Showing 6 open source projects for "development tools"

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  • Gemini 3 and 200+ AI Models on One Platform Icon
    Gemini 3 and 200+ AI Models on One Platform

    Access Google's best plus Claude, Llama, and Gemma. Fine-tune and deploy from one console.

    Build generative AI apps with Vertex AI. Switch between models without switching platforms.
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    MongoDB Atlas runs apps anywhere

    Deploy in 115+ regions with the modern database for every enterprise.

    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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  • 1
    Nerve

    Nerve

    The Simple Agent Development Kit

    Nerve is a developer-friendly Agent Development Kit (ADK) that utilizes YAML and a CLI to define, run, orchestrate, and evaluate LLM-driven agents. It supports declarative setups, tool integration, workflow pipelines, and both MCP client and server roles. Nerve is a simple yet powerful Agent Development Kit (ADK) to build, run, evaluate, and orchestrate LLM-based agents using just YAML and a CLI.
    Downloads: 2 This Week
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  • 2
    Upsonic

    Upsonic

    The most reliable AI agent framework that supports MCP

    Upsonic is a reliability-focused AI agent framework designed for real-world applications. It enables the development of trusted agent workflows within organizations by incorporating advanced reliability features, such as verification layers and output evaluation systems. The framework supports the Model Context Protocol (MCP), facilitating integration with various tools and enhancing agent capabilities. ​
    Downloads: 3 This Week
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  • 3
    Claude-Flow

    Claude-Flow

    The leading agent orchestration platform for Claude

    Claude-Flow v2 Alpha is an advanced AI orchestration and automation framework designed for enterprise-grade, large-scale AI-driven development. It enables developers to coordinate multiple specialized AI agents in real time through a hive-mind architecture, combining swarm intelligence, neural reasoning, and a powerful set of 87 Modular Control Protocol (MCP) tools. The platform supports both quick swarm tasks and persistent multi-agent sessions known as hives, facilitating distributed AI collaboration with persistent contextual memory. ...
    Downloads: 2 This Week
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  • 4
    AWS MCP Servers

    AWS MCP Servers

    Helping you get the most out of AWS, wherever you use MCP

    AWS MCP Servers are a collection of remotely hosted, fully-managed Model Context Protocol (MCP) servers by AWS, providing AI applications with real-time access to AWS documentation, API references, best practices, and infrastructure-management capabilities via natural-language workflows. An MCP Server is a lightweight program that exposes specific capabilities through the standardized Model Context Protocol. Host applications (such as chatbots, IDEs, and other AI tools) have MCP clients that...
    Downloads: 7 This Week
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    Try Google Cloud Risk-Free With $300 in Credit

    No hidden charges. No surprise bills. Cancel anytime.

    Use your credit across every product. Compute, storage, AI, analytics. When it runs out, 20+ products stay free. You only pay when you choose to.
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  • 5
    FlowLens MCP

    FlowLens MCP

    Open-source MCP server that gives your coding agent

    FlowLens MCP Server is an open-source tool designed to give AI-powered coding agents (like Claude Code, Cursor, GitHub Copilot / Codex, and others) full, replayable browser context to dramatically improve debugging, bug reporting, and regression testing for web applications. It works together with a companion browser extension: when a user reproduces a bug or a complicated UI interaction, the extension captures a rich session log, including screen/video recording, network traffic, console...
    Downloads: 0 This Week
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  • 6
    LitterBox

    LitterBox

    A secure sandbox environment for malware developers and red teamers

    ...The README frames typical use cases: testing evasion, validating detections, analyzing behavior, and keeping sensitive tooling in-house. Repo metadata and author pages highlight an active security-tools ecosystem around the maintainer, with CI and pull-request activity suggesting ongoing development. The project positions itself as a safe proving ground to reduce surprises in the field while minimizing operational risk. For teams exploring MCP integrations, notes mention pairing with LLM agents for assisted analysis.
    Downloads: 0 This Week
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