Understanding ReAct: The Reason‑Act Loop Behind LLM Agents

ReAct combines the words Reason and Act . When an LLM agent tackles a task, it first checks whether the current information is sufficient; if not, it invokes a tool, receives the result, and then continues reasoning until the task is completed.

Compared with ordinary question‑answering, a ReAct agent actively reasons when information is lacking and calls external tools (search, database, API, etc.) to supplement the needed data, whereas a standard model would simply generate an answer from its training knowledge.

The complete ReAct process consists of five steps:

Observe : Identify the current task and assess if existing information is enough.

Reason : If information is insufficient, the model determines what is missing and which tool to use.

Act : Execute the chosen tool, such as a web search, database query, or API call.

Feedback : Read the tool’s output and check whether the task is solved.

Adjust : If the task is still incomplete, use the new information to start another ReAct cycle.

ReAct is valuable because real‑world tasks rarely finish in a single step; the loop lets agents handle complex, multi‑step problems, leverage tools, and adapt strategies based on changing environment information, mirroring human decision‑making.

For example, when asked “Will it rain in Shanghai today? Should I bring an umbrella?”, a plain QA model would fabricate an answer. In ReAct mode, the agent recognizes the need for real‑time data, calls a weather API, obtains precipitation probability and timing, and then gives a concrete recommendation—bringing an umbrella if rain is likely, or further querying if the API response is incomplete.