GitHub Python API Docs | dltHub

GitHub is a platform for hosting Git repositories and provides a REST API to access and manage repositories, issues, pull requests, users, organizations and related resources. The REST API base URL is https://api.github.com and all requests that need elevated access require a Bearer token (personal access token, GitHub App token, or installation token)..

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 GitHub data in under 10 minutes.

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What data can I load from GitHub?

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

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How do I authenticate with the GitHub API?

Authenticate by sending an access token in the Authorization header (Authorization: Bearer YOUR_TOKEN or Authorization: token YOUR_TOKEN). Include Accept: application/vnd.github+json and optionally X-GitHub-Api-Version: 2022-11-28.

1. Get your credentials

1. Sign in to github.com. 2) Go to Settings -> Developer settings -> Personal access tokens. 3) Choose "Fine-grained tokens" or "Tokens (classic)" -> Generate new token. 4) Select required repositories/permissions (scopes) and expiration. 5) Create token and copy it (store securely). For app tokens, create a GitHub App in Settings -> Developer settings -> GitHub Apps and generate installation tokens per docs.

2. Add them to .dlt/secrets.toml

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[sources.github_source]
token = "your_personal_access_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 GitHub 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 github_pipeline.py

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

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Pipeline github_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset github_data
The duckdb destination used duckdb:/github.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 github_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 repos and issues from the GitHub 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 github_source(token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.github.com",
            "auth": {
                "type": "bearer",
                "token": token,
            },
        },
        "resources": [
            {"name": "repos", "endpoint": {"path": "repos/{owner}/{repo}"}},
            {"name": "issues", "endpoint": {"path": "repos/{owner}/{repo}/issues"}}
        ],
    }
    yield from rest_api_resources(config)


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

SQL (DuckDB example):

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SELECT * FROM github_data.issues LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("github_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 GitHub 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 Unauthorized or 403 Forbidden, verify your token: ensure you sent Authorization: Bearer (or Authorization: token ), the token has the required scopes/permissions or the fine-grained token includes access to the target repo/org, and the token is not expired or revoked.

Rate limits

GitHub enforces rate limits and returns headers: X-RateLimit-Limit, X-RateLimit-Remaining, X-RateLimit-Reset. Exceeding limits returns 403 or 429 with info. Monitor X-RateLimit-Remaining and retry after X-RateLimit-Reset (UNIX epoch seconds). Use conditional requests and caching where possible.

Pagination

Many list endpoints return paginated results. Use per_page and page query parameters. The response includes a Link header with rel="next" and rel="last" URLs. Iterate using Link header until no rel="next".

Common HTTP errors

401 Unauthorized — invalid/absent token. 403 Forbidden — insufficient permissions or rate-limited. 404 Not Found — resource does not exist or access denied. 422 Unprocessable Entity — validation error (e.g., bad input). 500/502/503 — server errors; retry with backoff.

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