Tavily
Tavily is a search API designed for LLMs, enabling AI applications to access real-time web data.
Installation
Python:
pip install tavily-python
JavaScript:
npm install @tavily/core
See [references/sdk.md](references/sdk.md) for complete SDK reference.
Client Initialization
from tavily import TavilyClient
# Uses TAVILY_API_KEY env var (recommended)
client = TavilyClient()
#With project tracking (for usage organization)
client = TavilyClient(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:
| Need |
Method |
| Web search results |
search() |
| Content from specific URLs |
extract() |
| Content from entire site |
crawl() |
| URL discovery from site |
map() |
For out-of-the-box research:
| Need |
Method |
| End-to-end research with AI synthesis |
research() |
Quick Reference
search() - Web Search
response = client.search(
query="quantum computing breakthroughs", # Keep under 400 chars
max_results=10,
search_depth="advanced"
)
print(response)
Key parameters: query, maxresults, searchdepth (ultra-fast/fast/basic/advanced), includedomains, excludedomains, time_range
See [references/search.md](references/search.md) for complete search reference.
extract() - URL Content Extraction
# Simple one-step extraction
response = client.extract(
urls=["https://docs.example.com"],
extract_depth="advanced"
)
print(response)
Key parameters: urls (max 20), extractdepth, query, chunksper_source (1-5)
See [references/extract.md](references/extract.md) for complete extract reference.
crawl() - Site-Wide Extraction
response = client.crawl(
url="https://docs.example.com",
instructions="Find API documentation pages", # Semantic focus
extract_depth="advanced"
)
print(response)
Key parameters: url, maxdepth, maxbreadth, limit, instructions, chunkspersource, selectpaths, excludepaths
See [references/crawl.md](references/crawl.md) for complete crawl reference.
map() - URL Discovery
response = client.map(
url="https://docs.example.com"
)
print(response)
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, outputschema, citationformat
See [references/research.md](references/research.md) for complete research reference.
Detailed Guides
For complete parameters, response fields, patterns, and examples:
- [references/sdk.md](references/sdk.md) - Python & JavaScript SDK reference, async patterns, Hybrid RAG
- [references/search.md](references/search.md) - Query optimization, search depth selection, domain filtering, async patterns, post-filtering
- [references/extract.md](references/extract.md) - One-step vs two-step extraction, query/chunks for targeting, advanced mode
- [references/crawl.md](references/crawl.md) - Crawl vs Map, instructions for semantic focus, use cases, Map-then-Extract pattern
- [references/research.md](references/research.md) - Prompting best practices, model selection, streaming, structured output schemas
- [references/integrations.md](references/integrations.md) - LangChain, LlamaIndex, CrewAI, Vercel AI SDK, and framework integrations
