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Tavily

App : A product you use directly

Search and extraction API optimized for LLMs, with MCP support.

Simple and well integrated into frameworks; acquired by Nebius in February 2026.

Official site

Overall score

7.9 /10

Security: nothing to flag

Utility

7.5

Reliability

6.0

Security

No caveat on record

Ease

5 min · Comfortable

Security reflects where data goes, requested permissions and incidents on record at the verification date. It is not a guarantee of zero risk: check the deployment mode and the connections you enable.

What it does, concretely

AI agents need access to up-to-date information from the web, but language models cannot browse on their own and tend to hallucinate when they lack context. Tavily solves this by providing a single API that connects agents to the web in real time.

In practice, Tavily offers three main operations: web search, content extraction from a URL, and site crawling. Results are returned structured and chunked in a format directly usable by a language model, sparing the agent from processing raw web pages.

Tavily is primarily aimed at developers building AI agents or RAG pipelines who need a reliable web data source in production. With over 2 million developer users and 300 million monthly requests, the tool is designed to scale.

However, Tavily is not suited for general public use without technical skills: there is no standalone search interface for end users, and integration requires working with an API or a compatible framework.

What it offers

·Real-time web search via the tavily.search() endpoint, with results structured and chunked for LLMs.
·Content extraction from a specific URL via the extract endpoint.
·Large-scale website crawling to index billions of pages.
·Dedicated /research endpoint for deep research, ranking at the top of SealQA and SimpleQA benchmarks.
·Built-in security layer: detection and blocking of PII leakage, prompt injection, and malicious sources.
·Intelligent caching and indexing to keep latency predictable under high load (p50 at 180 ms on /search).
·Native integration with major frameworks and LLM providers: OpenAI, Anthropic, Groq, plus MCP support for Claude.
·99.99% uptime SLA for mission-critical production use.

Our tips

Use the search_depth parameter to adjust search depth according to your needs: deeper search improves relevance but may increase latency.
Enable include_answer to get a synthesized answer directly in the API response, useful when the agent does not need to process raw sources.
Keep max_results to the strict minimum to reduce costs and the amount of context sent to the model.
Take advantage of the built-in security filters to block requests containing personal data before they leave your system.
To integrate Tavily into Claude, copy the setup prompt provided on the official website to enable MCP support in seconds.

Watch out

Tavily is a developer-facing API: without integration into an agent or framework, it cannot be used directly by an end user.
The acquisition by Nebius in February 2026 may raise questions about future pricing policy and the medium-term roadmap.

In practice

Price: Freemium
Commercial use: Commercial use: yes
Available on: web, claude-code

Security and data

Rating: Security: nothing to flag
Your data: Data with the vendor
Maintenance: Active

Links and repository checked on 25 October 2026 · added on 1 October 2023

How to install Tavily

1. Create an account on tavily.com and retrieve your API key from the dashboard. 2. Install the Python or JavaScript client depending on your environment (see install_cmd). 3. Store your API key in an environment variable (TAVILY_API_KEY) and never commit it to your source code. 4. Call tavily.search() passing your query and the desired parameters (search_depth, max_results, include_answer). 5. For Claude integration via MCP, copy the setup prompt available on the official website and paste it into Claude Code.

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