Which tools do AI coding agents pick? A benchmark of 96 runs

A Pixeltable co-founder benchmarked four AI engines across 96 responses to eight Python multimodal-application prompts, using three repeats per prompt and engine, to examine which libraries they recommend for storage, vector search, orchestration, serving, and model calls. FastAPI appeared in every response, while Qdrant and pgvector dominated vector-index choices but varied by engine, with ChatGPT Search favoring pgvector and Claude and gpt-5 more often selecting Qdrant; orchestration recommendations were more fragmented among Prefect, Celery, LangGraph, LangChain, and LlamaIndex. Search-grounded engines produced most citations, particularly Perplexity and ChatGPT Search, but cited sources often did not correspond to the tools ultimately recommended, illustrating a distinction between citation visibility and selection. Pixeltable received no mentions, picks, or citations, a finding the author frames as a measure of its current visibility while disclosing their commercial interest in the company. The study emphasizes that its results are a limited baseline because of the small sample, one-day testing window, prompt-specific scope, automatic first-mention parsing, output truncation, inconsistent search behavior, and uncertainty around the consumer ChatGPT model used.

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