web-research

Cited web research is generated by synthesizing search results into a structured markdown answer with inline citations. The process uses the OpenAI Responses API to perform web searches and optionally applies code-interpreter-assisted analysis for data tasks.

Yes, code interpreter can be used alongside web search for exploratory research. This combination allows the AI agent to fetch up-to-date web-backed facts and perform complex analysis, which is then synthesized into a markdown response.

Fast, normal, and deep research modes are configurable depth tiers that control how exhaustive the research process is. Fast mode handles quick factual lookups, while normal and deep modes execute multi-step exploratory research for more comprehensive results.

AI web research results are saved and reused automatically through an automation-friendly CLI workflow. This workflow persists results as markdown files with YAML frontmatter, allowing for consistent execution of repeated research tasks without manual saving.

Yes, OpenAI and python-dotenv are required dependencies to run automated web research tasks. The OpenAI library powers the Responses API for web search and code interpreter, while python-dotenv manages environment variables for API access.

The best way to automate multi-step web research and synthesize conflicting information is using a tool-based AI workflow with configurable depth modes. It eliminates manual searching by automatically synthesizing multiple sources into a clean, markdown-ready output with a sources list for verification.