Microsoftgraph Python API Docs | dltHub

Microsoft Graph is a unified REST API that provides access to Microsoft 365, Azure AD, and other Microsoft cloud service data. The REST API base URL is https://graph.microsoft.com/v1.0 and All requests require a Bearer access token (OAuth 2.0)..

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 Microsoftgraph data in under 10 minutes.

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What data can I load from Microsoftgraph?

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

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How do I authenticate with the Microsoftgraph API?

Use OAuth 2.0 client credentials flow to obtain an access token from Azure AD and include it in the Authorization header as "Bearer {access_token}".

1. Get your credentials

1. Sign in to the Azure portal (https://portal.azure.com) and open Azure Active Directory → App registrations.
2. Register a new application and note the Application (client) ID and Directory (tenant) ID.
3. Under Certificates & secrets, create a new client secret and copy its value.
4. In API permissions, add the required Microsoft Graph permissions (e.g., User.Read.All, Mail.Read) and grant admin consent.
5. Use the tenant ID, client ID and client secret to request an access token from https://login.microsoftonline.com/{tenant}/oauth2/v2.0/token.

2. Add them to .dlt/secrets.toml

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[sources.microsoftgraph_source]
client_id = "your_app_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 Microsoftgraph 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 microsoftgraph_pipeline.py

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

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Pipeline microsoftgraph_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset microsoftgraph_data
The duckdb destination used duckdb:/microsoftgraph.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 microsoftgraph_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 Microsoftgraph 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 microsoftgraph_source(tenant_id, client_id, client_secret=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://graph.microsoft.com/v1.0",
            "auth": {
                "type": "bearer",
                "access_token": tenant_id, client_id, client_secret,
            },
        },
        "resources": [
            {"name": "users", "endpoint": {"path": "users", "data_selector": "value"}},
            {"name": "messages", "endpoint": {"path": "me/messages", "data_selector": "value"}}
        ],
    }
    yield from rest_api_resources(config)


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

SQL (DuckDB example):

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SELECT * FROM microsoftgraph_data.users LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("microsoftgraph_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 Microsoftgraph 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 the access token is missing, expired, or lacks required scopes. Verify client_id, client_secret, tenant_id, and that the token request used scope=https://graph.microsoft.com/.default for app‑only flows; ensure admin consent for application permissions.

Rate limiting / throttling

Microsoft Graph may return 429 Too Many Requests or 503 Service Unavailable with a Retry-After header. Implement exponential backoff and respect Retry-After.

Pagination

Collection responses include a value array and an @odata.nextLink when more pages exist. Follow the URL in @odata.nextLink to retrieve the next page; results are not returned in a top‑level array but under the value property.

Common error format

Errors are returned as JSON with an error object containing code, message, and optionally innerError and correlation IDs. Example:

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{ "error": { "code": "Request_ResourceNotFound", "message": "Resource not found.", "innerError": { ... } } }

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