Glossary
ReAct
An agent loop where the model alternates between reasoning steps (thought) and acting steps (tool call), explicitly interleaving free-form deliberation with structured tool use.
The pattern that proposed combining chain of thought with tool use
in one explicit loop. The model emits a Thought (free-text reasoning),
then an Action (a tool call), gets an Observation (the tool result), and
repeats. The thought step is what distinguishes ReAct from pure tool
calling: it gives the model a place to plan, hypothesize, and self-
correct between actions.
Most modern agent frameworks implement some descendant of ReAct. LangChainagentsThe earliest widely-adopted LLM agent and RAG orchestration framework (2022), now with the LangGraph extension for stateful multi-step agent workflows. Open full entry ’s classic agent, AutoGenagentsA Microsoft Research framework for multi-agent systems, with a conversation-pattern API for orchestrating multiple specialized agents to solve tasks collaboratively. Open full entry ’s assistant pattern, and the research agents at Anthropic and OpenAI all do thought-action- observation loops in different packaging.
Whether the thought-action labels need to be explicit in the output depends on the model. Reasoning models (o1, R1) internalize the thought step into their hidden chain-of-thought; non-reasoning models often benefit from the explicit ReAct prompting scaffold to keep the loop coherent.