Load Lark Parser data to DuckDB

Lark is an enterprise collaboration platform providing a suite of RESTful APIs for interacting with its services like messaging, calendars, and contacts. Everything needed to build a working Lark Parser → 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 Lark Parser 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 Lark Parser 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 Lark Parser 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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Lark Parser API at a glance

These values come from the Lark Parser API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the Lark Parser API?

Authentication requires a Bearer token in the 'Authorization' HTTP header. The token is obtained via the Lark Open Platform API.

1. Get your credentials

1. Log in to the Lark Developer Console (https://open.larksuite.com/app/).\n2. Select the desired application from your list.\n3. Navigate to the Credentials & Basic Info page located in the sidebar.\n4. Copy the App ID and App Secret values displayed in the Credentials section. These will be used to authenticate and generate access tokens (tenant_access_token or app_access_token) via the Lark REST API.

2. Add them to .dlt/secrets.toml

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[sources.lark_parser_source]
access_token = "REPLACE_ME"

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 Lark Parser data can I load into DuckDB?

These are the Lark Parser endpoints dlt can load into DuckDB:

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How do I load only new Lark Parser records?

Lark Parser exposes page_token on open-apis/task/v2/tasks, 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": "tasks", "endpoint": {
    "path": "open-apis/task/v2/tasks",
    "data_selector": "items",
    "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 Lark Parser pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading /auth/v3/app_access_token/internal and /auth/v3/tenant_access_token/internal from the Lark Parser API into DuckDB:

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

@dlt.source
def lark_parser_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://open.larksuite.com",
            "auth": {"type": "bearer", "token": access_token},
        },
        "resources": [
            {"name": "tasks", "endpoint": {"path": "open-apis/task/v2/tasks", "data_selector": "items"}},
            {"name": "users", "endpoint": {"path": "open-apis/contact/v3/users", "data_selector": "users"}}
        ],
    }
    yield from rest_api_resources(config)


def load_lark_parser_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="lark_parser_pipeline",
        destination="duckdb",
        dataset_name="lark_parser_data",
    )
    load_info = pipeline.run(lark_parser_source())
    print(load_info)


if __name__ == "__main__":
    load_lark_parser_to_duckdb()

Run it with python lark_parser_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 Lark Parser 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("lark_parser_pipeline").dataset()
df = data.tasks.df()
print(df.head())

SQL:

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SELECT * FROM lark_parser_data.tasks LIMIT 10;

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

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How do I deploy the Lark Parser 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 Lark Parser 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 Lark Parser 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