tavily
AI-optimized web search using Tavily Search API. Use when you need comprehensive web research, current events lookup, domain-specific search, or AI-generated answer summaries. Tavily is optimized for LLM consumption with clean structured results, answer generation, and raw content extraction. Best for research tasks, news queries, fact-checking, and gathering authoritative sources.
- License 许可
- MIT
- 最近更新
- 2026-08-23
- Stars
- ★ 1
- Forks
- 0
01 前言 Front Matter
- name
- tavily
- description
- AI-optimized web search using Tavily Search API. Use when you need comprehensive web research, current events lookup, domain-specific search, or AI-generated answer summaries. Tavily is optimized for LLM consumption with clean structured results, answer generation, and raw content extraction. Best for research tasks, news queries, fact-checking, and gathering authoritative sources.
02 资源文件 6
- 参考文档_meta.json280 B
- 许可证LICENSE1.0 KB
- 参考文档references/api-reference.md5.1 KB
- 脚本scripts/tavily_search.py7.2 KB
- 参考文档SOURCE.md351 B
- 参考文档TRUST.auto.yaml2.2 KB
03 SKILL.md 正文
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:
- Need a quick fact or definition? → Use
basic - Researching a complex topic? → Use
advanced - Need multiple perspectives? → Use
advanced - 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:
- Query contains "latest", "recent", "current", "today"? → Use
news - Looking for historical or evergreen content? → Use
general - 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
- Visit https://tavily.com
- Sign up for an account
- Navigate to your dashboard
- Generate an API key (starts with
tvly-) - 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 titleurl: Source URLcontent: Extracted text snippetscore: Relevance score (0-1)raw_content: Full HTML (if--raw-contentenabled)
Images
When --images is enabled, returns URLs of relevant images found during search.
Best Practices
1. Choose the Right Search Depth
- Start with
basicfor most queries (faster, cheaper) - Escalate to
advancedonly when:- Initial results are insufficient
- Topic is complex or nuanced
- Need comprehensive coverage
2. Use Domain Filtering Strategically
Include domains for:
- Academic research (
.edudomains) - 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
basicdepth as default - Limit
max_resultsto what you'll actually use - Disable
include_raw_contentunless 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_resultsto conserve credits - Use
basicdepth instead ofadvanced - 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-pythonpackage (install:pip install tavily-python)- Valid Tavily API key
Credits & Attribution
- Tavily API: https://tavily.com
- Python SDK: https://github.com/tavily-ai/tavily-python
- Documentation: https://docs.tavily.com
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