Real ChatGPT answer · index seedfc80lL3gLDUcompleteB2B software · AI and automation toolsinformational

I am building a capstone project and need to know the current best practices for setting up LangChain agents to use tools without getting stuck in infinite loops.

ChatGPTgpt-62 queries10/31/2026
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Fan-out diagram

The searches the AI actually ran for this prompt.

SEED QUERY
I am building a capstone project and need to know the current best practices for setting up LangChain agents to use tools without getting stuck in infinite loops.
2 real searches · 1 real-search pass
LangChain agents documentation current create_agent recursion limit tool calling loops LangGraph 2026Google ↗
OBSERVEDReason
LangGraph documentation recursion limit step limit tool node error handling remaining steps agent loopGoogle ↗
OBSERVEDInstruction

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.

ChatGPT real searches (2)
Short factOther / navigationalComparisonConsequenceReason50%DefinitionInstruction50%Yes / no

Classes come from the query-classifier (a local Laya model). 2 of 2 calls are low-confidence (under 50%) — hover a query in the table to see its confidence.

Searches and sources

ChatGPT2 real searches · gpt-6
LangChain agents documentation current create_agent recursion limit tool calling loops LangGraph 2026 Google ↗
LangGraph documentation recursion limit step limit tool node error handling remaining steps agent loop Google ↗

Queries

2 queries · click a column to sort
Query / typeSearchIntentQuery classRouting formatClusterFreq ↓Weight
LangChain agents documentation current create_agent recursion limit tool calling loops LangGraph 2026
OBSERVED
Google ↗
AI Mode ↗
—Reason——1x
of 1
LangGraph documentation recursion limit step limit tool node error handling remaining steps agent loop
OBSERVED
Google ↗
AI Mode ↗
—Instruction——1x
of 1