Activepieces

Activepieces is an open‑source automation platform that replaces traditional integration services like Zapier while offering a fully extensible, type‑safe framework written in TypeScript. By packaging every integration as a “piece,” developers can publish new connectors that automatically become available as MCP servers. This means an AI assistant such as Claude or Cursor can call any Activepieces piece directly, turning a simple LLM prompt into a powerful workflow that spans dozens of services—from Google Sheets and Discord to custom APIs.

The core value proposition is two‑fold: first, it removes the friction of building and maintaining custom connectors. Pieces are npm packages with hot‑reloading for local development, allowing rapid iteration and immediate deployment to the MCP ecosystem. Second, it integrates seamlessly into AI‑centric workflows. Because each piece exposes a clear JSON schema for inputs and outputs, an LLM can generate the correct payload, trigger the piece, and consume its result—all without manual API calls. This unlocks use cases such as automated data extraction, scheduled reporting, or multi‑step business processes that can be orchestrated entirely through natural language commands.

Key capabilities include:

• Extensible, type‑safe pieces: 280+ pre‑built connectors and a developer kit for creating new ones with TypeScript.
• AI‑first features: Native AI pieces, an AI SDK for custom agents, and a Copilot that assists in building flows.
• Workflow builder: Visual editor with loops, branches, auto‑retries, and versioning, plus built‑in human‑input triggers like chat or form interfaces.
• Enterprise readiness: Self‑hosted, network‑gapped deployment with full branding and access controls.
• Human in the loop: Built‑in delay or approval pieces to ensure compliance and oversight.

Real‑world scenarios span from automating customer support ticket routing, generating dynamic dashboards from spreadsheet data, to orchestrating multi‑service marketing campaigns—all triggered by simple LLM prompts. Because the MCP server exposes each piece as a standard interface, integrating Activepieces into an AI workflow requires only minimal configuration: the assistant queries the MCP endpoint, receives a list of available pieces, and executes the chosen action with the supplied parameters. This tight coupling between AI intent and executable automation makes Activepieces a powerful bridge for developers looking to embed sophisticated, data‑driven logic into conversational agents.