Load Guru data to DuckDB

Guru is a knowledge management platform that provides a REST API for managing cards, collections, boards, and other organizational resources. Everything needed to build a working Guru → 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 Guru 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 Guru 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 Guru 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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Guru API at a glance

These values come from the Guru API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the Guru API?

The API uses HTTP Basic Authentication. Requests require an 'Authorization' header with the value 'Basic base64(username:token)', where the username is the user email (or Collection ID) and the token is the generated API token.

1. Get your credentials

To obtain Guru API credentials, follow these steps: 1. Sign in to your Guru web app as an Admin or a user with 'Manage Apps & Integrations' permissions. 2. Navigate to 'Manage' > 'Apps & Integrations'. 3. Select the 'API Access' tab. 4. Choose to 'Generate User Token' (for read/write access) or 'Generate a New Collection Token' (for read-only access). 5. If generating a User Token, provide a name for the token; if generating a Collection Token, select the specific collection from the dropdown list. 6. Copy the generated token immediately, as it cannot be retrieved again after leaving the page. Store this securely as it will be used as the password for API authentication.

2. Add them to .dlt/secrets.toml

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[sources.guru_source]
guru_api_key = "your_api_token_here"

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 Guru data can I load into DuckDB?

These are the Guru endpoints dlt can load into DuckDB:

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How do I load only new Guru records?

The Guru API reference does not document a timestamp or sequence field for these endpoints, so there is nothing to advertise here as verified. Pick a field from the endpoints table above that increases with every write, then set it as the cursor_path.

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{"name": "search_cards", "endpoint": {
    "path": "search/query",
    # Replace with a field that increases on every write.
    "incremental": {"cursor_path": "REPLACE_ME", "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 Guru pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading teams and cards from the Guru API into DuckDB:

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

@dlt.source
def guru_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.getguru.com/api/v1/",
            "auth": {"type": "http_basic", "username": "REPLACE_ME", "password": api_key},
        },
        "resources": [
            {"name": "search_cards", "endpoint": {"path": "search/query"}},
            {"name": "team_members", "endpoint": {"path": "members"}}
        ],
    }
    yield from rest_api_resources(config)


def load_guru_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="guru_pipeline",
        destination="duckdb",
        dataset_name="guru_data",
    )
    load_info = pipeline.run(guru_source())
    print(load_info)


if __name__ == "__main__":
    load_guru_to_duckdb()

Run it with python guru_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 Guru 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("guru_pipeline").dataset()
df = data.search_cards.df()
print(df.head())

SQL:

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SELECT * FROM guru_data.search_cards LIMIT 10;

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

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How do I deploy the Guru 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 Guru 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 Guru 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