Freshsuccess Python API Docs | dltHub

Freshsuccess is a customer success platform that provides APIs to retrieve and manage accounts, users, events, subscriptions, goals, contacts, custom metrics and other customer-success related data. The REST API base URL is https://api-us.freshsuccess.com/api/v2 and All requests require an API key (query param) or HTTP Basic auth using the API key as password..

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 Freshsuccess data in under 10 minutes.

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What data can I load from Freshsuccess?

Here are some of the endpoints you can load from Freshsuccess:

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How do I authenticate with the Freshsuccess API?

Authentication is performed using an API key. You can supply the key as an api_key query parameter (?api_key=YOUR_API_KEY) or via HTTP Basic Auth with username api and password YOUR_API_KEY. For event collector endpoints there are separate authKey and apiKey path components in the events endpoint URLs (events-.freshsuccess.com/v1/[authKey]/[apiKey]).

1. Get your credentials

1. Sign in to Freshsuccess. 2) Contact Freshsuccess support or the account admin to request an API key if not visible in the UI (documentation indicates API keys are issued via the website or by support: support@freshsuccess.com). 3) For the Event REST API obtain the JS Data apiKey and authKey from Freshsuccess (see Event API docs or support article).

2. Add them to .dlt/secrets.toml

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[sources.freshsuccess_source]
api_key = "your_api_key_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 Freshsuccess 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 freshsuccess_pipeline.py

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

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Pipeline freshsuccess_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset freshsuccess_data
The duckdb destination used duckdb:/freshsuccess.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 freshsuccess_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 accounts and events from the Freshsuccess 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 freshsuccess_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api-us.freshsuccess.com/api/v2",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "accounts", "endpoint": {"path": "accounts", "data_selector": "results"}},
            {"name": "events", "endpoint": {"path": "(events collector) v1/[authKey]/[apiKey]"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="freshsuccess_pipeline",
        destination="duckdb",
        dataset_name="freshsuccess_data",
    )
    load_info = pipeline.run(freshsuccess_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("freshsuccess_pipeline").dataset()
sessions_df = data.accounts.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM freshsuccess_data.accounts LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("freshsuccess_pipeline").dataset()
data.accounts.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 Freshsuccess 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

If you receive 401 Unauthorized, verify you are using the correct datacenter host (api-us or api-eu), and supply the API key either as ?api_key=KEY or via HTTP Basic auth (username 'api', password KEY). For event collector use correct authKey and apiKey path values.

Rate limits and concurrent uploads

Event ingestion and bulk operations are rate‑limited. The docs state only 1 outstanding POST/PUT at a time for bulk uploads; concurrent uploads may return 429. If you receive 429, back off and retry. POST/PUT request bodies are limited (10 MB) and timeout is ~90 s.

Pagination

List responses are paginated and return max_page_size and current_page. Pages are zero‑indexed; use the page query parameter. If fewer than max_page_size records are returned, there are no more pages. Many GET list responses place records in the "results" array.

Batch POST/PUT format quirks

POST batch inserts require a top‑level "records" array. PUT updates expect a top‑level "data" object. POST/PUT responses include status_is_ok and failed_results arrays.

Common API errors

• 401 Unauthorized (invalid API key)
• 429 Too Many Requests (rate limit)
• 500 Internal Server Error
• 504 Timeout For the Event REST API, a 200 response indicates success, while 400 indicates a bad request; the endpoint returns no body.

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