Front Python API Docs | dltHub

Front is a collaborative inbox and customer communication platform providing a REST Core API to manage conversations, messages, contacts, inboxes, teammates, tags, attachments and other resources. The REST API base URL is https://api2.frontapp.com and All requests require a Bearer token (OAuth or API token) provided in the Authorization header..

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 Front data in under 10 minutes.

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What data can I load from Front?

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

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How do I authenticate with the Front API?

Front supports OAuth for public integrations and personal/team API tokens for simpler setups. Send the token in the Authorization header as: Authorization: Bearer .

1. Get your credentials

1. Log into Front as an admin or teammate. 2) For a quick token: go to Settings > API & integrations > API tokens (or Personal settings > API tokens) and generate a new token with the required scopes. 3) For public apps: implement OAuth per Front's OAuth guide to obtain access tokens and request appropriate scopes (Access resources/read/write/send).

2. Add them to .dlt/secrets.toml

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[sources.front_source]
api_token = "your_front_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 Front 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 front_pipeline.py

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

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Pipeline front_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset front_data
The duckdb destination used duckdb:/front.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 front_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 conversations and messages from the Front 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 front_source(api_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api2.frontapp.com",
            "auth": {
                "type": "bearer",
                "token": api_token,
            },
        },
        "resources": [
            {"name": "conversations", "endpoint": {"path": "conversations", "data_selector": "_results"}},
            {"name": "messages", "endpoint": {"path": "messages", "data_selector": "_results"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="front_pipeline",
        destination="duckdb",
        dataset_name="front_data",
    )
    load_info = pipeline.run(front_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("front_pipeline").dataset()
sessions_df = data.conversations.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM front_data.conversations LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("front_pipeline").dataset()
data.conversations.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 Front 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, verify the Authorization header is exactly: Authorization: Bearer <token>. Ensure the token has the required scopes (Read/Write/Send) and that the token is not revoked or expired. For public integrations, use OAuth tokens as required.

Rate limiting

Front uses rate limiting on API requests and returns 429 Too Many Requests when limits are exceeded. Inspect response headers for rate limit details and implement exponential backoff and retry-after handling.

Pagination quirks

List endpoints are paginated and return _pagination (with next URL) and the actual records are in _results. Follow the _pagination.next link to iterate pages. Some singular GET endpoints return an object (no _results).

Common error responses

401 Unauthorized — invalid or missing token. 403 Forbidden — insufficient token scopes or permissions. 404 Not Found — invalid resource id. 422 Unprocessable Entity — validation errors for create/update. 429 Too Many Requests — rate limit exceeded. 5xx — server errors; retry with backoff.

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