Unverified50% confidenceFactExact time
与 Airflow、Temporal 等工业级工作流引擎不同，LangGraph 专为 LLM 输出的不确定性设计，条件路由可基于模型返回的自然语言内容
1
Sources
50%
Confidence
Long-term
Relevance
7/23/2026
First Seen
Sources
Related Claims
Unverified由于LLM的输出具有不确定性，工作流设计需要考虑提示词的鲁棒性和输出格式的可解析性73% similarUnverifiedLarge language models have limitations in precise numerical reasoning, so actual products often adopt a hybrid architecture in which the LLM understands intent and generates candidate solutions while a structured engine validates constraints.69% similarVerifiedThe output of LLMs is inherently non-deterministic; even with the same Prompt as input, the model may return results with different formats, content, or even logic at different times.68% similarUnverifiedWhen requirement descriptions are vague, LLMs tend to over-engineer and generate complex enterprise-level architectural solutions, stemming from their text-continuation characteristic based on conditional probability rather than engineering judgment.68% similarUnverifiedLLM推理框架正在从单纯的文本生成引擎演进为智能体运行时68% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/598177API
curl https://kongchang.com/api/v1/knowledge/claims/598177MCP
get_claim(id=598177)