Tools Reference

Each tool combines Tavily API endpoints with context engineering — formatting results for LLMs, managing token limits, deduplicating sources, and cleaning web noise. Your agent focuses on reasoning while the tools handle retrieval complexity.

Scenario	Tool
”Answer this question with web research”	`search_and_answer`
”Research this topic from multiple angles”	`search_dedup`
”What does this website say?”	`crawl_and_summarize`
”Summarize these specific pages”	`extract_and_summarize`
”What are people saying on Reddit/Twitter?”	`social_media_search`

`search_and_answer`

Answer a question using web research. Optionally generates subqueries for comprehensive coverage, handles token limits, and synthesizes an answer with your chosen model.

Parameters

Parameter	Type	Default	Description
`query`	str	Required	The question to answer
`api_key`	str	Required	Tavily API key
`model_config`	ModelConfig	None	LLM configuration for synthesis
`max_number_of_subqueries`	int	2-4	Number of subqueries to generate
`output_schema`	OutputSchema	None	Pydantic model for structured output
`token_limit`	int	50000	Maximum token budget for context
`threshold`	float	0.3	Minimum relevance score
`topic`	str	”general”	`"general"`, `"news"`, or `"finance"`
`time_range`	str	None	`"day"`, `"week"`, `"month"`, or `"year"`
`include_domains`	list	None	Only search these domains
`exclude_domains`	list	None	Exclude these domains

Example

from tavily_agent_toolkit import search_and_answer, ModelConfig, ModelObject

result = await search_and_answer(
    query="What are the pros and cons of Rust vs Go?",
    api_key="tvly-xxx",
    model_config=ModelConfig(model=ModelObject(model="anthropic:claude-sonnet-4-5")),
    max_number_of_subqueries=3,
)
print(result["answer"])

`search_dedup`

Run multiple search queries in parallel and consolidate results. Deduplicates by URL and merges content chunks from the same source.

Parameters

Parameter	Type	Default	Description
`queries`	list[str]	Required	List of search queries
`api_key`	str	Required	Tavily API key
`search_depth`	str	”advanced”	`"basic"` or `"advanced"`
`topic`	str	”general”	`"general"`, `"news"`, or `"finance"`
`max_results`	int	5	Results per query
`chunks_per_source`	int	3	Content chunks per source
`time_range`	str	None	`"day"`, `"week"`, `"month"`, or `"year"`
`include_domains`	list	None	Only search these domains
`exclude_domains`	list	None	Exclude these domains

Example

from tavily_agent_toolkit import search_dedup

results = await search_dedup(
    api_key="tvly-xxx",
    queries=[
        "transformer architecture explained",
        "attention mechanism neural networks",
        "BERT GPT comparison",
    ],
    search_depth="advanced",
    max_results=5,
)

for r in results["results"]:
    print(f"{r['title']}: {r['score']}")

`crawl_and_summarize`

Crawl an entire website and summarize the content with your chosen model. Useful for documentation sites, knowledge bases, or product catalogs.

Parameters

Parameter	Type	Default	Description
`url`	str	Required	Website URL to crawl
`model_config`	ModelConfig	Required	LLM for summarization
`api_key`	str	Required	Tavily API key
`instructions`	str	None	Specific extraction instructions
`output_schema`	OutputSchema	None	Pydantic model for structured output
`max_depth`	int	1-5	How deep to crawl from starting URL
`max_breadth`	int	20	Max pages to crawl per level
`limit`	int	50	Total max pages to crawl
`select_paths`	list	None	Only crawl URLs matching these regex patterns
`exclude_paths`	list	None	Skip URLs matching these regex patterns

Example

from tavily_agent_toolkit import crawl_and_summarize, ModelConfig, ModelObject

result = await crawl_and_summarize(
    url="https://docs.example.com",
    model_config=ModelConfig(model=ModelObject(model="anthropic:claude-sonnet-4-20250514")),
    instructions="Extract all API endpoints and their parameters",
    api_key="tvly-xxx",
    max_depth=2,
    select_paths=["/docs/.*", "/api/.*"],
)
print(result["summary"])

`extract_and_summarize`

Extract content from specific URLs and summarize with your model. Use when you already know which pages have the information.

Parameters

Parameter	Type	Default	Description
`urls`	list[str]	Required	URLs to extract (max 20)
`model_config`	ModelConfig	Required	LLM for summarization
`api_key`	str	Required	Tavily API key
`query`	str	None	Focuses extraction on specific information
`output_schema`	OutputSchema	None	Pydantic model for structured output
`chunks_per_source`	int	5	Content chunks per source
`extract_depth`	str	”basic”	`"basic"` or `"advanced"`

Example

from tavily_agent_toolkit import extract_and_summarize, ModelConfig, ModelObject

result = await extract_and_summarize(
    urls=["https://en.wikipedia.org/wiki/Artificial_intelligence"],
    model_config=ModelConfig(model=ModelObject(model="groq:llama-3.3-70b-versatile")),
    query="What are the main ethical concerns with AI?",
    api_key="tvly-xxx",
    chunks_per_source=5,
)
print(result["results"][0]["summary"])

`social_media_search`

Search specific social platforms for discussions and content.

Parameters

Parameter	Type	Default	Description
`query`	str	Required	Search query
`api_key`	str	Required	Tavily API key
`platform`	str	”combined”	`"reddit"`, `"x"`, `"linkedin"`, `"tiktok"`, `"instagram"`, `"facebook"`, or `"combined"`
`include_raw_content`	bool	False	Include full post content
`max_results`	int	5	Number of results
`time_range`	str	None	`"day"`, `"week"`, `"month"`, or `"year"`

Example

from tavily_agent_toolkit import social_media_search

results = social_media_search(
    query="best practices for LLM fine-tuning",
    api_key="tvly-xxx",
    platform="reddit",
    max_results=10,
    time_range="month",
)

Model Configuration

All tools that use an LLM accept a ModelConfig. Use the "provider:model" format, and optionally specify fallback models:

from tavily_agent_toolkit import ModelConfig, ModelObject

config = ModelConfig(
    model=ModelObject(model="openai:gpt-5.2"),
    fallback_models=[
        ModelObject(model="anthropic:claude-sonnet-4-20250514"),
        ModelObject(model="groq:llama-3.3-70b-versatile"),
    ],
    temperature=0.7,
)

Retry behavior:

With fallback_models: each model gets 1 attempt before moving to the next
Without fallback_models: primary model gets 1 retry (2 attempts total)

See the GitHub README for the full list of 20+ supported providers.

Hub

Agent Toolkit

Apps

Cookbook

Open Source

`search_and_answer`

Parameters

Example

`search_dedup`

Parameters

Example

`crawl_and_summarize`

Parameters

Example

`extract_and_summarize`

Parameters

Example

`social_media_search`

Parameters

Example

Model Configuration

Hub

Agent Toolkit

Apps

Cookbook

Open Source

​search_and_answer

​Parameters

​Example

​search_dedup

​Parameters

​Example

​crawl_and_summarize

​Parameters

​Example

​extract_and_summarize

​Parameters

​Example

​social_media_search

​Parameters

​Example

​Model Configuration

`search_and_answer`

Parameters

Example

`search_dedup`

Parameters

Example

`crawl_and_summarize`

Parameters

Example

`extract_and_summarize`

Parameters

Example

`social_media_search`

Parameters

Example

Model Configuration