--- title: "Olostep" type: "AI Tool" url: "https://aidemos.com/tools/olostep" description: "We ran Olostep’s /search endpoint through 52 benchmark calls; it returned SERP-style snippets, weak extraction, and /answers failed with HTTP 402." category: "developer-tools" website: "https://www.olostep.com/" published: "2026-08-20T10:53:16.060433+00:00" updated: "2026-08-24T11:27:48.040819+00:00" --- # Olostep A search-only baseline with mid-pack retrieval, weak extraction, and no usable answer mode. ## TL;DR Verdict **Search completes, but the answer layer is blocked.** **Where it wins:** - you only need a search endpoint that completes basic query calls reliably - you can work with SERP-style snippets instead of full extracted page text - you are benchmarking a search-layer baseline and can tolerate mid-pack retrieval quality **Main limitation:** you need a working answer API with verified citations `52-search-call run` · `Answer mode blocked` · `Joint-worst extraction` · `Estimated pricing` **Website:** [Visit Olostep](https://www.olostep.com/) > **Search completes, but the answer layer is blocked.** > > Olostep's \`/search\` endpoint completed all 52 calls with zero errors, but the benchmark does not support choosing it over stronger alternatives. It is weak on ambiguity and multi-source retrieval, usually returns SERP-style snippets rather than usable extracted text, omits published dates, and \`/answers\` returned HTTP 402 on every attempt, so there is no citation evidence to trust. ## Feature-by-Feature Breakdown ### Web search retrieval Olostep can take a plain-text query set and return ranked web results from its `/search` endpoint. In the benchmark, those results came back as SERP-style snippets with a median length of 161 characters and no published dates, so this card includes the retrieval output format and metadata behavior exercised on the same search calls. **Input:** > **File** **Output:** > **File** **Input:** > **File** **Output:** > **File** **Bottom line:** Reliable enough to issue search calls, but ranking quality is only moderate and clearly weak on ambiguity and multi-source questions. ### Direct answer API Olostep exposes an `/answers` mode intended to return direct answers. In the benchmark smoke tests, every attempt returned HTTP 402, so no answer content, citation output, or abstention behavior could be evaluated. **Input:** ``` Five `/answers` attempts across the unanswerable-query smoke test, repeated over both runs. ``` **Output:** ``` HTTP 402 on all five attempts across both runs. No usable answer, no citations, and no abstention record were produced. ``` **Bottom line:** Unusable in this test tier; the benchmark has no citation or answer-quality data for Olostep's answer mode. ## Is It Right For You? **Use it if** - you only need a search endpoint that completes basic query calls reliably - you can work with SERP-style snippets instead of full extracted page text - you are benchmarking a search-layer baseline and can tolerate mid-pack retrieval quality **Skip it if** - you need a working answer API with verified citations - you need strong ambiguity resolution or multi-source retrieval - your agent needs usable extracted content and published dates from the first call - you need low p95 latency ## Classification - **Category:** developer-tools - **Subcategory:** search-engine - **Type:** text - **Built for:** Other ## Frequently Asked Questions **Q: Does Olostep's `/search` endpoint actually work?** Yes. The report says `/search` completed all 52 calls with zero errors across both runs. **Q: How accurate was Olostep on the benchmark?** Overall retrieval on the 47 answerable queries was 28% top-1, 38% top-3, and 55% top-10. It was strongest on current facts (60% top-3) and weakest on ambiguity (14% top-3) and multi-source queries (0% top-3). **Q: Does Olostep return full page content or just snippets?** It returned SERP-style snippets. The report says the median returned content was 161 characters per result and 0% of results included published dates. Extraction quality was scored 1.33, which was joint-worst. **Q: What happened with `/answers`?** Every `/answers` attempt returned HTTP 402. The benchmark therefore has no answer-quality, citation, or abstention data for Olostep. **Q: How much does it cost and how fast is it?** For `/search`, the report estimates about $9.00 per 1,000 queries and shows p50 latency of 3,560 ms and p95 latency of 8,201 ms. The cost is explicitly labeled an estimate, not a measured response-derived figure. `/answers` was estimated at about $36 per 1,000 calls but did not run successfully. ## Similar Tools AI tools similar to Olostep: - [Exa](https://aidemos.com/tools/exa) — Best live-web search API here for agents that need ranked results, long page text, and cited answers. - [Jina](https://aidemos.com/tools/jina) — Rich web search results for agents, but this free-tier run was incomplete and not comparable. - [Linkup](https://aidemos.com/tools/linkup) — Standard mode is the usable tier: strong extraction, weak ranking; deep is slower, pricier, and weaker. - [OpenAI web_search](https://aidemos.com/tools/openai-web-search) — A citation-honest control arm for live-web answering, but not a reliable retrieval layer for fresh content. - [SerpAPI](https://aidemos.com/tools/serpapi) — Fast, zero-error live web search for agents, with stable mid-pack retrieval and snippet outputs. - [Serper](https://aidemos.com/tools/serper) — Fast, cheap raw web search for agents — strong on ambiguity, but too snippet-thin to replace a scrape. - [Tavily](https://aidemos.com/tools/tavily) — A web search API that returns full-page text well, but usually buries the best result. - [Valyu](https://aidemos.com/tools/valyu) — Returns usable web text for agents, but the citation layer is too error-prone to trust. - [You.com](https://aidemos.com/tools/you-com) — Best-in-benchmark top-1 web retrieval for agent queries, with full-page text and published dates — but at a measured high all-in cost. ## Need a custom AI solution for this use case? If you are looking to build a custom web search, information retrieval, or data extraction workflow for your business or internal workflow, email us at [contact@futuresmart.ai](mailto:contact@futuresmart.ai). ### Found something inaccurate or missing? We try to keep our AI research accurate and useful. If you found outdated information, an issue, or have a suggestion, email us at [collaborate@aidemos.com](mailto:collaborate@aidemos.com).