Semaphore UI Python API Docs | dltHub

Semaphore API is a REST API for interacting with Semaphore CI/CD (Semaphore UI) to manage projects, pipelines, jobs, agents, deployment targets, tokens and related resources. The REST API base URL is https://<organization-url>.semaphoreci.com/api/v1alpha and All requests require an API token sent in Authorization header (Bearer/Token style)..

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 Semaphore UI data in under 10 minutes.

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What data can I load from Semaphore UI?

Here are some of the endpoints you can load from Semaphore UI:

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How do I authenticate with the Semaphore UI API?

Generate an API token in the Semaphore UI (or via login + token creation endpoints). Send the token in the Authorization header for every request, e.g. Authorization: Token {api_token} or Authorization: Bearer {api_token} (documentation shows both ‘Token’ for SaaS and ‘Bearer’ in admin guide; use the header format required by your instance).

1. Get your credentials

1. Sign in to your Semaphore account or your self-hosted Semaphore UI instance.
2. Open Account / User Settings or Admin → API (Administration Guide → Getting Started with the API).
3. Create a new API token (or use the HTTP flow: POST /api/auth/login to obtain a session cookie then POST /api/user/tokens to create a token).
4. Copy the returned token id (response contains an "id" field that is the token). Use it in Authorization header.

2. Add them to .dlt/secrets.toml

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[sources.semaphore_ui_source]
api_token = "your_api_token_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 Semaphore UI 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 semaphore_ui_pipeline.py

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

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Pipeline semaphore_ui_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset semaphore_ui_data
The duckdb destination used duckdb:/semaphore_ui.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 semaphore_ui_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 jobs from the Semaphore UI 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 semaphore_ui_source(api_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://<organization-url>.semaphoreci.com/api/v1alpha",
            "auth": {
                "type": "bearer",
                "token": api_token,
            },
        },
        "resources": [
            {"name": "pipelines", "endpoint": {"path": "pipelines"}},
            {"name": "jobs", "endpoint": {"path": "jobs"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="semaphore_ui_pipeline",
        destination="duckdb",
        dataset_name="semaphore_ui_data",
    )
    load_info = pipeline.run(semaphore_ui_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("semaphore_ui_pipeline").dataset()
sessions_df = data.pipelines.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM semaphore_ui_data.pipelines LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("semaphore_ui_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 Semaphore UI 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 you receive 401/403 responses, verify the Authorization header contains a valid token. For SaaS use the organization-scoped endpoint and header exactly as documented: Authorization: Token {api_token}. For some Semaphore UI docs/examples the administration guide shows Authorization: Bearer {api_token} — confirm the header needed by your instance. Ensure token has not been expired/revoked.

Pagination and cursors

List endpoints that can return large results use cursor-based pagination. Responses often include a "cursor" field (string) when more pages exist. Use query parameters page_size and cursor (or cursor_type and cursor_value for some endpoints like deployment history) to page through results.

Rate limits and common HTTP errors

The docs do not list a global rate limit in the public reference; on 429 responses, back off and retry with exponential backoff. Common errors: 400 for invalid parameters, 401/403 for auth issues, 404 for missing resources, 422 for validation errors on POST/PUT. Check error body for details.

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