browserless Integrations

Selenium automates browsers. That's it! What you do with that power is entirely up to you. Primarily it is for automating web applications for testing purposes, but is certainly not limited to just that. Boring web-based administration tasks can (and should) also be automated as well. If you want to create robust, browser-based regression automation suites and tests, scale and distribute scripts across many environments, then you want to use Selenium WebDriver, a collection of language specific bindings to drive a browser - the way it is meant to be driven. If you want to create quick bug reproduction scripts, create scripts to aid in automation-aided exploratory testing, then you want to use Selenium IDE; a Chrome and Firefox add-on that will do simple record-and-playback of interactions with the browser. If you want to scale by distributing and running tests on several machines and manage multiple environments from a central point.

You should simply be able to work on your core product, grow, and get to wherever you want; without having to worry about infrastructure and how to deploy it. Great ideas becoming trainwrecks due to the complex nature of deployments, clusters, Docker, among the many, many other things that can go wrong. Starters allow you to deploy a fully configured project that is automatically connected to infrastructure. Every time you visit your project on Railway, we will check to see if the project it is based on has been updated by its maker. If it has, we will prompt you to update your project. On confirmation, we will create a branch on Github and open a PR deployment for you on Railway. This way, we don’t replace your production deployment and you can test things out within the PR deploy. Once you’re happy with the changes, you can merge the PR and your production deployment will be updated to the latest version.

The fastest way to integrate APIs and run code. Pipedream is a serverless integration and compute platform that makes it easy to connect apps and develop event-driven workflows. Event sources turn any API into a real-time event stream. Create event sources to listen for new Tweets, Github events, Airtable records, RSS items, webhook events and more. Inspect events in a human-friendly way, trigger Node.js workflows on every event, or consume events in your own app via API. Workflows are composed of Node.js code steps that run on every event. Write your own Node.js (and use any npm package) or reuse actions that scaffold popular APIs. Trigger via sources or a custom URL, email address, SDK code or schedule. Auth apps once, connect to those apps in any workflow. Pipedream supports OAuth and key-based auth, and handles the OAuth flow and token refresh for you. Just link accounts to steps and reference the relevant auth info in code.

Dendrite is a framework-agnostic platform that empowers developers to create web-based tools for AI agents, enabling them to authenticate, interact with, and extract data from any website. By simulating human-like browsing behavior, Dendrite facilitates seamless web navigation and data retrieval for AI applications. The platform offers a Python SDK, providing developers with the necessary tools to build AI agents capable of performing tasks such as interacting with web elements and extracting information. Dendrite's flexibility allows it to integrate with any tech stack, making it a versatile solution for developers aiming to enhance their AI agents' web interaction capabilities. Your Dendrite client syncs with website authentication sessions in your local browser, no need to share or store login credentials. Use our Chrome Extension, Dendrite Vault, to securely share authentication sessions from your browser with the Dendrite client.

Next-generation AI chatbot building platform, quickly create bots without coding and publish them on various platforms. Workflow is a useful tool for planning and implementing complex functional logic, often used for multi-step tasks like travel planning and report analysis. For tasks with complex functionality and high stability requirements, implementation with a workflow is ideal. Workflow provides a wide range of flexible, combinable nodes, and even supports custom code, to handle complex and controllable AI task flow needs. When the targeted AI task scenes involve many steps, and there are strict requirements on output accuracy and format, it is suitable to use a workflow.