The Tavily Python wrapper allows for easy interaction with the Tavily API, offering the full range of our search, extract, crawl, map, and research functionalities directly from your Python programs. Easily integrate smart search, content extraction, and research capabilities into your applications, harnessing Tavily's powerful features.
pip install tavily-python
You can try Tavily without an API key. Instantiate TavilyClient() with no arguments and the SDK runs in keyless mode against the public Tavily API. Keyless mode supports search() and extract() only; other methods raise an error explaining that an API key is required.
from tavily import TavilyClient, TavilyKeylessLimitError # No API key needed client = TavilyClient() try: response = client.search("Who is Leo Messi?") print(response) except TavilyKeylessLimitError as e: # Rate-limit cap reached. The exception carries the human-readable # message plus structured fields (code, window, retry_after_seconds, # next_actions) returned by the Tavily API. print(e) print("retry after:", e.retry_after_seconds, "seconds")
Keyless usage is rate-limited. For higher limits and the full set of endpoints (including crawl, map, and research), sign up for a Tavily API key and pass it as TavilyClient(api_key="tvly-...").
Search lets you search the web for a given query.
Below are some code snippets that show you how to interact with our search API. The different steps and components of this code are explained in more detail in the API Methods section further down.
from tavily import TavilyClient # Step 1. Instantiating your TavilyClient tavily_client = TavilyClient(api_key="tvly-YOUR_API_KEY") # Step 2. Executing a simple search query response = tavily_client.search("Who is Leo Messi?") # Step 3. That's it! You've done a Tavily Search! print(response)
from tavily import TavilyClient client = TavilyClient(api_key="tvly-YOUR_API_KEY") # Use exact_match=True to only return results containing the exact phrase(s) inside quotes response = client.search( query='"John Smith" CEO Acme Corp', exact_match=True ) print(response)
This is equivalent to directly querying our REST API.
from tavily import TavilyClient # Step 1. Instantiating your TavilyClient tavily_client = TavilyClient(api_key="tvly-YOUR_API_KEY") # Step 2. Executing a context search query context = tavily_client.get_search_context(query="What happened during the Burning Man floods?") # Step 3. That's it! You now have a context string that you can feed directly into your RAG Application print(context)
This is how you can generate precise and fact-based context for your RAG application in one line of code.
from tavily import TavilyClient # Step 1. Instantiating your TavilyClient tavily_client = TavilyClient(api_key="tvly-YOUR_API_KEY") # Step 2. Executing a Q&A search query answer = tavily_client.qna_search(query="Who is Leo Messi?") # Step 3. That's it! Your question has been answered! print(answer)
This is how you get accurate and concise answers to questions, in one line of code. Perfect for usage by LLMs!
Extract web page content from one or more specified URLs.
Below are some code snippets that demonstrate how to interact with our Extract API. Each step and component of this code is explained in greater detail in the API Methods section below.
from tavily import TavilyClient # Step 1. Instantiating your TavilyClient tavily_client = TavilyClient(api_key="tvly-YOUR_API_KEY") # Step 2. Defining the list of URLs to extract content from urls = [ "https://en.wikipedia.org/wiki/Artificial_intelligence", "https://en.wikipedia.org/wiki/Machine_learning", "https://en.wikipedia.org/wiki/Data_science", "https://en.wikipedia.org/wiki/Quantum_computing", "https://en.wikipedia.org/wiki/Climate_change" ] # You can provide up to 20 URLs simultaneously # Step 3. Executing the extract request response = tavily_client.extract(urls=urls, include_images=True) # Step 4. Printing the extracted raw content for result in response["results"]: print(f"URL: {result['url']}") print(f"Raw Content: {result['raw_content']}") print(f"Images: {result['images']}\n") # Note that URLs that could not be extracted will be stored in response["failed_results"]
Crawl lets you traverse a website's content starting from a base URL.
Note: Crawl is currently available on an invite-only basis. For more information, please visit crawl.tavily.com
Below are some code snippets that demonstrate how to interact with our Crawl API. Each step and component of this code is explained in greater detail in the API Methods section below.
from tavily import TavilyClient # Step 1. Instantiating your TavilyClient tavily_client = TavilyClient(api_key="tvly-YOUR_API_KEY") # Step 2. Defining the starting URL start_url = "https://wikipedia.org/wiki/Lemon" # Step 3. Executing the crawl request with instructions to surface only pages about citrus fruits response = tavily_client.crawl( url=start_url, max_depth=3, limit=50, instructions="Find all pages on citrus fruits" ) # Step 4. Printing pages matching the query for result in response["results"]: print(f"URL: {result['url']}") print(f"Snippet: {result['raw_content'][:200]}...\n")
Map lets you discover and visualize the structure of a website starting from a base URL.
Below are some code snippets that demonstrate how to interact with our Map API. Each step and component of this code is explained in greater detail in the API Methods section below.
from tavily import TavilyClient # Step 1. Instantiating your TavilyClient tavily_client = TavilyClient(api_key="tvly-YOUR_API_KEY") # Step 2. Defining the starting URL start_url = "https://wikipedia.org/wiki/Lemon" # Step 3. Executing the map request with parameters to focus on specific pages response = tavily_client.map( url=start_url, max_depth=2, limit=30, instructions="Find pages on citrus fruits" ) # Step 4. Printing the site structure for result in response["results"]: print(f"URL: {result['url']}")
Research lets you create comprehensive research reports on any topic, with automatic source gathering, analysis, and structured output.
Below are some code snippets that demonstrate how to interact with our Research API. Each step and component of this code is explained in greater detail in the API Methods section below.
from tavily import TavilyClient # Step 1. Instantiating your TavilyClient tavily_client = TavilyClient(api_key="tvly-YOUR_API_KEY") # Step 2. Creating a research task response = tavily_client.research( input="Research the latest developments in AI", model="pro", citation_format="apa" ) # Step 3. Retrieving the research results request_id = response["request_id"] result = tavily_client.get_research(request_id) # Step 4. Printing the research report print(f"Status: {result['status']}") print(f"Content: {result['content']}") print(f"Sources: {len(result['sources'])} sources found")
from tavily import TavilyClient # Step 1. Instantiating your TavilyClient tavily_client = TavilyClient(api_key="tvly-YOUR_API_KEY") # Step 2. Creating a streaming research task stream = tavily_client.research( input="Research the latest developments in AI", model="pro", stream=True ) # Step 3. Processing the stream as it arrives for chunk in stream: print(chunk.decode('utf-8'))
For enterprise environments that proxy Tavily traffic through an API gateway (e.g., for centralized auth, logging, or policy enforcement), you can pass a pre-configured HTTP session instead of a Tavily API key.
import requests from tavily import TavilyClient # Pre-configure a session with your gateway's auth session = requests.Session() session.headers["Authorization"] = "Bearer your-gateway-token" session.headers["X-Subscription-Key"] = "your-subscription-key" # No Tavily API key needed — auth is handled by the session client = TavilyClient( session=session, api_base_url="https://your-gateway.com/tavily", ) response = client.search("latest AI research")
import httpx from tavily import AsyncTavilyClient # Pre-configure an async client with your gateway's auth custom_client = httpx.AsyncClient( headers={"Authorization": "Bearer your-gateway-token"}, base_url="https://your-gateway.com/tavily", ) client = AsyncTavilyClient(client=custom_client) response = await client.search("latest AI research")
Key behaviors:
- If a custom session/client is provided,
api_keyis optional - Custom session headers take precedence over SDK defaults (e.g., your
Authorizationwon't be overwritten) - Custom session proxies take precedence over SDK proxy settings
- The SDK will not close externally-provided sessions — you manage the lifecycle
session_id, human_id, and client_name are optional identifiers that help attribute requests to a logical session, an end user, and a named client. All three are sent as HTTP headers (X-Session-Id, X-Human-Id, X-Client-Name) and are never persisted in raw form — human_id is hashed server-side.
Set them once at client init, or per-call (per-call wins):
from tavily import TavilyClient # Client-level — applied to every request client = TavilyClient( api_key="tvly-YOUR_API_KEY", session_id="my-session-123", human_id="internal-user-id-42", client_name="my-app", ) # Per-call override client.search("hello", session_id="ad-hoc-session")
All three are opt-in. Leave them unset and the SDK sends nothing — behavior is identical to earlier versions.
For a complete guide on how to use the different endpoints and their parameters, please head to our Python API Reference.
Tavily is free for personal use for up to 1,000 credits per month. Head to the Credits & Pricing in our documentation to learn more about how many API credits each request costs.
This project is licensed under the terms of the MIT license.
If you are encountering issues while using Tavily, please email us at [email protected]. We'll be happy to help you.
If you want to stay updated on the latest Tavily news and releases, head to our Developer Community to learn more!