Tavily + Graphlit: Web Search That Feeds Your Knowledge Base

Tavily has become the go-to web search API for AI applications. It's optimized for LLMs, returns clean results, and integrates easily with agent frameworks. If you're building AI that needs current web information, Tavily delivers.

Graphlit integrates Tavily as a native search backend. When you search with Tavily through Graphlit, results don't just return as text — they're automatically ingested, processed, and added to your knowledge base with full semantic infrastructure.

This means Tavily's excellent web search combined with Graphlit's knowledge graphs, entity extraction, and hybrid search. The best of both worlds.

Table of Contents

1. TL;DR — Quick Comparison
2. What Tavily Does Well
3. What Graphlit Adds
4. The Integration
5. When to Use Tavily Directly
6. When to Use Tavily Through Graphlit
7. Integration Example

TL;DR — Quick Comparison

What Tavily Does Well

Tavily is purpose-built for AI applications:

LLM-Optimized Results

Unlike traditional search APIs that return SEO-heavy snippets, Tavily extracts the actual content that LLMs need. Results are clean, relevant, and ready for context injection.

Smart Filtering

Tavily filters out low-quality content, paywalls, and SEO spam. You get substantive results, not clickbait.

Speed

Fast response times designed for real-time agent interactions. Your AI doesn't wait.

Simple Integration

Clean API, good documentation, easy to add to any agent framework. LangChain, LlamaIndex, and most agent tools have Tavily integrations.

Reasonable Pricing

Free tier for experimentation, affordable paid plans for production use.

For getting current web information into your AI application, Tavily is a solid choice.

What Graphlit Adds

Graphlit doesn't replace Tavily — it amplifies it:

Full Content Ingestion

Tavily returns snippets. Graphlit takes those URLs and scrapes the full content, extracting clean Markdown from web pages.

Automatic Processing

Every search result is automatically:

• Scraped for full content
• Converted to Markdown
• Embedded for vector search
• Entity-extracted (people, companies, topics)
• Added to your knowledge graph

Persistent Knowledge Base

Tavily results are ephemeral — search, use, forget. Graphlit results become part of your knowledge base, searchable alongside your other content.

Unified Search

Search across web results AND your documents, Slack messages, emails — everything in one query.

Automated Feeds

Set up recurring searches that automatically refresh. Monitor topics, track competitors, stay current — without manual API calls.

RAG-Ready

Web results are immediately available for conversations, with proper source citations.

The Integration

Tavily is one of Graphlit's SearchServiceTypes. Use it through the searchWeb method or create automated search feeds:

import { Graphlit, Types } from 'graphlit-client';

const client = new Graphlit();

// One-time web search with Tavily
const results = await client.searchWeb(
    "latest developments in AI agent memory",
    Types.SearchServiceTypes.Tavily,
    10  // limit
);

// Results include URLs, titles, and snippets
// Graphlit can then ingest full content from these URLs

For automated monitoring, create a search feed:

// Create a Tavily search feed that refreshes automatically
const feed = await client.createFeed({
    name: "AI Memory Research",
    type: Types.FeedTypes.Search,
    search: {
        type: Types.SearchServiceTypes.Tavily,
        text: "AI agent memory frameworks",
        readLimit: 10
    },
    schedulePolicy: {
        recurrenceType: Types.TimedPolicyRecurrenceTypes.Daily
    }
});

// New results are automatically:
// - Discovered via Tavily
// - Full content scraped
// - Processed and embedded
// - Added to your knowledge base

When to Use Tavily Directly

Use Tavily's API directly when:

• Ephemeral searches: Quick lookups that don't need persistence
• Agent tool calls: Simple search tools in agent frameworks
• Snippet-only needs: You only need result snippets, not full content
• Existing infrastructure: You've built your own ingestion pipeline
• Cost optimization: Minimizing per-search costs at high volume

Tavily is great as a standalone search API for simple use cases.

When to Use Tavily Through Graphlit

Use Graphlit's Tavily integration when:

• Building knowledge bases: Web research that accumulates over time
• Full content needed: Snippets aren't enough — you need the actual articles
• Entity tracking: Want to extract and connect people, companies, topics
• Unified search: Web results alongside your internal documents
• Automated monitoring: Recurring searches without manual API calls
• RAG applications: Web content as sources for AI conversations
• Team access: Multiple users searching and building shared knowledge

The integration turns ephemeral web searches into persistent, searchable, connected knowledge.

Integration Example

Tavily Direct: Search and Done

from tavily import TavilyClient

client = TavilyClient(api_key="...")

# Search
results = client.search(
    query="AI agent memory frameworks 2024",
    max_results=10
)

# Results are ephemeral — use them now or lose them
for result in results['results']:
    print(result['title'], result['url'])
    # result['content'] is a snippet, not full content

# To build a knowledge base, you'd need to:
# 1. Scrape each URL for full content
# 2. Extract clean text/Markdown
# 3. Generate embeddings
# 4. Store in vector database
# 5. Extract entities
# 6. Build search index
# 7. ...

Graphlit with Tavily: Search to Knowledge Base

import { Graphlit, Types } from 'graphlit-client';

const client = new Graphlit();

// Option 1: One-time search with automatic ingestion
const searchResults = await client.searchWeb(
    "AI agent memory frameworks 2024",
    Types.SearchServiceTypes.Tavily,
    10
);

// Ingest the full content from search results
for (const result of searchResults.searchWeb?.results || []) {
    await client.ingestUri(result.uri, result.title);
}

// Content is now:
// - Full pages scraped (not just snippets)
// - Converted to clean Markdown
// - Embedded for vector search
// - Entities extracted
// - Part of your knowledge base

// Option 2: Automated feed for ongoing monitoring
const feed = await client.createFeed({
    name: "AI Memory Research",
    type: Types.FeedTypes.Search,
    search: {
        type: Types.SearchServiceTypes.Tavily,
        text: "AI agent memory frameworks",
        readLimit: 10
    },
    schedulePolicy: {
        recurrenceType: Types.TimedPolicyRecurrenceTypes.Daily
    }
});

// Feed automatically discovers and ingests new content daily

// Search across everything — web results + your documents
const contents = await client.queryContents({
    search: "memory architecture comparison"
});

// RAG conversation with web research as sources
const response = await client.promptConversation(
    "What are the latest approaches to AI agent memory?",
    conversationId,
    { id: specificationId }
);

Other Web Search Options

Graphlit integrates multiple web search services. Choose based on your needs:

All integrate the same way — search results flow into your knowledge base with full processing.

Summary

Tavily is excellent for AI-powered web search — clean results, LLM-optimized, fast and affordable.

Graphlit integrates Tavily as a native backend, adding:

• Full content ingestion (not just snippets)
• Automatic embedding and entity extraction
• Persistent knowledge base storage
• Unified search across all your content
• Automated feed monitoring
• RAG-ready conversations

Use Tavily directly for quick, ephemeral searches. Use Tavily through Graphlit when web research should become part of your knowledge base.

Explore Graphlit Features:

• Web Scraping and Search — Web content ingestion
• Data Connectors — All integration options
• Building Knowledge Graphs — Entity extraction
• Complete Guide to Search — Hybrid search

Learn More:

• Graphlit Documentation
• Tavily Documentation
• Schedule a Demo
• Join our Discord

Web search finds information. Graphlit turns it into knowledge.