ReAct (Reason + Act)
Also known as: Reason and Act, ReAct pattern
Agent loop pattern: Thought → Action → Observation → Thought → … until the task is done.
An agent prompting pattern that interleaves reasoning (Thought) with tool calls (Action) and observations (Observation) in a loop. Predecessor to most modern function-calling agent loops.
In practice
The classical agent loop everyone reinvents. Knowing it makes function calling, tool use, and agent frameworks click instantly.
How it compares
ReAct is one specific reasoning pattern for agents; agents is the broader category of LLM-driven systems.
Comparisons that include ReAct (Reason + Act)
Related topics
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Related terms
Agent Loop
The runtime: LLM call → tool call → observation → LLM call → … until a final answer or step cap.
AI Agents
LLMs that loop: plan → call tools → observe results → repeat until done.
Chain-of-Thought (CoT)
Ask the model to think step by step before answering. It boosts accuracy on reasoning tasks.
Context Rot
Long-running chats degrade: early instructions get forgotten, and tool calls become less reliable.
Function Calling
The model emits structured JSON specifying a tool call instead of free text; the host executes it.
In-Context Learning (ICL)
Show the model a few examples in the prompt and it learns the pattern, no fine-tuning needed.