Gladly Python API Docs | dltHub

Gladly is a customer service platform that provides REST APIs to manage conversations, customers, agents, exports and related contact-center resources. The REST API base URL is https://{organization}.gladly.com/api/v1 and all requests use HTTP Basic auth with Gladly username and an API token (use token 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 Gladly data in under 10 minutes.

---

What data can I load from Gladly?

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

---

How do I authenticate with the Gladly API?

Gladly issues API tokens tied to a Gladly user. Use HTTP Basic authentication: set the Authorization type to Basic and send your Gladly login email as username and the API token as the password; requests are over HTTPS and responses are JSON.

1. Get your credentials

1. Log in to Gladly as a user with the "API User" permission. 2) Open Gladly Settings > API Tokens (or follow "Generate Gladly API token" doc). 3) Create a new API token; copy and securely store the token. 4) Use your Gladly login email as the Basic-auth username and the token as the password on API calls. (Rotate and revoke tokens via the Gladly dashboard.)

2. Add them to .dlt/secrets.toml

Copy
[sources.gladly_source]
username = "your_user_email@example.com"
password = "your_api_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.

---

How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

Copy
uv venv && source .venv/bin/activate
uv pip install "dlt[workspace]"

1. Install the dlt AI Workbench:

Copy
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:

Copy
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 Gladly 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:

Copy
python gladly_pipeline.py

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

Copy
Pipeline gladly_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset gladly_data
The duckdb destination used duckdb:/gladly.duckdb location to store data
Load package 1749667187.541553 is LOADED and contains no failed jobs

Inspect your pipeline and data:

Copy
dlt pipeline gladly_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.

---

Python pipeline example

This example loads conversations and customer_profiles from the Gladly API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:

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

@dlt.source
def gladly_source(username=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://{organization}.gladly.com/api/v1",
            "auth": {
                "type": "http_basic",
                "password": username,
            },
        },
        "resources": [
            {"name": "conversations_for_customer", "endpoint": {"path": "customers/{customerId}/conversations"}},
            {"name": "customer_profiles", "endpoint": {"path": "customer-profiles"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="gladly_pipeline",
        destination="duckdb",
        dataset_name="gladly_data",
    )
    load_info = pipeline.run(gladly_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.

---

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):

Copy
import dlt

data = dlt.pipeline("gladly_pipeline").dataset()
sessions_df = data.conversations_for_customer.df()
print(sessions_df.head())

SQL (DuckDB example):

Copy
SELECT * FROM gladly_data.conversations_for_customer LIMIT 10;

In a marimo or Jupyter notebook:

Copy
import dlt

data = dlt.pipeline("gladly_pipeline").dataset()
data.conversations_for_customer.df().head()

See how to explore your data in marimo Notebooks and how to query your data in Python with dataset.

---

What destinations can I load Gladly 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.

---

Troubleshooting

Authentication failures

If you receive 401 Unauthorized: verify you are using HTTP Basic auth with your Gladly login email as username and the API token as the password. Ensure the user has the "API User" permission and the token has not been revoked or rotated.

Rate limiting

Gladly returns 429 when organization rate limits are exceeded. The response headers include rate limit headers such as "Ratelimit-Limit-Second" and "Ratelimit-Remaining-Second" (docs show header names as "Ratelimit - Limit - Second" and "Ratelimit - Remaining - Second"). Implement exponential backoff and jitter on 429 responses.

Pagination / streaming

Many list endpoints return entire arrays in JSON; some large-data APIs (events) use JSON-lines (application/x-jsonlines) for streaming. Export job files are provided as .jsonl where each line is a JSON object. For very large result sets prefer export jobs instead of repeatedly listing resources.

Common error formats

Gladly returns structured error bodies like: { "errors": [ { "code": "blank", "detail": "one of emailAddress, phoneNumber must be present" } ] } and uses standard HTTP status codes (400, 401, 403, 404, 429, 5xx). Check endpoint-specific errors in documentation.

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

---

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.

Copy
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