> For the complete documentation index, see [llms.txt](https://agpt.co/docs/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://agpt.co/docs/integrations/block-integrations/search-5.md).

# Tavily Search

Blocks for searching the web with Tavily's AI-native search API.

## Tavily Search

### What it is

Searches the web using Tavily's AI-native search API

### How it works

The block sends your query to Tavily's search endpoint and returns ranked web results, each with a relevance score and a query-relevant content snippet. You can scope the search by `topic` (general, news, or finance), `time_range`, and domain include/exclude lists, and trade cost for quality with `search_depth` (basic/fast/ultra-fast at 1 credit, advanced at 2).

Beyond the raw `results` list (also emitted one `result` at a time), the block can return an LLM-generated `answer` synthesized from the results when `include_answer` is enabled, and always emits a `context` string — the results formatted as markdown, ready to feed straight into an LLM block. Actual credit spend is read from the API's usage report and reported to the platform's cost tracking.

### Inputs

| Input                 | Description                                                                        | Type                                            | Required |
| --------------------- | ---------------------------------------------------------------------------------- | ----------------------------------------------- | -------- |
| query                 | The search query                                                                   | str                                             | Yes      |
| topic                 | Search category: general, news, or finance                                         | "general" \| "news" \| "finance"                | No       |
| search\_depth         | Depth of the search: basic, fast or ultra-fast (1 credit), or advanced (2 credits) | "basic" \| "advanced" \| "fast" \| "ultra-fast" | No       |
| max\_results          | Maximum number of results to return                                                | int                                             | No       |
| time\_range           | Only include results published within this time range                              | "day" \| "week" \| "month" \| "year"            | No       |
| include\_domains      | Domains to include in search                                                       | List\[str]                                      | No       |
| exclude\_domains      | Domains to exclude from search                                                     | List\[str]                                      | No       |
| include\_answer       | Include an LLM-generated answer to the query, based on the search results          | bool                                            | No       |
| include\_raw\_content | Include the full page content for each result                                      | bool                                            | No       |

### Outputs

| Output  | Description                                                    | Type                      |
| ------- | -------------------------------------------------------------- | ------------------------- |
| error   | Error message if the search failed                             | str                       |
| results | List of search results                                         | List\[TavilySearchResult] |
| result  | Single search result                                           | TavilySearchResult        |
| answer  | LLM-generated answer to the query, based on the search results | str                       |
| context | A formatted string of the search results ready for LLMs.       | str                       |

### Possible use case

**Research Automation**: Pull current, ranked sources on a topic and feed the `context` output directly into an LLM block for summarization or synthesis.

**Grounded Q\&A**: Enable `include_answer` to get a concise, source-backed answer for chatbots or agents that need up-to-date facts.

**News & Market Monitoring**: Set `topic` to news or finance and narrow `time_range` to recent windows to track breaking developments.

***


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