LogMeIn Rescue Python API Docs | dltHub

The LogMeIn Rescue API allows integration with other support applications and provides tools for remote support. It includes documentation for API reference and customization. The API enhances IT workflows and remote support experiences. The REST API base URL is https://secure.logmeinrescue.com/API/ and https://secure.logmeinrescue.com/id-srv-api/api/ and Supports OAuth2 Bearer tokens (Rescue Identity Service 2) and legacy authcode query parameter..

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 LogMeIn Rescue data in under 10 minutes.

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What data can I load from LogMeIn Rescue?

Here are some of the endpoints you can load from LogMeIn Rescue:

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How do I authenticate with the LogMeIn Rescue API?

Obtain an access_token from the OAuth token endpoint and include it in the Authorization: Bearer header for JSON endpoints; legacy calls can pass authcode=... in the query string.

1. Get your credentials

1. Log in to https://secure.logmeinrescue.com/id-srv/ with an admin account. 2) Add a new OAuth client and record the client_id (and client_secret for confidential clients). 3) For server‑to‑server access, POST to https://secure.logmeinrescue.com/id-srv-api/api/OAuth/token with grant_type=client_credentials, client_id, client_secret, and scope=Rescue;API to receive an access_token. 4) For user‑based access, configure a redirect URL, request an authorization code via GET /api/oauth/code, then exchange it for a token at /api/oauth/token. 5) Use the returned access_token in the Authorization: Bearer header.

2. Add them to .dlt/secrets.toml

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[sources.logmein_rescue_source]
access_token = "your_access_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 LogMeIn Rescue 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 logmein_rescue_pipeline.py

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

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Pipeline logmein_rescue_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset logmein_rescue_data
The duckdb destination used duckdb:/logmein_rescue.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 logmein_rescue_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 get_session_v3 and get_user from the LogMeIn Rescue 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 logmein_rescue_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://secure.logmeinrescue.com/API/ and https://secure.logmeinrescue.com/id-srv-api/api/",
            "auth": {
                "type": "bearer",
                "access_token": access_token,
            },
        },
        "resources": [
            {"name": "get_session_v3", "endpoint": {"path": "API/getSession_v3.aspx"}},
            {"name": "get_user", "endpoint": {"path": "API/getUser.aspx"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="logmein_rescue_pipeline",
        destination="duckdb",
        dataset_name="logmein_rescue_data",
    )
    load_info = pipeline.run(logmein_rescue_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("logmein_rescue_pipeline").dataset()
sessions_df = data.get_session_v3.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM logmein_rescue_data.get_session_v3 LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("logmein_rescue_pipeline").dataset()
data.get_session_v3.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 LogMeIn Rescue 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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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