AI agents need Search Engine Results Pages (SERP), data extraction and web crawling. For tasks like Deep Research, an agent needs to do the following.
- Interpret a prompt.
- Decide which tools to use.
- Perform a search.
- Interpret the results.
- Further research relevant results.
- Provide the user with insights, summaries and citations.
This workflow hinges on two main assumptions. The agent needs to access a search engine and it also needs access to individual websites. In this piece, we’ll explore both Tavily and Brave as SERP tools that can power your Deep Research workflow.
Each of these tools follows a different design philosophy and by the end, you should be able to make an educated decision for building your own AI agents.
Brave overview
Brave is primarily known as a privacy-focused browser and alternative search engine. Its initial popularity came from offering a privacy-friendly browser with user rewards in the form of Basic Attention Token (BAT) in the crypto community. However, Brave has carved out quite the niche for itself.
Brave Search API
The Brave Search API is a fast growing alternative to traditional SERP APIs based on other engines like Google and Bing. This API gives teams the following.
- Specialized endpoints: Using different endpoints, teams can get AI summaries, locality-driven results as well as images.
- Fresh results: Brave’s web index includes over 30,000,000,000 pages and receives over 100,000,000 page updates daily.
- Search Goggles: This feature allows teams to filter results and apply custom re-ranking rules for task-specific results.
- Extra snippets: AI agents can access site snippets in order to better filter results based on context.
- Schema-based results: Results can come in JSON format. This is one of the most important points in SERP APIs. JSON is already formatted for machines, not humans.
Tavily overview
Tavily solves this problem differently than Brave. With Tavily, results are served primarily from semantic search. Brave was built primarily for users — the Brave Search API and AI tooling were added later. Tavily has been built from the ground up specifically for AI agents.
Tavily API
Tavily offers a variety of APIs for LLMs to use. When combined, these features aim to deliver a comprehensive web toolkit for AI agents. Rather than offering a single API to deliver search results, Tavily aims to provide an all-in-one hub for your AI agents to use the web.
- Search: Enter a query and receive relevant results.
- Extract: Extract page content from specified URLs.
- Crawl: Traverse entire site directories with your AI agent for automated data discovery.
- Map: Generate comprehensive maps by traversing entire sites.
Given its overlap with Brave Search API, we’ll primarily examine the Tavily Search API. It offers the following.
- Singular endpoint: All search parameters are handled via the
/searchendpoint. Results can be customized by changing parameters. - Fresh results: With Tavily, teams get more granular control over result freshness. They specify a start date and end date.
- Semantic ranking: Tavily’s API re-ranks results automatically based on context inferred from the prompt. Teams can also apply custom re-ranking using Cohere.
- Chunks: Users can specify a “chunks per source” parameter. This allows AI agents to use website snippets for added context.
- Schema-based results: API results can be delivered in JSON format with a variety of useful fields such as URL, title, content and score. The score parameter is particularly useful. It represents how well the content matches the query —
0indicates no relevance whatsoever and1is indicative of a 100% match.
Testing them out
Next, we’ll try out the playground for each of these services. This way, we can see how they look and feel in a real world scenario. We’ll use a rather difficult query: what is the meaning of life? — this should stir up some diverse results — allowing us to really look into which sites are given weight within the results.
Brave Search API
Head on over to their API playground. Users can generate requests using shell, JavaScript or Python. In the image below, you can see our query as well as the autogenerated code.
When you’re ready, you can either copy the code and paste it into your local environment or you can click the Submit button. After clicking the button, we receive the following results.
As you can see in the image above, Wikipedia comes in ranked first. A Reddit thread on existentialism comes in second. Each result shows a variety of important fields. We won’t go through all of them but we will review the important ones so you can get a solid understanding of the data structure here.
title: The title of the page.url: The actual URL used to access the page.description: The site description offered by site metadata.page_age: The Wikipedia result shows December of 2025. The Reddit thread shows 2021.age: Alongside thepage_agefield, we also get the site’s age displayed in natural language.
Tavily Search API
Now, we’ll do the same thing using Tavily. As you can see below, the interface is pretty similar. Teams should note that code is not generated using a REST API like in our previous example. The Tavily playground generates its code using the Tavily Python SDK.
Now, let’s look at the results. Tavily makes our results available in both JSON and a human-readable preview. We’ll look at the JSON first.
Our first result is the same Wikipedia page we saw in the Brave results. Our number two result is where things get a bit more interesting. We can’t say that Tavily serves better results but we receive a result that attempts to answer the question directly. Pay attention to the following result fields.
url: The address used to access the site.title: The title of the page.content: The URL content most related to the query we entered.score: A relevance score showing how well the content matches the query.
Before moving on, we’ll take a look at these results from the preview tab. This isn’t a major differentiator for your AI agent. However, when playing with the API, it is useful for human eyes.
As you can see, the preview takes our results and displays them in a way that people can read. Users should note that this removes the relevance score. At the moment, Brave’s playground does not offer a similar feature. However, Brave’s frontend search pages are designed to meet this exact need.
Pricing
Now let’s take a look at each provider’s pricing options. Both Brave and Tavily offer custom plans for enterprise usage. Brave explicitly states which plans allow you to use the API within AI apps. Tavily doesn’t explicitly state this in the plans but their product is designed for AI agents and AI application usage is implied — you can view their full terms here.
Brave Search API
- Free AI: Teams can make up to 2,000 queries/month for free. Rate limits are imposed at 1 query/second.
- Base AI: For $5/1,000 requests, teams get up to 20 queries/second and 20,000,000 queries/month. Teams also get explicit permission to use the API within their own AI applications.
- Pro AI: For $9/1,000 requests, teams get up to 50 queries/second, unlimited queries as well as usage rights.
Tavily Search API
NOTE: The image above only shows the “Project” plan — not other monthly plans. You can verify them for yourself here.
- Free: Users get access to 1,000 API credits/month with no purchase required and free email support.
- Pay As You Go: At $0.008/credit, teams get to pay only for their actual usage and email support.
- Project: 4,000 API credits for a total of $30/month with higher rate limits.
- Bootstrap: $100/month for 15,000 API credits.
- Startup: $220/month for 38,000 credits.
- Growth: $500/month for 100,000 API credits.
Key breakdown: Brave vs. Tavily
| Category | Brave Search API | Tavily Search API |
|---|---|---|
| Primary focus | General-purpose search engine with API access | AI-first search and retrieval layer |
| Original target user | Human users (browser and search) | AI agents and LLM-based applications |
| Ranking transparency | Explicit ranking with optional re-ranking via Search Goggles | Automatic semantic re-ranking inferred from prompt |
| Freshness control | Implicit, based on index updates | Explicit start and end date parameters |
| Result explainability | Metadata-heavy, source-forward | Score-based relevance emphasis |
| Output structure | JSON optimized for machine parsing | JSON optimized for downstream LLM consumption |
Conclusion
Brave and Tavily attack the same problem from different angles. Brave gives developers access to their search engine through a more traditional API architecture delivering structured results with a large set of fields and metadata. Tavily keeps the fields lighter and returns a relevance score along with some actual site content for AI agents to evaluate themselves.
Both of these products allow for custom ranking — Tavily re-ranks results based on context and Brave offers the Search Goggles feature. For teams building AI agents, both of these tools can be incredibly helpful. Brave is better suited for teams who need a more traditional SERP API. Tavily is a natural choice for teams who need precise context-based formatting specifically designed for LLMs.
