Showing 2 open source projects for "security operating system"

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  • Auth0 B2B Essentials: SSO, MFA, and RBAC Built In Icon
    Auth0 B2B Essentials: SSO, MFA, and RBAC Built In

    Unlimited organizations, 3 enterprise SSO connections, role-based access control, and pro MFA included. Dev and prod tenants out of the box.

    Auth0's B2B Essentials plan gives you everything you need to ship secure multi-tenant apps. Unlimited orgs, enterprise SSO, RBAC, audit log streaming, and higher auth and API limits included. Add on M2M tokens, enterprise MFA, or additional SSO connections as you scale.
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  • Ship Agents Faster Icon
    Ship Agents Faster

    Transform your applications and workflows into powerful agentic systems at global scale.

    Gemini Enterprise Agent Platform lets you rapidly build, scale, govern and optimize production-ready agents grounded in your organization's data. The platform enables developers to build custom or pre-built agents for virtually any use case. New customers get $300 in free credits.
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    SmythOS

    SmythOS

    Cloud-native runtime for agentic AI

    SmythOS SRE (Smyth Runtime Environment) is an open-source runtime and development platform designed for building and operating production-grade AI agents. It provides a foundational infrastructure layer that functions similarly to an operating system for agentic AI systems, managing resources such as language models, storage, vector databases, and caching through a unified interface. Developers can use the runtime to create, deploy, and orchestrate intelligent agents across local machines, cloud environments, or hybrid infrastructures without rewriting their application logic. ...
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    AWS GenAI LLM Chatbot

    AWS GenAI LLM Chatbot

    A modular and comprehensive solution to deploy a Multi-LLM

    ...The project is built as a modular blueprint that helps organizations stand up a production-oriented chat experience rather than a simple demo, combining model access, knowledge retrieval, storage, security, and user interface components into one deployable system. It supports multiple model providers and endpoints, giving teams flexibility to work with Amazon Bedrock, SageMaker-hosted models, and additional model access patterns through related integrations. A major part of the design is its RAG layer, which enables the chatbot to pull contextual knowledge from connected data sources so responses can be grounded in enterprise content rather than relying only on model memory.
    Downloads: 0 This Week
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