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Integrations serve various purposes, including working with legacy APIs, implementing custom file exchange solutions, or integrating with RPAs.

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High-level architecture

Integrations involve interaction with legacy systems and require custom development to integrate them into your FlowX.AI setup.

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Developing a custom integration

Developing custom integrations for the FlowX.AI platform is a straightforward process. You can use your preferred technology to write the necessary custom code, with the requirement that it can send and receive messages from the Kafka cluster.

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Steps to create a custom integration

Follow these steps to create a custom integration:
  1. Develop a microservice, referred to as a “Connector,” using your preferred tech stack. The Connector should listen for Kafka events, process the received data, interact with legacy systems if required, and send the data back to Kafka.
  2. Configure the process definition by adding a message send action in one of the nodes. This action sends the required data to the Connector.
  3. Once the custom integration’s response is ready, send it back to the FlowX.AI Engine. Keep in mind that the process will wait in a receive message node until the response is received.
For Java-based Connector microservices, you can use the following startup code as a quickstart guide:

Quickstart connector

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Managing an integration

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Managing Kafka topics

It’s essential to configure the engine to consume events from topics that follow a predefined naming pattern. The naming pattern is defined using a topic prefix and suffix, such as “ai.flowx.dev.engine.receive.”
We recommend the following naming convention for your topics:
 topic:
    naming:
      package: "ai.flowx."
      environment: ""
      version: ".v1"
      prefix: ${kafka.topic.naming.package}${kafka.topic.naming.environment}
      suffix: ${kafka.topic.naming.version}
      engineReceivePattern: engine.receive

    pattern: ${kafka.topic.naming.prefix}${kafka.topic.naming.engineReceivePattern}*

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Integration types

FlowX.AI supports multiple integration approaches to connect with external systems and data sources:

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Integration Designer

The Integration Designer provides a visual interface for managing data sources and building integration workflows without extensive coding.

RESTful Systems

Connect to external REST APIs for data exchange and system integration

FlowX Database

Store and retrieve data using the FlowX internal database

MCP Integration

Connect to Model Context Protocol servers to enable AI agents to use external tools

Knowledge Base

Create contextual knowledge repositories for AI agents to query

Learn more about Integration Designer

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Custom integrations

For more complex scenarios or when working with legacy systems, you can develop custom integrations using Kafka messaging.

Building a custom connector

Last modified on December 24, 2025