Load Front data to DuckDB

Front is a customer communication platform providing a REST API to manage conversations, contacts, and messages. Everything needed to build a working Front → DuckDB pipeline is on this page: the API's base URL, authentication, endpoints, pagination and incremental field — plus a prompt that hands the whole job to your coding agent.

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Build your Front to DuckDB pipeline

Paste this prompt into Claude, Codex, or Cursor. The agent does the rest.

PromptCopy prompt
Run uvx dlthub-init@latest to build a pipeline from Front to DuckDB and run it on dltHub

That scaffolds a dltHub workspace and installs the dltHub AI harness — the project rules, the secrets-management skill, and the dlt MCP server your agent needs to work safely. From there it reads the Front API, proposes the endpoints to load, then writes, runs and validates the pipeline while you review rather than type. Credentials are inspected through MCP tools, so your agent never reads secrets.toml itself. How the LLM-native workflow works →

Prefer to write it yourself? Every fact the agent uses is below.

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Front API at a glance

These values come from the Front API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the Front API?

Front uses Bearer token authentication for all API requests. The token must be sent in the 'Authorization' header, prefixed with the string 'Bearer ' (e.g., 'Authorization: Bearer ').

1. Get your credentials

1. Sign in to your Front account as an administrator.
2. Navigate to Settings (the gear icon).
3. Select the Developers section under the Company header.
4. Click the API tokens tab.
5. Click Create API token.
6. Provide a descriptive name and select the required scopes/permissions for your integration.
7. Save the token. Once created, you can reveal or copy the token value from the API token details page. Note that the full secret value is typically only shown upon creation or when revealed.

2. Add them to .dlt/secrets.toml

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[sources.front_source]
api_key = "Bearer YOUR_FRONT_API_TOKEN"

dlt reads this file automatically at runtime. With the harness, the setup-secrets skill prompts you for the values and never handles the raw credential in chat. For production, see setting up credentials with dlt.

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What Front data can I load into DuckDB?

These are the Front endpoints dlt can load into DuckDB:

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How do I load only new Front records?

Front exposes page_token on conversations, so dlt can request only the records that changed since the last run. Set it as the cursor_path and dlt tracks the high-water mark for you between runs.

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{"name": "conversations", "endpoint": {
    "path": "conversations",
    "data_selector": "_results",
    "incremental": {"cursor_path": "page_token", "initial_value": "2024-01-01T00:00:00Z"},
}}

On the first run dlt loads everything from initial_value; on every run after that it requests only what changed and appends with write_disposition="merge" if you set a primary key. See incremental loading.

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What does the generated Front pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading /me and /conversations from the Front API into DuckDB:

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import dlt
from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources

@dlt.source
def front_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api2.frontapp.com",
            "auth": {"type": "bearer", "token": api_key},
        },
        "resources": [
            {"name": "conversations", "endpoint": {"path": "conversations", "data_selector": "_results"}},
            {"name": "contacts", "endpoint": {"path": "contacts", "data_selector": "_results"}}
        ],
    }
    yield from rest_api_resources(config)


def load_front_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="front_pipeline",
        destination="duckdb",
        dataset_name="front_data",
    )
    load_info = pipeline.run(front_source())
    print(load_info)


if __name__ == "__main__":
    load_front_to_duckdb()

Run it with python front_pipeline.py. The agent iterates on this until it loads cleanly — you review and approve, rather than write it from scratch.

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How do I query Front data in DuckDB?

dlt creates one table per resource. Query the loaded data with Python or SQL — or ask your agent to, through the MCP server's execute_sql_query tool.

Python (pandas DataFrame):

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import dlt

data = dlt.pipeline("front_pipeline").dataset()
df = data.conversations.df()
print(df.head())

SQL:

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SELECT * FROM front_data.conversations LIMIT 10;

See querying your data with dataset and exploring it in marimo notebooks.

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How do I deploy the Front to DuckDB pipeline in production?

The pipeline runs locally, which is ideal for prototyping and one-off analysis. When you need it on a schedule, monitored on every load, and shared with your team, deploy the same dlt code on the dltHub platform — no infrastructure to maintain. The prompt above already ends with "run it on dltHub", so your agent can take it there directly.

• Deploy & schedule — run the pipeline as a managed job with automatic retries.
• Monitor — observable job queues, alerting, and load metrics for every run.
• Transform — promote raw Front loads into governed, documented models.
• Visualize & share — explore data in notebooks and publish live dashboards instead of static screenshots.

Book a demo →

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What other destinations can I load Front data to?

dlt loads into any of these — only the destination argument changes:

Set dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. On the dltHub platform the same pipeline runs against a managed Iceberg lakehouse. See the full destinations list.

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Next steps

• Ready for production? Book a demo of the dltHub platform →
• dlt LLM-native workflow docs
• REST API source reference
• Join the dlt community on Slack