Tavily Skill

This skill extends Manus's capabilities by providing highly specialized workflows, reference guides, and helper scripts to integrate Tavily's search, extraction, and research APIs. It is designed to be highly token-efficient, leveraging progressive disclosure.

Quick Navigation

• Core Search & RAG Workflow: See workflows.md for step-by-step sequential patterns.
• API Reference & Specifications: See api_reference.md for endpoint details, parameter ranges, and JSON schemas.
• Troubleshooting & Diagnostics: See troubleshooting.md for diagnostic checklists and error handling strategies.

Basic Principles

1. Match Search Depth to Query Complexity

• Use basic (1 credit) for simple queries (e.g., factual lookups, company names).
• Use advanced (2 credits) for complex research questions requiring multi-query expansion and synthesis.
• Use fast or ultra-fast (1 credit) for latency-critical applications (e.g., real-time chat, voice assistants).

2. Prefer Extract over Raw Search for RAG

• Avoid downloading full HTML or uncleaned pages.
• Use the Extract API with query to trigger Intent-Based Extraction.
• Limit the chunks returned per source using chunks_per_source (range: 1 to 5) to save downstream LLM context window tokens.

3. Handle Rate Limits Gracefully

• Development keys are capped at 100 RPM. Production keys are capped at 1,000 RPM.
• Always wrap Tavily API calls in a retry wrapper that parses the retry-after header during 429 Too Many Requests responses.

Code Boilerplate

Synchronous Python Search

from tavily import TavilyClient

client = TavilyClient(api_key="tvly-YOUR_API_KEY")
response = client.search(
    query="quantum computing trends 2026",
    search_depth="advanced",
    max_results=5
)

Asynchronous Python Extract

import asyncio
from tavily import AsyncTavilyClient

async def extract_links():
    client = AsyncTavilyClient(api_key="tvly-YOUR_API_KEY")
    response = await client.extract(
        urls=["https://example.com/ai-agents"],
        query="agent orchestration",
        chunks_per_source=3
    )
    return response