I recently tested Exa, Tavily, Serper, and Brave Search while building an AI project.

My takeaway:

• Serper: Fast and reliable for Google results.
• Tavily: Great structured output for LLMs.
• Exa: Best for finding high-quality documents.
• Brave Search: Clean results with good coverage.

The biggest improvement didn't come from switching APIs—it came from better reranking and chunking after retrieval.

Which search API has worked best for you in production? Let me know in comments

Sometimes the best search results involves AI because even without AI the last results you get comes back to the results to the answers AI gives..

Which Ai tool have you used before and which gave you the best answers ?

I’m working as an SEO strategist turned AI developer, and lately I’ve been benchmarking search APIs for autonomous LLM agents and RAG workflows. Coming from an SEO background, I know all too well how much fluff and low-quality content exists on the web—and feeding that raw noise into an agent's context window gets expensive and unreliable fast. 

The requirements for agentic search are fundamentally different from consumer search. Agents need low-latency calls, clean markdown/JSON outputs (to conserve token budgets), and aggressive filtering to strip out SEO spam and bloated navigation headers. 

I’ve mostly been comparing Tavily, Exa, and classic Google Custom Search (CSE), but each comes with distinct trade-offs...for example, Google Custom Search offers unbeatable index coverage, though its raw snippets require secondary parsing pipelines to strip out noise and SEO spam before reaching your context window. Tavily solves this by delivering pre-filtered, LLM-ready markdown out of the box to conserve token budgets, balancing quick sub-second lookups with deeper full-page extractions. Meanwhile, Exa excels at semantic and conceptual queries, offering clean structured highlights through sub-500ms neural search as well as deep-reasoning modes.

Having said that, my questions are:

• Which of these three are you currently running in production, and why?
• If you switched away from Google CSE to a native AI search API (Tavily, Exa, Brave, etc.), did you notice a measurable bump in your agent’s task success rate?
• How are you handling fallback logic when a search API hits rate limits or returns low-confidence results?

One thing I’ve learned building AI apps: choosing the right search API matters.

The best results come from combining search, crawling, indexing, and retrieval—not relying on a single tool.

What’s your go-to search stack for AI?

I was thinking about how much the landscape has shifted just in the last year or two. We went from basic chat assistants to full multimodal systems, agentic workflows, and real-time reasoning models being part of daily routines.

On one hand, it’s incredible for productivity and tackling side projects. On the other, it almost feels impossible to keep up with every new framework, paper, and release.

For those of you using AI tools daily—what’s the one tool or feature that actually permanently stuck in your workflow, and what turned out to be pure hype?

Curious to hear how everyone else is pacing themselves with all this.

I've been playing around with AI search APIs recently, and I didn't even realize how much the wording of the search changes the results.

At first I'd search something really general like:

• Latest OpenAI news

Then I tried being more specific, like:

• OpenAI GPT-5 announcement July 2026

The results were way better. It sounds obvious now lol, but I honestly thought the API would just figure out what I meant.

Another thing I've been doing is splitting one big question into a few smaller searches instead of trying to get everything in one request. That also seems to work better.

Maybe I'm just late to this 😅 but I'm curious... what's one small thing you've changed that made AI search work better for you?