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There is no generally available Google endpoint that exports the exact consumer AI Overview or AI Mode answer object as JSON. For a current, web-grounded answer that your application controls, use the Gemini API with the Google Search tool and a JSON Schema, then parse and validate the response. That produces a separately generated Gemini answer—not a byte-for-byte copy of the Search page. Custom Search JSON API can return search results and metadata, but its documentation does not expose an AI Overview field. Vertex AI Search offers a managed answer workflow for configured enterprise content and is also separate from consumer Search.
Can I get Google AI Overviews as JSON?
Not through a documented, generally available public API. Google describes AI Overviews as a Search experience that summarizes complex topics and links to further reading; it does not document an export endpoint for the answer object shown in the consumer results page.
The practical substitute depends on what you need. If you need a fresh answer about the public web in a schema your software can consume, call Gemini with Google Search grounding and structured output. If you need raw result links, use Custom Search JSON API. If you need generated answers over content that your organization controls, evaluate Vertex AI Search.
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|---|---|---|---|---|
| Gemini API + Google Search tool | A Gemini-generated answer grounded by Search results | Current web information retrieved during the request | JSON Schema, required fields, arrays, enums and other supported schema features | Not the exact consumer AI Overview or AI Mode payload |
| Custom Search JSON API | Search or image result records from a configured Programmable Search Engine | Results from the engine and query | Google-defined result structure | No documented AI answer field; Google says the API is closed to new customers and existing customers have until January 1, 2027 to transition |
| Vertex AI Search | A managed answer or search summary for a configured app | Your configured unstructured or website data, with product-specific indexing | Product-defined answer workflow | Separate enterprise product, not consumer AI Overviews or AI Mode |
What are AI Overviews and AI Mode actually returning?
AI Overviews are shown when Google’s systems decide that a generated summary adds value beyond ordinary Search results. AI Mode is intended for nuanced questions, follow-up exploration, reasoning and comparisons. Google notes that the two experiences can use different models and techniques.
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Both experiences may fan a question out into related searches and consult multiple data sources before composing a response. That internal process is not documented as a public JSON contract. A scraper that imitates the consumer page would therefore depend on an unstable interface and would still not be an official API integration.
For a developer, the key distinction is between retrieving Google’s consumer answer and asking a Google model to produce a new, grounded answer. The first is not documented as generally available; the second is the supported Gemini pattern.
How do I force Gemini to return web-grounded JSON?
Use three controls in the same request: the Google Search built-in tool for current web grounding, a response MIME type of application/json, and a JSON Schema describing the object you will accept. Treat the returned text as untrusted input: parse it, validate the schema, and check the meaning of important values before your application acts on it.
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Keep the schema narrow enough that a model can fill it consistently. For example, an answer service might require an answer string and a list of source URLs:
{
"type": "object",
"properties": {
"answer": { "type": "string" },
"sources": {
"type": "array",
"items": { "type": "string" }
}
},
"required": ["answer", "sources"],
"additionalProperties": false
}Structured outputs support schema types including string, number, integer, boolean, object, array and null. They also support features such as required and enum, but Google does not support every keyword in the full JSON Schema specification. Design against the subset documented for the Gemini API rather than assuming that any validator keyword will be honored by the model.
2. Add Google Search as a built-in tool
When freshness matters, include the Google Search tool in the request. Google describes this as a managed tool executed inside the API call: Search retrieves grounding information and Gemini then generates the final response from those results. This is different from fetching search results yourself and pasting them into a prompt.
3. Request JSON explicitly
Set the response format to JSON with a MIME type of application/json and attach the schema. A conceptual Interactions API request looks like this:
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{
"model": "<supported Gemini model>",
"input": "Answer the question using current web sources.",
"tools": [
{ "type": "google_search" }
],
"response_format": {
"type": "text",
"mime_type": "application/json",
"schema": {
"type": "object",
"properties": {
"answer": { "type": "string" },
"sources": {
"type": "array",
"items": { "type": "string" }
}
},
"required": ["answer", "sources"]
}
}
}The model name and endpoint are deliberately deployment-time values. Google’s supported models and API surfaces change, so select a currently supported model in your project instead of hard-coding the placeholder above into production documentation.
4. Parse and validate before using the result
- Read the returned text from the API response.
- Parse it with your language’s JSON parser. If parsing fails, record the raw response for diagnosis and retry according to your service’s error policy; do not silently treat it as a successful answer.
- Validate the parsed value against the same schema you sent. Check required properties, data types, array contents and any allowed enum values.
- Perform semantic checks that a schema cannot express. For example, verify that a URL has the format your application accepts, that a number is in a safe range, and that the answer actually addresses the user’s question.
- Only then pass the object to downstream code, storage or a user interface.
Google explicitly recommends validation and robust error handling because JSON can be syntactically valid while containing incorrect or unusable values. A schema guarantees shape, not truth.
How should I represent sources and citations in the JSON?
Make provenance an explicit part of your contract instead of hoping that a prose citation format will remain stable. A simple sources array can hold URLs, while a richer object can reserve fields for a title, publisher and excerpt if your application needs them.
{
"type": "object",
"properties": {
"answer": { "type": "string" },
"sources": {
"type": "array",
"items": {
"type": "object",
"properties": {
"url": { "type": "string" },
"title": { "type": "string" }
},
"required": ["url"]
}
}
},
"required": ["answer", "sources"]
}In your prompt, tell the model what each field means and whether every material claim must have a corresponding source. Then validate the URLs and apply your own policy for allowed domains. Do not present a source list as proof that every sentence is correct; grounding improves currency, but the generated answer still requires application-level review for high-impact uses.
Is there an API for Google AI Mode answers?
Google’s public Search documentation does not identify a generally available API that returns the exact answer object from AI Mode. AI Mode is a consumer Search experience with its own models, follow-up behavior and presentation, not a documented response schema for external applications.
If your requirement is “give my service a nuanced question and return a predictable object,” Gemini structured output with Search grounding is the supported design. You control the fields, can require or omit arrays, and can reject responses that fail validation. You should describe the result to users as a Gemini-generated, Search-grounded answer rather than labeling it an exported AI Mode response.
If your requirement is “capture exactly what a signed-in user sees in Google Search,” the Gemini workflow does not satisfy it. The public documentation provides no stable JSON contract for that consumer interface, so building a production dependency on page markup would carry an undocumented compatibility risk.
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Does Custom Search JSON API return AI Overviews?
No documented field in Custom Search JSON API represents an AI Overview or AI Mode answer. The API returns web or image search results from a Programmable Search Engine as JSON, which makes it useful for links, titles and result metadata—not for exporting Google’s generated Search summary.
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Google’s current overview says the API is closed to new customers and that existing customers have until January 1, 2027 to transition to an alternative. Confirm the lifecycle status and migration terms for your account before starting new work, because availability can change independently of the endpoint format.
When Custom Search is still the right tool
- You need a machine-readable list of matching pages rather than a synthesized answer.
- Your application already owns the answer-generation step and only needs retrieval data.
- You need the result structure supplied by a configured Programmable Search Engine, including its search scope and ranking behavior.
Do not add a field named ai_overview to your own output and imply that Custom Search populated it. If you generate a summary from returned links, label that as your application’s summary and retain the underlying result records for traceability.
When is Vertex AI Search a better fit?
Vertex AI Search is designed for managed search and answer workflows over configured enterprise content. Google Cloud documents a search-summary capability and an answer method for unstructured or website search apps.
It is a fit when you need an answer over a controlled corpus—such as indexed internal documents or a configured website—rather than an answer that reproduces consumer Search. Website search has additional prerequisites: generative responses must be enabled, and Google Cloud documents enterprise features and advanced website indexing for that scenario.
Because it is a separate product, its answer format, controls, permissions and operational costs should be evaluated against your Cloud project requirements. It should not be presented to users as an API for AI Overviews or AI Mode.
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Which approach should I choose?
Choose based on the object you need to receive, not on the name of the Search feature you want to imitate.
| Your requirement | Recommended starting point | Why | What to disclose |
|---|---|---|---|
| Current public-web answer in a strict schema | Gemini API with Google Search and structured output | Combines managed Search grounding with an application-defined JSON contract | The result is generated by Gemini and is not the consumer Search payload |
| Search links and metadata only | Custom Search JSON API, subject to account lifecycle | Returns JSON result records from a configured engine | No documented AI Overview or AI Mode answer field |
| Answers over controlled enterprise or website content | Vertex AI Search | Provides managed search-summary and answer capabilities for configured apps | Requires product-specific setup and is separate from consumer Search |
| Byte-for-byte export of the Search page answer | No documented public option | Google does not publish a general export contract for the consumer experiences | Do not promise stable access through page scraping |
What can go wrong, and how do I recover?
The response is not valid JSON
Check that the request sets the JSON MIME type and includes the schema in the response format. Capture the raw response, classify the failure, and retry only under a defined policy. A retry should not bypass validation or feed malformed text directly into a parser that can crash your service.
The JSON parses but fields are missing
Run schema validation after parsing. Confirm that the required list in the request matches the validator in your client, and reject objects that omit required properties. If optional data is genuinely optional, represent that explicitly with nullable or optional fields supported by the documented schema subset.
The object has the right shape but questionable facts
Separate structural validation from semantic review. Require grounding for current claims, inspect the returned source list, apply range and domain checks, and route high-risk answers for human approval. Structured output cannot determine whether a statement is true.
Search grounding is stale or incomplete
Make the user’s time frame and geography explicit in the input, and ask for current web sources when that matters. Search fan-out can retrieve several related queries, but no API promise means that every relevant page will be found or that all sources agree. Your application should expose uncertainty rather than converting an incomplete retrieval into a definitive claim.
A model or endpoint stops working
Keep model and endpoint configuration outside your business logic, monitor deprecation notices, and test the schema against the replacement model before switching traffic. Google’s supported models and API surfaces change, so deployment-time configuration is safer than embedding a model identifier in a long-lived article or client library.
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You planned a new Custom Search integration
Check whether your account is an existing customer and review the transition deadline before committing architecture. If you only need generated answers, compare Gemini Search grounding or Vertex AI Search instead of building a new dependency on an API that Google says is closed to new customers.
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What does this mean for publishers?
Google says publishers do not need a special schema.org vocabulary or a new machine-readable file for AI features. The stated eligibility baseline is being indexed and eligible to appear with a normal Search snippet; existing fundamentals such as crawl access, helpful content, textual accessibility and accurate structured data still matter.
That guidance concerns whether pages may be used or linked in Search experiences. It does not grant a publisher an API for downloading the generated answer, nor does adding structured data create an AI Overview JSON feed. Treat search visibility and API access as separate engineering questions.
Implementation checklist
- Decide whether you need a generated answer, raw results, or an enterprise-controlled corpus.
- For a generated answer, select a currently supported Gemini model at deployment time.
- Write a minimal JSON Schema with explicit required fields and allowed values.
- Enable the Google Search built-in tool when freshness and public-web grounding are required.
- Request
application/jsonand send the schema with the request. - Parse the returned text and validate both schema and semantics.
- Store source URLs and the prompt or query context needed for auditability.
- Define retries, timeout behavior, human review thresholds and a fallback response.
- Monitor model, API and Custom Search lifecycle changes before they affect production.
Frequently Asked Questions
Do I need special schema markup to make my page eligible for AI Overviews?
No. Google says there are no additional technical requirements beyond being indexed and eligible for a normal Search snippet. Existing crawl access, helpful content, accessible text and accurate structured data remain the relevant foundations.
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Can a JSON Schema guarantee that a grounded answer is correct?
No. The schema constrains the shape and data types of the response, not the truth of its claims. Use source checks, domain-specific rules and human review where an incorrect answer would have material consequences.
Is Vertex AI Search the same service as AI Mode?
No. Vertex AI Search is an enterprise search and answer product for configured unstructured or website content. Its managed answer workflow is separate from the consumer AI Mode experience and does not export that interface’s response.
What should I do if my application depends on Custom Search JSON API?
Verify your customer status and migration path before expanding the integration. Google’s current overview says the API is closed to new customers and gives existing customers until January 1, 2027 to transition, so a replacement plan may be necessary.
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