Contele field team manager Python API Docs | dltHub

Contele Field Team Manager is a REST API for managing field teams, locations (POIs), tasks, forms, users and related operational data for Contele's field team management platform. The REST API base URL is https://integration.contelege.com.br/v2 and all requests require an x-api-key header (API key) 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 Contele field team manager data in under 10 minutes.

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What data can I load from Contele field team manager?

Here are some of the endpoints you can load from Contele field team manager:

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How do I authenticate with the Contele field team manager API?

Authentication is performed with an API key sent in the request header named "x-api-key". Some SDK examples also show an "authorization" parameter, but the documented primary method is the x-api-key header.

1. Get your credentials

1. Sign in to your Contele (Gestor de Equipes) account or open the Contele integrations / API settings in the admin panel. 2) Navigate to the API / Integração or developer section (API keys). 3) Create/generate a new API key. 4) Copy the generated key and store it securely; use it in requests as the x-api-key header.

2. Add them to .dlt/secrets.toml

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[sources.contele_field_team_manager_source]
api_key = "your_contele_api_key_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 Contele field team manager 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 contele_field_team_manager_pipeline.py

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

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Pipeline contele_field_team_manager_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset contele_field_team_manager_data
The duckdb destination used duckdb:/contele_field_team_manager.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 contele_field_team_manager_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 tasks and pois from the Contele field team manager 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 contele_field_team_manager_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://integration.contelege.com.br/v2",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "tasks", "endpoint": {"path": "tasks"}},
            {"name": "pois", "endpoint": {"path": "pois"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="contele_field_team_manager_pipeline",
        destination="duckdb",
        dataset_name="contele_field_team_manager_data",
    )
    load_info = pipeline.run(contele_field_team_manager_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("contele_field_team_manager_pipeline").dataset()
sessions_df = data.tasks.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM contele_field_team_manager_data.tasks LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("contele_field_team_manager_pipeline").dataset()
data.tasks.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 Contele field team manager 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 requests return 401/403, verify the x-api-key header is present and the API key is valid. Example header: x-api-key: YOUR_API_KEY. Some SDK examples pass an additional authorization string parameter — confirm whether your integration requires that extra header; primary auth is x-api-key.

Rate limiting

The public docs do not specify rate limits. If you receive 429 responses, implement exponential backoff and retry, and contact Contele support or your account manager to request rate limit details.

Pagination quirks

Many list endpoints accept page and perPage query parameters. Responses may return paginated arrays — always check response metadata (if present) for total pages/total items. When iterating pages, stop when an empty array is returned.

Missing fields / includes

Several endpoints accept an includes or include query parameter (e.g., includes=portfolios or include=lastLocation) to include related objects. If expected fields are missing, retry the request adding the appropriate include param.

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