Perplexity AI Releases WANDR: An Open Benchmark for Research Agents That Search Wide and Deep

Perplexity AI has open-sourced WANDR, a benchmark specifically designed to evaluate research agents on tasks that require both broad topic coverage (wide search) and multi-hop deep investigation (deep search). Existing agent benchmarks have struggled to capture the dual requirement of breadth and depth that characterizes real research workflows, making WANDR a timely contribution to evaluation infrastructure. For developers building or evaluating research agents, RAG pipelines, or multi-step search systems, WANDR provides a standardized way to compare approaches and identify where agents fall short on complex queries. The open nature of the benchmark means teams can run it against their own systems without sending data to a third party. This is the kind of evaluation tooling the agentic AI space has badly needed, and Perplexity's domain expertise in search makes them a credible author for it.