Codefresh Python API Docs | dltHub

Codefresh is a CI/CD platform and GitOps solution that provides pipelines, build artifacts, and integrations via a REST API. The REST API base URL is https://g.codefresh.io/api and All requests require an API key sent in the Authorization header..

dlt is an open-source Python library that handles authentication, pagination, and schema evolution automatically. dlthub provides AI context files that enable code assistants to generate production-ready pipelines. Install with uv pip install "dlt[workspace]" and start loading Codefresh data in under 10 minutes.

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What data can I load from Codefresh?

Here are some of the endpoints you can load from Codefresh:

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How do I authenticate with the Codefresh API?

Authenticate using an API key (created in the Codefresh UI). Include it in every request as the Authorization header value (Authorization: <API_KEY>). The API uses an API-key security scheme.

1. Get your credentials

1. In the Codefresh UI open User Settings -> API Keys (or navigate to Integrations -> API Keys).
2. Click Generate (or Create) to create a new key.
3. Give the key a descriptive name and select scopes required for the key (e.g. Pipeline, Build, Repos).
4. Copy the generated token immediately and store it securely; it is shown only once.
5. Use that token in the Authorization header for API requests.

2. Add them to .dlt/secrets.toml

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[sources.codefresh_source]
api_key = "your_codefresh_api_key_here"

dlt reads this automatically at runtime — never hardcode tokens in your pipeline script. For production environments, see setting up credentials with dlt for environment variable and vault-based options.

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How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

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uv venv && source .venv/bin/activate
uv pip install "dlt[workspace]"

1. Install the dlt AI Workbench:

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dlt ai init --agent <your-agent> # <agent>: claude | cursor | codex

This installs project rules, a secrets management skill, appropriate ignore files, and configures the dlt MCP server for your agent. Learn more →

2. Install the rest-api-pipeline toolkit:

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dlt ai toolkit rest-api-pipeline install

This loads the skills and context about dlt the agent uses to build the pipeline iteratively, efficiently, and safely. The agent uses MCP tools to inspect credentials — it never needs to read your secrets.toml directly. Learn more →

3. Start LLM-assisted coding:

Use /find-source to load data from the Codefresh API into DuckDB.

The rest-api-pipeline toolkit takes over from here — it reads relevant API documentation, presents you with options for which endpoints to load, and follows a structured workflow to scaffold, debug, and validate the pipeline step by step.

4. Run the pipeline:

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python codefresh_pipeline.py

If everything is configured correctly, you'll see output like this:

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Pipeline codefresh_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset codefresh_data
The duckdb destination used duckdb:/codefresh.duckdb location to store data
Load package 1749667187.541553 is LOADED and contains no failed jobs

Inspect your pipeline and data:

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dlt pipeline codefresh_pipeline show

This opens the Pipeline Dashboard where you can verify pipeline state, load metrics, schema (tables, columns, types), and query the loaded data directly.

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Python pipeline example

This example loads pipelines and builds from the Codefresh API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:

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import dlt
from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources

@dlt.source
def codefresh_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://g.codefresh.io/api",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "pipelines", "endpoint": {"path": "pipelines", "data_selector": "docs"}},
            {"name": "builds", "endpoint": {"path": "builds", "data_selector": "docs"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="codefresh_pipeline",
        destination="duckdb",
        dataset_name="codefresh_data",
    )
    load_info = pipeline.run(codefresh_source())
    print(load_info)

To add more endpoints, append entries from the resource table to the "resources" list using the same name, path, and data_selector pattern.

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How do I query the loaded data?

Once the pipeline runs, dlt creates one table per resource. You can query with Python or SQL.

Python (pandas DataFrame):

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import dlt

data = dlt.pipeline("codefresh_pipeline").dataset()
sessions_df = data.pipelines.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM codefresh_data.pipelines LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("codefresh_pipeline").dataset()
data.pipelines.df().head()

See how to explore your data in marimo Notebooks and how to query your data in Python with dataset.

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What destinations can I load Codefresh data to?

dlt supports loading into any of these destinations — only the destination parameter changes:

Change the destination in dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. See the full destinations list.

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Troubleshooting

Authentication failures

If the Authorization header is missing or the API key is invalid the API returns 401 Unauthorized with an error body (example: {"status":401,"code":"2401","name":"UNAUTHORIZED_ERROR","message":"Failed to authenticate request because no token was provided"}). Ensure your Authorization header contains the API key exactly as generated.

Rate limits and throttling

Codefresh API endpoints may impose rate limiting per account. On hitting limits you will receive 429 responses; implement exponential backoff and retries. Pagination parameters (limit/offset) are provided on list endpoints to reduce page sizes.

Pagination and list shapes

Many list endpoints return a paginated envelope containing a "docs" array plus "total", "limit", and "offset" fields (examples: /builds, /images, /pipelines). Other endpoints return a top-level JSON array (examples: /projects, /triggers). Always inspect the endpoint response for either a top-level array or the "docs" key and set the dlt data selector accordingly.

Not found / invalid resource

GET requests for missing resources (e.g. /pipelines/{name} or /builds/{id} if not found) return 404 Not Found. Check identifiers and URL-encoding (pipeline names containing "/" must be URL-encoded).

Ensure that the API key is valid to avoid 401 Unauthorized errors. Also, verify endpoint paths and parameters to avoid 404 Not Found errors.

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Next steps

Continue your data engineering journey with the other toolkits of the dltHub AI Workbench:

• data-exploration — Build custom notebooks, charts, and dashboards for deeper analysis with marimo notebooks.
• dlthub-runtime — Deploy, schedule, and monitor your pipeline in production.

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dlt ai toolkit data-exploration install
dlt ai toolkit dlthub-runtime install

• How to deploy a pipeline
• How to explore your data in marimo Notebooks
• How to query your data in Python with dataset
• Join the dlt community on Slack