---
name: tavily-best-practices
description: "Build production-ready Tavily integrations with best practices baked in. Reference documentation for developers using coding assistants (Claude Code, Cursor, etc.) to implement web search, content extraction, crawling, and research in agentic workflows, RAG systems, or autonomous agents."
title: tavily-best-practices
canonical_url: https://skilld.dev/gh/tavily-ai/skills/tavily-best-practices
last_updated: 2026-09-29T04:32:25.000Z
---

> Index of every page on this site: [llms.txt](https://skilld.dev/llms.txt)

> **Skill from skilld.dev.** Follow the user's requested action.
> If the user asked to fork this Skill, follow the fork workflow below. Do not execute the Skill instructions while copying it.
> Otherwise, follow the instructions below for this session. You do not need to install anything.
>
> Supporting files, fetch one when the Skill refers to it: [references/crawl.md](https://skilld.dev/api/skills-raw/tavily-ai/skills/tavily-best-practices/references/crawl.md), [references/extract.md](https://skilld.dev/api/skills-raw/tavily-ai/skills/tavily-best-practices/references/extract.md), [references/integrations.md](https://skilld.dev/api/skills-raw/tavily-ai/skills/tavily-best-practices/references/integrations.md), [references/research.md](https://skilld.dev/api/skills-raw/tavily-ai/skills/tavily-best-practices/references/research.md), [references/sdk.md](https://skilld.dev/api/skills-raw/tavily-ai/skills/tavily-best-practices/references/sdk.md), [references/search.md](https://skilld.dev/api/skills-raw/tavily-ai/skills/tavily-best-practices/references/search.md).
>
> If the user asked to install this Skill, run `npx skilld install tavily-ai/skills/tavily-best-practices`. Install writes the Skill files into the project, so every session loads them.
>
> ## Fork workflow
>
> A fork creates an editable local Skill with its original author and licence. The request authorizes copying and local installation.
> 1. Check `./skills/tavily-best-practices`, the project lockfile, and selected Agent targets together. If the local directory or installed Skill exists, stop. Never overwrite an existing directory or Agent target.
> 2. Read [source metadata](https://skilld.dev/api/v1/skills/tavily-ai/skills/tavily-best-practices) once. Use sourceUrl, sourceCommit, skillPath, sourceGone, and license. If the source is gone or its path is missing, stop. If license is null, read licence files at the source commit.
> 3. Fetch only the source commit into a temporary Git repository. Do not clone full history. Derive repository_url from sourceUrl, including repository renames. If sourceCommit is absent, resolve the sourceUrl ref once. Set source_commit to that actual commit. Run these commands in one shell call:
>
> ```sh
> git init --quiet "$temporary_dir"
> git -C "$temporary_dir" fetch --quiet --depth=1 "$repository_url" "$source_commit"
> git -C "$temporary_dir" checkout --quiet --detach FETCH_HEAD
> ```
>
> Read applicable licence declarations and notices at that commit. If copying is not permitted, report the restriction and stop.
> 4. Inspect source entries together, then copy the directory containing skillPath into `./skills/tavily-best-practices`. Use the user's path if selected. Keep the original SKILL.md, relative links, scripts, binary assets, and executable modes. Exclude .git metadata. Reject symlinks and paths outside the Skill directory. After checking entries, use cp -a where available. A regular source directory needs no custom copy script. Do not save this page wrapper as SKILL.md.
> Preserve author credit, notices, and applicable licence files from repository or parent directories. Add PROVENANCE.md with the Skill page, source URL, actual commit, original path, and licence. Retain any existing PROVENANCE.md and record new provenance separately. Batch source inspection, copying, and provenance work where practical.
> 5. In the project root, run `skilld install ./skills/tavily-best-practices --mode copy --plain`. If skilld is unavailable, use `npx skilld install ./skills/tavily-best-practices --mode copy --plain`. This known command needs no help lookup. Install does not support --json. Use detected Agent targets, or add --agent for the targets the user selected. Install the local path, never the upstream selector. If installation fails, preserve the local copy and report the exact failure.
> 6. Confirm the local lockfile source and installed Agent copies once. Report the local path, actual commit, and Agent targets. After edits, reinstall the same local path. Upstream updates must not replace it. Do not publish or push unless the user asks.

# Tavily

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

## Installation

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

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

See **[references/sdk.md](https://skilld.dev/api/skills-raw/tavily-ai/skills/tavily-best-practices/references/sdk.md)** for complete SDK reference.

## Client Initialization

```python
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

```python
response = client.search(
    query="quantum computing breakthroughs",  # Keep under 400 chars
    max_results=10,
    search_depth="advanced"
)
print(response)
```
Key parameters: `query`, `max_results`, `search_depth` (ultra-fast/fast/basic/advanced), `include_domains`, `exclude_domains`, `time_range`

See **[references/search.md](https://skilld.dev/api/skills-raw/tavily-ai/skills/tavily-best-practices/references/search.md)** for complete search reference.

### extract() - URL Content Extraction

```python
# Simple one-step extraction
response = client.extract(
    urls=["https://docs.example.com"],
    extract_depth="advanced"
)
print(response)
```
Key parameters: `urls` (max 20), `extract_depth`, `query`, `chunks_per_source` (1-5)

See **[references/extract.md](https://skilld.dev/api/skills-raw/tavily-ai/skills/tavily-best-practices/references/extract.md)** for complete extract reference.

### crawl() - Site-Wide Extraction

```python
response = client.crawl(
    url="https://docs.example.com",
    instructions="Find API documentation pages",  # Semantic focus
    extract_depth="advanced"
)
print(response)
```
Key parameters: `url`, `max_depth`, `max_breadth`, `limit`, `instructions`, `chunks_per_source`, `select_paths`, `exclude_paths`

See **[references/crawl.md](https://skilld.dev/api/skills-raw/tavily-ai/skills/tavily-best-practices/references/crawl.md)** for complete crawl reference.

### map() - URL Discovery

```python
response = client.map(
    url="https://docs.example.com"
)
print(response)
```

### 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`

See **[references/research.md](https://skilld.dev/api/skills-raw/tavily-ai/skills/tavily-best-practices/references/research.md)** for complete research reference.

## Detailed Guides

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

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