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What a web-search API does—and what it does not
A search API accepts a query and returns information about matching public-web pages in a machine-readable response. An agent can use those results to find sources, decide whether it needs more evidence, and produce an answer linked to the pages it used. Depending on the provider and product, the returned material may be search-result snippets, citation annotations, or context intended for language models.
Search is a retrieval step, not a guarantee that an answer is correct. Search ranking can surface weak or outdated pages; snippets can omit important qualifications; and a citation is useful only if it supports the claim attached to it. Your application remains responsible for deciding which sources to trust and checking that the final answer is grounded in them.
- Search API: discovers pages and returns results or context.
- Browser automation: opens pages and interacts with rendered sites; it is useful when a task depends on page behavior or content not returned by search.
- Crawler: fetches pages according to a broader collection or indexing process.
- Vector database: retrieves from material already ingested into a collection; by itself, it does not discover current public-web pages.
- Model browsing tool: may let a model orchestrate web retrieval within a hosted model workflow rather than exposing only a conventional search-results call.
These can be combined. For example, an agent might search for relevant pages, fetch a selected page when snippets are insufficient, and use a vector store for the organization’s internal documents.
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How the main options differ
The available products are not all the same kind of integration. OpenAI documents web search as a tool for its Responses API, alongside legacy Chat Completions search models. Microsoft’s material covers both the Bing Web Search API v7 request/response interface and a separate Microsoft Foundry agent tool using Grounding with Bing Search or Bing Custom Search. Brave presents a developer-facing Search API backed by its independently maintained index.
| Option | What the provider documents | Useful distinction | What to verify before choosing |
|---|---|---|---|
OpenAI Responses API with web_search |
Model access to up-to-date web information, agentic search controls, domain filtering, and URL citation annotations. The guide distinguishes fast non-reasoning search, agentic search managed by reasoning models, and deep-research workflows. | Search is integrated as a model tool; the guide recommends Responses API for new integrations and documents Chat Completions search models for legacy integrations. | Which workflow and controls your account and application need; current availability, commercial terms, and output behavior. |
| Microsoft Bing Web Search API v7 | Reference material for its endpoint, parameters, headers, and JSON response objects. Microsoft describes Bing as safe, ad-free, and location-aware across billions of web documents. | A documented search API request/response interface. | Current availability and purchasing path for your region and account, plus the exact response fields and limits that apply. |
| Microsoft Foundry web-search tool | A real-time public-web retrieval tool for agents that can return inline citations and uses Grounding with Bing Search or Bing Custom Search. | An agent-oriented Foundry tool, distinct from treating the legacy Bing API as the only Microsoft search option. | Which grounding product, Azure setup, and current availability apply to your intended deployment. |
| Brave Search API | A developer API backed by Brave’s independently maintained index. Brave’s product page reports over 30 billion indexed pages and over 100 million page updates each day, figures reported by Brave and accessed September 29, 2026. | Its documentation includes freshness filtering and an LLM Context endpoint designed for machine consumption. | Whether its result format, freshness controls, geography, terms, limits, and cost suit your workload. Brave’s index figures are not an independent relevance benchmark. |
Provider descriptions establish capabilities, not a universal quality ranking. The cited product materials do not establish an independent head-to-head benchmark, nor should one infer that index size alone predicts the best results for a particular query set. Prices, quotas, endpoint availability, and partner terms can change; check current provider documentation and commercial pages before procurement.
Choose based on your agent’s job
Start with the decision the agent needs to make, not a feature checklist. A support assistant restricted to a small set of approved domains has different requirements from a local discovery agent, a multilingual research workflow, or a system that needs machine-oriented context rather than snippets.
Rank #2
- Source control and citations: determine whether you need inline citations, URL annotations, domain allow/block filters, or just URLs and snippets that your application will cite itself. Verify that the returned citation can be connected to the claim in the final answer.
- Freshness: decide whether the task needs very recent material. Check whether the API supports a recency or freshness filter and how it behaves; do not assume that a recent-looking result or crawl implies a reliable publication date.
- Geography and language: test representative local and multilingual queries. Confirm which region, language, and safe-search controls are supported by the specific endpoint or tool you plan to use.
- Content depth: snippets may be enough for discovery, but not for resolving a nuanced claim. Check whether the product returns extracted context or whether you must fetch and parse pages separately.
- Runtime and ecosystem: consider whether you need a model-managed tool, a direct HTTP API, a particular cloud environment, or a stable JSON response that your own orchestration can process.
- Privacy and terms: review the current data handling, retention, and usage terms for the product and account type. Do not put secrets or private user data in search queries unless your policies and provider terms allow it.
- Economics and capacity: compare current request or token pricing, quotas, concurrency, rate limits, and overage behavior for your expected workload. The available provider descriptions do not establish comparable current prices or quotas.
A safe integration pattern
- Define acceptable evidence. Specify which sources count as authoritative for the task and which query types require current information, local results, or a human escalation.
- Call the provider from a trusted server. Keep API credentials in server-side secret storage. Do not expose a provider key in browser code, public mobile apps, prompts, or logs.
- Constrain and shape the query. Apply domain, freshness, language, region, or safe-search controls when available and relevant. Avoid sending unnecessary personal or confidential information.
- Normalize and preserve provenance. Store the title, canonical URL, snippet or returned context, publisher when available, and retrieval timestamp. Keep provider metadata that helps identify the source and result.
- Give the model evidence, not just an instruction to browse. Require it to answer from retrieved material, distinguish evidence from inference, and attach citations to the URLs that support each material claim. If the evidence is missing or contradictory, the agent should say so rather than fill the gap.
- Make the call resilient. Set a timeout, handle rate limits with backoff, retry only appropriate transient failures, cache results where freshness and policy permit, and remove duplicate URLs before synthesis.
- Evaluate on a fixed query set. Score whether results answer the information need, whether sources are authoritative, how quickly fresh pages appear, whether citations are correct, and the latency and cost under your own configuration.
When comparing providers, run the same representative queries with the same region, language, freshness settings, and evaluation rules. Include time-sensitive, multilingual, local, and adversarial queries. Record the API version, region, retrieval timestamp, and configuration so a later change in results can be interpreted rather than mistaken for a stable provider difference.
The Tool Desk
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For a new OpenAI integration, the documented approach is to use the Responses API with the web_search tool. The following cURL request illustrates a server-side call and asks the model to search for current public information. Set OPENAI_API_KEY in your shell or secret manager before running it. Inspect the actual response structure for the text and URL citation annotations rather than assuming a citation is plain text.
curl https://api.openai.com/v1/responses
-H "Authorization: Bearer $OPENAI_API_KEY"
-H "Content-Type: application/json"
-d '{
"model": "gpt-4.1",
"tools": [{ "type": "web_search" }],
"input": "Find current official guidance on API key security. Summarize the relevant guidance and cite the source URLs."
}'This example is a starting point, not a substitute for checking the provider’s current API reference for model availability, supported options, domain controls, and the precise response shape for your account. In a production agent, validate citation annotations and persist their URLs alongside the generated claims. If you instead use Bing Web Search API v7, implement against that endpoint’s documented parameters, headers, and response objects; if you choose Microsoft Foundry grounding, treat its agent-tool configuration as its own integration path. For Brave, check the current API documentation for freshness controls and the LLM Context endpoint parameters before building against them.
Rank #3
Evaluate retrieval quality, not just attractive results
Build an evaluation set from the kinds of questions your agent will actually receive. Include questions with known authoritative answers, questions where no good public source exists, and questions that tempt the system to accept a misleading result. Keep the set small enough to inspect by hand, but diverse enough to expose failures that a simple general query will not show.
- Relevance: do the top results address the user’s specific information need, rather than merely sharing keywords?
- Authority: are primary or otherwise trustworthy sources present when the question calls for them?
- Freshness: does a time-sensitive answer surface current material, and can the agent distinguish publication date from retrieval time?
- Citation correctness: does each material answer claim have a URL that actually supports it?
- Coverage and disagreement: can the agent find corroboration or identify when reliable sources conflict?
- Operational behavior: how do latency, rate limits, retries, and cost behave under expected concurrency?
Save examples of both successes and failures. When a provider, model, region, or query setting changes, rerun the set. That gives you a meaningful basis for a choice without treating a provider’s product-page claims as an independent benchmark.
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Reliability, cost, and failure handling
Search is an external dependency, so the agent should have a defined response to missing, delayed, or unusable results. A timeout should not silently become an uncited answer. A rate-limit response should trigger bounded backoff rather than an unbounded retry loop. Cache only when the freshness requirement and provider terms permit it, and use a cache key that accounts for relevant query controls such as region or language.
Rank #4
Track operational metrics separately from answer quality: request success and timeout rates, latency, rate-limit events, cache hits, and spend. Measure answer quality with your evaluation set and citation review. A fast response is not useful if the sources are irrelevant; a plausible answer is not adequately grounded if its links do not support it.
Before release, decide how the agent behaves when it cannot retrieve evidence: ask the user to narrow the question, report that it cannot verify a current answer, or use an approved fallback. Never silently substitute model memory for a failed live search if the application promises current web-grounded answers.
Or skip the browser setup
If the job is to capture a page image or PDF rather than discover web pages, ScreenshotNeo is the adjacent alternative to try first—not a web-search API. It accepts a URL in one GET request and returns a screenshot or PDF. The call below saves a WebP capture; API details are in the ScreenshotNeo documentation.
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curl -G "https://api.screenshotneo.com/v1/shot" -d access_key=YOUR_API_KEY --data-urlencode url=https://stripe.com -o shot.webpimport requests
r = requests.get("https://api.screenshotneo.com/v1/shot", params={"access_key": "YOUR_API_KEY", "url": "https://stripe.com"}, timeout=90)
open("shot.webp", "wb").write(r.content)const q = new URLSearchParams({ access_key: 'YOUR_API_KEY', url: 'https://stripe.com' });
const res = await fetch(`https://api.screenshotneo.com/v1/shot?${q}`);ScreenshotNeo removes cookie banners, newsletter popups, and chat widgets before a capture; bot checks, blank pages, and failed loads are never billed. Its MCP server lets AI agents take screenshots, and the free plan includes 1,000 screenshots a month with no card; paid plans start at $5 for 3,000. See ScreenshotNeo for the service, then sign up free for 1,000 screenshots a month with no card.
Frequently Asked Questions
Can an agent rely on a search result snippet as its evidence?
Treat snippets as discovery aids unless your evaluation shows they contain enough context for the task. For consequential claims, inspect the linked source or request fuller context.
Should I always make multiple search calls for every question?
No. Use additional retrieval when the first results leave an evidence gap, conflict, or fail a defined confidence or source-quality threshold; measure the trade-off in your workload.
Quick Recap
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