Sumo Logic Python API Docs | dltHub

Sumo Logic is a cloud‑native machine‑data analytics platform providing log management, metrics, and security analytics via REST APIs. The REST API base URL is https://api.sumologic.com/api/ and All requests require Access ID and Access Key using HTTP Basic authentication..

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 Sumo Logic data in under 10 minutes.

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What data can I load from Sumo Logic?

Here are some of the endpoints you can load from Sumo Logic:

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How do I authenticate with the Sumo Logic API?

Sumo Logic uses Access ID and Access Key credentials. Authenticate by sending Basic auth (curl -u ":") or by adding an Authorization: Basic <base64(accessId:accessKey)> header on each request.

1. Get your credentials

1. Log into the Sumo Logic UI as an admin. 2) Navigate to Manage > Security > Access Keys (or Account > Access Keys). 3) Click "Create New Access Key". 4) Copy the displayed Access Key immediately (it is shown only once). 5) Use the Access ID and Access Key pair for API calls.

2. Add them to .dlt/secrets.toml

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[sources.sumo_logic_source]
access_id = "your_access_id"
access_key = "your_access_key"

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 Sumo Logic 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 sumo_logic_pipeline.py

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

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Pipeline sumo_logic_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset sumo_logic_data
The duckdb destination used duckdb:/sumo_logic.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 sumo_logic_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 collectors and users from the Sumo Logic 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 sumo_logic_source(access_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.sumologic.com/api/",
            "auth": {
                "type": "http_basic",
                "access_key": access_key,
            },
        },
        "resources": [
            {"name": "collectors", "endpoint": {"path": "api/v1/collectors", "data_selector": "collectors"}},
            {"name": "users", "endpoint": {"path": "api/v1/users", "data_selector": "users"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="sumo_logic_pipeline",
        destination="duckdb",
        dataset_name="sumo_logic_data",
    )
    load_info = pipeline.run(sumo_logic_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("sumo_logic_pipeline").dataset()
sessions_df = data.collectors.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM sumo_logic_data.collectors LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("sumo_logic_pipeline").dataset()
data.collectors.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 Sumo Logic 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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Troubleshooting

Authentication failures

401/403 indicate invalid credentials or insufficient role capabilities. Ensure you use the correct deployment API endpoint and that the Access Key user has required role permissions. Use Basic auth with accessId:accessKey or Authorization: Basic .

Rate limiting

Sumo Logic enforces 4 requests/second (240/min) and up to 10 concurrent in‑flight requests per access key. Exceeding limits returns 429 rate.limit.exceeded. Implement exponential backoff and honor the Retry-After header if present.

Pagination

Many list endpoints are paginated. Responses include a "next" token (string or null). Use ?limit= and ?token=<next> query parameters to page through results until next is null.

Ensure that the API key is valid to avoid 401 Unauthorized errors. Also, verify endpoint paths and parameters to avoid 404 Not Found errors.

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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