Overview
We are continuously improving Pipefy’s integration capabilities to make them more stable, scalable, and reliable.
This update introduces two improvements to how our integration engine handles data mapping and flow execution time, helping ensure more consistent performance across the platform.
Structural improvement: More resilient integrations with System IDs
What changed?
Integrations built using Pipefy actions (such as Get Card by ID) now rely on System IDs instead of field and phase display names when mapping data.
System IDs are unique and immutable identifiers that remain stable even if fields or phases are renamed in your process.
Why this matters
Previously, integrations relied on the visible names of fields and phases. If those names were changed, integrations could fail to locate the correct references.
By using System IDs, integrations remain stable even when process structures evolve, allowing teams to rename fields or phases without affecting existing integrations.
Do I need to take action?
🟢 Published flows – No action required
If your flows are already published and you do not edit them, they will continue running normally.
🟡 Edited flows – Action required
If you open and edit an existing flow, the system will automatically upgrade it to the new System ID–based structure.
Before publishing, you will need to remap dynamic variables using the updated references.
Platform performance: 5-minute flow execution limit
To maintain consistent platform performance, we have implemented a maximum execution time of 5 minutes for a single flow execution.
Why this change?
Pipefy’s integration environment operates on shared infrastructure. Extremely long-running executions can create bottlenecks and affect overall platform performance.
This execution limit helps ensure that integrations remain responsive and efficient for all customers.
Pipefy’s integration engine is designed primarily for event-driven orchestration, rather than large-scale data processing workloads.
About timeouts and execution times
Execution times may vary depending on factors such as:
- The volume of data being processed
- The complexity of the flow logic
- The payload size returned by external systems
If a flow approaches the execution limit, consider optimizing the logic or splitting the process into smaller steps.
Most relevant for
- Developers
- IT teams
- Pipefy Admins
- Teams managing integrations and automation workflows