Consensus accelerates research with GPT-5 and Responses API

Every year, millions of new scientific papers are published—far more than any one person can read. 

For scientists, the challenge isn’t access to knowledge but the overwhelming task of finding, interpreting, and connecting it. Breakthroughs happen at the edge of what’s known, yet researchers spend most of their time just finding the edges instead of pushing past them.

Consensus⁠(opens in a new window), a research assistant used by more than 8 million people, was built to change that. Founded by Christian Salem and Eric Olson, the platform searches, reads, and synthesizes peer-reviewed literature across more than 220 million papers. Its newest capability, Scholar Agent, is a multi-agent system built on GPT‑5 and the Responses API. It mirrors how researchers actually work, helping them get from question to conclusion in minutes instead of weeks.

But the goal isn’t just faster research—it’s a faster path to discovery. “Science advances when it’s more accessible,” Salem says. “Our job is to give researchers everywhere the ability to find, trust, and act on evidence.”

From search engine to agentic assistant

The first version of Consensus worked like a vertical search engine for science: it indexed academic papers, retrieved relevant results, and generated summaries grounded in citations. But search alone wasn’t enough. 

“Research isn’t just finding papers,” Salem says. “It’s interpreting results, comparing findings, and connecting ideas. The more time scientists spend searching, reading, and interpreting past knowledge for the right study, the less time they have to discover and createdo real research.”

So the team began re-architecting Consensus around a new concept: a multi-agent system called “Scholar Agent” that works the way a human researcher does.

Built on GPT‑5 and the Responses API, the system now runs a coordinated workflow of agents: