ...Rather than being a simple chatbot, NagaAgent emphasizes persistent thought cycles, context retention, and the ability to decompose complex tasks into smaller executable units, earning it a place in research explorations of agent design. Its architecture facilitates extensibility, allowing developers to plug in different reasoning modules or knowledge sources depending on the domain of use.
    Downloads: 6 This Week
    Last Update:
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  • 3
    DeerFlow

    DeerFlow

    Deep Research framework, combining language models with tools

    DeerFlow is an open-source, community-driven “deep research” framework / multi-agent orchestration platform developed by ByteDance. It aims to combine the reasoning power of large language models (LLMs) with automated tool-use — such as web search, web crawling, Python execution, and data processing — to enable complex, end-to-end research workflows. Instead of a monolithic AI assistant, DeerFlow defines multiple specialized agents (e.g. “planner,” “searcher,” “coder,” “report generator”)...
    Downloads: 9 This Week
    Last Update:
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