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
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
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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