Aircall Python API Docs | dltHub

Aircall is a cloud‑based phone system and call‑center platform providing REST APIs and webhooks to access calls, contacts, users, numbers, webhooks, teams and related resources. The REST API base URL is https://api.aircall.io/v1 and All requests require HTTP Basic authentication (api_id and api_token) or OAuth for multi‑customer applications..

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 Aircall data in under 10 minutes.

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What data can I load from Aircall?

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

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How do I authenticate with the Aircall API?

Aircall supports HTTP Basic Auth where api_id is the username and api_token is the password; the Authorization header must be set to Basic <base64(api_id:api_token)>. OAuth2 is available for integrations across customers using a Bearer token.

1. Get your credentials

1. Sign in to the Aircall Dashboard and navigate to Company Settings. 2) Open the API Keys section and click Add a new API key. 3) Copy the generated api_id and api_token (the token is shown only once). 4) Base64‑encode api_id:api_token and use it in the Authorization: Basic <encoded> header.

2. Add them to .dlt/secrets.toml

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[sources.aircall_source]
api_id = "your_api_id_here"
api_token = "your_api_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 Aircall 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 aircall_pipeline.py

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

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Pipeline aircall_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset aircall_data
The duckdb destination used duckdb:/aircall.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 aircall_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 calls and users from the Aircall 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 aircall_source(api_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.aircall.io/v1",
            "auth": {
                "type": "http_basic",
                "api_token": api_token,
            },
        },
        "resources": [
            {"name": "calls", "endpoint": {"path": "v1/calls", "data_selector": "calls"}},
            {"name": "users", "endpoint": {"path": "v1/users", "data_selector": "users"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="aircall_pipeline",
        destination="duckdb",
        dataset_name="aircall_data",
    )
    load_info = pipeline.run(aircall_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("aircall_pipeline").dataset()
sessions_df = data.calls.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM aircall_data.calls LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("aircall_pipeline").dataset()
data.calls.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 Aircall 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 the Authorization header is missing or malformed, the API returns 401 Unauthorized with a JSON body like { "error": "Unauthorized", "troubleshoot": "Check your API key" }. Ensure you encode api_id:api_token correctly or use a valid OAuth Bearer token.

Rate limiting

Aircall enforces per‑minute request limits. Exceeding the limit returns 429 Too Many Requests. Reduce request frequency or contact support to raise limits. Paginated calls respect a maximum per_page of 50.

Pagination and data limits

List endpoints are paginated and include a top‑level meta object with pagination info. Use ?page= and ?per_page= (1‑50) query parameters. Calls and contacts are capped at 10,000 items; use the from timestamp parameter to retrieve older records or request an export for full history.

Common error payloads

All error responses contain an error field and a troubleshoot hint, e.g., { "error": "Invalid parameter", "troubleshoot": "Check query syntax" }. HTTP status codes follow standard semantics (4xx client errors, 5xx server errors).

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