What are the hidden API costs associated with maintaining state in multi-agent conversational frameworks built on top of OpenAI models?
Download run
Fan-out diagram
The searches the AI actually ran for this prompt.
SEED QUERY
What are the hidden API costs associated with maintaining state in multi-agent conversational frameworks built on top of OpenAI models?
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
OpenAI API pricing input cached input output tokens Responses API conversation state storage pricing official documentation Google ↗
OpenAI Agents SDK sessions memory tracing token usage documentation Google ↗
- Observability and usage · developers.openai.com
- Tracing · developers.openai.com
- Usage · openai.github.io
- Pricing · developers.openai.com
- Pricing · developers.openai.com
Queries
2 queries · click a column to sort
| Query / type | Search | Intent | Query class | Routing format | Cluster | Freq ↓ | Weight |
|---|---|---|---|---|---|---|---|
OpenAI API pricing input cached input output tokens Responses API conversation state storage pricing official documentation OBSERVED | Google ↗ AI Mode ↗ | — | Other / navigational | — | — | 1x of 1 | |
OpenAI Agents SDK sessions memory tracing token usage documentation OBSERVED | Google ↗ AI Mode ↗ | — | Other / navigational | — | — | 1x of 1 |