Fullstory Python API Docs | dltHub

Fullstory is a digital experience analytics platform that captures user sessions, events, and settings to help teams analyze and reproduce user behavior. The REST API base URL is https://api.fullstory.com and All requests require an API key sent via HTTP Basic Authorization..

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 Fullstory data in under 10 minutes.

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What data can I load from Fullstory?

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

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How do I authenticate with the Fullstory API?

FullStory server/API requests use an API Key in the Authorization header using HTTP Basic scheme. Use header: Authorization: Basic YOUR_API_KEY. API keys have permission levels (Standard, Admin, Architect); some endpoints (privacy, settings/history, delete user) require Admin or Architect keys.

1. Get your credentials

1. Sign in to FullStory as a user with permission to create API keys. 2) In FullStory settings navigate to “API Keys” (Settings > API Keys or via Help Center link). 3) Click “Create key”, name it and choose permission level (Standard vs Admin/Architect). 4) Save and copy the generated key value (displayed only once). 5) Store securely and use as Authorization: Basic {key}.

2. Add them to .dlt/secrets.toml

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[sources.fullstory_source]
api_key = "your_fullstory_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 Fullstory 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 fullstory_pipeline.py

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

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Pipeline fullstory_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset fullstory_data
The duckdb destination used duckdb:/fullstory.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 fullstory_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 users and settings_privacy from the Fullstory 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 fullstory_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.fullstory.com",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "users", "endpoint": {"path": "v2/users"}},
            {"name": "settings_blocking", "endpoint": {"path": "settings/recording/v1/blocking", "data_selector": "blockedIps"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="fullstory_pipeline",
        destination="duckdb",
        dataset_name="fullstory_data",
    )
    load_info = pipeline.run(fullstory_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("fullstory_pipeline").dataset()
sessions_df = data.users.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM fullstory_data.users LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("fullstory_pipeline").dataset()
data.users.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 Fullstory 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 or 403 Forbidden, verify that the Authorization header is present and formatted as: Authorization: Basic YOUR_API_KEY. Ensure the key was copied at creation time (FullStory shows key only once) and that key has the required permission level (Admin required for privacy and some delete operations).

Rate limits and quotas

Exceeding server event quota or request limits returns 429 Too Many Requests. Check account Server Event Quota in Settings > Account Management. Implement backoff and retry when 429 returned.

Pagination and history endpoints

Several history endpoints return a "versions" array and a "next_pagination_token" (or next_pagination_token) for paging. Use the token parameter as documented to page through results. Some settings endpoints return arrays under specific keys (e.g., blockedIps, domains, recordGeoZones) rather than a generic top-level array.

Admin-only endpoints and operations

Endpoints such as /settings/recording/v1/privacy, /privacy/history, and user delete operations require an Admin API key. Calls with insufficient permissions will return 403 Forbidden.

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