Can I create an AI agent to search the web or search data in real time using Lovable?

Yes. You can build an AI agent in Lovable that searches the live web, retrieves current data, reads webpages, and returns the results inside your application.

Lovable can build the frontend and server-side application logic, but the live data has to come from a search or web data service. Lovable supports external APIs through built-in connectors, custom connectors, or direct server-side integrations. For APIs that require credentials, Lovable can keep the key outside the browser and call the service from server-side code.

For an agent built with Olostep, the basic architecture looks like this:

User → Lovable app → Olostep → live web → structured results → Lovable UI

Depending on what the agent needs to do, it can search for relevant URLs, read individual webpages, extract structured information, or return a source-backed answer.

How real-time web search works in a Lovable app

Lovable is the application layer. It can generate the interface, handle user input, run server-side functions, manage application state, and display the response.

The web search API becomes the retrieval layer.

Suppose you build a competitor research assistant in Lovable. A user asks:

Find the latest pricing for Notion, Coda, and ClickUp and show the source for each result.

The Lovable app receives that request and sends it from a server-side function to a web data API. The API searches current web sources, retrieves the relevant information, and sends the result back. Lovable then renders the data as an answer, list, table, dashboard, or whatever interface you designed.

This matters because generating an AI interface and accessing current web information are separate problems. Lovable can build the application, but current information has to be retrieved when the request happens.

Lovable explicitly supports integrations with external internet-accessible APIs. Its documentation recommends connectors for managed credentials, although APIs can also be integrated directly into the application.

Using Olostep as the web data layer for a Lovable AI agent

Olostep exposes separate APIs for different retrieval jobs rather than forcing every request through the same workflow.

For a Lovable application, the most relevant endpoints are:

What the agent needs	Olostep endpoint	What comes back
Find relevant webpages	POST /v1/searches	Deduplicated URLs, titles, and descriptions
Answer a question using the live web	POST /v1/answers	Source-backed answer and optional structured JSON
Read a specific webpage	POST /v1/scrapes	Markdown, HTML, text, JSON, screenshots, or extracted fields
Run repeatable web research	Olostep Agents	Multi-step search, scraping, extraction, validation, and scheduled output

The Search endpoint is useful when your application needs to decide which pages to inspect. The Answers endpoint is the shorter route when the end result is already known: the user asks a question and expects an answer instead of a list of search results. Scrapes becomes useful once the workflow already knows which page it wants to read.

Use Search when the agent needs discovery

Olostep Search accepts a natural-language query through POST /v1/searches and returns deduplicated results containing the URL, title, and description.

A Lovable research tool could use this for requests such as:

• find recent articles about a company;
• discover pricing pages for a group of competitors;
• find relevant documentation for a technical question;
• identify candidate sources before doing deeper research.

The application can show those results directly or pass selected URLs into the Scrape endpoint for full-page retrieval.

That creates a retrieval pipeline such as:

Question → Search → relevant URLs → Scrape → page content → AI response

This gives the application control over which sources are used rather than treating search and answer generation as one opaque operation.

Use Answers when you want the agent to return the result directly

You do not always need to build the Search → Scrape → LLM sequence yourself.

Olostep's Answers endpoint accepts a natural-language task, searches the live web, reads relevant pages, and returns an answer with the sources used. You can also request a defined JSON structure when the data needs to feed another part of the application.

For example, a Lovable app could send a task equivalent to:

Find the latest funding round, valuation, and CEO of this company.

Instead of asking Lovable to separately search, scrape several URLs, combine their text, and call another model, the Answers request can handle the web retrieval and return the resulting data.

A structured result is especially useful for product interfaces because the frontend does not have to parse an unpredictable paragraph before rendering it.

You could ask for fields such as:

{
  "company": "",
  "latest_funding_round": "",
  "valuation": "",
  "ceo": ""
}

Olostep's Answers API supports structured outputs and includes the source URLs used for the result. Its documented behavior also returns NOT_FOUND when requested information cannot be supported, rather than requiring the application to invent a value for the field.

How to connect Olostep to Lovable

There are two practical ways to do it.

Option 1: Create a custom Lovable connector

Lovable lets workspace admins and owners create a custom connector for a REST API. You define the API base, authentication method, and technical instructions once, and the connector can then be used by projects in that workspace. Custom connectors support Bearer-token authentication, which matches APIs that authenticate with an Authorization: Bearer header.

For a reusable Olostep integration, the connector can describe the endpoints your application is allowed to call:

• POST /v1/searches
• POST /v1/answers
• POST /v1/scrapes

You can also add connector knowledge explaining which endpoint Lovable should choose for each job.

This approach makes sense if several Lovable projects will use the same web data infrastructure.

Option 2: Ask Lovable to integrate Olostep directly

Lovable can also write the API integration into the application.

Its documentation says that for credentialed APIs, direct integrations keep the credential in the project's secrets and make the request through server-side code rather than exposing the private API key to visitors. Newer Lovable applications use server functions; older React + Vite projects can use Cloud Edge Functions.

That makes the direct approach suitable for a single app or prototype.

Do not put an Olostep private API key in client-side JavaScript. Lovable's Secrets feature exists specifically for API keys and other sensitive server-side values. Secrets are encrypted and do not reach the browser.

A prompt you can give Lovable

You can describe the entire feature instead of manually building the integration.

Build an AI web research assistant.

Create a search input where users can enter a natural-language question.

Connect the backend to the Olostep API using a server-side integration. Store the Olostep API key as a private secret called OLOSTEP_API_KEY. Never expose the key in frontend code.

For questions that require a direct answer, call POST /v1/answers and send the user's question as the task.

Display the returned answer in the interface and show the source URLs underneath it.

Add loading, empty, timeout, and error states.

If the returned data contains NOT_FOUND, show that the information could not be verified instead of generating a replacement answer.

Keep all calls to Olostep on the server side.

You can make the workflow more advanced by telling Lovable when to use Search and when to use Scrape:

If the user requests search results, use POST /v1/searches.

If the user asks to inspect or summarize a specific URL, use POST /v1/scrapes.

If the user asks a factual question that needs current web information, use POST /v1/answers.

The important part is defining the routing logic. "Add web search" leaves Lovable with much more architectural freedom than explicitly stating what should happen for discovery, extraction, and answer generation.

What can you build with Lovable and real-time web data?

A simple search box is only one implementation.

An AI research assistant

A user enters:

What changed in Anthropic's API documentation this month?

The app searches current sources and returns the relevant information with links rather than relying only on a model's stored knowledge.

A competitor intelligence app

Users enter a set of competitors. The agent can search for recent product announcements, find pricing pages, retrieve those pages, and convert selected fields into structured data.

If the same research needs to happen repeatedly, Olostep also provides Agents for scheduled web research workflows that can search, scrape, extract, validate, and deliver structured results.

A lead research tool

The Lovable interface can accept a company or domain and request current fields such as company information, recent announcements, executives, product details, or other web-accessible data.

Structured JSON is useful here because the returned fields can be written directly into a database or CRM workflow rather than presented only as prose.

A fact-checking assistant

The app can send a factual claim to the Answers endpoint and surface the retrieved sources next to the result.

The UI can then make the distinction between the generated answer and the evidence visible to the user.

A current-information chatbot

A normal chatbot can answer conversational questions, but a web-connected chatbot can decide that a question requires fresh information and trigger retrieval first.

A useful pattern is:

User question → decide whether fresh data is required → call Olostep → add retrieved information to the workflow → return the answer

Questions about recent news, current prices, product changes, new documentation, company announcements, or information published after an LLM's training data are natural candidates for retrieval.

Does Lovable already have web search?

Yes.

Lovable now has web-search integrations of its own. Its current Perplexity connector can provide managed web search without requiring the builder to supply a Perplexity API key. Lovable documents the managed option as supporting Perplexity's Search API, while other Perplexity capabilities such as its cited-answer models and deep research require the builder's own Perplexity credentials.

So adding Olostep is not required simply to prove that Lovable can access the live web.

The choice depends on the retrieval workflow you are building.

If you only need the search capability already supplied by a Lovable connector, using that connector may be enough.

Olostep becomes relevant when the application needs its web data endpoints as part of the product architecture: semantic web discovery through Search, extraction of known pages through Scrapes, live source-backed answers through Answers, or repeatable web research through Agents.

Is "real-time" search actually real time?

For this type of application, "real time" normally means the system retrieves current web information when the user makes the request. It does not mean every website on the internet is being streamed continuously into Lovable.

For example:

1. A user asks for today's pricing.
2. The Lovable app sends the request.
3. Olostep searches or reads the relevant live pages.
4. The result is returned to the app.

If the requirement is continuous tracking rather than on-demand retrieval, use a scheduled workflow or monitoring system instead.

Olostep's Monitors endpoint is designed for repeated checks of sources such as pricing pages, changelogs, blogs, and status pages, while Olostep Agents can run scheduled research workflows.

What if I want to search my own data instead of the public web?

That is a different retrieval problem.

Lovable can connect to external APIs and data services, so an application can query information stored in a database, warehouse, SaaS product, or internal API when that source exposes the required interface. Lovable's connector catalog includes data platforms, and its general API integration supports internet-accessible REST APIs.

Olostep is relevant when the missing information lives on the web.

A production agent can use both:

internal data + live web data + an LLM

For example, a sales assistant could retrieve a customer's existing CRM record first, use Olostep to find recent company developments, and then generate a briefing using both sources.

Do I need to know how to code?

Not necessarily for the initial implementation.

Lovable can generate the application, server-side API integration, interface, and backend function from natural-language instructions. Its Edge Functions documentation specifically describes external API calls as a server-side use case and says Lovable can write and deploy those functions from instructions provided in project chat.

You still need to understand what the application is supposed to do.

For a web-search agent, define:

• when the agent should search;
• whether it needs links or a finished answer;
• whether it should read complete webpages;
• what fields should be returned;
• whether sources need to be displayed;
• what should happen when information cannot be found.

Those decisions affect the reliability of the application more than the visual design.

Can I create an AI agent to search the web in Lovable without building my own scraper?

Yes.

You do not need to maintain browser infrastructure just to give a Lovable application access to current web information.

For an Olostep implementation, use Search when the agent needs to discover pages, Scrapes when it needs content from known URLs, and Answers when it needs a direct, web-grounded response. The Lovable application handles the product experience around those API calls.

For the simplest question-answering agent, the workflow can be reduced to:

Lovable input → /v1/answers → live web retrieval → answer + sources → Lovable interface

If you later need more control over retrieval, change the workflow to:

Lovable input → /v1/searches → select URLs → /v1/scrapes → process extracted content → response

That separation lets you start with a simple agent and add deeper web research only when the product actually needs it.