AI Agents Explained: What Is a ReAct Loop and How Does It Work?

2026-07-03 · Source: Towards Data Science · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Intermediate, medium

Summary

The ReAct loop is a mechanism for AI agents to perform multi-step tasks by iteratively reasoning, acting, and observing. Unlike basic parallel tool calling, which executes independent functions concurrently, ReAct allows an AI model to use the result of one tool call to dynamically decide subsequent actions. This is crucial for tasks where later steps are conditional on earlier outcomes, such as determining if a currency conversion is needed only after checking weather conditions. The article demonstrates this with Python code using `get_current_weather` and `convert_currency` functions, leveraging Open-Meteo and Frankfurter APIs, and an OpenAI `gpt-4o-mini` model. The core loop involves sending the full conversation history to the model, executing requested tools, and feeding observations back as new context until a final text response is generated, with a `max_iterations` cap to prevent indefinite looping and manage costs.

Key takeaway

For AI Engineers designing agentic workflows, if your tasks involve conditional logic or arguments dependent on prior tool outputs, you should implement a ReAct loop. This approach, demonstrated with `gpt-4o-mini`, prevents unnecessary API calls and reduces costs compared to parallel tool calling, which executes all functions upfront. Consider setting a `max_iterations` cap to manage potential indefinite loops and control expenses.

Key insights

ReAct loops empower AI agents to handle complex, conditional multi-step tasks via iterative reasoning, action, and observation.

Principles

• Tool calls can be dynamically chained based on prior results.
• Conditional task execution reduces unnecessary API calls and costs.
• Agents adapt plans based on real-time observations.

Method

Implement a `for` loop that sends the full conversation to the model, executes requested tools, and appends tool results as observations, repeating until a text response is generated.

In practice

• Use `gpt-4o-mini` for ReAct loop demonstrations.
• Integrate Open-Meteo for weather data.
• Integrate Frankfurter for currency conversion.

Topics

• AI Agents
• ReAct Loop
• Tool Calling
• Multi-step Reasoning
• OpenAI GPT-4o-mini
• API Integration

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

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Editorial summary, takeaway, and curation by AIssential. Original article published by Towards Data Science.