New Developer Tools
Foxentry
Strategy & Training, Custom Development
4 SDKs developed
API documentation management
Detail
AI & Automation, Custom Development
Connecting Apify web scraping to 20+ automation and AI platforms
Deep, platform-specific integrations that bring Apify's web scraping into the automation and AI tools teams already use, built from the ground up rather than as thin wrappers.
Key Highlights
01
Automation platform connectors
Native connectors for N8N, Workato, Power Automate, Camunda, Active Pieces, IFTTT and more, so web scraping runs inside the automation builders that teams already use.
02
AI agent plugins
Plugins for Cursor, Claude Code, GitHub Copilot, Codex and other agent platforms that let AI tools pull live web data into their context.
03
Beyond thin wrappers
Every connector exposes the platform's own primitives — actors, pre-configured tasks, dataset items, key-value stores and webhooks — rather than wrapping everything in a single generic adapter.
04
AI-native delivery
Claude Code and Cursor cut the build cycle, so a small team moves several platform integrations forward in parallel.
The challenge
Web scraping is most valuable when it reaches the tools people already work in. Apify wanted to improve adoption by meeting users where they are — inside automation builders, AI agent platforms and CRMs — rather than asking them to come to Apify first. In the AI era that matters twice over: integrations extend the context available to AI agents, and they provide a reliable path to data on sites that block general-purpose bots. Covering every requested platform was more than the internal team could take on alone, so Apify brought in DX Heroes for our integration track record and a shared TypeScript stack.
The approach
Starting in May 2025, we built integrations across three categories: automation and integration platforms, AI agent platforms, and CRM. The principle throughout: every platform gets a deep, platform-specific implementation rather than a thin generic wrapper. Each connector exposes Apify's primitives natively — listing actors, running actors and pre-configured tasks, fetching dataset items — and supports key-value stores and webhooks for triggering.
Where a platform imposes hard execution-time limits, we worked around them with workflow-based waiting instead of pushing those constraints onto the user. TypeScript was the primary stack, with Python, Ruby and C# where a platform required it. Build time ranged from about two weeks for a straightforward connector to several months for the complex ones that needed their own backend and UI.
The result
N8N was the biggest early win and saw the highest user traction; Active Pieces, IFTTT and Diffy followed with solid adoption. Power Automate and Camunda are in preparation and are expected to make the largest impact once released. Each integration widens the surface area where Apify data flows without custom glue code.
AI-native development
The delivery leaned on the same kind of tooling we build integrations for. Claude Code and Cursor cut the time to understand a new platform and stand up its connector, letting the team progress several integrations in parallel. An AI setup turns scoped work into epics and user stories pushed straight into GitHub, and platform-native assistants for tools like Camunda and Workato remove most of the manual documentation hunting. The result is a broad catalogue of deep integrations shipped faster than a wrapper-by-wrapper approach would allow.
