Direct developer access to the big engines has mostly closed, and a raw results page isn't something an agent can use cleanly. Microsoft retired the Bing Search APIs on August 11, 2025 and pointed developers to Azure and agent tools instead. Consumer Google output is full of ads, cookie banners, and navigation your model pays tokens to skip. A search API built for machines gives you clean, ranked results and stays stable when an agent calls it in a loop.

The Browserbase Search API integrates as a single REST API or Python and TypeScript SDKs methods for a swift migration path. Get quick and accurate results, optimized for agents.

Fast, because agents loop and the delay stacks. A single ReAct agent might run three or four searches before it lands on an answer, so every 100 ms per call adds up across a task. The agent-search providers now compete on sub-200 ms response times, with Tavily citing "180ms p50". Browserbase Search aims for sub-second responses with small payloads, around 5–10 KB, to stay quick inside those loops.

It returns ranked results (URLs, titles, and snippets), not full page bodies. The agent gets a short, token-light list to work from, then pulls the content of the page it actually needs with the Fetch API, which returns a live copy as markdown or JSON. Splitting search from fetch keeps search responses fast and small, and it means the content the agent finally reads is current rather than a copy from an index. Firecrawl Search takes the other approach and returns full markdown for every result in one call, which is handy for a one-shot answer but heavier and cached.

Results are ranked from an up-to-date index, and the content the agent reads afterward is fetched live, not pulled from a stale copy. That matters for agents: a cached snapshot can send one down a path based on something the site has already changed.

The right one for RAG returns relevant, clean, chunk-ready results with low latency and current content, since those are what cut hallucination and token waste. Browserbase pairs ranked, token-light Search to find sources with live Fetch to pull fresh markdown or JSON for grounding, so retrieval stays current and your context window isn't full of page furniture.

Direct developer access to the big engines has mostly closed, and a raw results page isn't something an agent can use cleanly. Microsoft retired the Bing Search APIs on August 11, 2025 and pointed developers to Azure and agent tools instead. Consumer Google output is full of ads, cookie banners, and navigation your model pays tokens to skip. A search API built for machines gives you clean, ranked results and stays stable when an agent calls it in a loop.

The Browserbase Search API integrates as a single REST API or Python and TypeScript SDKs methods for a swift migration path. Get quick and accurate results, optimized for agents.

Fast, because agents loop and the delay stacks. A single ReAct agent might run three or four searches before it lands on an answer, so every 100 ms per call adds up across a task. The agent-search providers now compete on sub-200 ms response times, with Tavily citing "180ms p50". Browserbase Search aims for sub-second responses with small payloads, around 5–10 KB, to stay quick inside those loops.

It returns ranked results (URLs, titles, and snippets), not full page bodies. The agent gets a short, token-light list to work from, then pulls the content of the page it actually needs with the Fetch API, which returns a live copy as markdown or JSON. Splitting search from fetch keeps search responses fast and small, and it means the content the agent finally reads is current rather than a copy from an index. Firecrawl Search takes the other approach and returns full markdown for every result in one call, which is handy for a one-shot answer but heavier and cached.

Results are ranked from an up-to-date index, and the content the agent reads afterward is fetched live, not pulled from a stale copy. That matters for agents: a cached snapshot can send one down a path based on something the site has already changed.

The right one for RAG returns relevant, clean, chunk-ready results with low latency and current content, since those are what cut hallucination and token waste. Browserbase pairs ranked, token-light Search to find sources with live Fetch to pull fresh markdown or JSON for grounding, so retrieval stays current and your context window isn't full of page furniture.