Flexport Python API Docs | dltHub

Flexport API is a RESTful service that provides programmatic access to freight and logistics data. The REST API base URL is https://api.flexport.com and All requests require a Bearer token for authentication..

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 Flexport data in under 10 minutes.

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What data can I load from Flexport?

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

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How do I authenticate with the Flexport API?

Flexport uses OAuth2 client_credentials to obtain a bearer token, which must be sent in the Authorization: Bearer <token> header. API Keys can also be used directly as bearer tokens.

1. Get your credentials

1. Log in to the Flexport dashboard.
2. Navigate to Developer Settings → API Credentials.
3. Click Create New Credential.
4. Give the credential a name and select OAuth2 Client Credentials.
5. After creation, copy the Client ID and Client Secret.
6. Use these values to request an access token via the /oauth/token endpoint.

2. Add them to .dlt/secrets.toml

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[sources.flexport_source]
access_token = "your_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 Flexport 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 flexport_pipeline.py

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

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Pipeline flexport_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset flexport_data
The duckdb destination used duckdb:/flexport.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 flexport_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 products and documents from the Flexport 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 flexport_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.flexport.com",
            "auth": {
                "type": "bearer",
                "token": access_token,
            },
        },
        "resources": [
            {"name": "products", "endpoint": {"path": "products", "data_selector": "data.data"}},
            {"name": "documents", "endpoint": {"path": "documents", "data_selector": "data.data"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="flexport_pipeline",
        destination="duckdb",
        dataset_name="flexport_data",
    )
    load_info = pipeline.run(flexport_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("flexport_pipeline").dataset()
sessions_df = data.products.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM flexport_data.products LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("flexport_pipeline").dataset()
data.products.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 Flexport 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 errors

• 401 Unauthorized – occurs when the bearer token is missing, malformed, or expired. Obtain a fresh token via the /oauth/token endpoint.

Rate limits

• Flexport limits token requests to 10 per day. Exceeding this limit results in a 429 Too Many Requests response.

Pagination issues

• Endpoints expect page and per query parameters. Supplying invalid values returns 400 Invalid pagination with an error object.

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