A ReAct agent is an AI agent that alternates between reasoning, using tools, and evaluating results to complete a task. It uses a large language model to choose the next action, such as searching documentation, querying a database, or running code. Unlike a single-prompt chatbot, it can revise its approach after receiving real-world tool output.
ReAct agent
What Is a ReAct Agent?
A ReAct agent is an AI agent that alternates between reasoning, using tools, and evaluating results to complete a task. ReAct means “reasoning and acting”: a large language model decides what to do next, such as searching documentation, querying a database, or running code, then uses the result to guide its next step. The pattern was introduced in LLM agent research on ReAct. It is not React, the JavaScript library for building user interfaces.
How the ReAct Loop Works
The agent repeats a small decision loop until it has enough evidence to finish safely.
- It interprets the goal and identifies the next useful check.
- It selects an available tool and performs one action, such as reading a test log.
- It examines the observation returned by that tool.
- It updates its approach, repeats the loop if needed, or returns a final answer.
For example, a coding agent can inspect a failed test, read the relevant framework documentation, change the suspected code path, rerun the test, and report whether the fix worked.
ReAct Agent vs. LLM Agent and Function Calling
These terms overlap, but they describe different levels of agent behavior.
| Approach | Planning and tools | Best fit |
|---|---|---|
| LLM agent | Broad category. It may use tools and planning in many ways. | Any software agent built around an LLM. |
| ReAct agent | Selects tools iteratively after each observation, creating a trace of actions. | Debugging, research, and changing problems. |
| Direct function calling | Calls a defined function, often with a predictable flow and limited iteration. | Simple, bounded actions such as creating a ticket. |
Benefits of ReAct Agents
ReAct is useful when a task cannot be reliably solved in one prompt.
- It adapts when a tool result disproves an early assumption.
- It can use current data from approved APIs, repositories, or databases.
- Tool logs make execution easier to inspect than a one-shot response.
- It can combine short-term context with agent memory when a task spans multiple steps.
Practical Limits and Guardrails
A tool-using agent needs engineering controls, not just a good prompt.
- Models can choose an unsuitable tool, misread output, or loop without reaching a useful result.
- Retrieved web pages and documents can contain prompt injection instructions.
- Repeated tool calls increase latency and make cost less predictable.
- Use allowlisted tools, structured inputs, least-privilege permissions, validation, step and budget limits, and observable logs.
When to Use a ReAct Agent in Software Development
Use a ReAct agent for debugging, repository research, integration troubleshooting, and guided coding workflows where each result affects the next decision. Prefer deterministic workflows or direct function calls when the path, inputs, and permitted action are already known. ReAct-style tool use is central to agentic coding, and Emergent’s autonomous coding agents apply these principles to help build, test, integrate, and deploy applications. For sound implementations, follow the principles of building AI agents.
Frequently Asked Questions
Your Questions, Answered
This will automatically populate, don't change
Don't change this element unless you know what you are doing
What is the difference between a ReAct agent and React?
A ReAct agent is an AI software pattern for reasoning and tool use. React is a JavaScript library used to build web user interfaces. The similar names refer to unrelated technologies.
What are the key differences between an LLM agent and a ReAct agent?
An LLM agent is a broad term for software that uses a language model to pursue a goal. A ReAct agent is a specific type of LLM agent that follows an iterative reasoning, action, and observation loop.
How do you build a ReAct agent?
Define a clear task, provide a small allowlisted set of tools, instruct the model to choose one next action at a time, feed each tool result back into the agent, and enforce stopping, permission, and validation rules. Test it with expected failures as well as successful tasks.
When should you use a ReAct agent instead of function calling?
Use ReAct when the agent must investigate, adapt, or make several evidence-based decisions. Use direct function calling when one known function can complete a bounded request safely and predictably.
Can you build an AI agent without coding?
Yes. No-code and agent-building platforms can provide tool connections, prompts, and workflow controls. However, reliable production agents still need careful permissions, testing, monitoring, and clear boundaries for every action.
Start Building
on Emergent today
on Emergent today
Start Building
