tavily-best-practices

# 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`:
```json
{
  "env": {
    "TAVILY_API_KEY": "tvly-YOUR_API_KEY"
  }
}
```

Restart Claude Code after adding your API key.

## Installation

**Python:**
```bash
pip install tavily-python
```

**JavaScript:**
```bash
npm install @tavily/core
```

See **[references/sdk.md](references/sdk.md)** for complete SDK reference.

## Client Initialization

```python
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:**

| 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

```python
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

```python
# 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

```python
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

```python
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

```python
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](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