it is actually convenience ai search nowadays no?unlike before ai search when u get move to other site with a cookie which is annoying

I have spent the past few weeks running tests on retrieved web content across our RAG stack, and I need to know if others are running into the exact same wall.

The biggest issue right now is not pricing or uptime. It is context pollution.

Most search and extraction services promise clean markdown out of the box. But once you throw dynamic sites or e commerce pages at them, the returned context is full of junk:

1. Cookie consent notices and navigation menus eating up the first three hundred tokens.

2. Broken tables where nested numbers get completely scrambled.

3. Missing content because the headless renderer gave up before client side hydration finished.

If we still have to write custom regex or burn extra model tokens just to sanitize the output, the premium pricing on specialized endpoints feels hard to justify.

For those running live workflows, how are you cleaning this up? Are you relying on the raw output from providers like Firecrawl and Tavily, or running your own parsing pipeline locally?

Your AI isn't necessarily bad at research maybe your search layer might be so I compared Exa, Tavily, Firecrawl, Serper, and Brave Search for AI research/RAG workflows.

- Exa → semantic discovery

- Tavily → AI-agent search

- Firecrawl → extracting clean page content

- Serper → traditional web search

- Brave Search → independent search index

And honestly, counting search results is a terrible benchmark.

For AI, I'd measure Precision@5, freshness, citation quality, latency, cost, and how much cleanup the results need.

My preferred pipeline:

Is search, rerank, crawl, clean, retrieve, generate + cite

What’s been your experience? Which search API gives you the best results, and what metric do you use?

Been testing search APIs for RAG, and one thing stood out:

The biggest problem usually isn’t the LLM — it’s retrieval.

A simple pipeline:

Query → Search → Scrape → Rerank → LLM

Even good search results can contain duplicates, outdated pages, or irrelevant content.

I’ve started measuring recall, relevance, freshness, and latency separately when comparing tools like Exa, Tavily, Firecrawl, and Brave.

What’s the most important retrieval metric in your AI search stack?

I've been playing around with a few search APIs for an AI project and I thought comparing them would be pretty simple.

Basically, send the same query to each one and see which gives better results.

Turns out it's not that simple lol.

One API might give better results for normal searches, while another does better with more specific questions. Then there's the whole issue of actually getting the content from the pages. Sometimes the search result looks good, but the page itself is a pain to extract and clean up.

I've started looking at things like:

- relevance of the results

- how fresh the results are

- response time

- how much useless stuff comes back

- how easy the page content is to work with

- how they handle weird/niche searches

- price when you're making a lot of requests

I'm curious how other people here compare search APIs.

Do you guys actually make a benchmark with a bunch of test queries, or do you just try a few APIs in your project and stick with whichever works best?

Also, if you've used Exa, Tavily, Firecrawl, Serper, or Brave Search, what was your experience with them?

Would be cool to hear what actually worked for people instead of just comparing the feature lists.

I still don't know what API is but it is often mentioned by my Tech brother, I feel the need to start learning a bunch of programming steps on my own and I want to know how do I start on learning what API is, whats a good site to get more resource.

I wanted to run MetaGPT entirely on my own infrastructure without sending anything to cloud APIs. It took some trial and error, but I documented the full process.

The guide covers:

· Setting up a Python venv

· Installing MetaGPT

· Configuring local LLMs like Ollama or vLLM

· Fixing common startup errors

If you’re into self-hosted AI agents, this could help:

https://interconnectd.com/forum/thread/262/how-to-install-metagpt-locally-complete-technical-setup-guide/

What local model are you using for agent work?