perplexity-research

Real-time web research with citations is performed by running a Python script that queries Perplexity models via OpenRouter. It gathers current information and returns structured outputs with traceable sources, enabling quick fact retrieval and deep competitive analysis.

Export research results to Markdown or JSON by executing the Python script with specific output formatting flags. This allows you to save structured insights, market analyses, and citations in your preferred format for downstream processing.

Yes, this approach supports deep research for competitive analyses by adding a deep research flag to the script execution. It generates comprehensive research briefs and structured product insights with traceable citations.

Yes, you need an OpenRouter account to run Perplexity web searches. The script uses python-dotenv to manage OpenRouter API authentication, which routes requests to Perplexity models for generating search results and citations.

The best way to generate a research brief with traceable sources is to use the deep research mode. This queries Perplexity via OpenRouter to compile current web information into a structured report complete with citations.

Yes, you can control the output language when running deep research queries. The script satisfies requirements for model selection and language control, allowing you to tailor the generated Markdown or JSON research outputs.