Tavily 101: AI-powered Search for Developers

AI has fundamentally changed how we access information from the web.

What used to be a direct loop between humans → web browsers → websites now often looks like humans → AI agents → the web. But for agents to be useful in real-world applications, they need fast, reliable, up-to-date access to live web data, returned in formats they can reason over and cite, with safety built in.

We kicked off our first live virtual event, Tavily 101, with exactly this challenge: how developers bridge the gap between static language models and the dynamic web.

In this recap, we’ll cover how Tavily solves this challenge with a single API, when to use each endpoint, the real-world agent patterns they enable, and a sneak peek at our newest Research API.

What is Tavily?

Tavily is the web access layer for AI agents. It is a single API for agents to search, extract, and crawl the live web in formats designed specifically for RAG and agent workflows.

Unlike traditional search engines that’s built for humans, Tavily is built for AI systems. It provides:

• Fresh, grounded results optimized for LLM ingestion
• Low-latency even at production scale, powered by dynamic caching and an agent-native index
• Agent-native firewall to prevent against prompt injection and data leakage

The Endpoints: Search, Extract, and Crawl

During the live coding demo, we walked through the three main ways you can interact with the web using the Tavily Python SDK.

1. /search - find and rank sources

Use /search to discover relevant pages fast. It performs semantic search optimized for AI workflows and returns concise, ranked snippets designed for downstream reasoning and RAG.

Code Example:

from tavily import TavilyClient

# Initialize Client
tavily_client = TavilyClient(api_key="YOUR_API_KEY")

# Perform a search on a current topic
search_results = tavily_client.search(
    query="Anthropic model release?",
    max_results=5,
    time_range="month",
    include_domains=["techcrunch.com"], # Filter by trusted domains
    topic="news"
)

# Returns snippets optimized for LLMs
for result in search_results["results"]:
    print(result["title"], result["content"])

2. /extract - turn URLs into clean text

Use /extract to pull the full page content from URLs. Rather than reading one page at a time, agents can extract content from up to twenty URLs in a single call, returning clean text, markdown, and images.

Code Example:

# Extract full-page content from search results
extract_results = tavily_client.extract(
    urls=[result["url"] for result in search_results["results"]]
)

for result in extract_results["results"]:
    print(result["raw_content"]) # The full, clean text

Tip: if you want a single call, you can also enable extraction directly inside /search via include_raw_content, handy for prototyping, though a two-step flow is often better for precision.

3. /crawl - site-level discovery and coverage

Use /crawl when you need site-level discovery or coverage

For deep research, you often need to explore an entire domain.

• /crawl enables deep discovery. It navigates a URL, follows links, and scrapes the content of all nested pages.
• /map is a lightweight version of /crawl. It returns a sitemap (list of all nexted URLs) without the heavy scraping.

Tip: You can use natural language instructions to guide the crawler. Instead of scraping everything, you can tell the AI exactly what to look for.

Code Example:

# Map a site with specific AI instructions
guided_map_results = tavily_client.map(
    url="tavily.com", 
    instructions="find only the developer docs"
)

These are designed to be composable, so you can build everything from lightweight assistants to full research agents.

To explore the full walkthrough from the live session, including setup and examples, view the complete notebook here: https://github.com/NirDiamant/agents-towards-production/blob/main/tutorials/agent-with-tavily-web-access/search-extract-crawl.ipynb

Real-World Agent Patterns

The endpoints are building blocks. The value comes from how you compose them into workflows. Here are the most common patterns we see teams ship.

1. Research Agents: Agents that perform deep, multi step research by searching, refining queries, deduplicating sources, and synthesizing findings. For example, monitoring competitor pricing changes and alerting teams with a clear summary.
2. Enrichment Agents: Agents that keep internal systems up to date with fresh web data. A common example is CRM enrichment, where agents surface recent funding, hires, product launches, or market expansion with source links.
3. Enrichment Agents Agents that continuously update datasets with fresh web data. Common examples include CRM enrichment, company monitoring, and market intelligence (e.g., "What did this company do last quarter?").
4. AI Assistants: Low latency, real-time assistants that fetch live information on demand. For example, customer support bots that pull the latest policies and status updates to generate accurate, order specific responses.

Each use case balances latency and accuracy differently, and Tavily is designed to support all of them.

Announcement: The Research API

We saved the best for last. While /search, /extract, and /crawl are the building blocks, we are now offering a complete Agent-in-a-Box.

The new Research endpoint enables deep, end-to-end web research through a single API call. Given a query, it performs multiple iterative searches, reasons over the data, supports multi agent coordination, deduplication, and structured JSON outputs and returns a comprehensive report.

Since the endpoints operate directly on live web data, security is foundational. Tavily acts as a firewall between your agents and the internet, scanning retrieved content and blocking malicious prompt injection attempts before they ever reach your models. This ensures enterprise-grade safety when running autonomous research workflows.

State-of-the-Art Performance: In benchmarks (DeepResearch Bench on Hugging Face), the Tavily Research API is currently outperforming implementations from major labs, including OpenAI and Perplexity.

The API is currently in private beta, with early access available upon request. Find more: https://deepresearch.tavily.com/

Integrations & Ecosystem

We are model-agnostic. Whether you are using OpenAI, Anthropic, Llama, or Mistral, Tavily provides the data layer. We are also integrated directly into the frameworks you already use, like LangChain, LlamaIndex, Agno, and n8n, and available on major cloud marketplaces.

Final Thoughts

Tavily 101 showed how developers can move beyond static language models and build agents that truly understand what is happening on the web in real-time.

By combining semantic search, scalable extraction, intelligent crawling, and enterprise grade security, Tavily provides the foundation for the next generation of AI systems.

If you are ready to get started, explore the full session, experiment with the Tavily playground, and begin building agents that can truly search, read, and research the live web.

• Watch the event: https://tavily.ondemand.goldcast.io/on-demand/f2ab67f3-e62e-4c3a-9762-2761bdfe325d
• Sign up at https://app.tavily.com to get your API key
• Explore the docs: https://docs.tavily.com/welcome