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Comprehensive best practices in AI Tools for Every Need

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best practices in AI

  • CrewAI Quickstart

    CrewAI Quickstart provides a Node.js template to rapidly configure, run, and manage conversational AI agents via CrewAI API.
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    What is CrewAI Quickstart?
    CrewAI Quickstart is a developer toolkit designed to streamline the creation and deployment of AI-driven conversational agents using the CrewAI framework. It offers a preconfigured Node.js environment, example scripts for interacting with CrewAI APIs, and best-practice patterns for prompt design, agent orchestration, and error handling. With this quickstart, teams can prototype chatbots, automate workflows, and integrate AI assistants into existing applications in minutes, reducing boilerplate code and ensuring consistency across projects.

  • AI Engineer DevTools

    AI Engineer DevTools is a CLI toolkit offering scaffolding, code generation, testing, deployment, and monitoring for AI agents.
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    What is AI Engineer DevTools?
    AI Engineer DevTools is a comprehensive developer toolkit that simplifies building and maintaining AI agents. It offers command-line scaffolding for project structure, code generation for standard agent patterns, environment configuration scripts, integrated testing frameworks, CI/CD pipeline examples, deployment automation, and monitoring setup. By reducing boilerplate and enforcing best practices, it ensures consistency, reliability, and rapid iteration in AI agent projects across development and production stages.

  • Azure AI Foundry Agents Samples

    A GitHub repository showcasing code samples for building autonomous AI agents on Azure with memory, planning, and tool integration.
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    What is Azure AI Foundry Agents Samples?
    Azure AI Foundry Agents Samples provides developers with a rich set of example scenarios that illustrate how to leverage Azure AI Foundry SDKs and services. It includes conversational agents with long-term memory, planner agents that break down complex tasks, tool-enabled agents that call external APIs, and multimodal agents combining text, vision, and speech. Each sample is preconfigured with environment setups, LLM orchestration, vector search, and telemetry to accelerate prototyping and deployment of robust AI solutions on Azure.

  • Build with ADK

    A CLI toolkit to scaffold, test, and deploy autonomous AI agents with built-in workflows and LLM integrations.
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    What is Build with ADK?
    Build with ADK streamlines the creation of AI agents by providing a CLI scaffolding tool, workflow definitions, LLM integration modules, testing utilities, logging, and deployment support. Developers can initialize agent projects, select AI models, configure prompts, connect external tools or APIs, run local tests, and push their agents to production or container platforms—all with simple commands. The modular architecture allows easy extension with plugins and supports multiple programming languages for maximum flexibility.
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