Chatlio Python API Docs | dltHub

Chatlio is a live‑chat widget platform that integrates Slack conversations with website visitors. The REST API base URL is https://api.chatlio.com/v1/ and All requests require a Chatlio API key provided via Basic authentication or Bearer token..

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 Chatlio data in under 10 minutes.

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What data can I load from Chatlio?

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

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How do I authenticate with the Chatlio API?

Provide the API key as the username in HTTP Basic authentication, or include it in an Authorization header as a Bearer token (e.g., "Authorization: Bearer YOUR_API_KEY").

1. Get your credentials

1. Log in to your Chatlio account at https://app.chatlio.com.
2. Click on your profile avatar in the top‑right corner and select Settings.
3. In the Settings menu, choose API or Integrations.
4. Locate the API Key section and click Generate New Key (or copy the existing key).
5. Copy the generated key; you will use it as the api_key in dlt configuration.
6. Store the key securely, for example in secrets.toml.

2. Add them to .dlt/secrets.toml

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[sources.chatlio_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 Chatlio 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 chatlio_pipeline.py

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

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Pipeline chatlio_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset chatlio_data
The duckdb destination used duckdb:/chatlio.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 chatlio_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 conversation_summary and chat_endpoints/:widget-id from the Chatlio 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 chatlio_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.chatlio.com/v1/",
            "auth": {
                "type": "bearer",
                "token": api_key,
            },
        },
        "resources": [
            {"name": "conversation_summary", "endpoint": {"path": "conversation_summary"}},
            {"name": "chat_endpoints_widget_id", "endpoint": {"path": "chat_endpoints/:widget-id"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="chatlio_pipeline",
        destination="duckdb",
        dataset_name="chatlio_data",
    )
    load_info = pipeline.run(chatlio_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("chatlio_pipeline").dataset()
sessions_df = data.conversation_summary.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM chatlio_data.conversation_summary LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("chatlio_pipeline").dataset()
data.conversation_summary.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 Chatlio 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 Errors

• 401 Unauthorized – occurs when the API key is missing, malformed, or revoked. Ensure the API key is supplied correctly via Basic auth username or Bearer token.
• 403 Forbidden – indicates the key is valid but does not have permission for the requested resource.

Rate Limiting

• 429 Too Many Requests – Chatlio enforces a rate limit per account. Respect the Retry-After header and implement exponential back‑off.

Pagination

• Many list endpoints return a maximum number of items per page. Use the page or cursor parameters (as documented per endpoint) to retrieve subsequent pages. Failure to provide these parameters may result in incomplete data.

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