What is a deep research API, and how do you build with one?

Exa describes deep research APIs as systems that plan multi-step web investigations, search and evaluate sources, and produce cited reports or structured outputs for uses such as competitive analysis, due diligence, literature reviews, and technical comparisons. It distinguishes consumer research tools from APIs and self-hosted open-source options, comparing providers including Google, OpenAI, Perplexity, and Exa on benchmarks, pricing models, citation granularity, effort controls, depth, latency, and search data sources. Exa’s offerings include deep search, a synchronous retrieval API with configurable depth, query expansion, freshness controls, and optional structured cited answers, and the asynchronous Exa Agent, which independently performs the research loop and returns JSON validated against a supplied schema with field-level citations. The article presents Exa benchmark results and fixed- or capped-cost Agent tiers, while noting that other providers may bill by task, tokens, or search calls. It also provides Python examples for using both products to research managed vector-database pricing and concludes that deep search suits faster, application-controlled workflows, whereas the Agent is intended for more complex, multi-source verification tasks that can tolerate longer processing times.

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