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cover of episode 🤖 Guide: How to Build Your First OpenAI Agent
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主持人：我今天深入研究了一个来自AI Unraveled Builder's Toolkit的具体教程，该教程由Etienne Newman提供，他是一位资深软件工程师。了解信息来源很重要，因为这有助于我们更轻松地开展工作。AI Unraveled Builder's Toolkit提供每周更新的教程，包含详细的PDF、音频和视频格式的指导。我们的任务是解析Etienne的教程，展示如何构建第一个OpenAI代理，基于视频中展示的步骤和细节。该教程演示了如何使用OpenAI服务在Google Colab中构建一个简单的AI代理。构建像天气代理这样的功能性工具，突显了代理的核心思想：让AI能够与外部信息或工具互动以完成特定任务。第一步是设置OpenAI API密钥，这是使用OpenAI服务的关键。API密钥就像密码一样，需要保密，以防止他人使用你的密钥访问服务。生成密钥后，视频展示了在Google Colab笔记本中放置密钥的位置，以便代码从一开始就知道使用哪些凭据。在获得凭据后，下一步是确保在编码环境中拥有正确的工具，教程展示了如何安装OpenAI agent库。使用pip install命令可以下载并设置其他人创建的预先编写的代码模块或库，从而简化与OpenAI代理工具的交互。Colab具有内置功能，可以使用AI（如Gemini）来解释代码，帮助理解pip install命令的实际作用。安装库后，下一步是将这些工具导入到当前会话中，并将其与API密钥连接起来。使用import语句使OpenAI agent库中的函数和类可供使用，并初始化库以使用特定的API密钥与OpenAI通信。库已导入且密钥已初始化，意味着代理构建工具已加载并已通过您的帐户进行身份验证，舞台已准备就绪。教程的核心是创建代理实例本身，定义代理要执行的魔法。教程重点介绍了构建天气代理，该代理能够获取并告知您任何城市的天气。教程的最后一步是测试代理的功能，但出现了一个错误消息，提示密钥有问题。即使遵循了步骤，实际问题也可能出现，有时是帐户问题或连接问题。Etienne解释说，最可能的原因是他的OpenAI帐户信用额度已用完，这表明开发并非总是线性的，故障排除是常见的部分。尽管密钥错误被标记，但视频显示代理仍然设法提供了纽约市的天气答案。即使存在密钥警告，代理仍然能够为其他城市（如巴黎和卡尔加里）提供结果，这表明核心代理逻辑可能工作正常，但API连接或信用状态检查存在一些问题。构建第一个代理的过程可以分解为四个主要步骤：设置凭据、准备编码环境、创建代理实例并测试其功能。通过遵循视频中显示的具体示例，包括关键的故障排除时刻，可以更直接地理解在实践中这些基础步骤的实际样子。遇到API密钥问题等错误并不是失败，而是构建过程的基本组成部分，通常也很有启发性。教程不仅展示了完美的路径，还展示了当事情没有完全按计划进行时，人们如何React，这对于将理论知识转化为实际工作应用程序至关重要。

Deep Dive

Chapters
This chapter details the process of setting up your OpenAI API keys within Google Colab, emphasizing the importance of keeping these keys private and secure. It covers accessing API keys from your OpenAI account settings, creating a new key, and then correctly integrating it into your Google Colab notebook.
  • Obtain OpenAI API key from account settings
  • Treat API keys as passwords; maintain privacy
  • Integrate API key into Google Colab notebook

Shownotes Transcript

Building a basic AI agent using OpenAI's platform involves several key steps. First, developers need to clearly define the agent's objective and select an appropriate OpenAI model (such as GPT-4o, o3-mini, or GPT-4.1) based on the complexity of the task and desired latency. After setting up the development environment with an OpenAI API key, clear instructional prompts are crafted to define the agent's behavior, role, and response style. For more advanced functionalities, agents can be equipped with tools like web search, file search, or the ability to call external functions (APIs). Frameworks like OpenAI's Agents SDK or libraries such as LangChain can then be used for orchestrating multi-step tasks, managing memory, and integrating the agent with other applications, followed by thorough testing and iteration.

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**STEP BY STEP: **

Go to Google Colab and install OpenAI agents with pip install openai-agents

Get your API key from OpenAI’s platform and add some credits to your account

Import libraries and create your agent with a model (e.g., gpt-4o or o3-mini), instructions, and web search tool

Run your agent and print the results

**What this means: **OpenAI is providing increasingly powerful and accessible tools and APIs that simplify the process for developers to create custom AI agents. This empowers builders of varying skill levels to design specialized AI solutions capable of performing complex, autonomous tasks across a wide range of applications, from simple automation to more sophisticated agentic workflows.