Tavily vs Aegis Research API: Which AI Search API Should You Use?

If you're building AI agents or applications that need web research capabilities, you've probably encountered Tavily. It's one of the most popular search APIs in the AI ecosystem, integrated into LangChain, CrewAI, and dozens of agent frameworks.

But after building with Tavily for several projects, we kept running into the same problems. So we built Aegis Research API to solve them. Here's an honest comparison.

The Core Difference

Tavily returns raw search results. You get URLs, snippets, and metadata. Your application (or another LLM call) needs to synthesize these into useful information.

Aegis returns AI-synthesized research. You get a structured response with a summary, key findings, detailed analysis, and cited sources. The synthesis is included in the API call.

This fundamental difference affects everything else.

Reliability

Tavily's Issues

Search the Tavily GitHub issues or Reddit discussions and you'll find a pattern:

• "Random 400 errors on the same requests"
• "Extract API returning AttributeError"
• "Broken/dead links in results"
• "Inconsistent 500/502/504 errors"

The common workaround? Use Tavily for intent detection, then verify with your own scraper. That defeats the purpose of using an API.

Aegis Approach

We validate and verify before returning results. Our synthesis layer catches dead links, contradictory information, and low-quality sources before they reach your application.

Current uptime: 99.9% over the last 30 days.

Pricing Comparison

At first glance, Tavily looks cheaper. But consider the full cost:

With Tavily: 1. Pay for search API ($0.008) 2. Pay for LLM to synthesize results (~$0.01-0.05) 3. Handle retries for failed requests 4. Build error handling for broken links

With Aegis: 1. Pay for research API ($0.018) 2. Get synthesized results directly

For simple "find me URLs" use cases, Tavily is cheaper. For "research this topic and give me findings," Aegis is often more cost-effective when you factor in the LLM synthesis cost you'd need anyway.

API Response Comparison

Tavily Response

{
  "results": [
    {
      "title": "Rate Limiting Best Practices",
      "url": "https://example.com/rate-limiting",
      "content": "Rate limiting is a technique...",
      "score": 0.95
    },
    // ... more results
  ]
}

You get raw results. Now you need to: - Filter out low-quality sources - Handle dead links - Call an LLM to synthesize - Format the output

Aegis Response

{
  "research_id": "res_abc123",
  "status": "completed",
  "summary": "Rate limiting APIs requires a multi-layered approach...",
  "key_findings": [
    "Token bucket algorithms provide the best balance...",
    "Redis-based solutions scale to 100K+ req/sec...",
    "Client-side retry with exponential backoff is essential..."
  ],
  "analysis": "The research reveals three dominant patterns...",
  "sources": [
    {"url": "...", "title": "...", "relevance": 0.95}
  ],
  "cached": false,
  "credits_used": 3
}

You get a complete research artifact ready to use.

When to Use Each

Use Tavily When:

• You need raw search results for custom processing
• You're building a search UI that shows individual results
• You have existing LLM synthesis pipelines
• Cost is the primary concern and you're doing high volume basic searches
• You want the largest free tier to experiment

Use Aegis When:

• You need synthesized research findings
• You're building AI agents that consume research
• You want reliability over the cheapest price
• You don't want to manage a separate synthesis step
• You need consistent, structured output

Integration Comparison

Both APIs integrate easily with modern frameworks:

Tavily + LangChain

from langchain_community.tools.tavily_search import TavilySearchResults

tool = TavilySearchResults()
results = tool.invoke({"query": "rate limiting best practices"})
# Returns raw results - needs synthesis

Aegis + Any Framework

import requests

response = requests.post(
    "https://aegisagent.ai/api/v1/research",
    headers={"X-API-Key": "your_key"},
    json={"topic": "rate limiting best practices", "depth": "medium"}
)
# Returns synthesized research
findings = response.json()["key_findings"]

The Verdict

Tavily has earned its place in the AI ecosystem. The large free tier makes it accessible, and raw search results are exactly what some applications need.

But if you've been frustrated by reliability issues, tired of building synthesis pipelines, or want a simpler architecture, Aegis Research API offers a different approach: pay slightly more per request, get synthesized research directly, and skip the complexity.

Try the free tier (500 credits/month) and see which approach works better for your use case.

Ready to try Aegis Research API? Get 500 free credits - no credit card required.

Disclosure: This comparison was written by the Aegis team. We've tried to be fair, but we obviously have a bias. Test both APIs with your actual use case before deciding.