9 projects for "vendor" with 2 filters applied:

  • $300 Free Credits to Build on Google Cloud Icon
    $300 Free Credits to Build on Google Cloud

    New customers can spin up VMs, build with AI, and query data at no cost.

    Put your $300 in credit toward real workloads, then keep building with free monthly usage for 20+ products. No commitment and no charge until you upgrade.
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  • 99.99% Uptime for MySQL and PostgreSQL Databases Icon
    99.99% Uptime for MySQL and PostgreSQL Databases

    Sub-second maintenance. 2x read/write performance. Built-in vector search for AI apps.

    Cloud SQL Enterprise Plus delivers near-zero downtime with 35 days of point-in-time recovery. Supports MySQL, PostgreSQL, and SQL Server.
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  • 1
    Oh My OpenAgent

    Oh My OpenAgent

    The best agent harness

    ...The system is designed as a comprehensive agent harness where tasks are automatically decomposed, delegated, and executed across a network of specialized agents. It emphasizes openness and flexibility, allowing developers to integrate different providers and avoid dependency on any single ecosystem or vendor. The framework includes robust tooling for managing agent workflows, monitoring execution, and integrating external tools, making it suitable for complex, production-level use cases. It also fosters a strong community-driven development approach, with features evolving in real time.
    Downloads: 10 This Week
    Last Update:
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  • 2
    Big-AGI

    Big-AGI

    AI suite powered by state-of-the-art models and providing advanced AI

    ...The workspace includes advanced features like Beam, which enables multi-model consensus and comparative responses to improve reliability and reduce hallucination, and robust persona management to tailor responses to specific roles or workflows. Big-AGI can be self-hosted or deployed in cloud environments, giving users full control over data and model access limits and avoiding vendor lock-in.
    Downloads: 2 This Week
    Last Update:
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  • 3
    BeeAI Framework

    BeeAI Framework

    Build production-ready AI agents in both Python and Typescript

    ...It includes a unified backend layer that connects seamlessly to multiple large language model providers, allowing flexible deployment across different AI infrastructures without vendor lock-in. BeeAI also provides orchestration tools for designing dynamic workflows, enabling multiple agents to coordinate tasks through structured execution flows, retries, and parallel processing.
    Downloads: 2 This Week
    Last Update:
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  • 4
    QM

    QM

    Multiplayer agent harness for work

    ...The same agent identity and configuration can operate through Slack or the web interface. Teams can choose among supported models and harnesses, including Pi, OpenCode, Codex, and Claude Code, without binding the deployment to one vendor. Administrators control organization settings, available models, shared skills, and security posture. Scheduled jobs and watches let agents continue background work when no user is present. QM can search company information, maintain projects, build internal apps, work in repositories, and publish results to selected users.
    Downloads: 0 This Week
    Last Update:
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  • Custom VMs From 1 to 96 vCPUs With 99.95% Uptime Icon
    Custom VMs From 1 to 96 vCPUs With 99.95% Uptime

    General-purpose, compute-optimized, or GPU/TPU-accelerated. Built to your exact specs.

    Live migration and automatic failover keep workloads online through maintenance. One free e2-micro VM every month.
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  • 5
    RuoYi AI

    RuoYi AI

    Enterprise AI platform for building, deploying, and managing apps

    ...It provides a unified framework for integrating multiple AI models from different providers, allowing teams to switch or combine models through a consistent interface without vendor lock-in. RuoYi AI includes built-in support for retrieval-augmented generation, enabling organizations to create secure, private knowledge bases with high-accuracy search and reasoning capabilities. It also offers visual workflow orchestration tools that allow users to design complex AI pipelines, automate tasks, and coordinate multi-agent systems for advanced decision-making scenarios. ...
    Downloads: 2 This Week
    Last Update:
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  • 6
    One API

    One API

    The LLM API management & distribution system

    ...Its architecture is designed for scalability, allowing teams to deploy it as a centralized layer in their AI infrastructure. By abstracting the complexity of working with multiple APIs, it simplifies development and reduces vendor lock-in.
    Downloads: 0 This Week
    Last Update:
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  • 7
    Docker Agent

    Docker Agent

    AI Agent Builder and Runtime by Docker Engineering

    ...The runtime supports multi-agent collaboration, allowing specialized agents to delegate tasks to each other and operate as coordinated systems rather than isolated units. It is provider-agnostic, meaning it can integrate with multiple AI model providers such as OpenAI, Anthropic, and local inference engines, helping avoid vendor lock-in. cagent also supports the Model Context Protocol, enabling seamless integration with external tools, APIs, and services.
    Downloads: 0 This Week
    Last Update:
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  • 8
    Puck

    Puck

    Open source visual editor for building React drag-and-drop pages

    ...It operates with a structured data model that stores content as a JSON payload, allowing the same configuration to power both the editing interface and the production rendering of pages. This approach gives developers full ownership of their content data while avoiding vendor lock-in.
    Downloads: 0 This Week
    Last Update:
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  • 9
    UCCL

    UCCL

    UCCL is an efficient communication library for GPUs

    ...It supports a variety of communication patterns including collective operations such as all-reduce as well as peer-to-peer transfers that are commonly used in modern machine learning architectures. UCCL is designed to work with heterogeneous hardware environments, allowing GPUs from different vendors and network interfaces to communicate efficiently without vendor lock-in. The system also supports specialized workloads such as reinforcement learning weight transfers, key-value cache sharing, and expert parallelism for mixture-of-experts models. Its architecture emphasizes flexibility and extensibility so that developers can implement custom communication protocols tailored to specific machine learning workloads.
    Downloads: 0 This Week
    Last Update:
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