I am stuck trying to set up LangChain agents for our customer support desk and need a working code example right now.
Download run
Fan-out diagram
The searches the AI actually ran for this prompt.
SEED QUERY
I am stuck trying to set up LangChain agents for our customer support desk and need a working code example right now.
2 real searches · 1 real-search pass
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)
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 Python agents create_agent current documentation tools middleware 2026 Google ↗
LangChain OpenAI ChatOpenAI package documentation current Python Google ↗
- create_agent | langchain · reference.langchain.com
- What's new in LangChain v1 · langchaindocs.sitemirror.store
- LangChain Agents · docs.langchain.com
- ChatOpenAI integration · docs.langchain.com
Queries
2 queries · click a column to sort
| Query / type | Search | Intent | Query class | Routing format | Cluster | Freq ↓ | Weight |
|---|---|---|---|---|---|---|---|
LangChain Python agents create_agent current documentation tools middleware 2026 OBSERVED | Google ↗ AI Mode ↗ | — | Other / navigational | — | — | 1x of 1 | |
LangChain OpenAI ChatOpenAI package documentation current Python OBSERVED | Google ↗ AI Mode ↗ | — | Other / navigational | — | — | 1x of 1 |