# 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

{% hint style="success" %}
Start with the **Builds** entity — it's the most actionable dataset for tracking pipeline health and catching failure patterns early.
{% endhint %}

## How to connect

{% stepper %}
{% step %}
**Create a new data flow in Coupler.io.** From your Coupler.io dashboard, click **Add data flow** and search for **Codefresh** as your source.
{% endstep %}

{% step %}
**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.
{% endstep %}

{% step %}
**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.
{% endstep %}

{% step %}
**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.
{% endstep %}

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

{% step %}
**Run the data flow.** Click **Run** to execute a manual sync and confirm data is arriving correctly.
{% endstep %}
{% endstepper %}

## Available entities

| Entity             | Description                                       |
| ------------------ | ------------------------------------------------- |
| Accounts           | User accounts and account information             |
| Account settings   | Configuration settings and preferences            |
| Agents             | Runtime agents that execute pipelines             |
| Builds             | Build execution records with status and metadata  |
| Audits             | Audit logs of user actions and system events      |
| Analytics metadata | Metadata definitions for analytics reports        |
| Analytics reports  | Generated reports with pipeline and build metrics |
| Execution contexts | Runtime environments for pipeline executions      |
| Contexts           | Shared variables and configuration contexts       |
| Projects           | Project definitions organizing pipelines          |
| Pipelines          | CI/CD pipeline definitions and configurations     |
| Step types         | Custom step type definitions used in pipelines    |
| Helm repos         | Helm chart repositories configured in Codefresh   |