Tubegraph

OpenAIs Agents SDK

2 min read

From: aidotengineer

OpenAI’s Agents SDK is highlighted as a preferred agent framework for building and improving AI agents in a full-stack, serverless environment [00:01:24].

Overview and Preference

The speaker, Brook Riio, chose OpenAI’s Agents SDK from its initial announcement due to its capabilities [00:03:16]. This SDK offers significant “agentic power” for developing AI applications [00:15:41].

Key Features

The OpenAI Agents SDK includes several notable features:

  • Native Tool Calling It supports native tool calling capabilities [00:03:20].
  • One-Shot Multi-Agent Calls It facilitates one-shot multi-agent calls [00:03:22].
  • Built-in Tracing and Eval Hooks These features provide robust observability, allowing users to understand the agent’s behavior [00:03:26].
  • Strong Backing and Model Interchangeability The SDK has strong support from OpenAI, ensuring its longevity and continuous development [00:03:31]. It also allows for interchangeability of models, preventing developers from being locked into a single ecosystem [00:03:37].
  • Python Only Currently, the OpenAI Agents SDK is exclusively available for Python [00:06:23].

Integration in a Full-Stack Architecture

In a recommended full-stack serverless architecture, OpenAI agents run within Python serverless functions [00:06:19]. When deployed to a platform like Vercel, the platform automatically recognizes and hosts these Python functions [00:06:34]. These functions handle AI inference and interact with OpenAI to perform tasks [00:06:41].

Example Application

An example application demonstrated uses AI agents to create a newsletter [00:07:08]. The AI agents are powered by Fast API, which allows for quick assembly and execution [00:12:32]. The example includes:

  • A research agent [00:12:40].
  • A formatting agent [00:12:43].
  • Each agent has its own endpoint in the Fast API application [00:12:45].
  • Vercel can deploy these agents as independent cloud functions with minimal configuration [00:12:49].

The workflow for these agents is orchestrated by Ingest, with each step invoking a specific agent function [00:13:51]:

  1. Call the research agent [00:13:55].
  2. Format the newsletter with another agent [00:13:57].
  3. Save the results to blob storage for the front end [00:14:01].

The results from the initial research agent are type-checked and then passed to the formatting agent [00:14:55]. The formatted content is then type-checked and saved to blob storage [00:15:04].

Graph View

Backlinks