Activepieces is an open-source AI automation and agent platform whose goal is to bring chat, AI agents, automated flows, tables, and 760+ app integrations together in a single workspace, so that an entire team can use AI within the tools they already use. Its core is open source, and its product positioning emphasizes enterprise-grade control and flexibility.
In terms of use cases, Activepieces is aimed at teams' daily repetitive work and cross-tool collaboration. Official examples include: answering routine repetitive questions and escalating to a human when needed; extracting invoices from an inbox, matching them against purchase orders, and filing them for approval; researching every demo request, scoring it, and handing it to the owner within a minute. It is suited to teams that want to reduce manual work and let AI execute tasks end-to-end across their existing tools.
Main capabilities include:
- AI Agents: Describe a task in natural language, and the agent can execute it end-to-end across tools, deciding on its own the steps, order, and what to open. It can choose its own tools, including all connected apps and any specified MCP servers; set approval gates on steps involving money, customers, or production environments, and let the remaining steps run automatically. Officially, no prompt engineering is required.
- Flows: Supports branching, retries, wait points, subflows, error handling, and human approval. Simple by default, with depth when needed. Most flows can be drafted through chat, and the builder provides full control when things get difficult; the hardest steps can be written inline in TypeScript code while the rest remain visual.
- App connections: Each app only needs to be connected once, after which every flow, agent, and team member can access it. It includes 760+ open-source pieces, covering apps already in use; for services not covered, any REST or GraphQL endpoint can be called through the HTTP piece, with authentication and pagination handled.
- Tables and data: Provides Tables, which flows and agents can read from and write to; documents can be searched by every agent.
- AI building: Describe a step in plain English, and AI will write it; you can also switch to writing TypeScript code.
- Runs and recovery: When using your own keys and models, runs can be resumed from the last checkpoint rather than starting over.
For enterprise-grade control and management: each team has its own independently sealed space; supports Okta or Entra login, with role-to-permission granularity; a secrets manager keeps credentials in your own vault; audit logs record who did what and when, and stream it in real time.Lost to SIEM. You can also see which teams are building and what is actually running.
In terms of how to use it: you can start by describing a task that a team repeatedly does manually, you can start without a credit card, and it runs on the official cloud or the user's own servers. Deployment options include the official cloud (running in Frankfurt), the user's own servers, or a fully air-gapped environment; it can be run on your own infrastructure using Docker or Kubernetes. Teams can directly describe requirements in natural language to build processes, and later can ask again to modify them or open the builder. Claude and Cursor can run what the user builds.
Applicable scenarios include: automatic report generation, routing documents to the right people, sales presentations pulling the latest data, automatic scheduling for user research, and other repetitive team work. It is officially said that every team can build what it needs on its own, and engineering teams can focus on difficult problems instead of handling ticket queues.
Limitations and billing information: every plan provides unlimited processes, and the logic within a process is free; payment depends on how often the process actually runs, and the price per run decreases as usage grows. A free plan is provided, no credit card required. The core code uses the MIT license, so you can read the code, fork it, or run your own version. Currently, the official materials do not provide specific prices, specific run-count limits, or more detailed restrictions, and no related content is available for now.
