How should I structure human in the loop review gates inside a LangGraph state machine to handle low-confidence extraction outputs safely?
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Fan-out diagram
The searches the AI actually ran for this prompt.
SEED QUERY
How should I structure human in the loop review gates inside a LangGraph state machine to handle low-confidence extraction outputs safely?
1 real search · 1 real-search pass
LangGraph human in the loop interrupt Command resume checkpointer official docsGoogle ↗
OBSERVED
Badge and edge weight = share of passes that produced the query. Hover a query for its intent and routing format.
Query classes
Google's semantic query class for every sub-query: what kind of answer each search is after, simulated vs real.
Classifying 1 queries…
Searches and sources
ChatGPT1 real searches · gpt-6
LangGraph human in the loop interrupt Command resume checkpointer official docs Google ↗
- docs/src/oss/langgraph/interrupts.mdx at main · langchain-ai/docs · github.com
- Interrupts · langchain-ai.github.io
- langgraphjs/docs/docs/concepts/human_in_the_loop.md at main · langchain-ai/langgraphjs · github.com