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Consensus accelerates research with GPT-5 and Responses API
Builders can learn how to leverage GPT-5 and the Responses API to create their own multi-agent research assistants, automating evidence gathering and synthesis for domain-specific tasks.
What happened
Consensus, a research platform, has integrated GPT-5 and OpenAI's Responses API to power a multi-agent assistant that can read, analyze, and synthesize scientific evidence rapidly. According to the OpenAI Blog, this enables over 8 million researchers to accelerate discovery. The practical angle for builders: this demonstrates how to combine large language models with a multi-agent architecture to automate complex research tasks, from retrieving papers to summarizing findings. For developers building AI workflows, this case study shows the potential of the Responses API for orchestrating specialized agents that handle different parts of a research pipeline—like searching, extracting, and cross-referencing data. It also highlights the importance of grounding outputs in real evidence to maintain credibility. The move reflects a broader trend of AI moving from general-purpose chat to domain-specific, tool-augmented agents.
Key takeaways
- Consensus integrated GPT-5 and OpenAI's Responses API to create a multi-agent research assistant.
- The tool can read, analyze, and synthesize evidence in minutes.
- Over 8 million researchers use Consensus to accelerate discovery.
- The approach uses multiple specialized agents working in coordination.
- The Responses API enables orchestration of these agents for complex workflows.
Why it matters
Builders can learn how to leverage GPT-5 and the Responses API to create their own multi-agent research assistants, automating evidence gathering and synthesis for domain-specific tasks.
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