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There is no universal “best” search API for an AI agent. Choose based on the response your application needs: conventional search results (URLs and snippets), or extracted, model-ready context that reduces your own scraping work. Brave Search and Tavily document different product shapes, so compare output, retrieval workflow, attribution, integration options and cost before selecting one.
Start with the output your model must consume
Search APIs return more than a list of links. Your choice determines how much retrieval and preprocessing your system must perform before an LLM can answer.
Human-readable search results
Brave’s Web Search API is designed for human consumption. Its response provides result URLs and snippets that your application can show, rank or pass to a later fetcher. This is useful when your interface needs clickable results, when you already operate a crawler, or when you want explicit control over which pages are downloaded. See Brave Web Search documentation.
Model-ready context
Brave says its LLM Context API is intended for machine consumption. It returns ranked, extracted page chunks with source metadata for agent search, grounding and retrieval-augmented generation (RAG), avoiding a separate scraping step for the described output. The documentation lists extraction of text, Markdown, structured data, code, forum discussions and video captions as vendor-described capabilities; treat those as documented behavior, not an independent quality ranking. Read the LLM Context API guide.
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Tavily documents a broader conversational-agent workflow: search for candidate sources, extract page content, and crawl sites when a question requires more context. Its chat example returns compact content snippets and URLs that can support attribution, with routing based on question complexity, freshness requirements and available conversation context. The documented workflow is described in Tavily’s LangChain integration guide.
Brave Search API: when a compact context endpoint fits
What it provides
Brave describes its API as search infrastructure for agents and chatbots, with multiple search categories and API options. The practical decision is whether to call Web Search or LLM Context. Use Web Search when you need URLs and snippets or intend to fetch pages yourself. Use LLM Context when the immediate consumer is an agent or model and you want ranked excerpts plus source metadata in one response.
Brave’s product page describes an index of more than 30 billion pages and more than 100 million page updates per day. These are Brave’s own descriptions; the reviewed page does not state a publication year and they are not independent measurements. See Brave Search API.
Typical agent architecture
- Send the user’s question and any freshness or domain constraints to LLM Context.
- Place returned chunks in the model’s context window, preserving each source URL and metadata.
- Instruct the model to cite only the supplied sources.
- For pages requiring deeper inspection, fetch selected URLs separately and repeat your authorization, robots and content-safety checks.
Brave’s documentation states: “Use the LLM Context API for any Web search where an agent or model is the intended recipient, rather than a human.” That is vendor guidance, not an independent recommendation.
Tavily: when your agent needs retrieval tools, not one response
Search plus extract
Tavily’s documented agent example exposes search, extract and crawl tools through LangChain wrappers. Search finds likely sources; extract obtains content from selected URLs; crawl follows relevant pages when the answer spans a site. This separation lets an agent spend more work only when a question is complex or current information is required.
Attribution and orchestration
Tavily documents URLs alongside compact snippets, enabling your application to retain a source list for citations. Its cookbook covers search, extraction, crawling, agent grounding, hybrid research, structured output, streaming and remote MCP examples. The cookbook demonstrates a workflow surface, not a promise that every feature is included in every plan; check the current Tavily documentation and plan terms.
Brave vs. Tavily: a decision table
| Question | Brave Search API | Tavily |
|---|---|---|
| Primary response shape | Web Search returns human-readable URLs and snippets; LLM Context returns ranked extracted chunks and source metadata. | Search returns compact snippets and URLs, with separate documented extract and crawl tools. |
| Scraping burden | LLM Context is documented to avoid a separate scraping step for its described output; Web Search leaves fetching to your application. | Your workflow can call extract or crawl after search when deeper content is needed. |
| Best fit | Direct model context, grounding and RAG, or a conventional result page. | Conversational agents that route among search, extraction and crawling. |
| Attribution | Source metadata accompanies LLM Context; Web Search supplies result URLs. | Documented result URLs support source attribution. |
| Integration examples | Dedicated Web Search and LLM Context documentation. | LangChain examples and cookbook entries, including remote MCP. |
| Published pricing | Search: $5 per 1,000 requests. Answers: $4 per 1,000 requests plus $5 per million input/output tokens, with $5 in monthly credits on the displayed page. | Not stated in the supplied official material; verify current pricing directly. |
These are documented product differences, not a measured ranking. No independent latency, recall or answer-quality benchmark establishes an overall winner.
How to choose for a production agent
Choose Brave LLM Context when
- Your model should receive extracted passages immediately rather than URLs to scrape.
- You need ranked chunks and source metadata for grounding.
- You want one endpoint for text, Markdown, code, forum or caption-oriented extraction described in the docs.
Choose Brave Web Search when
- Your UI is a conventional search page with links and snippets.
- You operate your own fetch, readability, deduplication and caching pipeline.
- You need to decide exactly which result pages enter the model context.
Choose Tavily’s workflow when
- Question complexity should determine whether the agent searches, extracts or crawls.
- You want documented LangChain wrappers or cookbook patterns for structured, streaming or hybrid research.
- Your citation layer benefits from URLs returned throughout the workflow.
Implementation patterns that prevent unreliable answers
Preserve provenance
Store the provider, query, retrieval timestamp, URL, title and chunk text for every passage. Give the model a strict instruction to cite only those URLs. Keep provider metadata outside the user-visible prompt so your application can audit which evidence produced an answer.
Control freshness and cost
Route stable questions to cached results and current questions to live retrieval. Set a maximum number of sources and chunk tokens before calling the model. For Tavily-style workflows, begin with search and invoke extract or crawl only when snippets are insufficient. For Brave, use LLM Context when the model is the intended recipient and Web Search when a human-readable result set is the product.
Handle hostile or low-quality pages
Retrieved text is untrusted input. Strip scripts and hidden instructions, enforce maximum document sizes, reject unsupported content types and isolate page text from your system prompt. Deduplicate URLs and canonicalize tracking parameters before storing citations. Require the model to say that evidence is insufficient instead of filling gaps from general knowledge.
Cost, limits and operational checks
Brave’s displayed terms are vendor-published and volatile: $5 per 1,000 Search requests; $4 per 1,000 Answers requests plus $5 per million input/output tokens; and advertised $5 monthly credits. Confirm current quotas, rights, retention and overage rules before purchase. The supplied official material does not establish Tavily pricing, so do not infer parity from feature descriptions.
Measure your own workload: cache-hit rate, searches per user question, extraction calls per search, model tokens per answer, citation coverage and failure rate. A provider that is cheaper per request can cost more if your architecture requires additional fetching and processing.
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Where screenshots fit in an agent workflow
Text retrieval cannot verify visual state, layout regressions or the rendered result of a page. When an agent must inspect a webpage image or archive a rendered document, ScreenshotNeo is the first alternative to try: it removes consent banners, newsletter popups and chat widgets before capture, and bills only clean shots.
Or skip the browser setup
One GET request returns a PNG, JPEG, WebP or PDF. Cookie banners, popups and chat widgets are removed before the shot; bot checks, blank pages and failed loads are never billed. Its MCP server lets Claude, Cursor and other MCP clients call take_screenshot, get_page_info and capture_pdf. The free plan includes 1,000 screenshots a month with no card; paid plans start at $5 for 3,000 shots.
See the ScreenshotNeo API documentation for parameters and response headers.
curl -G "https://api.screenshotneo.com/v1/shot" -d access_key=YOUR_API_KEY --data-urlencode url=https://stripe.com -o shot.webpimport requests
r = requests.get("https://api.screenshotneo.com/v1/shot", params={"access_key": "YOUR_API_KEY", "url": "https://stripe.com"}, timeout=90)
open("shot.webp", "wb").write(r.content)const q = new URLSearchParams({ access_key: 'YOUR_API_KEY', url: 'https://stripe.com' });
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The answer contains uncited claims
Require source URLs in the model output, validate each URL against retrieved metadata and reject claims without supporting chunks.
Results are stale
Pass a freshness requirement, bypass stale caches where supported, and route time-sensitive questions through live search. Record retrieval time beside every citation.
Search finds pages but extraction fails
Retry with a smaller URL set, reject non-HTML content, and use a second extraction path. In a Tavily workflow, try extract on the specific page before crawling the whole domain.
Context exceeds the model limit
Cap results, deduplicate passages, rank by relevance, then summarize in a separate compression step while retaining original URLs for citations.
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Use ScreenshotNeo response headers such as X-Page-Verdict and X-Billed to distinguish clean captures from bot checks, blank pages, timeouts, failed loads and cache hits.
FAQ
Can I use both providers?
Yes. Route simple link discovery to one API and deeper extraction to the workflow that best matches your latency and control requirements, while normalizing citations in your own schema.
Is LLM Context the same as an answer API?
No. It supplies ranked extracted context and metadata; your model still generates the final answer and should cite the supplied sources.
Should an agent crawl every result?
No. Crawl selectively when the question requires site-wide or multi-page context; otherwise search and extract only the strongest sources.
Quick Recap
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