Backlog Python API Docs | dltHub

Backlog is a project management and issue tracking platform with a REST API for accessing spaces, projects, issues, users, attachments and related resources. The REST API base URL is https://{space}.backlog.com/api/v2 and Supports API key (apiKey query parameter) or OAuth2 Bearer tokens..

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 Backlog data in under 10 minutes.

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What data can I load from Backlog?

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

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How do I authenticate with the Backlog API?

You can authenticate by appending apiKey=YOUR_API_KEY as a query parameter to requests, or by using OAuth2 Authorization Code flow to obtain an access token and sending Authorization: Bearer YOUR_ACCESS_TOKEN in request headers.

1. Get your credentials

1. For API key: log into your Backlog space -> My profile / Personal settings -> API Key -> Generate API Key (copy it).
2. For OAuth2: register an application at Backlog Developer Applications (https://backlog.com/developer/applications/) to get client_id and client_secret; perform Authorization Code flow to obtain access and refresh tokens via /api/v2/oauth2/token.

2. Add them to .dlt/secrets.toml

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[sources.backlog_source]
api_key = "your_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 Backlog 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 backlog_pipeline.py

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

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Pipeline backlog_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset backlog_data
The duckdb destination used duckdb:/backlog.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 backlog_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 issues and projects from the Backlog 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 backlog_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://{space}.backlog.com/api/v2",
            "auth": {
                "type": "api_key or bearer",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "issues", "endpoint": {"path": "api/v2/issues"}},
            {"name": "projects", "endpoint": {"path": "api/v2/projects"}}
        ],
    }
    yield from rest_api_resources(config)


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

SQL (DuckDB example):

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SELECT * FROM backlog_data.issues LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("backlog_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 Backlog 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 append an invalid apiKey or omit Authorization header for OAuth2, API returns 401 Unauthorized. OAuth bearer failures include WWW-Authenticate header with an error_description (e.g. "The access token is invalid" or "The access token expired").

Rate limits

Backlog exposes rate limit info via the Rate Limit endpoint and may return headers indicating limits; respect pagination and use count/offset parameters.

Pagination and large result sets

List endpoints support offset and count query parameters (count 1-100). Use offset/count to page through results; responses are top-level arrays for list endpoints (no wrapper key).

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