Quercle vs Tavily: Which Web API is Best for AI Agents?

TL;DR

Overview

Both Quercle and Tavily offer fetch and search APIs for AI applications. The key differences are in security, JavaScript rendering, and fetch output format. Both provide LLM-synthesized search answers - Tavily via the include_answer parameter, Quercle by default.

Key Differences

1. Security

Quercle includes built-in prompt injection detection. When a webpage contains hidden malicious instructions, Quercle detects and blocks them before they reach your LLM.

Tavily passes through content as-is, including any hidden prompt injection attacks embedded in pages.

2. JavaScript Rendering

Quercle fully renders JavaScript-heavy pages. SPAs, React apps, and dynamic content work out of the box.

Tavily's JavaScript rendering behavior is poorly documented. Their documentation doesn't mention how they handle JS-rendered content. In our tests, initial requests to JS-heavy pages (like Artifact Hub) failed with "Failed to fetch url" errors. However, subsequent requests to the same URL sometimes succeeded, suggesting Tavily may process pages offline in the background. This means dynamic content may not be up-to-date, and first-time requests to uncached JS-heavy pages will likely fail.

3. Fetch Output

Quercle processes fetched content through an LLM with your custom prompt:

{
  "url": "https://example.com/docs",
  "prompt": "Extract the API authentication steps"
}
// Returns: Clean markdown with just the auth steps

Tavily's /extract endpoint returns raw content chunks. The query parameter reranks chunks by relevance, but you still get raw chunks rather than a processed answer:

{
  "urls": ["https://example.com/docs"],
  "query": "authentication steps",  // Reranks chunks by relevance
  "extract_depth": "advanced"       // retrieves tables & embedded content
}
// Returns: Raw content chunks your agent still needs to process

Tavily's extract has many parameters to configure (extract_depth, format, timeout, include_images, etc.) - you may need to dig through their docs to find the right options for your use case.

4. Search Output

Both provide LLM-synthesized answers for search queries:

Quercle /v1/search:

{ "query": "How to implement OAuth2?" }
// Returns: LLM-synthesized answer with inline source citations

Tavily /search with include_answer:

{ "query": "...", "include_answer": "advanced" }
// Returns: LLM-synthesized answer (basic or advanced) with search results

Both generate answers using an LLM - Tavily offers "basic" (quick) or "advanced" (detailed) answer modes via the include_answer parameter.

When to Use Quercle

• AI agents browsing untrusted websites autonomously
• Security-sensitive applications where prompt injection is a concern
• JavaScript-heavy sites like documentation, dashboards, or SPAs
• Applications needing processed, ready-to-use content from fetches

When to Use Tavily

• RAG applications where you control and trust the document sources
• Site crawling/mapping - Tavily offers dedicated crawl and map endpoints
• Multi-step research - Tavily's research endpoint handles complex queries
• Simple static pages that don't require JS rendering

Pricing Comparison (smallest volume plan)

Tavily's "advanced" extraction (2 credits per 5 URLs) is used for comparison as it retrieves tables and embedded content, making it more comparable to Quercle's full page processing.

For comparable operations, Quercle is 3x cheaper for fetching and 37% cheaper for search.

The Bottom Line

Choose Quercle if security matters or you need JavaScript rendering. For AI agents browsing the open web, prompt injection detection is essential.

Choose Tavily if you need crawling, mapping, or multi-step research capabilities, or you're building RAG systems with controlled, trusted content sources.

Both offer fetch and search - the choice depends on your security needs, content sources, and whether you need crawling or research capabilities.