Exa Agent Collapses Web Research Into One API Call at Half the Cost

Web research has always been one of the messiest parts of building AI pipelines. You either pay top-dollar to run a frontier model with browsing tools, or you stitch together a fragile chain of search calls and hope the context doesn't explode. Exa Agent is a new API endpoint that tries to collapse that entire workflow into a single async call, and it does it at a price point that undercuts the obvious alternatives by more than half.

One endpoint, any research task

Exa Agent combines top language models with Exa's state-of-the-art web search tools to achieve exhaustive and accurate results, now used in a variety of agentic products. The endpoint is called /agent, and it is available in the API today.

Exa Agent is highly effective on deep research, list-building, and entity enrichment tasks. The practical range is wide: from enriching a CRM list of 500 companies to running a multi-hop literature review that requires chaining dozens of searches together.

The architecture behind the cost savings

The cost story is the headline, but the more interesting part is how Exa gets there. Two mechanisms do most of the work.

• Model fusion: When working with large datasets, Exa Agent divides the task into many subtasks and assigns subagents to research various domains at once. When researching, it uses a fusion of frontier and cost-effective models to find the most cost-effective methodology for the given research task.
• Token-efficient highlights: Highlights are snippets from a page that now offer higher quality results for ~94% fewer tokens on some evals, significantly reducing costs and leading to latency benefits. In practice, on benchmarks like SimpleQA, 500 characters of Exa's highlights match the accuracy of the first 8,000 characters of the page, and use 16x fewer tokens.

This is especially important in agentic search use cases, where doing multiple rounds of search is the norm and reducing context bloat is critical. The highlights model runs per-request (not cached) and completes in under 100ms.