How to Build a Multiagent AI Research Assistant with OpenAI Agents SDK
This guide explains how to create a multiagent AI research assistant using the OpenAI Agents SDK, the GPT-54 Mini Model, and the Olostep Web API. You will learn how to define a manager agent, integrate callable APIs, and build a web application for generating structured, source-grounded research reports in Python.
Defining the Manager Agent
The manager agent serves as the central coordinator of the multiagent system. It oversees the workflow by delegating tasks to specialist subagents, such as a judge agent for evaluating evidence and an analyst agent for synthesizing findings. This orchestration allows the system to break down complex tasks into manageable steps while ensuring that the end product is accurate and organized.
To define the manager agent, developers configure its decision-making logic. This involves programming it to determine when to call upon subagents or external tools. Using the OpenAI Agents SDK, developers can structure the manager to prioritize tasks, manage dependencies, and ensure that no critical steps are overlooked.
Integrating Olostep Web API
The Olostep Web API enables the research assistant to access live web data. Key components include the Answer Search, SearchWithScrape, and Scrape APIs. These APIs are integrated into the system as callable tools, allowing the assistant to search the web, scrape relevant data, and retrieve answers in real-time.
To integrate the Olostep API, developers must acquire and configure API keys. These keys are loaded into the Python environment using the python-dotenv package. Once set up, the APIs can be called directly within the multiagent workflow to fetch and process live web content.
Building the Multiagent Workflow
Constructing the multiagent workflow involves connecting the manager agent with its subagents and tools. The OpenAI Agents SDK provides a flexible framework for defining these connections. For instance, the manager can instruct the judge agent to evaluate the relevance of data fetched by the analyst agent, ensuring that only high-quality information is included in the research output.
Each agent in the workflow is implemented as a modular component, making it easy to expand or modify the system. Developers can fine-tune the behavior of individual agents by adjusting parameters such as input filters, decision thresholds, and output formatting rules.
Exposing the Assistant as a Web Application
To make the research assistant accessible to users, the system can be deployed as a web application using Reflex. Reflex simplifies the process of building interactive web interfaces, enabling developers to focus on functionality rather than front-end design. The application allows users to input queries, view research outputs, and even export reports as PDF files.
Building the web application requires configuring routes, designing user interfaces, and integrating the multiagent system with the front-end. Reflex offers pre-built components that can be customized to meet specific user needs, ensuring a user-friendly experience.
Setting Up the Development Environment
Before starting development, it is essential to set up a Python environment and install the necessary packages. The primary libraries required are openaiagents, olostep, pydantic, and python-dotenv. These libraries facilitate the creation of the multiagent workflow, data handling, and secure API key management.
Developers can set up the environment by running installation commands for each library. Proper configuration of the environment ensures seamless integration of all components, minimizing potential errors during development. Additionally, using a virtual environment is recommended to maintain project dependencies.
Producing Structured Research Reports
One of the primary objectives of the assistant is to generate structured, source-grounded research reports. To achieve this, the system leverages the strengths of its multiagent architecture. The analyst agent gathers data, the judge agent evaluates its reliability, and the manager agent organizes the findings into a coherent format.
The reports are designed to be easy to read and share, with a focus on clarity and accuracy. Developers can customize the report template using Pydantic models, which provide a clear schema for the output. This ensures that the final product meets the desired standards for both content and presentation.