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Built to power knowledge work AIs, Olostep is the best agentic search, scraping and crawling API for AI

San Francisco
Joined April 2024
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Gandalf and Marx are building our infrastructure. Behind the beards are two Olostep engineers working on the distributed systems and data pipelines that knowledge work AIs run on. We're hiring more people like them. The dress code is optional, but opinions about distributed systems are mandatory. Find our open roles in the comments! 👇
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The $5 top-up is live. 2,000 credits, valid for 6 months, no subscription. Built for agents that buy their own web data. Available now in the dashboard. nitter.cf/hmz_ali7/status/211995…
AI agents wanted to pay us. But they couldn’t afford our cheapest top-up. Last night, we added a $5 top-up to @olostep. By morning, agents were already buying. Your next customer might be an AI agent with $5 to spend.
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Your coding agent can manage your Olostep account now. Through the CLI, right where you write code: > Check balances and manage API keys > Buy top-ups and invite team members > Use API endpoints CLI docs in the reply.
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We grew 10x compared to 2025. Now we're growing the team at @olostep. Bare metal, AI models, agentic harnesses, rigorous benchmarking. Working directly with customers, human or agent. If that sounds like your kind of work, come build web data infrastructure for AI with our small team. Link in the reply.
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Get answers from the web in a single API call
I’ll always defend the ideological purity of object-oriented APIs. 𝗣𝗢𝗦𝗧 /𝗰𝗿𝗮𝘄𝗹𝘀 → a crawl object you can come back to. But @olostep now also lets you 𝗣𝗢𝗦𝗧 /𝗰𝗿𝗮𝘄𝗹 with an action oriented API design. Technically impure but we will allow it
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Olostep retweeted
I’ll always defend the ideological purity of object-oriented APIs. 𝗣𝗢𝗦𝗧 /𝗰𝗿𝗮𝘄𝗹𝘀 → a crawl object you can come back to. But @olostep now also lets you 𝗣𝗢𝗦𝗧 /𝗰𝗿𝗮𝘄𝗹 with an action oriented API design. Technically impure but we will allow it
🤖 Made with AI
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Your procurement agent can monitor availability with a plain natural language instruction. Olostep Monitors evaluates page changes against that instruction. A product coming back in stock triggers an alert. A website logo change doesn't.
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With up to 100,000 URLs in a single batch, a finished job can still contain failed URLs. Some pages return a 404, some are temporarily unavailable, and some don’t return content before the batch’s completion window closes. Olostep Batch separates successful URLs from failed ones, so you can retrieve the extracted content and see exactly which URLs need another attempt. Next, we’re working on returning the exact website status for every URL, successful or failed, so each result is easier to classify.
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Olostep is now live on @composio. You can now let your agents search, scrape, and monitor any website with structured JSON output and managed auth. nitter.cf/hmz_ali7/status/211477…
Olostep and @composio are bringing live web data to agent workflows. You can now give your agents full web extraction tools: • Scrape dynamic pages with managed auth and proxies • Extract structured JSON instead of noisy raw HTML • Schedule automated site diffs and pricing alerts Full tutorial on building a competitor intel agent in the reply.
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Web data shouldn't be ephemeral. When an AI pipeline regresses, you need to inspect the exact page your agent saw. When building knowledge bases, you need to reuse extracted data without scraping everything again. Now, you can persist your extraction results with Olostep. Choose your retention period: 10, 30, 90 days, or forever. No S3 buckets. No custom retention pipelines. Just persistent web data, built into Olostep.
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Olostep works with @mastra. Give your Mastra agents web search, page extraction and structured data with one package: 𝘯𝘱𝘮 𝘪𝘯𝘴𝘵𝘢𝘭𝘭 @𝘰𝘭𝘰𝘴𝘵𝘦𝘱/𝘮𝘢𝘴𝘵𝘳𝘢-𝘵𝘰𝘰𝘭𝘴 Ask an agent to find suppliers for a part. It searches, reads their product pages, extracts and returns price and stock as structured data the next step can use.
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Olostep runs inside @AirOpsHQ, powering brand and competitor research. AirOps uses four of our Web Data API endpoints: • /maps: discover pages on brand and competitor websites • /crawls: extract those pages' content for research • /answers: research company facts, hiring and product announcements • /batches: extract data from many URLs at once We're also working with AirOps to track what's being said about brands and their competitors across reviews, social, news and their own channels, so teams can see how sentiment and customer feedback change over time. Full case study in the reply.
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If your coding agent uses an outdated API parameter, you're debugging code that looked right. Olostep's MCP tools let Cursor fetch current doc pages as markdown before writing the integration, instead of you copying them into chat.
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