Sentry Python API Docs | dltHub

Sentry is an error‑monitoring and observability platform that exposes a REST API (v0) for managing organizations, projects, teams, issues, events and users. The REST API base URL is https://sentry.io/api/0 and All requests require a Bearer token passed 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 Sentry data in under 10 minutes.

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What data can I load from Sentry?

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

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How do I authenticate with the Sentry API?

Provide a Bearer token in the Authorization header (e.g., Authorization: Bearer ) for all API calls.

1. Get your credentials

1. Log in to your Sentry account at https://sentry.io.
2. For a personal token: Open User Settings → Personal Tokens → Create New Token, assign the required scopes, and copy the generated token.
3. For an internal integration token: Navigate to Settings → Integrations → Internal Integrations → Create New Integration, then generate a token for the integration and copy it.
4. Store the token securely; it will be used as the Bearer token in API requests.

2. Add them to .dlt/secrets.toml

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[sources.sentry_error_tracking_source]
token = "your_sentry_bearer_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 Sentry 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 sentry_error_tracking_pipeline.py

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

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Pipeline sentry_error_tracking_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset sentry_error_tracking_data
The duckdb destination used duckdb:/sentry_error_tracking.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 sentry_error_tracking_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 organizations and projects from the Sentry 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 sentry_error_tracking_source(api_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://sentry.io/api/0",
            "auth": {
                "type": "bearer",
                "token": api_token,
            },
        },
        "resources": [
            {"name": "organizations", "endpoint": {"path": "organizations/"}},
            {"name": "projects", "endpoint": {"path": "organizations/{org_slug}/projects/"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="sentry_error_tracking_pipeline",
        destination="duckdb",
        dataset_name="sentry_error_tracking_data",
    )
    load_info = pipeline.run(sentry_error_tracking_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("sentry_error_tracking_pipeline").dataset()
sessions_df = data.issues.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM sentry_error_tracking_data.issues LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("sentry_error_tracking_pipeline").dataset()
data.issues.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 Sentry 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 Errors

• 401 Unauthorized – Occurs when the Bearer token is missing, malformed, or lacks required scopes. Verify that the token is correct and includes the project:read or org:read scopes as needed.

Rate Limiting

• 429 Too Many Requests – Sentry enforces rate limits per organization. The response includes a Retry-After header indicating when to retry. Implement exponential backoff and respect the header.

Pagination

• Responses are paginated using Link headers (rel="next"). Continue fetching subsequent pages until the next link is absent. The documentation notes that each page contains up to 100 items.

Common Request Issues

• 400 Bad Request – Invalid query parameters or malformed URLs. Double‑check endpoint paths and query syntax.
• 403 Forbidden – Token does not have permission for the requested resource. Ensure the token’s scopes cover the targeted endpoint.

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