Scalable Automation Starts Here: Meet Stagehand and MongoDB Atlas

While APIs deliver clean, structured data on a silver platter, the most valuable insights for AI applications often hide in the messy, unstructured corners of the web. AI's potential is immense, and many organizations face a significant challenge: their existing data infrastructure isn't ready for the scale needed by AI. The web is a vast ocean of data, and ~80% of it is unstructured.

But what if you could reliably automate web interactions, extract complex data, and seamlessly integrate it into a database that offers a variety of query and search methods? This is where the powerful combination of Stagehand (by Browserbase) and MongoDB Atlas redefines what's possible for building AI applications.

Fig. 1- Stagehand and MongoDB Atlas: Seamlessly automating web data collection and AI-ready integration.

Copy linkStagehand: An SDK for developers to write automations with natural language

The browser is a powerful tool for collecting data, but it's hard to control and scale. Traditional browser automation tools like Playwright, Puppeteer, and Selenium often force developers to write fragile code that breaks with even slight UI changes on a website. This makes maintaining scripts on live websites a significant pain point. Stagehand, however, is designed specifically for the AI era.

Stagehand allows you to automate browsers using a combination of natural language and code. It's built to be more reliable and adaptable than legacy frameworks, enhancing Playwright's determinism with large language models (LLMs) to account for page changes and volatility. This means you can write code once, and it adapts to that website if it changes.

Key capabilities of Stagehand include:

• Reading and Interacting with Page Content: Stagehand can read page content by parsing the DOM using accessibility trees, interact with, and continue to work even when the page changes.
• Natural Language Operations: You can use natural language to extract data or instruct the browser to take actions. For instance, page.extract("the price of the first cookie") or page.act("add the first cookie to cart").
• Agentic Workflows: Stagehand allows you to use a simple agent to automate complex workflows with commands like stagehand.agent.execute("complete checkout") or stagehand.agent.execute("Extract the top contributor's username").
• Full Control: By extending the page and context objects from Playwright, Stagehand gives developers full control over the browser session, allowing for deterministic and repeatable automation while being resilient to unpredictable DOM changes.
• Performance-Oriented: Traditionally, developers would inspect the DOM manually, right-click elements, test CSS or XPath selectors in the console, and tweak them repeatedly to get automation working using the browser’s DevTools, such as the example below to select a button nested in div’s.
  await page.click('div.container > div:nth-child(3) button.primary');
  With Stagehand, those selectors are automatically inferred and stabilized, so you can focus on logic instead of fragile DOM paths.

For scaled, production deployments, Stagehand works seamlessly with Browserbase, allowing you to launch hundreds of browsers at once through an API, with advanced features like session replay, prompt observability, and captcha solving.

Copy linkMongoDB Atlas: The AI-ready data foundation

Once Stagehand has done its work, gathering dynamic web content and insights, you need a robust, flexible, and scalable database to store, process, and make sense of this data for your AI applications. This is where MongoDB Atlas shines as the ideal data foundation.

MongoDB Atlas is built to handle today's need for flexible, real-time data processing, unlike traditional databases that are rigid and not designed for an AI-first world.

Here's how MongoDB Atlas becomes the memory and content hub for Stagehand's extracted data:

• Flexible document model: Stagehand can extract highly diverse information from websites—from product details and customer reviews to financial reports and real estate listings. MongoDB's flexible document model mirrors the way developers structure data in their code, making it a natural fit for complex, semi-structured, and constantly evolving web data. It eliminates the need for cumbersome schema “day 1” definitions and “day 2” migrations, which are a constant bottleneck in relational databases.

• Native vector search: A significant blind spot in traditional data analysis is the vast amount of semi-structured data. MongoDB Atlas addresses this by enabling modern vector search workflows. With Stagehand, you can define the structure in every extract call, catering to a variety of concepts—like product reviews, customer support transcripts, or even images from visually rich documents. Once scraped, they can be vectorized via embedding models (such as Voyage AI's voyage-3-large) and ingested directly into MongoDB Atlas. Storing these vector embeddings next to the original text chunks along with semi-structured data allows for native semantic search queries directly inside the database, reducing system complexity, development and testing time, and enabling low-latency results.

• Support for agentic AI architectures: AI agents often require dynamic evaluation criteria and the ability to enrich feature sets based on diverse data. MongoDB Atlas is uniquely suited to store dynamic criteria as flexible JSON documents and serves as the ideal foundation for agentic architectures, allowing captured web data to be used to generate new features for machine learning models without requiring schema redesigns. Checkout another blog post defining 7 Practical Design Patterns for Agentic Systems.

• Massive scalability: Whether Stagehand is scraping millions of product listings or billions of clinical data points, MongoDB Atlas's distributed architecture and flexible schema simplify scaling, handling unpredictable workloads and massive data volumes effortlessly. Companies like Ubuy manage over 300 million products and 150 million annual searches, while SHARE NOW handles 2 TB of IoT data per day, all powered by MongoDB Atlas. Additionally, Browserbase enabled Structify to eliminate gigabytes of RAM usage, bringing memory consumption on their production web servers down to virtually zero, and can support several thousand concurrent browser sessions, allowing them to scrape tens of thousands of websites in a single campaign with ease.

Copy linkThe powerful synergy: Stagehand + MongoDB Atlas in action

Fig. 2 - Data flow: From web automation via Stagehand to scalable AI processing in MongoDB Atlas.

Imagine the possibilities when Stagehand reliably harvests information from the web, and MongoDB Atlas makes that data immediately actionable for AI:

• Customer engagement & product discovery: Stagehand can extract vast amounts of customer reviews and product information from various e-commerce platforms. MongoDB Atlas stores this data as both operational data and vector embeddings, enabling AI-driven recommendations and intent-driven product discovery, as seen with Ubuy, reducing search times from seconds to milliseconds, but still providing traditional query and insights over data as required by human consumers, enterprise systems and external partners and consumers

• Real-time market and financial intelligence: Stagehand can be programmed to extract live financial news, market trends, or competitive pricing data. This real-time stream can be fed into MongoDB Atlas Stream Processing for immediate analysis, enabling dynamic pricing models or allowing financial analysts to sift through dense reports with AI-powered multimodal search for specific trends.

• Enhanced content curation and recommendation: For media companies, Stagehand can gather articles, reports, and visual content from diverse sources. MongoDB Atlas, with its vector search and multimodal capabilities, allows for a hybrid search experience, blending keyword precision with AI-driven discovery, delivering hyper-relevant recommendations instantly, much like the Financial Times' solution.

• Next-gen inventory classification: By using Stagehand to collect qualitative metrics from product reviews or social media posts (e.g., customer expectations, re-purchase probability), this unstructured data can be vectorized and integrated into MongoDB Atlas. AI agents can then use this enriched dataset to dynamically classify inventory, transforming reactive inventory management into predictive and customer-centric decision-making.

By combining Stagehand's robust web data acquisition, MongoDB Atlas's flexible and AI-ready data foundation (including vector search capabilities), and MongoDB's ability to expose this data and its management tools to diverse AI applications and platforms, organizations can create a truly seamless and powerful data pipeline for their AI initiatives. This setup empowers developers and AI agents to access, understand, and leverage web data at an unprecedented scale and efficiency.

Ready to build smarter, adapt faster, and scale more confidently? Explore Stagehand and Browserbase for your browser automation needs and connect it with a free MongoDB Atlas cluster to power your next AI application – get started with the MongoDB + Stagehand integration on GitHub.