The Problem with Traditional Search
When you search Google, you get a page full of blue links, ads, "People also ask" boxes, and knowledge panels. That's great for humans scanning a screen. It's terrible for an AI agent that needs structured, machine-readable data.
Here's what happens when an AI agent uses Google: 1. Parse HTML (thousands of lines of noise) 2. Extract relevant text (ads, nav, cookie banners mixed in) 3. Hope the result is actually relevant 4. Wait 200-500ms per query 5. Pay $5 per 1,000 queries
Here's what happens with SearchX: 1. Call one API endpoint 2. Get clean JSON with title, snippet, trust score, citation 3. Results are semantically ranked — meaning, not just keywords 4. Response in under 5ms 5. 1,000 queries/day free
Why Agents Need Different Search
AI agents aren't browsing. They're executing tasks. A coding agent needs API documentation, not a blog post about coding. A research agent needs papers and data, not opinion pieces.
SearchX solves this with the agent parameter. Pass agent=coder and results prioritize documentation, GitHub repos, and Stack Overflow. Pass agent=researcher and academic papers float to the top.
The Search Grounding Effect
The #1 cause of AI hallucination is lack of real-time data. An LLM trained on 2024 data will confidently make up facts about 2026. Web search grounding — feeding real-time search results as context — reduces hallucinations by up to 90%.
SearchX provides exactly this: fresh, structured, semantic search results that agents can use as grounding context before generating responses.
Try It Now
curl https://searchx.dev/api/v1/search \ -G -d "q=kubernetes latest version" \ -d "mode=hybrid" \ -d "agent=coder"
No signup. No API key. Just results.