{"repo":"tavily-ai/tavily-python","free":true,"listed":false,"github":"https://github.com/tavily-ai/tavily-python","clone":"git clone https://github.com/tavily-ai/tavily-python.git","description":"The Tavily Python SDK 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.","language":"Python","stars":1362,"topics":["agent","crawl","extract","map","python","search","tavily"],"license":"MIT","category":"api-integrations-sdks","readme_excerpt":"Tavily Python SDK 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. Installing Keyless mode 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. 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-...\") . Tavily Search Search lets you search the web for a given query. Usage 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. Getting and printing the full Search API response Using exact match to find specific names or phrases This is equivalent to directly querying our REST API. Generating context for a RAG Application This is how you can generate precise and fact-based context for your RAG application in one line of code. Getting a quick answer to a question This is how you get accurate and concise an","default_branch":null,"files":null,"tree":[],"storefront":"/r/tavily-ai","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/tavily-ai/tavily-python/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}