Quercle vs Exa: Choosing the Right Web API for Your AI Agents

TL;DR

Overview

Both Quercle and Exa offer fetch and search capabilities for AI applications. The key differences are in security, JavaScript rendering reliability, and search approach. Quercle prioritizes security with prompt injection detection, while Exa focuses on semantic search quality with embeddings-based retrieval.

Key Differences

1. Security

This is the most significant difference.

Quercle detects prompt injection attacks hidden in web pages - critical for autonomous agents browsing untrusted sites.

Exa is vulnerable to prompt injection. In our tests, Exa's model actually followed malicious instructions hidden in a page, asking users for API keys instead of summarizing the content. This is a serious concern for AI agents browsing the open web.

2. JavaScript Rendering

Quercle fully renders JavaScript-heavy pages. SPAs, React apps, and dynamic content work reliably.

Exa's JavaScript rendering is inconsistent and poorly documented. Their documentation doesn't clearly explain whether or how they handle JS-rendered content. In practice, it works on some pages but fails unpredictably on others with crawl timeouts. In our tests, Exa returned empty results for some Artifact Hub pages (a React-based Helm chart registry) with a "Crawl Livecrawl Timeout" error, while other pages on the same site worked fine. For AI agents that need reliable access to modern web applications, this lack of clarity and unpredictable behavior can be problematic.

3. Search Approach

Exa uses embeddings-based semantic search. Their search finds pages by meaning rather than just keywords - if you search for "companies that pivoted successfully," Exa understands the concept, not just the literal words.

Quercle uses traditional web search with LLM synthesis. Effective for most queries, but Exa's semantic approach can find more conceptually relevant results for abstract queries.

4. API Structure

Both offer similar capabilities:

Quercle:

• /v1/fetch - Fetch URL with prompt-based extraction
• /v1/search - Search with LLM-synthesized answer

Exa has 5 endpoints: /search, /contents, /findsimilar, /answer, and /research - offering more specialized operations for different use cases.

5. Output Quality

Quercle processes content through an LLM with your custom prompt, returning exactly what you asked for in markdown format.

Exa's /contents endpoint returns summaries guided by a query. The /answer endpoint provides synthesized answers similar to Quercle's search.

When to Use Quercle

• AI agents browsing untrusted websites
• Security-sensitive applications where prompt injection is a concern
• JavaScript-heavy sites like documentation, dashboards, or SPAs that need reliable rendering
• Autonomous systems that need to safely process web content
• Any application where the LLM shouldn't follow malicious instructions

When to Use Exa

• Semantic search - Exa's embeddings-based approach excels at finding conceptually related content
• Finding similar pages - The /findsimilar endpoint finds pages similar to a given URL
• Multi-step research - Exa's /research endpoint handles complex research tasks
• Websets - Exa offers curated datasets for specific domains (companies, research papers, etc.)
• RAG systems needing high-quality semantic retrieval from trusted sources

Pricing Comparison

Both have similar pricing for comparable operations.

The Bottom Line

Choose Quercle if security matters or you need reliable JavaScript rendering. For AI agents browsing the open web, prompt injection detection adds a critical security layer.

Choose Exa if you need semantic search, similar page discovery, curated websets, or multi-step research capabilities.

Both offer fetch and search - the choice depends on your security needs and whether you need specialized discovery features like semantic search or websets.