Rethinking search as code generation

Perplexity introduces Search as Code (SaC), an agent-oriented search architecture that replaces fixed, monolithic search pipelines with modular retrieval, ranking, filtering, fan-out, and rendering primitives exposed through a Python-based Agentic Search SDK. Rather than issuing serial function calls and receiving preprocessed result sets, models generate and execute task-specific code in secure sandboxes, enabling asynchronous workflows, thousands of retrieval operations, deterministic processing, and more selective use of context. SaC combines models as planning and control layers, sandboxes for code execution and state management, and an SDK built from atomized search components; Perplexity reports favoring filesystem-based state serialization over persistent REPL environments for long-running tasks. A CVE-advisory case study illustrates how agents can generate targeted vendor searches, adapt coverage strategies, and verify evidence linking vulnerabilities to product fixes. Across five benchmarks, including DSQA, BrowseComp, HLE, WideSearch, and the new WANDR benchmark, Perplexity reports that its SaC implementation outperformed competing evaluated systems on four benchmarks and was effectively tied on HLE, while also showing favorable cost-performance results. The company frames SaC as a hybrid computing model in which language-model reasoning determines research strategies while deterministic code handles parallelism, filtering, aggregation, and other operational tasks.

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