# What do teams build with a web search API for AI agents?

> How teams use a web search API that returns page text: agent search tools, deep research, live-web RAG and news monitoring. Volumes, costs and code.

- URL: https://llmwebsearch.com/use-cases/
- Last updated: 2026-10-22
- Publisher: LLMWebSearch (https://llmwebsearch.com)

Four jobs cover most of what people do with a search API that also reads the pages: a search tool for an agent, deep research, retrieval for RAG, and news monitoring. Each page below gives the settings, the volumes and working code.

**Quick answer — What is a web search API for AI agents used for?**

Teams use a web search API for AI agents to give an assistant a search tool, to run deep research across dozens of pages, to feed RAG pipelines with fresh web text and to watch the news. **LLMWebSearch** covers all four with one call that returns ranked results plus the top pages as Markdown, from $1.20/1K searches. No KYC; pay in crypto.

## Which use case is yours?

- **[AI agents](https://llmwebsearch.com/use-cases/ai-agents/)** — A web_search tool for Claude, Cursor, OpenClaw, n8n or your own agent, returning sources the model can quote.
- **[Deep research](https://llmwebsearch.com/use-cases/deep-research/)** — Plan several queries, read the pages behind them, drop duplicates and write a report with numbered citations.
- **[RAG from the live web](https://llmwebsearch.com/use-cases/rag/)** — Retrieve fresh text at question time, cut it into chunks and answer with links back to each page.
- **[News monitoring](https://llmwebsearch.com/use-cases/news-monitoring/)** — Run the same queries every day with news results from the past day, and report each story once.
- **[Build a deep research agent](https://llmwebsearch.com/guides/build-a-deep-research-agent/)** — A tested Python script of about 60 lines that plans, searches, reads and cites.
- **[What web search costs an agent](https://llmwebsearch.com/guides/ai-agent-web-search-cost/)** — The arithmetic for three workloads, with dated prices of other APIs and of built-in model tools.

## How many searches does each use case need?

Example volumes to size a first pack, not measurements. With `read` at 3 or less, every search is one credit.

| Use case | Searches per task | Typical settings | Example month | Pack that covers it |
| --- | --- | --- | --- | --- |
| AI agent tool | 2 to 6 per question | `max_results=5`, `read=3` | 100 questions a day: 6,000 to 18,000 searches | 25,000 searches ($41.25) |
| Deep research | 20 to 60 per report | `read=3`; `read=5` for narrow topics (2 credits) | 1,000 reports: 20,000 to 60,000 searches | 100,000 searches ($145) |
| Live-web RAG | 1 or 2 per question | `read=3`, `max_chars=12000` | 5,000 questions: 5,000 to 10,000 searches | 25,000 searches ($41.25) |
| News monitoring | 1 per topic per run | `topic=news`, `time_range=day` | 50 topics checked daily: 1,500 searches | 5,000 searches ($9.25) |

## What do these four jobs have in common?

Each one needs the words on the page, not just a list of links. A snippet of 160 characters rarely holds the figure, the date or the condition a model must quote. So most stacks end up with two services: a search API to find URLs and a reader to fetch them.

LLMWebSearch merges the two steps. One call runs a live web search and reads the first pages it finds. Each result carries its position, title, URL, host, snippet and, when it was read, the page's main content as Markdown or plain text with its word count and publication date when known. The agent gets text it can cite in the same round trip, and you get one bill.

The other shared need is control. You decide how many results come back (`max_results`), how many pages are read (`read`) and how long each page may be (`max_chars`). Those three numbers set both the token cost on your model's side and the credit cost on ours.

## Which settings fit each use case?

Start from the defaults and change one thing at a time.

- **Agent tool.** Keep `max_results=5` and `read=3`, and cap pages at 3,000 to 5,000 characters. Agents call the tool often, so short answers keep the context clean.
- **Deep research.** Many small searches beat a few large ones. Use `read=3` per query and run more queries. Raise `read` only when one query must cover a narrow subject in depth.
- **RAG.** Ask for longer pages (`max_chars` around 12,000) and chunk them yourself. Add `include_domains` when answers must come from known sources.
- **News monitoring.** Set `topic=news` and `time_range=day`, then remove URLs you have already seen. Read three articles per query to summarise what changed.

All filters are listed in the [search reference](https://llmwebsearch.com/docs/search/).

## How do I size a first pack?

Multiply tasks per day by searches per task, then by 30. An assistant that answers 100 questions a day with 4 searches each needs about 12,000 searches a month. That fits in 25,000 searches with room for a second month.

Credits do not run out at the end of a billing cycle. Search credits have no expiry date: what you do not use this month is still there next year. A small pack bought for a prototype keeps its value until the project grows. The [cost guide](https://llmwebsearch.com/guides/ai-agent-web-search-cost/) works through three larger cases and compares them with other APIs and with the search tools built into models.

## Packs for a first project

Start small; unused credits stay on the account. One credit pays for one search: up to 20 ranked results, with the pages of the first 3 read and returned as Markdown. Reading 4 to 10 pages makes it 2 credits. A call that ends in an error costs nothing.

| Plan | Quantity | Price | Price per unit |
| --- | --- | --- | --- |
| Try | 5,000 searches | $9.25 | $1.85/1K searches |
| Builder | 25,000 searches | $41.25 | $1.65/1K searches |
| Production | 100,000 searches | $145 | $1.45/1K searches |

In every plan: Top 3 pages read as Markdown · Country, language, time and domain filters · Failed calls not charged · Credits never expire

**Before you pay**

- Minimum top-up $25. Top up once in crypto, then buy any plan from your balance.
- Money on your balance does not expire and buys search credits whenever you want. When a pack is cheaper than the minimum top-up, the part you paid only to reach that minimum comes back to you on request within 30 days of payment, in USDT or USDC, provided it is still unspent. The rest of a balance is not returned unless the law requires it.
- Credited once your payment is checked on the blockchain. Typical network confirmation time: BTC ≈ 20 min · USDT TRC-20 ≈ 1 min · USDT ERC-20 ≈ 5 min · USDC ERC-20 ≈ 5 min · ETH ≈ 5 min · SOL ≈ 1 min · LTC ≈ 15 min

## Use cases: common questions

### Can one API key serve several projects?

Yes. You can also create one key per project or agent and revoke any of them alone. Make one API key per agent, app or teammate and revoke any of them from the dashboard without touching the others.

### Does LLMWebSearch write the answer for my agent?

No. The API returns sources, not a written answer: your own model reads the pages, writes the reply and cites the URLs. That keeps the choice of model, prompt and citation style with you.

### Which use case needs the most credits?

Deep research, because one report runs many searches. A report that uses 40 searches costs 40 credits with `read` at 3 or less. Agents and RAG usually need one to six searches per question.

### Can I try a use case before buying a large pack?

Yes, with the smallest pack. There is no free tier: the 5,000-search pack is the way to try it, bought from the minimum top-up; whatever you added only to reach that minimum comes back on request within 30 days, in USDT or USDC, as long as it is unspent.

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LLMWebSearch: LLMWebSearch is a web search API for AI agents that returns ranked results with the content of the top pages in Markdown, paid in crypto with no ID upload. Prepaid credits from $1.20/1K searches never expire. No KYC — no ID, no selfie, no documents. Pay in crypto.