Load Tactical RMM data to DuckDB

Tactical RMM is a remote monitoring and management platform that provides a REST API for programmatic access to agents, checks, tasks, and system configuration. Everything needed to build a working Tactical RMM → 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 Tactical RMM 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 Tactical RMM 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 Tactical RMM 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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Tactical RMM API at a glance

These values come from the Tactical RMM API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the Tactical RMM API?

All programmatic API requests require an X-API-KEY header containing the generated static API key. For alternative Knox token-based authentication (typically used for frontend sessions), the Authorization header is used with the format 'Token '.

1. Get your credentials

1. Log into your Tactical RMM web dashboard. 2. Navigate to Settings > Global Settings in the left-hand menu. 3. Select API Keys from the sub-menu. 4. Click the + Add Key button (or 'Create New Key'). 5. Assign the key to a user, provide a descriptive name, and select the appropriate permissions based on that user's role. 6. Save the configuration; copy the generated API key value immediately, as it will be used for authentication in the X-API-KEY header.

2. Add them to .dlt/secrets.toml

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[sources.tactical_rmm_source]
api_key = "your_api_key_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 Tactical RMM data can I load into DuckDB?

These are the Tactical RMM endpoints dlt can load into DuckDB:

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How do I load only new Tactical RMM records?

Tactical RMM exposes id on agents/, 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": "agents", "endpoint": {
    "path": "agents/",
    "incremental": {"cursor_path": "id", "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 Tactical RMM pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading /agents and /automation/policies from the Tactical RMM API into DuckDB:

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

@dlt.source
def tactical_rmm_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "The base URL is the domain of your specific Tactical RMM instance, typically accessed via https://api.yourdomain.com",
            "auth": {"type": "api_key", "api_key": api_key, "name": "X-API-KEY", "location": "header"},
        },
        "resources": [
            {"name": "agents", "endpoint": {"path": "agents/"}},
            {"name": "checks", "endpoint": {"path": "checks/"}}
        ],
    }
    yield from rest_api_resources(config)


def load_tactical_rmm_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="tactical_rmm_pipeline",
        destination="duckdb",
        dataset_name="tactical_rmm_data",
    )
    load_info = pipeline.run(tactical_rmm_source())
    print(load_info)


if __name__ == "__main__":
    load_tactical_rmm_to_duckdb()

Run it with python tactical_rmm_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 Tactical RMM 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("tactical_rmm_pipeline").dataset()
df = data.agents.df()
print(df.head())

SQL:

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SELECT * FROM tactical_rmm_data.agents LIMIT 10;

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

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How do I deploy the Tactical RMM 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 Tactical RMM 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 Tactical RMM 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