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Kafka Trigger Integration #8208

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opened 2026-02-21 18:24:29 -05:00 by yindo · 2 comments
yindo commented 2026-02-21 18:24:29 -05:00
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Originally created by @fzozyurt on GitHub (Feb 12, 2025).

Self Checks

  • I have searched for existing issues search for existing issues, including closed ones.
  • I confirm that I am using English to submit this report (我已阅读并同意 Language Policy).
  • [FOR CHINESE USERS] 请务必使用英文提交 Issue，否则会被关闭。谢谢！:）
  • Please do not modify this template :) and fill in all the required fields.

1. Is this request related to a challenge you're experiencing? Tell me about your story.

This feature enables DifyAI to automatically initiate processes by listening to messages from KAFKA queues. Essentially, when a new message arrives in a specific APACHE KAFKA topic, DifyAI will receive this message and trigger a pre-defined workflow. This workflow can then utilize the data within the message as input to execute a scenario or application within DifyAI.

Why is it Beneficial?

  • Real-time Data Processing: KAFKA is commonly used for real-time data streams. With this feature, DifyAI becomes capable of immediately processing data generated in real-time (e.g., sensor data, logs, event streams, etc.).
  • Event-Driven Workflows: It transforms DifyAI into a system that reacts to events. When an event (a KAFKA message) occurs, a process is automatically initiated, enhancing automation and rapid response capabilities.
  • Integration with Existing KAFKA Infrastructure: For organizations already using KAFKA, this allows for seamless integration of DifyAI into their existing data flows. Data from sources can be directly routed to DifyAI.
  • Scalability and Reliability: KAFKA is designed for high-volume and reliable data streaming. This feature allows DifyAI to leverage KAFKA's advantages, becoming more scalable and reliable itself.
  • Automation and Efficiency: By automatically initiating processes without manual intervention, it accelerates business processes and reduces the risk of human error.

2. Additional context or comments

How it Works (Basic Level):

  • KAFKA Configuration: Within DifyAI, settings such as the KAFKA server address, the topic to listen to, and the message format are configured.
  • KAFKA Listener (Consumer): DifyAI establishes a mechanism (typically a KAFKA consumer) that continuously listens to the specified KAFKA topic.
  • Message Reception: When a new message arrives in the KAFKA topic, DifyAI receives this message.
  • Data Parsing and Usage as Input: DifyAI parses the message content (e.g., JSON, text, etc.) and uses this data as input for the pre-defined workflow.
  • Workflow Initiation: The relevant DifyAI workflow is automatically initiated with the received data.

3. Can you help us with this feature?

  • I am interested in contributing to this feature.
Originally created by @fzozyurt on GitHub (Feb 12, 2025). ### Self Checks - [x] I have searched for existing issues [search for existing issues](https://github.com/langgenius/dify/issues), including closed ones. - [x] I confirm that I am using English to submit this report (我已阅读并同意 [Language Policy](https://github.com/langgenius/dify/issues/1542)). - [x] [FOR CHINESE USERS] 请务必使用英文提交 Issue，否则会被关闭。谢谢！:） - [x] Please do not modify this template :) and fill in all the required fields. ### 1. Is this request related to a challenge you're experiencing? Tell me about your story. This feature enables DifyAI to automatically initiate processes by listening to messages from KAFKA queues. Essentially, when a new message arrives in a specific APACHE KAFKA topic, DifyAI will receive this message and trigger a pre-defined workflow. This workflow can then utilize the data within the message as input to execute a scenario or application within DifyAI. Why is it Beneficial? - Real-time Data Processing: KAFKA is commonly used for real-time data streams. With this feature, DifyAI becomes capable of immediately processing data generated in real-time (e.g., sensor data, logs, event streams, etc.). - Event-Driven Workflows: It transforms DifyAI into a system that reacts to events. When an event (a KAFKA message) occurs, a process is automatically initiated, enhancing automation and rapid response capabilities. - Integration with Existing KAFKA Infrastructure: For organizations already using KAFKA, this allows for seamless integration of DifyAI into their existing data flows. Data from sources can be directly routed to DifyAI. - Scalability and Reliability: KAFKA is designed for high-volume and reliable data streaming. This feature allows DifyAI to leverage KAFKA's advantages, becoming more scalable and reliable itself. - Automation and Efficiency: By automatically initiating processes without manual intervention, it accelerates business processes and reduces the risk of human error. ### 2. Additional context or comments How it Works (Basic Level): - KAFKA Configuration: Within DifyAI, settings such as the KAFKA server address, the topic to listen to, and the message format are configured. - KAFKA Listener (Consumer): DifyAI establishes a mechanism (typically a KAFKA consumer) that continuously listens to the specified KAFKA topic. - Message Reception: When a new message arrives in the KAFKA topic, DifyAI receives this message. - Data Parsing and Usage as Input: DifyAI parses the message content (e.g., JSON, text, etc.) and uses this data as input for the pre-defined workflow. - Workflow Initiation: The relevant DifyAI workflow is automatically initiated with the received data. ### 3. Can you help us with this feature? - [x] I am interested in contributing to this feature.
yindo added the 💪 enhancementstale labels 2026-02-21 18:24:29 -05:00
yindo closed this issue 2026-02-21 18:24:30 -05:00
yindo commented 2026-02-21 18:24:30 -05:00
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@junjiem commented on GitHub (Feb 14, 2025):

Dify is not a streaming computing platform, and this requirement can encapsulate the Dify Api itself outside of Dify.

@junjiem commented on GitHub (Feb 14, 2025): Dify is not a streaming computing platform, and this requirement can encapsulate the Dify Api itself outside of Dify.
yindo commented 2026-02-21 18:24:30 -05:00
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@dosubot[bot] commented on GitHub (Mar 17, 2025):

Hi, @fzozyurt. I'm Dosu, and I'm helping the Dify team manage their backlog. I'm marking this issue as stale.

Issue Summary:

  • You suggested adding a Kafka trigger to DifyAI for automatic workflow initiation via Apache Kafka.
  • This feature aims to enhance real-time data processing and integration.
  • @junjiem mentioned that Dify is not intended as a streaming computing platform.
  • It was suggested that the functionality could be implemented externally using the Dify API.

Next Steps:

  • Please let me know if this issue is still relevant to the latest version of Dify by commenting here.
  • If there is no further activity, I will automatically close this issue in 15 days.

Thank you for your understanding and contribution!

@dosubot[bot] commented on GitHub (Mar 17, 2025): Hi, @fzozyurt. I'm [Dosu](https://dosu.dev), and I'm helping the Dify team manage their backlog. I'm marking this issue as stale. **Issue Summary:** - You suggested adding a Kafka trigger to DifyAI for automatic workflow initiation via Apache Kafka. - This feature aims to enhance real-time data processing and integration. - @junjiem mentioned that Dify is not intended as a streaming computing platform. - It was suggested that the functionality could be implemented externally using the Dify API. **Next Steps:** - Please let me know if this issue is still relevant to the latest version of Dify by commenting here. - If there is no further activity, I will automatically close this issue in 15 days. Thank you for your understanding and contribution!
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Reference: langgenius/dify#8208
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