Codefresh

Codefresh is a CI/CD platform built for Kubernetes and container-based workflows. It helps engineering teams build, test, and deploy software with pipelines, Helm charts, and runtime agents. Connecting Codefresh to Coupler.io lets you pull your pipeline runs, build records, audit logs, and analytics into any destination for reporting and analysis.

Why connect Codefresh to Coupler.io?

• Track build success rates, failure trends, and pipeline performance over time

• Centralize audit logs alongside data from other tools for compliance reporting

• Send CI/CD metrics to Google Sheets, BigQuery, or AI tools like ChatGPT or Gemini for deeper analysis

• Combine Codefresh data with project management sources using Join or Append transformations

Prerequisites

• A Codefresh account with access to the data you want to export

• A Codefresh API key (generated from your Codefresh user settings)

Quick start

Start with the Builds entity — it's the most actionable dataset for tracking pipeline health and catching failure patterns early.

How to connect

1

Create a new data flow in Coupler.io. From your Coupler.io dashboard, click Add data flow and search for Codefresh as your source.

2

Enter your Codefresh API key. In Codefresh, go to User Settings → API Keys and generate a new key. Copy it and paste it into the API key field in Coupler.io.

3

Select the entity you want to import. Choose from Builds, Pipelines, Audit logs, Analytics reports, or any of the other available entities. You can add more sources to the same data flow later.

4

Set your start date and report parameters. Use the date picker to set the earliest date for your data export. If you're pulling Analytics reports, also set the report granularity (daily, weekly, or monthly) and the report date range.

5

Choose a destination. Select where your data should land — Google Sheets, Excel, BigQuery, Looker Studio, or an AI destination like Claude, ChatGPT, or Gemini.

6

Run the data flow. Click Run to execute a manual sync and confirm data is arriving correctly.

Codefresh data overview

Codefresh exposes data across your entire CI/CD operation — from pipeline definitions and build records to audit trails, runtime agents, and Helm repositories. Here's a breakdown of what each entity contains and how you can use it.

Entities at a glance

Entity	Best used for
Builds	Pipeline run history, success/failure rates, duration trends
Pipelines	Pipeline definitions, triggers, and configuration
Audits	Compliance tracking, user activity monitoring
Analytics reports	Aggregated CI/CD metrics over time
Analytics metadata	Understanding report structure and available dimensions
Projects	Organizing and filtering pipelines by project
Agents	Monitoring runtime infrastructure
Contexts	Reviewing shared variables and secrets metadata
Execution contexts	Debugging runtime environments
Accounts	User management and account inventory
Account settings	Configuration auditing
Step types	Cataloging custom pipeline steps
Helm repos	Helm chart repository inventory

Builds

Key fields

Field	Description
Build ID	Unique identifier for the build run
Pipeline name	The pipeline that triggered the build
Status	Outcome: success, failure, error, terminated
Started at	Timestamp when the build began
Finished at	Timestamp when the build completed
Duration	Total execution time
Triggered by	User or event that triggered the build
Branch	Git branch associated with the build
Commit SHA	Git commit hash
Error message	Failure reason if the build did not succeed

Pipelines

Key fields

Field	Description
Pipeline ID	Unique identifier
Name	Pipeline display name
Project	Associated project
Created at	Pipeline creation date
Updated at	Last modification date
Triggers	Events configured to trigger the pipeline
Steps	Step definitions in the pipeline

Audits

Key fields

Field	Description
Event type	Action performed (e.g., pipeline updated, user added)
User	Who performed the action
Timestamp	When the event occurred
Entity type	What was affected (pipeline, account, context, etc.)
Entity ID	Identifier of the affected resource
IP address	Origin IP of the action

Analytics reports

Key fields

Field	Description
Report period	Date range covered by the report
Granularity	Daily, weekly, or monthly aggregation
Total builds	Count of builds in the period
Success rate	Percentage of successful builds
Average duration	Mean build execution time
Pipeline breakdown	Per-pipeline performance metrics

Common metric combinations

• Builds + Pipelines — Join on pipeline ID to enrich build records with pipeline metadata for full-context reporting

• Builds over time — Use Aggregate transformation to calculate daily or weekly success rates from raw build records

• Audits + Accounts — Join on user ID to map audit events to named users for compliance reports

• Analytics reports (Append) — Append reports from multiple date ranges to build a continuous historical dataset

Use cases by role

Engineering leads

• Monitor build success and failure rates across all pipelines to identify flaky tests or unstable stages

• Track average build duration over time to catch performance regressions before they slow down the team

• Compare pipeline activity across projects to balance workload and infrastructure costs

DevOps / Platform teams

• Audit agent activity and execution contexts to ensure runtime infrastructure is healthy

• Review Helm repo configurations and context settings to maintain consistent environments

• Export audit logs to BigQuery or a SIEM tool for compliance and incident investigation

Engineering managers

• Use Analytics reports in Google Sheets or Looker Studio to report on deployment frequency and change failure rate

• Feed build data into ChatGPT or Gemini to generate narrative summaries of team delivery health

• Track pipeline trends by project to identify teams that may need additional tooling support

Platform-specific notes

• Build history depth depends on your Codefresh plan — enterprise accounts retain longer build history

• Analytics reports require setting both granularity and date range parameters; missing either may result in empty data

• The Audits entity only captures events within your account's retention window as configured in Codefresh

• Contexts contain metadata about shared variables but do not expose secret values

• Agent data reflects the current state of registered agents and is not historical

Codefresh common issues

Connection issues

API key is rejected or returns an authentication error

Double-check that you copied the full API key from Codefresh — truncated keys are a common cause of auth failures. In Codefresh, go to User Settings → API Keys and regenerate a new key if you're unsure. Make sure the key has not been revoked or expired. Paste it fresh into the Coupler.io source settings without any leading or trailing spaces.

Connection succeeds but returns no data

Check your start date setting. If the start date is set too far in the future or beyond your account's data retention window, the sync will return empty results. Use the date picker to set a start date within the range where you know builds or events exist.

Missing data

Analytics reports entity returns empty results

Analytics reports require both report granularity and report date range to be set. If either is missing or the date range doesn't match any completed reporting periods, the entity will return no rows. Make sure your date range covers at least one full granularity period (e.g., a full day for daily granularity).

Builds from a specific pipeline are missing

Codefresh build history is scoped to your account. If a pipeline was deleted, its build records may no longer be accessible via the API. Also confirm that the API key belongs to an account that has access to that pipeline — builds from sub-accounts or teams you're not a member of won't appear.

Audit logs don't go back far enough

Audit log retention is controlled by your Codefresh plan and account settings. Coupler.io can only retrieve what Codefresh exposes via its API — if events older than a certain date aren't returned, they've likely been purged from Codefresh's retention window.

Permission errors

Certain entities return a 403 or permission denied error

Some Codefresh entities (like Accounts or Account settings) require admin-level access. If your API key is tied to a non-admin user, you'll get permission errors on those entities. Generate an API key from an admin account, or limit your data flow to entities your current user has access to.

Data discrepancies

Build counts in Coupler.io don't match what I see in the Codefresh UI

The Codefresh UI may apply default filters (e.g., showing only the last 30 days, or filtering by a specific pipeline). Your Coupler.io data flow pulls based on the start date you configured with no additional filters applied by default. Adjust your start date or filter the exported data in your destination to match the UI view you're comparing against.

Analytics report metrics differ from in-app analytics

The in-app analytics dashboard may use a different aggregation method or exclude certain build types (e.g., cancelled builds). Check that your report granularity and date range in Coupler.io exactly match the period shown in the Codefresh analytics UI.

Rate limits

Sync fails or slows down with large build histories

Codefresh API rate limits apply based on your account tier. If you're syncing a very large build history (months of data on a busy account), the initial sync may hit rate limits and slow down. Start with a shorter date range to get recent data first, then extend the range in subsequent runs.

Codefresh FAQ

Where do I find my Codefresh API key?

Log into Codefresh and go to User Settings (click your avatar in the bottom left). Under the API Keys section, click Generate to create a new key. Copy it immediately — Codefresh won't show it again after you leave the page.

Can I connect multiple Codefresh accounts to the same data flow?

Each Codefresh source in Coupler.io is tied to one API key and therefore one account. To combine data from multiple accounts, create a separate source for each account and use the Append transformation in your data flow to merge them into a single output.

Which entities update in real time and which are static?

Builds and Audits are updated continuously as your pipelines run. Entities like Pipelines, Projects, Step types, Helm repos, and Contexts are relatively static — they change only when your team updates configurations. Agents reflect current registration status. Schedule your data flow refresh frequency based on how often the data actually changes.

Can I use Codefresh data with AI tools?

Yes. Coupler.io supports AI destinations including ChatGPT, Claude, Gemini, Perplexity, Cursor, and OpenClaw. You can send Builds or Analytics reports data directly to these tools — for example, to generate a plain-language summary of your deployment health or to identify failure patterns across pipelines.

Why does the Analytics reports entity return no data even though I configured everything?

This almost always comes down to the date range not covering a complete reporting period. For example, if your granularity is set to weekly but your date range doesn't include at least one full week, Codefresh won't return a report row. Check that your date range aligns with complete periods at your chosen granularity. See the Common Issues article for more detail.

Does Coupler.io pull the actual build logs or just metadata?

The Builds entity pulls metadata — status, duration, triggered by, branch, commit SHA, and error messages. Full raw build logs are not exported. If you need log content, you'd need to access those directly in the Codefresh UI or via their API outside of Coupler.io.

Can I track which team members triggered the most builds?

Yes. The Builds entity includes a "triggered by" field. Export Builds data to Google Sheets or BigQuery, then aggregate by that field to see build volume per user. Joining with the Accounts entity on user ID will give you full name and account details alongside the build counts.

For a full breakdown of available fields per entity, see Data Overview. For help with syncs that return empty data or permission errors, check Common Issues.