Tavily

Tavily is a search API designed for LLMs, enabling AI applications to access real-time web data.

Prerequisites

Tavily API Key Required - Get your key at https://app.tavily.com (1,000 free API credits/month, no credit card required)

Add to ~/.claude/settings.json:

{
  "env": {
    "TAVILY_API_KEY": "tvly-YOUR_API_KEY"
  }
}

Restart Claude Code after adding your API key.

Installation

Python:

pip install tavily-python

JavaScript:

npm install @tavily/core

See references/sdk.md for complete SDK reference.

Client Initialization

from tavily import TavilyClient

# Option 1: Uses TAVILY_API_KEY env var (recommended)
client = TavilyClient()

# Option 2: Explicit API key
client = TavilyClient(api_key="tvly-YOUR_API_KEY")

# Option 3: With project tracking (for usage organization)
client = TavilyClient(api_key="tvly-YOUR_API_KEY", project_id="your-project-id")

# Async client for parallel queries
from tavily import AsyncTavilyClient
async_client = AsyncTavilyClient()

Choosing the Right Method

For custom agents/workflows:

For out-of-the-box research:

Quick Reference

search() - Web Search

response = client.search(
    query="quantum computing breakthroughs",  # Keep under 400 chars
    max_results=10,
    search_depth="advanced",  # 2 credits, highest relevance
    topic="general"  # or "news", "finance"
)

for result in response["results"]:
    print(f"{result['title']}: {result['score']}")

Key parameters: query, max_results, search_depth (ultra-fast/fast/basic/advanced), topic, include_domains, exclude_domains, time_range

extract() - URL Content Extraction

# Two-step pattern (recommended for control)
search_results = client.search(query="Python async best practices")
urls = [r["url"] for r in search_results["results"] if r["score"] > 0.5]
extracted = client.extract(
    urls=urls[:20],
    query="async patterns",  # Reranks chunks by relevance
    chunks_per_source=3  # Prevents context explosion
)

Key parameters: urls (max 20), extract_depth, query, chunks_per_source (1-5)

crawl() - Site-Wide Extraction

response = client.crawl(
    url="https://docs.example.com",
    max_depth=2,
    instructions="Find API documentation pages",  # Semantic focus
    chunks_per_source=3,  # Token optimization
    select_paths=["/docs/.*", "/api/.*"]
)

Key parameters: url, max_depth, max_breadth, limit, instructions, chunks_per_source, select_paths, exclude_paths

map() - URL Discovery

response = client.map(
    url="https://docs.example.com",
    max_depth=2,
    instructions="Find all API and guide pages"
)
api_docs = [url for url in response["results"] if "/api/" in url]

research() - AI-Powered Research

import time

# For comprehensive multi-topic research
result = client.research(
    input="Analyze competitive landscape for X in SMB market",
    model="pro"  # or "mini" for focused queries, "auto" when unsure
)
request_id = result["request_id"]

# Poll until completed
response = client.get_research(request_id)
while response["status"] not in ["completed", "failed"]:
    time.sleep(10)
    response = client.get_research(request_id)

print(response["content"])  # The research report

Key parameters: input, model ("mini"/"pro"/"auto"), stream, output_schema, citation_format

Detailed Guides

For complete parameters, response fields, patterns, and examples:

• references/sdk.md - Python & JavaScript SDK reference, async patterns, Hybrid RAG
• references/search.md - Query optimization, search depth selection, domain filtering, async patterns, post-filtering
• references/extract.md - One-step vs two-step extraction, query/chunks for targeting, advanced mode
• references/crawl.md - Crawl vs Map, instructions for semantic focus, use cases, Map-then-Extract pattern
• references/research.md - Prompting best practices, model selection, streaming, structured output schemas
• references/integrations.md - LangChain, LlamaIndex, CrewAI, Vercel AI SDK, and framework integrations