How to Build a Multi-Agent Research Assistant in Python

2026-05-21 · Source: MachineLearningMastery.com - Machinelearningmastery.com · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, extended

Summary

This guide details building a multi-agent AI research assistant in Python using the OpenAI Agents SDK, the GPT-5.4 mini model, and the Olostep Web API. The system orchestrates a manager agent, a judge agent, and an analyst agent, integrating live web tools for structured, source-grounded research reports. Key steps include setting up the Python environment with `openai-agents`, `olostep`, `pydantic`, and `python-dotenv`, and configuring API keys for OpenAI and Olostep. The workflow defines structured outputs like `Judgment` and `MarkdownResearchReport`, and creates tools for Olostep's Answer, Search, Search-with-Scrape, and Scrape APIs. The manager agent employs a tiered research strategy, starting with a quick answer, evaluating its quality with the judge agent, and escalating to deeper web searches and scraping if evidence is insufficient, before the analyst agent compiles the final report. The solution also includes tracing for debugging and a Reflex-based web application with PDF export.

Key takeaway

For AI Engineers building agentic research applications, you should adopt a tiered multi-agent architecture to optimize for speed, cost, and accuracy. Implement a manager agent to dynamically escalate research depth based on evidence quality, using a judge agent for validation. This approach ensures efficient resource use for simple queries while enabling thorough investigation for complex topics. Consider integrating web APIs and structured outputs for robust, source-grounded reports.

Key insights

Multi-agent systems can efficiently produce source-grounded research reports by dynamically escalating research depth based on evidence quality.

Principles

• Orchestrate agents for tiered research.
• Validate evidence quality before reporting.
• Integrate external APIs as callable tools.

Method

The workflow starts with a quick answer, then a judge agent evaluates quality. If insufficient, it escalates to search-with-scrape, then targeted searches and URL scraping, before an analyst agent compiles the final report.

In practice

• Implement a manager agent for workflow control.
• Use Pydantic for structured agent outputs.
• Integrate web APIs (e.g., Olostep) for live data.

Topics

• Multi-Agent Systems
• OpenAI Agents SDK
• Olostep Web API
• GPT-5.4 mini
• Research Automation
• Reflex Framework

Code references

• kingabzpro/Multi-Agent-Research-Assistant
• reflex-dev/reflex
• kingabzpro/Multi-Agent-Research-Assistant

Best for: AI Engineer, Machine Learning Engineer, AI Student

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Editorial summary, takeaway, and curation by AIssential. Original article published by MachineLearningMastery.com - Machinelearningmastery.com.