Companycam-community Python API Docs | dltHub

CompanyCam is a photo‑based construction documentation platform that provides a REST API to read and manage projects, photos, users, and other entities. The REST API base URL is https://api.companycam.com/v1 and All requests require a Bearer token obtained via OAuth 2.0 Authorization Code flow..

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 Companycam-community data in under 10 minutes.

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What data can I load from Companycam-community?

Here are some of the endpoints you can load from Companycam-community:

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How do I authenticate with the Companycam-community API?

Obtain an access token via the OAuth 2.0 Authorization Code flow and include it in the HTTP Authorization: Bearer <token> header for every request.

1. Get your credentials

1. Log in to your CompanyCam account.
2. Navigate to Settings → Integrations → API or Developer Settings.
3. Create a new OAuth application if needed.
4. Copy the generated Client ID and Client Secret.
5. Configure the redirect URI used by your application.
6. Use these credentials in the OAuth Authorization Code flow to obtain an access token.

2. Add them to .dlt/secrets.toml

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[sources.companycam_community_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 Companycam-community 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 companycam_community_pipeline.py

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

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Pipeline companycam_community_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset companycam_community_data
The duckdb destination used duckdb:/companycam_community.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 companycam_community_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 projects and photos from the Companycam-community 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 companycam_community_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.companycam.com/v1",
            "auth": {
                "type": "bearer",
                "token": access_token,
            },
        },
        "resources": [
            {"name": "projects", "endpoint": {"path": "projects", "data_selector": "projects"}},
            {"name": "photos", "endpoint": {"path": "photos", "data_selector": "photos"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="companycam_community_pipeline",
        destination="duckdb",
        dataset_name="companycam_community_data",
    )
    load_info = pipeline.run(companycam_community_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("companycam_community_pipeline").dataset()
sessions_df = data.projects.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM companycam_community_data.projects LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("companycam_community_pipeline").dataset()
data.projects.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 Companycam-community 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 – Returned when the Bearer token is missing, expired, or invalid. Refresh the token using the refresh token endpoint or obtain a new token via the Authorization Code flow.

Rate Limits

• 429 Too Many Requests – CompanyCam enforces per‑minute request caps. When received, back off for at least the number of seconds indicated in the Retry-After header before retrying.

Pagination

• List endpoints return paginated results. Use the page and per_page query parameters (if supported) and check the total_pages or next_page fields in the response to iterate through all records.

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