ReAct
Reasoning + Acting
In one line
ReAct (Reasoning + Acting) is the classic agent pattern where an LLM loops through Thought, Action and Observation steps — reasoning out loud and calling tools as it goes.
Going deeper
ReAct came out of a 2022 paper by Yao et al. (Princeton + Google Brain, arXiv 2210.03629). Instead of producing one big answer, the LLM cycles through Thought, Action and Observation — reasoning, calling a tool, looking at the result, and feeding it back into the next thought.
It matters because almost every modern agent framework — LangChain, OpenAI Assistants, Claude's tool-use loop — is a variation or extension of ReAct.
The limits are real. Long loops accumulate hallucinations and bad tool calls, and tokens add up fast. In production teams usually wrap ReAct with planner and critic components rather than running it raw.
Sources
Related terms
AI Agent
An AI agent is an LLM-driven system that takes a goal, plans the steps, calls the tools it needs and runs the task end-to-end with limited human input.
AI AgentTool Use
Tool use is an LLM calling external APIs, calculators or search systems directly to ground its answers — the foundational behaviour of every agent.
AI AgentPlanning Agent
A planning agent is an agent that first drafts a multi-step plan for a goal and then executes it step by step, calling tools as needed.
AI AgentLangChain
LangChain is the most widely used open-source framework for building LLM apps and agents, bundling prompts, tools, memory and execution flow into composable modules.
LLMPrompt Engineering
Prompt engineering is the practice of crafting inputs that steer an LLM toward better outputs — a way to dramatically change result quality without retraining the model.
AI AgentPrompt Chaining
Prompt chaining is the simplest agent pattern — a sequence of prompts where each step's output feeds the next step's input.
AI AgentReflexion
Reflexion is an agent pattern where the agent reviews its own previous attempt, writes a critique, and uses that critique to do better on the next try.
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