Salesforce Python API Docs | dltHub

Salesforce REST API is a RESTful interface to access and manipulate Salesforce platform data and metadata. The REST API base URL is https://{instance}.my.salesforce.com/services/data/v{version} and All requests require an OAuth 2.0 Bearer access token (session) obtained via a Connected App..

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 Salesforce data in under 10 minutes.

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What data can I load from Salesforce?

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

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How do I authenticate with the Salesforce API?

Use OAuth 2.0: obtain an access token from the token endpoint and include header Authorization: Bearer <access_token> on all requests.

1. Get your credentials

1. In Salesforce Setup create a Connected App; 2) Configure OAuth scopes (e.g., 'Full access' or 'api'); 3) Note Consumer Key (client_id) and Consumer Secret (client_secret); 4) Configure callback URL for Authorization Code flow; 5) Use OAuth token endpoint (https://login.salesforce.com/services/oauth2/token or https://test.salesforce.com/... for sandbox) to exchange credentials for access_token.

2. Add them to .dlt/secrets.toml

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[sources.salesforce_data_source]
client_id = "your_consumer_key"
client_secret = "your_consumer_secret"
instance = "your_instance_domain_or_login_host"

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 Salesforce 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 salesforce_data_pipeline.py

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

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Pipeline salesforce_data_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset salesforce_data_data
The duckdb destination used duckdb:/salesforce_data.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 salesforce_data_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 query and sobjects from the Salesforce 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 salesforce_data_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://{instance}.my.salesforce.com/services/data/v{version}",
            "auth": {
                "type": "bearer",
                "access_token": access_token,
            },
        },
        "resources": [
            {"name": "query", "endpoint": {"path": "query?q={soql}", "data_selector": "records"}},
            {"name": "sobjects", "endpoint": {"path": "sobjects/{sObject}"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="salesforce_data_pipeline",
        destination="duckdb",
        dataset_name="salesforce_data_data",
    )
    load_info = pipeline.run(salesforce_data_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("salesforce_data_pipeline").dataset()
sessions_df = data.query.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM salesforce_data_data.query LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("salesforce_data_pipeline").dataset()
data.query.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 Salesforce 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: the access token is invalid or expired. Re-authenticate and obtain a new access_token; ensure the Authorization header is 'Bearer <access_token>'. Check token scope includes 'api'.

Rate limits and request limits

If you receive 403 with error REQUEST_LIMIT_EXCEEDED or messages about limits, you have exceeded org API limits. Implement exponential backoff and monitor org limits in Salesforce Setup or REST API usage endpoints.

Pagination quirks

SOQL Query and QueryAll paginate results: responses include 'done' (boolean), 'totalSize', 'nextRecordsUrl' and a 'records' array. Use nextRecordsUrl (relative path under /services/data/v{version}) to fetch subsequent pages. For list views use offset/limit parameters or nextRecordsUrl when provided.

Common error responses

400 Bad Request: malformed JSON or MALFORMED_QUERY; 401 Unauthorized: invalid/expired token; 403 Forbidden: insufficient permissions or REQUEST_LIMIT_EXCEEDED; 404 Not Found: resource doesn't exist; 409 Conflict: resource conflict (e.g., duplicate external ID). Error bodies are JSON arrays of objects with 'message' and 'errorCode' fields.

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