Microsoft Graph Python API Docs | dltHub

Microsoft Graph is the gateway to data and intelligence in Microsoft 365 and related Microsoft cloud services, exposing a single REST endpoint to access users, groups, mail, calendars, files, sites, Teams, and more. The REST API base URL is https://graph.microsoft.com and all requests require a Bearer token obtained via OAuth2.

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 Microsoft Graph data in under 10 minutes.

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What data can I load from Microsoft Graph?

Here are some of the endpoints you can load from Microsoft Graph:

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How do I authenticate with the Microsoft Graph API?

Microsoft Graph uses OAuth 2.0 (OpenID Connect). Applications obtain an access token via Azure AD and include it in an Authorization: Bearer <access_token> header on each request.

1. Get your credentials

1. Sign in to the Azure portal (portal.azure.com) and go to Microsoft Entra ID (Azure Active Directory).
2. Register a new app (App registrations > New registration).
3. Note the Application (client) ID and Directory (tenant) ID.
4. Under Certificates & secrets, create a client secret (or upload a certificate) and copy the secret value.
5. Under API permissions, add the required Microsoft Graph permissions and grant admin consent if needed.
6. To obtain an access token, POST to https://login.microsoftonline.com/{tenant}/oauth2/v2.0/token with client_id, client_secret, scope (e.g., https://graph.microsoft.com/.default), and grant_type.

2. Add them to .dlt/secrets.toml

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[sources.microsoft_graph_source]
client_id = "your_client_id"
client_secret = "your_client_secret"
tenant_id = "your_tenant_id"

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 Microsoft Graph 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 microsoft_graph_pipeline.py

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

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Pipeline microsoft_graph_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset microsoft_graph_data
The duckdb destination used duckdb:/microsoft_graph.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 microsoft_graph_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 users and messages from the Microsoft Graph 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 microsoft_graph_source(client_secret=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://graph.microsoft.com",
            "auth": {
                "type": "bearer",
                "token": client_secret,
            },
        },
        "resources": [
            {"name": "users", "endpoint": {"path": "v1.0/users", "data_selector": "value"}},
            {"name": "messages", "endpoint": {"path": "v1.0/me/messages", "data_selector": "value"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="microsoft_graph_pipeline",
        destination="duckdb",
        dataset_name="microsoft_graph_data",
    )
    load_info = pipeline.run(microsoft_graph_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("microsoft_graph_pipeline").dataset()
sessions_df = data.users.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM microsoft_graph_data.users LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("microsoft_graph_pipeline").dataset()
data.users.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 Microsoft Graph 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

401 Unauthorized or 403 Forbidden are returned when an access token is missing, expired, or lacks required scopes. Verify token acquisition, tenant, client credentials, and that the token includes the correct scopes/roles (delegated permissions for user flows, application permissions for app‑only). Use the Azure portal to grant consent and inspect token scopes.

Throttling and rate limits

When throttled Microsoft Graph returns 429 (or 503) and includes a Retry‑After header. Implement exponential backoff and retry using the Retry‑After response header when present.

Pagination

Collection responses return results in "value" and include an "@odata.nextLink" property when there are more pages. Follow the URL in @odata.nextLink to retrieve subsequent pages; do not recompose paging URLs manually.

Common error format

Errors return JSON with an "error" object containing code and message. Example structure: { "error": { "code": "ErrorCode", "message": "text", "innerError": { ... } } } and standard HTTP status codes (4xx, 5xx) apply.

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