Unverified50% confidenceFactExact time
In the ReAct loop, the LLM first judges whether it can answer directly, and if not, it examines the available tools and decides which one to call.
1
Sources
50%
Confidence
Long-term
Relevance
10/16/2026
First Seen
Sources
Deconstructing ReAct: A Hands-On Guide to Building AI Agents from Scratch
bilibili枫叶边城10/16/2026
Related Entities
Related Claims
VerifiedReAct 智能体的核心是「推理-行动-观察」循环，LLM 分析问题后决定调用工具，观察返回结果，若不满足则继续推理直到给出最终回复78% similarUnverifiedThe core idea of ReAct is to have LLMs perform explicit reasoning before generating actions, and to feed execution results back to the model as observations to form a closed loop.74% similarUnverifiedThe three core elements of a custom ReAct agent are an LLM model (as the reasoning engine), Tools, and an observation loop.73% similarUnverifiedBasic LLM Chain is a one-shot input-to-output pipeline, whereas the Agent node introduces a ReAct reasoning-action loop that can independently decide which tools to call.72% similarUnverifiedThe first step in evaluating an LLM should be to clarify what is to be measured, rather than running the model directly71% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1042397API
curl https://kongchang.com/api/v1/knowledge/claims/1042397MCP
get_claim(id=1042397)