Documentation Index
Fetch the complete documentation index at: https://docs.tavily.com/llms.txt
Use this file to discover all available pages before exploring further.
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"])
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,
)
- 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.
