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There is no single search-depth or recency setting shared by every AI research agent. First identify the surface you use—Responses API, Agents API, Agents SDK, or ChatGPT Deep Research—then configure search effort, eligible sources, and freshness using that integration’s documented controls. OpenAI notes that tool availability, configuration, and call handling depend on the integration (OpenAI tools guide).

Identify the integration before changing settings

“AI research agent” can mean an API tool call or a product interface, and the controls do not map one-to-one. Responses API web search, Agents API web search, ChatGPT Deep Research, and Agents SDK integrations expose different options. Use the documentation for the integration you actually run; do not assume that a field name or behavior transfers between them.

What the controls do on each surface

Surface Search effort and context Source controls Freshness
Responses API web_search search_context_size controls how much search context is made available before generation. Reasoning effort can affect agentic search depth and latency. For GPT-5+ reasoning-model search runs, return_token_budget controls how much search-result content may be returned. filters supports allowed_domains and blocked_domains; the current guide permits up to 100 domains in either list. Web search is described as access to up-to-date information. The guide does not establish a general date-window parameter.
Agents API web_search context_size accepts low, medium, or high; when omitted, it defaults to medium. Optional allowed_domains; omitting it means no domain filter. mode can be live, cached, or disabled. Live is the default when the mode is omitted.
ChatGPT Deep Research Research is agent-driven and extended; you can review and modify the proposed research plan. Restrict research to entered websites/domains, or prioritize those sources while still allowing broader web search. The help material does not establish an explicit date-window field.
Agents SDK Exact controls depend on the SDK and hosted-tool behavior; consult the current SDK and underlying integration documentation. The SDK guide exposes web-search configuration such as allowed domains. Confirm the freshness controls for the specific underlying integration; a shared date-window setting is not established here.

References: Responses API web-search guide, Agents API web-search guide, ChatGPT Deep Research help, and the OpenAI tools guide. Controls and interfaces can change, so check the relevant current reference when implementing.

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Configure an agent in a practical order

  1. Name the integration and the outcome

    Record which API or product surface you are configuring and what the answer must establish. A quick factual lookup and a multi-part comparison have different search needs.

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  2. Choose strict source control or source preference

    Use an allow-list or a restriction when the agent must stay within specified domains. This can exclude useful evidence elsewhere. If you want preferred sources without giving up discovery, use prioritization where supported; ChatGPT Deep Research documents both restricting research to chosen sites and prioritizing them while permitting broader search.

  3. Match search effort to the question

    For straightforward lookups, use quick, non-reasoning retrieval where available. For questions that require comparing sources and synthesizing several issues, use agentic search with reasoning or an extended deep-research workflow. The Responses API guide notes that deeper agentic research may increase latency.

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  4. Set the available context or result budget

    Choose the documented context size or search-context setting for your integration. In a supported Responses API research run, set return_token_budget if you need to control returned search-result content. These settings affect how much retrieved material reaches the model; they do not certify source quality or freshness.

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  5. Select live or cached search when supported

    In the Agents API, choose live for live retrieval, cached for cached retrieval, or disabled to turn that search mode off. This is an access mode, not a universal instruction to return pages within a particular date range. The documentation reviewed does not establish a shared recency-window field.

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  6. Inspect sources and dates

    Check whether the resulting sources include the domains you required and whether broader sources remain available when intended. The Responses API guide describes a sources field that can expose all URLs consulted—often more than the inline citations. For volatile claims, check the publication or update date on the individual page rather than inferring freshness from the search mode alone.

  7. Test with a representative question

    Run a realistic prompt and inspect whether the answer has enough sourced context, includes or excludes the expected domains, and handles dates appropriately. If the result is thin, adjust the documented context or effort controls; if sources are missing, revisit the filter choice. Treat this as a configuration check, not a guarantee of answer quality.

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Keep depth, source choice, and recency separate

  • Search effort is how much reasoning or research work the agent applies; deeper agentic work can take longer.
  • Context or result budget is how much retrieved search material is available to the model or returned in a research run.
  • Source filtering determines which domains may be used or which ones are emphasized, depending on the surface.
  • Freshness mode, where available, selects live, cached, or disabled access; it is not itself a date cutoff.
  • Page dates help you assess whether a particular source supports a time-sensitive claim.

Accordingly, do not look for a universal numeric search_depth field or assume that “live” means “only results from the last N days.” The documented controls vary by integration, and the cited official material does not establish those shared settings.

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