Grounding is the new "garbage in, garbage out." We spent years worrying about model weights and context windows, but in twenty twenty-six, your agent is only as good as the search layer feeding it. If that layer is noisy or slow, the agent just hallucinates with more confidence. Today's prompt from Daniel is about the modern search and grounding stack, and it is a fascinating architectural rabbit hole.
Herman Poppleberry here, and I have been waiting for us to really tear into this. The landscape has shifted so fast. We are no longer just talking about "Googling something" for an AI. We are talking about a sophisticated middleware layer that sits between the LLM and the live web. It's the difference between giving a researcher a library card and giving them a team of interns who pre-read, summarize, and highlight the relevant bits before the researcher even wakes up. By the way, today's episode is powered by Google Gemini three Flash, which is fitting since we are talking about the very infrastructure that makes models like this useful in production.
It’s interesting you mention the "intern" analogy—though I promised I’d keep the analogies to a minimum today. The reality is that as of the first quarter of twenty twenty-six, the number one bottleneck we see in production agent deployments isn't the reasoning capability of the model. It's the latency and the token waste of bad grounding. If you send an agent a raw HTML dump of a modern news site, you're burning thousands of tokens on navigation menus and tracking scripts.
And you're likely triggering the model to get lost in the weeds. The "grounding stack" is really about five or six key players right now. You’ve got SearXNG for the self-hosted meta-search crowd, Tavily for the "it just works" commercial API side, Perplexica if you want a full-stack answer engine, and then the extraction specialists like Firecrawl and Jina Reader.
Let’s start with the heavy hitter in the open-source world: SearXNG. If you look at any popular local LLM project or a privacy-focused agent framework on GitHub, SearXNG is almost always the default recommendation for search. Why is a meta-search engine from the pre-agent era suddenly the darling of the AI world?
It’s about the "federated" model. SearXNG doesn’t have its own index; it’s a proxy aggregator. It parallelizes queries across Google, Bing, DuckDuckGo, and about seventy other specialized engines. For a developer, that’s a massive win because it acts as a buffer. You aren't hitting one single provider that might shadow-ban your IP for making ten thousand automated requests an hour. SearXNG handles the rotation and the anonymity.
But there’s a maintenance tax there, right? I see people acting like SearXNG is "free" because it’s open source, but if you’re running a production agent, you aren't just running the container. You’re managing proxies, you’re dealing with CAPTCHAs, and you’re constantly tweaking the engines because Google changed their CSS selectors again.
That is the hidden cost. It is a "high-ops" solution. However, the reason it dominates the open-source stack is two-fold: privacy and sovereignty. If you are a developer building a research tool that handles sensitive corporate data, the last thing you want is your search queries—which often contain the "meat" of what you're working on—being sent to a third-party commercial API where they might be logged or used for training. With SearXNG, you can keep that query traffic inside your own VPC.
It’s the "anti-black-box" choice. You know exactly how the results are being ranked because you can see the configuration files. But then you look at the commercial side, and Tavily is everywhere. LangChain, CrewAI, AutoGPT—they all treat Tavily as the gold standard. What is Tavily doing that a meta-search engine isn't?
Tavily is a "result processor," not just a "query router." This is the fundamental shift. If you query SearXNG, you get a list of URLs and snippets. Then your agent has to decide which URL to click, fetch it, and clean it. Tavily optimizes for the LLM context window from the jump. When you hit their API, it doesn't just give you links; it fetches the content, strips out the SEO junk, removes the headers and footers, and ranks the results based on semantic relevance to the query, not just keyword density.
So it’s essentially a search engine that speaks "AI" instead of "Human." Humans want a pretty page with images and layout; agents want a clean markdown string that contains the answer to the specific question asked.
Well, not "exactly," but you've hit the nail on the head. Tavily’s API handles over fifty million queries per month now because it solves the "token bloat" problem. If I can get the same information in five hundred tokens of cleaned text versus five thousand tokens of raw HTML, the API pays for itself in reduced LLM costs alone.
I’ve seen some benchmarks suggesting that Tavily’s ranking is actually tuned for "truthfulness" rather than "clickability." Is that just marketing, or is there a technical mechanism there?
There is a mechanism. Traditional search engines prioritize "freshness" and "authority" in a way that often favors large media sites over specialized technical documentation or niche forums where the actual answer might live. Tavily uses a secondary LLM-based scoring pass to look at the content of the retrieved pages before returning them to you. It’s essentially doing a mini-RAG cycle inside the search API call.
Okay, let’s talk about the tradeoff. If I’m a startup, I’m looking at Tavily and thinking "great, I don't have to manage proxies." But if I scale to a million users, those per-query costs start looking like a mortgage payment. Is there a middle ground?
The middle ground is usually a hybrid architecture. You use something like SearXNG for the broad "discovery" phase where you're just looking for links, and then you use a specialized extraction tool for the "deep dive" phase. This brings us to the "Reader" layer—Jina Reader and Firecrawl. This is where the real engineering happens in twenty twenty-six.
I love Jina Reader for its simplicity. It’s the "one-shot" solution. You give it a URL, and it gives you back beautiful, clean markdown. It’s fast, it’s often free for a certain tier, and it’s very predictable. But it has a major blind spot: it’s a single-page tool.
It’s a "fetcher." If you want to summarize a news article or a single blog post, Jina is perfect. It’s lightweight. But agents today are being asked to do much more complex tasks. "Go to this company's website and find every mention of their sustainability policy across all their subpages." Jina can't do that. You’d have to write a custom crawler to feed Jina the URLs.
And that’s where Firecrawl comes in. I’ve been playing with Firecrawl lately, and it feels like the "heavy artillery" of the grounding world. It doesn’t just fetch; it crawls. It handles JavaScript rendering—which is huge because so much of the web is just a blank white screen until the React components load—and it can traverse sitemaps.
Firecrawl is fascinating because it’s effectively "browser-as-a-service." It’s designed to turn an entire website into an LLM-ready knowledge base. Their search endpoint is particularly interesting because it’s one of the only ones that returns the full scraped content of the pages alongside the search results in a single API call. You skip the "fetch" step entirely.
But that’s a lot of data. If I’m building a quick-response chatbot, sending it the full scraped content of five pages might be overkill. Doesn't that just move the token bloat problem further down the line?
It can, which is why the selection of these tools has to be task-specific. If you're doing "Deep Research"—the kind of agentic work where the model takes five minutes to think and write a ten-page report—you use Firecrawl. You want every scrap of data. If you're doing a "Quick Answer" tool like a customer support bot, you use Tavily or Jina Reader.
Let’s look at the "Answer Engine" wildcard: Perplexica. This one is rising in popularity because it’s basically a self-hosted Perplexity clone. But it’s more than just a UI; it’s a full RAG pipeline.
Perplexica is for the "I want to own the whole stack" crowd. It bundles the search—usually via SearXNG—with the scraping and the LLM synthesis. What's clever about it is the "Focus Modes." You can tell it to only search YouTube, or only search academic papers, or only search WolframAlpha.
That "Focus Mode" thing is actually a huge deal for grounding. One of the biggest causes of hallucinations is "context contamination." If I ask a medical question and the agent searches the general web, it might get a peer-reviewed study and a Reddit thread from "WellnessGuru42" in the same context window. The agent might give them equal weight.
Perplexica tries to solve that by narrowing the "search space" before the LLM even sees the results. It’s an architectural decision to move the "filtering" logic from the agent's prompt into the search infrastructure. I’ve seen legal tech firms using Perplexica because they can run it entirely on-premise. They link it to their internal document stores and a self-hosted SearXNG instance. No data ever touches a third-party search API.
So we have a clear spectrum emerging. On one end, you have the "Convenience/Speed" stack: Tavily plus Jina Reader. You pay a premium, but you ship your product in a weekend and the results are high-quality right out of the box. On the other end, you have the "Control/Privacy" stack: SearXNG plus Firecrawl plus Perplexica. It’s free in terms of licensing, but it’s expensive in terms of DevOps and compute.
And don't forget the "middle-ware" complexity of SearXNG. There are over two hundred public instances of SearXNG listed on their GitHub, but most production users are running their own. If you’re self-hosting, you have to worry about "search engine saturation." If you hit Google too hard from a single IP, they’ll start serving you different results or CAPTCHAs that your agent can't solve.
I think people underestimate how much "dark magic" goes into what Tavily does. They aren't just scraping; they are managing a global network of proxies and headless browsers. When you pay for Tavily, you're paying for their ability to stay one step ahead of the anti-bot measures that websites use.
That’s the "Vendor SDK Moat" we’ve talked about before. Once you integrate Tavily’s specific way of returning cleaned markdown, switching to a self-hosted SearXNG setup feels like a massive downgrade in terms of data cleanliness. You suddenly have to write your own cleaning scripts using BeautifulSoup or something similar, and you realize how much "noise" was being filtered out for you.
Let’s talk about the "Reader" evolution specifically. Jina Reader recently added a feature where it can actually "interact" with a page—clicking buttons or scrolling—to find hidden content. This starts to blur the line with Firecrawl.
It does, but Firecrawl is still the king of "depth." Think of it this way: Jina is a sniper, Firecrawl is a carpet-bombing run. If you need to know what is on a specific URL that is behind a "Read More" button, Jina is your tool. If you need to map out the entire structure of a competitor's documentation site to see how their API has changed over the last six months, you need Firecrawl’s crawling logic.
What I find wild is that we are essentially building a "parallel internet" for machines. The human internet is a mess of ads, pop-ups, and auto-playing videos. The "agentic internet" is this clean, structured, markdown-only world that these tools are carving out.
It’s a literal translation layer. We are translating "Human-Web" to "LLM-Web." And the tools that do that translation best are the ones winning. This is why SearXNG is so popular in the "Local LLM" community. If you’re running a model on your own hardware, you probably have a philosophical commitment to decentralization. You don't want to depend on a single API provider who could change their pricing or their "safety filters" tomorrow.
There is also the "Latency Tax." If my agent has to wait three seconds for SearXNG to aggregate results, and then another two seconds for a scraper to clean the text, and then ten seconds for the LLM to process it... that’s a fifteen-second delay for the user. In the world of twenty-six, that’s an eternity.
Tavily’s big selling point is that they’ve optimized that entire pipeline to happen in under two seconds. They’ve already cached the cleaned versions of popular pages. They’ve pre-indexed the "AI-relevant" parts of the web. It’s a specialized index that sits on top of the general web.
So if I'm a developer listening to this, and I’m trying to choose my stack... let’s give them some concrete decision points. If I’m building a "Personal Assistant" agent that helps me plan trips and find recipes, what’s the move?
For a personal assistant, I’d go Tavily. You want the "it just works" experience. You need high-quality, cleaned results quickly, and the query volume for a single user isn't going to break the bank. Plus, Tavily’s "context" feature—where it returns a single string of the most relevant facts across multiple sources—is perfect for a chatbot.
Okay, what if I’m building a "Competitive Intelligence" agent for a large enterprise? It needs to monitor fifty different news sites and company blogs every hour.
That is a Firecrawl use case. You need the crawling capability to see what has changed on a site-wide level. You’re less concerned about the cost per query because the value of the insights is high, and you need the robustness of a tool that can handle complex, JavaScript-heavy corporate sites. I’d probably pair Firecrawl with a self-hosted SearXNG instance to handle the initial "discovery" of new articles without paying a per-query fee for every "check for updates" ping.
And what about the "Privacy Maxis"? The folks building "Local-First" software or handling legal/medical data?
Perplexica is the clear winner there. It gives you that "Perplexity-style" experience where you get a cited, structured answer, but you can run the whole thing on your own infrastructure. You point it at a local instance of SearXNG and a local LLM like a fine-tuned Llama three or Mistral, and you have a world-class research tool that never leaks a single byte of data to the outside world.
I think a lot of people make the mistake of thinking they can just use the "Search" tool built into their LLM provider—like the "Google Search" tool in the Gemini API or the "Bing" tool in OpenAI. What are the downsides of just using the "native" grounding?
Control and transparency. When you use the native tool, you're at the mercy of the model provider's ranking. You can't tell the Gemini search tool to "only prioritize GitHub repos and ignore StackOverflow." You can't see the raw snippets it's picking from. You're getting a "pre-digested" version of the web. For simple stuff, it's fine. But for building an "Expert Agent," you need to be able to tune the grounding. You need to be able to say, "Hey, for this specific task, fetch the full content of these three specific technical docs, but for this other task, just give me a broad overview from news sites."
It’s the difference between a "black box" and a "glass box." With the external grounding stack, you can log exactly what the agent "saw" versus what it "said." That is crucial for debugging. If the agent gives a wrong answer, you can look at the Tavily or SearXNG logs and see: "Oh, the search result it got was actually incorrect," or "The search result was correct, but the agent ignored it."
That "traceability" is the only way to solve the hallucination problem in production. If you can't see the input data, you can't fix the output. I’ve seen teams spend weeks trying to "prompt engineer" their way out of a hallucination, only to realize that their search tool was just feeding the agent the wrong version of a documentation page.
There’s a misconception that open-source tools like SearXNG are always the "cheap" option. I’ve talked to developers who spent more on the compute and the proxy subscriptions to keep SearXNG running than they would have spent on a Tavily Pro plan.
It’s the "Free as in Puppy" problem. SearXNG is free to download, but it requires constant care and feeding. If you don't have a dedicated DevOps person, or at least someone who enjoys wrestling with Docker and proxy rotations, it can become a massive time-sink. Tavily and Firecrawl are "Free as in Beer"—well, they have free tiers, but eventually, you’re paying for the convenience of not having to be a "web scraping engineer."
Let’s talk about the "Reader" layer again. Jina Reader vs. Firecrawl. Is there a scenario where you’d use both?
A common architecture I’m seeing is: Use SearXNG for the wide net, use Jina Reader for the "quick look" at a specific page to see if it’s relevant, and then if it is, trigger a Firecrawl job to do a deep scrape of that entire domain. It’s a tiered approach to data acquisition. You save your "high-cost" crawling for the sources that you’ve already verified are high-value.
That’s a smart way to manage the "Token-to-Value" ratio. You don't want to spend five dollars scraping a site that turns out to be a parked domain or an ad farm.
And that brings up the "SEO Spam" problem. The web in twenty twenty-six is increasingly flooded with AI-generated content designed to rank for specific keywords. A human can spot a "low-effort" AI blog post in about two seconds. An agent might see a high keyword density and think it’s the definitive source.
Does Tavily or SearXNG have a "Human-Written Only" filter? That feels like the "Holy Grail" for grounding right now.
Tavily is getting close. They have a "Domain Authority" filter that lets you whitelist specific trusted sources. SearXNG has a "Reliability" score that is community-maintained. But the real solution is actually the "Answer Engine" layer like Perplexica. Because it uses an LLM to "pre-read" the results, it can be prompted to say, "If this result looks like generic AI-generated filler, discard it and move to the next one."
It’s AI fighting AI. We’re using a "Gatekeeper LLM" to protect our "Worker LLM" from "Spam LLMs." It’s models all the way down.
It really is. What most people don't realize is that "search" for agents is no longer about finding a needle in a haystack. It’s about building a machine that can sift through a thousand haystacks a second and find the one needle that isn't made of plastic.
I want to touch on the "Developer Experience" of these tools. Tavily’s SDK is literally one line of code. You pass a query, you get back a list of objects with title, url, and content. It’s beautiful. SearXNG, on the other hand, gives you this massive JSON object with "engines," "category," "positions," and about fifty other fields you don't need.
That’s because SearXNG is trying to be everything to everyone. It’s a power user tool. Tavily is an "Agent Developer" tool. They’ve made the opinionated choice to only give you what an LLM needs. That "opinionated" nature is why they dominate the commercial frameworks. If I’m building a LangChain app, I don't want to write a "SearXNG-to-Markdown" parser. I just want the text.
It’s the "Vendor Lock-in" by way of "Developer Joy." If you make my life easy, I’m going to stay with you even if you’re more expensive. But I do worry about the "Black Box" aspect of Tavily. If they decide to stop indexing certain sites—maybe for "safety" or "copyright" reasons—your agent suddenly loses a limb and you don't even know why.
That is the big risk of the "Commercial Grounding" route. We saw this with some of the early search APIs where they started "filtering" results for political or corporate reasons. If your agent is relying on that API, its "worldview" is being shaped by the API provider. This is why I think the "Hybrid" model is the future. You use Tavily for the ninety percent of "normal" queries, but you have a SearXNG fallback for the "unfiltered" or specialized research.
What’s your take on the "Scraping vs. Search" distinction? Firecrawl calls itself a "Search Engine for Agents," but it feels like a scraper that happens to have a search bar.
It’s a blurring of categories. In the old world, "Search" meant finding a URL, and "Scraping" meant getting the data from that URL. In the "Agentic" world, those two steps are merging. Firecrawl’s "Search" endpoint actually does the scraping in real-time. It doesn't just show you what’s in its index; it goes out and fetches the live pages for you.
That’s a huge latency hit, though. If it’s scraping five pages in real-time, that’s going to take ten to twenty seconds.
It is, but for "Deep Research," that is acceptable. If I’m asking an agent to "Write a comprehensive report on the current state of solid-state battery manufacturing in Japan," I don't expect it in two seconds. I want it to take its time, scrape the relevant company sites, and give me the ground truth. Firecrawl is for "Accuracy-First" tasks. Tavily is for "Speed-First" tasks.
So we have a decision matrix. Let’s summarize it for the listeners. If you need speed and ease of use, it’s Tavily plus Jina Reader. If you need privacy and cost-control at scale, it’s SearXNG plus a self-hosted scraper. If you need deep, site-wide data, it’s Firecrawl. And if you want a full-blown "Research Agent" in a box, it’s Perplexica.
And if you're building in twenty twenty-six, you're probably using a mix. The most sophisticated stacks I’ve seen are using Tavily for the initial "broad search" to narrow down the top five most relevant URLs, and then using Firecrawl or Jina Reader to go deep on those specific five. It’s about being "surgical" with your grounding.
I think the "Open Source" popularity of SearXNG is also driven by the "Local LLM" movement. If you’re running a model on your own hardware, you probably have a philosophical commitment to decentralization. You don’t want to depend on a single API provider who could change their pricing or their "safety filters" tomorrow.
There is also the "Data Sovereignty" aspect. If you are a developer building a research tool for a law firm or a hospital, you cannot send their queries to a third-party search API. Even if that API claims they don’t store data, the "risk" is too high. In those cases, SearXNG is the only option.
We should mention the "Perplexica" installation process. It’s not just a "pip install." You’re setting up a database, a vector store, a search backend, and a frontend. It’s a "Product," not just a "Tool."
It is. But for an enterprise that wants their own "Internal Perplexity," it’s a bargain. You can point it at your internal Confluence, your Jira, and the public web all at once. That "Unified Grounding" is the real value. The agent doesn’t have to switch between "Internal Search" and "External Search"; it just asks the "Answer Engine" and gets the best answer from both worlds.
I’ve seen some benchmarks suggesting that Tavily's ranking is actually tuned for "truthfulness" rather than "clickability." Is that just marketing, or is there a technical mechanism there?
There is a mechanism. Traditional search engines prioritize "freshness" and "authority" in a way that often favors large media sites over specialized technical documentation or niche forums where the actual answer might live. Tavily uses a secondary LLM-based scoring pass to look at the content of the retrieved pages before returning them to you. It’s essentially doing a mini-RAG cycle inside the search API call.
That "semantic scoring" is the secret sauce. A meta-search engine like SearXNG is just giving you what the underlying engines (Google, Bing, etc.) think is good. Tavily is saying, "I don't care what Google thinks; I care what an LLM thinks is useful for this specific query."
And that is why it’s so much more expensive. You’re paying for the compute of that secondary LLM pass. But again, if it saves you ten thousand tokens of "junk" text in your final prompt, it’s a net win for your AWS bill.
Let’s talk about the "Reader" evolution specifically. Jina Reader recently added a feature where it can actually "interact" with a page—clicking buttons or scrolling—to find hidden content. This starts to blur the line with Firecrawl.
It does, but Firecrawl is still the king of "depth." Think of it this way: Jina is a sniper, Firecrawl is a carpet-bombing run. If you need to know what is on a specific URL that is behind a "Read More" button, Jina is your tool. If you need to map out the entire structure of a competitor's documentation site to see how their API has changed over the last six months, you need Firecrawl’s crawling logic.
What I find wild is that we are essentially building a "parallel internet" for machines. The human internet is a mess of ads, pop-ups, and auto-playing videos. The "agentic internet" is this clean, structured, markdown-only world that these tools are carving out.
It’s a literal translation layer. We are translating "Human-Web" to "LLM-Web." And the tools that do that translation best are the ones winning. This is why SearXNG is so popular in the "Local LLM" community. If you’re running a model on your own hardware, you probably have a philosophical commitment to decentralization. You don't want to depend on a single API provider who could change their pricing or their "safety filters" tomorrow.
There is also the "Latency Tax." If my agent has to wait three seconds for SearXNG to aggregate results, and then another two seconds for a scraper to clean the text, and then ten seconds for the LLM to process it... that’s a fifteen-second delay for the user. In the world of twenty-six, that’s an eternity.
Tavily’s big selling point is that they’ve optimized that entire pipeline to happen in under two seconds. They’ve already cached the cleaned versions of popular pages. They’ve pre-indexed the "AI-relevant" parts of the web. It’s a specialized index that sits on top of the general web.
So if I'm a developer listening to this, and I’m trying to choose my stack... let’s give them some concrete decision points. If I’m building a "Personal Assistant" agent that helps me plan trips and find recipes, what’s the move?
For a personal assistant, I’d go Tavily. You want the "it just works" experience. You need high-quality, cleaned results quickly, and the query volume for a single user isn't going to break the bank. Plus, Tavily’s "context" feature—where it returns a single string of the most relevant facts across multiple sources—is perfect for a chatbot.
Okay, what if I’m building a "Competitive Intelligence" agent for a large enterprise? It needs to monitor fifty different news sites and company blogs every hour.
That is a Firecrawl use case. You need the crawling capability to see what has changed on a site-wide level. You’re less concerned about the cost per query because the value of the insights is high, and you need the robustness of a tool that can handle complex, JavaScript-heavy corporate sites. I’d probably pair Firecrawl with a self-hosted SearXNG instance to handle the initial "discovery" of new articles without paying a per-query fee for every "check for updates" ping.
And what about the "Privacy Maxis"? The folks building "Local-First" software or handling legal/medical data?
Perplexica is the clear winner there. It gives you that "Perplexity-style" experience where you get a cited, structured answer, but you can run the whole thing on your own infrastructure. You point it at a local instance of SearXNG and a local LLM like a fine-tuned Llama three or Mistral, and you have a world-class research tool that never leaks a single byte of data to the outside world.
I want to touch on the "Developer Experience" of these tools. Tavily’s SDK is literally one line of code. You pass a query, you get back a list of objects with title, URL, and content. It’s beautiful. SearXNG, on the other hand, gives you this massive JSON object with "engines," "category," "positions," and about fifty other fields you don't need.
That’s because SearXNG is trying to be everything to everyone. It’s a power user tool. Tavily is an "Agent Developer" tool. They’ve made the opinionated choice to only give you what an LLM needs. That "opinionated" nature is why they dominate the commercial frameworks. If I’m building a LangChain app, I don't want to write a "SearXNG-to-Markdown" parser. I just want the text.
It’s the "Vendor Lock-in" by way of "Developer Joy." If you make my life easy, I’m going to stay with you even if you’re more expensive. But I do worry about the "Black Box" aspect of Tavily. If they decide to stop indexing certain sites—maybe for "safety" or "copyright" reasons—your agent suddenly loses a limb and you don't even know why.
That is the big risk of the "Commercial Grounding" route. We saw this with some of the early search APIs where they started "filtering" results for political or corporate reasons. If your agent is relying on that API, its "worldview" is being shaped by the API provider. This is why I think the "Hybrid" model is the future. You use Tavily for the ninety percent of "normal" queries, but you have a SearXNG fallback for the "unfiltered" or specialized research.
What’s your take on the "Scraping vs. Search" distinction? Firecrawl calls itself a "Search Engine for Agents," but it feels like a scraper that happens to have a search bar.
It’s a blurring of categories. In the old world, "Search" meant finding a URL, and "Scraping" meant getting the data from that URL. In the "Agentic" world, those two steps are merging. Firecrawl’s "Search" endpoint actually does the scraping in real-time. It doesn't just show you what’s in its index; it goes out and fetches the live pages for you.
That’s a huge latency hit, though. If it’s scraping five pages in real-time, that’s going to take ten to twenty seconds.
It is, but for "Deep Research," that is acceptable. If I’m asking an agent to "Write a comprehensive report on the current state of solid-state battery manufacturing in Japan," I don't expect it in two seconds. I want it to take its time, scrape the relevant company sites, and give me the ground truth. Firecrawl is for "Accuracy-First" tasks. Tavily is for "Speed-First" tasks.
We should mention the "Perplexica" installation process. It’s not just a "pip install." You’re setting up a database, a vector store, a search backend, and a frontend. It’s a "Product," not just a "Tool."
It is. But for an enterprise that wants their own "Internal Perplexity," it’s a bargain. You can point it at your internal Confluence, your Jira, and the public web all at once. That "Unified Grounding" is the real value. The agent doesn’t have to switch between "Internal Search" and "External Search"; it just asks the "Answer Engine" and gets the best answer from both worlds.
I’ve seen some teams try to build their own "Tavily" using SearXNG and a bunch of Python scripts. It always starts easy and ends in a nightmare of regex and proxy management.
It’s the classic "Build vs. Buy" decision. We talked about this back in the "Vendor SDK Moat" episode—not that I’m supposed to mention previous episodes, but the principle holds. You are deciding whether your core competency is "Search Infrastructure" or "Agent Application Logic." If you are building a medical diagnostic agent, your value is in the medical reasoning, not in figuring out how to bypass Cloudflare’s anti-bot protection on a research journal’s website.
One thing that often gets overlooked in these discussions is "Metadata." Jina Reader and Tavily provide really clean metadata—publication dates, authors, etc. SearXNG is notoriously flaky with that. If your agent needs to know "Is this information from twenty twenty-six or twenty twenty-two?", having reliable metadata is a game-changer.
That is a huge point. Grounding isn't just about the "what," it's about the "when." An agent that gives me twenty twenty-two financial data as if it’s current is worse than an agent that says "I don't know." Tavily’s ability to filter by time—"Give me results from the last twenty-four hours"—is actually reliable because they are monitoring the crawl frequency.
Okay, let's wrap this up with some practical takeaways. If someone is starting a new agent project tomorrow, what is the "Starter Pack" stack?
Starter Pack: Tavily for search and Jina Reader for any specific URLs you want to "deep dive." It’s the fastest way to get to high-quality results. You’ll spend your time on the prompt and the agent logic, not the plumbing.
And if they have a zero-dollar budget but a lot of time?
Then it’s SearXNG running in a Docker container, paired with a self-hosted instance of Firecrawl. You’ll have to manage your own proxies, but you’ll have a professional-grade scraping and search stack for the cost of your electricity bill.
And the "Enterprise" move?
Perplexica for the internal research team, and a high-volume Tavily Enterprise plan for the customer-facing bots. You want the consistency and speed for the customers, and you want the deep, private research capability for the internal team.
It’s a fascinating time to be building. We’re moving from "LLMs as calculators" to "LLMs as researchers." And you can’t be a good researcher if you’re looking at a blurry, noisy version of the world.
The grounding stack is the "eyeglasses" for the LLM. Without it, the model is just squinting at the internet. With the right stack, it sees everything in high definition.
I'm still waiting for the "Smell-O-Vision" grounding for the cooking agents, but I guess we're a few years away from that.
I think your "Sloth-O-Vision" is grounding enough for most of our listeners, Corn.
Hey, I may be slow, but my search results are highly curated.
Fair enough. Well, that’s our deep dive into the grounding stack. It’s a complex, fast-moving space, but hopefully, this gives you a roadmap for your next project.
Big thanks as always to our producer, Hilbert Flumingtop, for keeping the gears turning behind the scenes. And a huge thank you to Modal for providing the GPU credits that power this show—if you’re running heavy agentic workloads, Modal is where you want to be.
This has been My Weird Prompts. If you found this architectural breakdown useful, a quick review on Apple Podcasts or Spotify helps more than you know. It helps other developers find the show and keeps us going.
You can find us at myweirdprompts dot com for the full archive and our RSS feed. We're also on Telegram—just search for My Weird Prompts to get notified when new episodes drop.
Until next time, keep your prompts weird and your grounding solid.
See ya.
