Setup & Installation

Install Tavily AI Search using the ClawHub CLI or OpenClaw CLI:

clawhub install tavily

If the CLI is not installed:

npx clawhub@latest install tavily

Or install with OpenClaw CLI:

openclaw skills install tavily

View on ClawHub · View on GitHub

What This Skill Does

Tavily AI Search is a Productivity & Workflow skill for OpenClaw by bert-builder.

Tavily AI Search

Overview

Tavily is a search engine specifically optimized for Large Language Models and AI applications. Unlike traditional search APIs, Tavily provides AI-ready results with optional answer generation, clean content extraction, and domain filtering capabilities.

Key capabilities:

  • AI-generated answer summaries from search results
  • Clean, structured results optimized for LLM processing
  • Fast (basic) and comprehensive (advanced) search modes
  • Domain filtering (include/exclude specific sources)
  • News-focused search for current events
  • Image search with relevant visual content
  • Raw content extraction for deeper analysis

Architecture

graph TB
    A[User Query] --> B{Search Mode}
    B -->|basic| C[Fast Search<br/>1-2s response]
    B -->|advanced| D[Comprehensive Search<br/>5-10s response]
    
    C --> E[Tavily API]
    D --> E
    
    E --> F{Topic Filter}
    F -->|general| G[Broad Web Search]
    F -->|news| H[News Sources<br/>Last 7 days]
    
    G --> I[Domain Filtering]
    H --> I
    
    I --> J{Include Domains?}
    J -->|yes| K[Filter to Specific Domains]
    J -->|no| L{Exclude Domains?}
    K --> M[Search Results]
    L -->|yes| N[Remove Unwanted Domains]
    L -->|no| M
    N --> M
    
    M --> O{Response Options}
    O --> P[AI Answer<br/>Summary]
    O --> Q[Structured Results<br/>Title, URL, Content, Score]
    O --> R[Images<br/>if requested]
    O --> S[Raw HTML Content<br/>if requested]
    
    P --> T[Return to Agent]
    Q --> T
    R --> T
    S --> T
    
    style E fill:#4A90E2
    style P fill:#7ED321
    style Q fill:#7ED321
    style R fill:#F5A623
    style S fill:#F5A623

Quick Start

Basic Search

# Simple query with AI answer
scripts/tavily_search.py "What is quantum computing?"

# Multiple results
scripts/tavily_search.py "Python best practices" --max-results 10

Advanced Search

# Comprehensive research mode
scripts/tavily_search.py "Climate change solutions" --depth advanced

# News-focused search
scripts/tavily_search.py "AI developments 2026" --topic news

Domain Filtering

# Search only trusted domains
scripts/tavily_search.py "Python tutorials" \
  --include-domains python.org docs.python.org realpython.com

# Exclude low-quality sources
scripts/tavily_search.py "How to code" \
  --exclude-domains w3schools.com geeksforgeeks.org

With Images

# Include relevant images
scripts/tavily_search.py "Eiffel Tower architecture" --images

Search Modes

Basic vs Advanced

Mode Speed Coverage Use Case
basic 1-2s Good Quick facts, simple queries
advanced 5-10s Excellent Research, complex topics, comprehensive analysis

Decision tree:

  1. Need a quick fact or definition? → Use basic
  2. Researching a complex topic? → Use advanced
  3. Need multiple perspectives? → Use advanced
  4. Time-sensitive query? → Use basic

General vs News

Topic Time Range Sources Use Case
general All time Broad web Evergreen content, tutorials, documentation
news Last 7 days News sites Current events, recent developments, breaking news

Decision tree:

  1. Query contains "latest", "recent", "current", "today"? → Use news
  2. Looking for historical or evergreen content? → Use general
  3. Need up-to-date information? → Use news

API Key Setup

Option 1: Clawdbot Config (Recommended)

Add to your Clawdbot config:

{
  "skills": {
    "entries": {
      "tavily": {
        "enabled": true,
        "apiKey": "tvly-YOUR_API_KEY_HERE"
      }
    }
  }
}

Access in scripts via Clawdbot's config system.

Option 2: Environment Variable

export TAVILY_API_KEY="tvly-YOUR_API_KEY_HERE"

Add to ~/.clawdbot/.env or your shell profile.

Getting an API Key

  1. Visit https://tavily.com
  2. Sign up for an account
  3. Navigate to your dashboard
  4. Generate an API key (starts with tvly-)
  5. Note your plan's rate limits and credit allocation

Common Use Cases

1. Research & Fact-Finding

# Comprehensive research with answer
scripts/tavily_search.py "Explain quantum entanglement" --depth advanced

# Multiple authoritative sources
scripts/tavily_search.py "Best practices for REST API design" \
  --max-results 10 \
  --include-domains github.com microsoft.com google.com

2. Current Events

# Latest news
scripts/tavily_search.py "AI policy updates" --topic news

# Recent developments in a field
scripts/tavily_search.py "quantum computing breakthroughs" \
  --topic news \
  --depth advanced

3. Domain-Specific Research

# Academic sources only
scripts/tavily_search.py "machine learning algorithms" \
  --include-domains arxiv.org scholar.google.com ieee.org

# Technical documentation
scripts/tavily_search.py "React hooks guide" \
  --include-domains react.dev

4. Visual Research

# Gather visual references
scripts/tavily_search.py "modern web design trends" \
  --images \
  --max-results 10

5. Content Extraction

# Get raw HTML content for deeper analysis
scripts/tavily_search.py "Python async/await" \
  --raw-content \
  --max-results 5

Response Handling

AI Answer

The AI-generated answer provides a concise summary synthesized from search results:

{
  "answer": "Quantum computing is a type of computing that uses quantum-mechanical phenomena..."
}

Use when:

  • Need a quick summary
  • Want synthesized information from multiple sources
  • Looking for a direct answer to a question

Skip when (--no-answer):

  • Only need source URLs
  • Want to form your own synthesis
  • Conserving API credits

Structured Results

Each result includes:

  • title: Page title
  • url: Source URL
  • content: Extracted text snippet
  • score: Relevance score (0-1)
  • raw_content: Full HTML (if --raw-content enabled)

Images

When --images is enabled, returns URLs of relevant images found during search.

Best Practices

1. Choose the Right Search Depth

  • Start with basic for most queries (faster, cheaper)
  • Escalate to advanced only when:
    • Initial results are insufficient
    • Topic is complex or nuanced
    • Need comprehensive coverage

2. Use Domain Filtering Strategically

Include domains for:

  • Academic research (.edu domains)
  • Official documentation (official project sites)
  • Trusted news sources
  • Known authoritative sources

Exclude domains for:

  • Known low-quality content farms
  • Irrelevant content types (Pinterest for non-visual queries)
  • Sites with paywalls or access restrictions

3. Optimize for Cost

  • Use basic depth as default
  • Limit max_results to what you'll actually use
  • Disable include_raw_content unless needed
  • Cache results locally for repeated queries

4. Handle Errors Gracefully

The script provides helpful error messages:

# Missing API key
Error: Tavily API key required
Setup: Set TAVILY_API_KEY environment variable or pass --api-key

# Package not installed
Error: tavily-python package not installed
To install: pip install tavily-python

Integration Patterns

Programmatic Usage

from tavily_search import search

result = search(
    query="What is machine learning?",
    api_key="tvly-...",
    search_depth="advanced",
    max_results=10
)

if result.get("success"):
    print(result["answer"])
    for item in result["results"]:
        print(f"{item['title']}: {item['url']}")

JSON Output for Parsing

scripts/tavily_search.py "Python tutorials" --json > results.json

Chaining with Other Tools

# Search and extract content
scripts/tavily_search.py "React documentation" --json | \
  jq -r '.results[].url' | \
  xargs -I {} curl -s {}

Comparison with Other Search APIs

vs Brave Search:

  • ✅ AI answer generation
  • ✅ Raw content extraction
  • ✅ Better domain filtering
  • ❌ Slower than Brave
  • ❌ Costs credits

vs Perplexity:

  • ✅ More control over sources
  • ✅ Raw content available
  • ✅ Dedicated news mode
  • ≈ Similar answer quality
  • ≈ Similar speed

vs Google Custom Search:

  • ✅ LLM-optimized results
  • ✅ Answer generation
  • ✅ Simpler API
  • ❌ Smaller index
  • ≈ Similar cost structure

Troubleshooting

Script Won't Run

# Make executable
chmod +x scripts/tavily_search.py

# Check Python version (requires 3.6+)
python3 --version

# Install dependencies
pip install tavily-python

API Key Issues

# Verify API key format (should start with tvly-)
echo $TAVILY_API_KEY

# Test with explicit key
scripts/tavily_search.py "test" --api-key "tvly-..."

Rate Limit Errors

  • Check your plan's credit allocation at https://tavily.com
  • Reduce max_results to conserve credits
  • Use basic depth instead of advanced
  • Implement local caching for repeated queries

Resources

See api-reference.md for:

  • Complete API parameter documentation
  • Response format specifications
  • Error handling details
  • Cost and rate limit information
  • Advanced usage examples

Dependencies

  • Python 3.6+
  • tavily-python package (install: pip install tavily-python)
  • Valid Tavily API key

Credits & Attribution

Version History

Latest version: 1.0.0

First published: Jan 24, 2026. Last updated: Jan 24, 2026.

1 version released.

Frequently Asked Questions

Is Tavily AI Search free to use?
Yes. Tavily AI Search is a free, open-source skill available on the OpenClaw Skills Registry. You can install and use it at no cost, and the source code is publicly available for review and contribution.
What platforms does Tavily AI Search support?
It runs on any platform that supports OpenClaw, including macOS, Linux, and Windows. As long as you have the OpenClaw runtime installed, Tavily AI Search will work seamlessly across operating systems.
How do I update Tavily AI Search?
Run openclaw skills update tavily to get the latest version. OpenClaw will download and apply the update automatically, preserving your existing configuration.
Can I use Tavily AI Search with other skills?
Yes. OpenClaw skills are composable — you can combine Tavily AI Search with any other installed skill in your workflows. This allows you to build powerful multi-step automations by chaining skills together.