What Is a Research API? Choosing One That Returns Cited Answers

TLDR: A research API takes a question and returns a researched answer, not raw results. It runs searches, reads pages, cross-references sources, and synthesizes a response with inline citations. The You.com Research API is one implementation. This guide covers what the category does, the decision between a search API and a research API, and the parameters that decide cost, latency, and answer shape.

What Does a Research API Do?

A research API returns grounded, natural language answers to questions of varying complexity. Instead of handing you a list of URLs and snippets to process, it reads, reasons over, and synthesizes the sources for you, and returns a thorough answer with inline citations such as [[1, 2]] that reference a sources array.

Every You.com Research API response carries three things: output.content, which is a Markdown-formatted answer by default, output.content_type, which tells you the format, and output.sources, the pages the API read and cited, each with a URL, title, and relevant snippets. If your users can verify any claim by following a numbered citation to its source, the response is doing its job.

The value is traceability. Raw LLM generation cannot show where its facts came from. A research API can, because the citations are structural rather than decorative.

Research API or Web Search API: Which Do You Need?

Decide with one question: who does the synthesis?

If your pipeline already has ranking, deduplication, and synthesis logic that you control, start with a web search API. It returns raw results, URLs with snippets and metadata, and your code decides what to do with them. You get full control over how results are used, and a fast single-round-trip response.

If you want the answer itself, and especially if the question cannot be answered from a single source, use a research API. You give up control over the intermediate steps and gain a synthesized, cited answer. Latency is higher because multiple searches and reasoning steps happen server-side. The tradeoff is speed for depth, and it is worth making whenever the deliverable is an answer rather than a result list.

A useful boundary test: does your feature render a results page, or does it answer a question? Results pages belong to search APIs. Questions that span multiple domains, comparative analyses, and multi-factor evaluations belong to research APIs.

How Do You Control How Deep the Research Goes?

The research_effort parameter controls how much compute the API allocates to your question. Higher effort means more searches, deeper source reading, and more cross-referencing, at the cost of longer response times.

You.com exposes five levels: lite for quick factual lookups, standard as the balanced default for most production use, deep for complex multi-source research, exhaustive for comprehensive analysis across dozens of sources, and frontier for long-running deep research tasks. The frontier level requires background mode and cannot run synchronously.

Match the tier to the question, not to your ambition. A simple factual lookup does not become better because it ran exhaustive research, it just becomes slower and more expensive. Save the high tiers for questions where thoroughness and accuracy justify the wait.

How Do You Get Structured Output From a Research API?

Free-form Markdown does not fit every consumer. When the answer feeds a typed system, an entity extraction step, or a database row, you want JSON with predictable fields. The You.com Research API does this with output_schema: define a JSON Schema, and output.content comes back as an object that follows it, with content_type set to object and sources still attached.

Here is a working pattern for due diligence questions that need a typed verdict.

from youdotcom import You
from youdotcom.models import ResearchEffort
from youdotcom.research_helpers import research_and_wait

with You() as you:
    task = research_and_wait(
        you,
        input=(
            "Are 'Acme Logistics LLC' (Delaware) and "
            "'Acme Logistics' (Newark, NJ) the same business?"
        ),
        research_effort=ResearchEffort.FRONTIER,
        output_schema={
            "type": "object",
            "properties": {
                "same_entity": {"type": "boolean"},
                "confidence": {"type": "number"},
                "evidence": {
                     "type": "array",
                     "items": {"type": "string"},
                },
            },
            "required": ["same_entity", "confidence", "evidence"],
            "additionalProperties": False,
        },
        timeout_s=600,
    )

    verdict = task.result.output["content"]
    print(f"Same entity: {verdict['same_entity']}")
    print(f"Confidence: {verdict['confidence']}")

Every field you declare must appear in required, which is what makes generation reliable: the model always emits every field. If a value can legitimately be unknown, keep the field in required but make its type nullable, so you get a clean null instead of a fabricated value. Schemas that violate the rules are rejected with a 422 before any model execution, which is far better than discovering a malformed schema downstream.

How Do You Keep the Agent on Trusted Sources?

Citations are only as good as the sources behind them. The source_control parameter constrains which domains the research agent can search and visit. You can restrict to an allowlist with include_domains, block domains with exclude_domains, prefer some sources without excluding others with boost_domains, filter by freshness from day up to a custom date range, and focus results by country.

The combination matters for regulated and high-stakes work. For a medical research question, restricting to fda.gov, nih.gov, and pubmed paired with a freshness filter means the answer draws only from official sources that are current. That pattern generalizes to finance, legal, and any domain where a citation from the wrong source class is worse than no citation. The JSON Schema vocabulary is the reference for what output_schema can express, and the WHATWG Server-Sent Events specification defines the progress stream format you consume in background mode.

What Failure Modes Should You Handle?

Three failure modes account for most production pain with research APIs.

The blocked worker. A synchronous request at deep or exhaustive effort can outlive client-side timeouts and tie up a worker. The fix is background mode: set background to true, receive a task handle immediately, then poll or stream progress until the task completes. The youdotcom research_helpers module ships research_and_wait so you do not have to write the submit-and-poll loop yourself.

The wasted tier. Running frontier effort on a lookup question burns budget and adds minutes for no quality gain. Detection is a latency dashboard segmented by effort level. The fix is routing: classify question complexity first, then pick the tier.

The unverified citation. The API cites what its sources say, but for legal, financial, or medical contexts the documentation recommends building a verification step that follows citation URLs to confirm claims before surfacing them to end users. The citations make verification straightforward. Skipping it in high-stakes flows is a product decision, not an engineering one.

Where Does This Fit With the Rest of the Stack?

A research API sits at the synthesis end of the retrieval stack. Search discovers, the Contents API reads known URLs deeply, and research synthesizes. The deep research API hub article covers the agentic internals in more depth. Sibling spokes cover alternative data sourcing, earnings call research, and the grounding API pattern for LLM responses.

What Should You Evaluate Before Committing?

Before you wire a research API into a user-facing feature, evaluate it on your own questions rather than the vendor's. Collect 20 questions your team actually answers in production, including the awkward multi-domain ones and the ones with no clean answer. Run them at standard effort, then run the same set at a higher tier and compare. Score three things: whether the answer addresses the question asked, whether every load-bearing claim carries a citation, and whether the cited sources actually support the claims when you follow them. The third check is the one that separates a citation-backed answer from a decorated one, and it is the one most teams skip.

Next action: get a key from the You.com platform, take one question your team answers by hand today, and run it at standard effort. Compare the returned answer and its citations against the manual work. That single comparison tells you more than any evaluation matrix.