Chatbot Python API Docs | dltHub

ChatBot is a natural language chatbot platform and REST API for managing bots, stories, interactions, users and reports. The REST API base URL is https://api.chatbot.com and all requests require a Bearer token (Developer Access Token) in the Authorization header..

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 Chatbot data in under 10 minutes.

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What data can I load from Chatbot?

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

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How do I authenticate with the Chatbot API?

Include header Authorization: Bearer ${DEVELOPER_ACCESS_TOKEN} on every request. Developer Access Tokens are obtained in the ChatBot dashboard (Settings → Developers).

1. Get your credentials

1. Sign in to https://app.chatbot.com. 2) Open Settings → Developers. 3) Create or copy your Developer Access Token (private token). 4) Store it securely; use it as the Bearer token in Authorization header.

2. Add them to .dlt/secrets.toml

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[sources.chatbot_source]
api_key = "your_developer_access_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.

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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 Chatbot 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 chatbot_pipeline.py

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

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Pipeline chatbot_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset chatbot_data
The duckdb destination used duckdb:/chatbot.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 chatbot_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 welcome_interaction from the Chatbot 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 chatbot_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.chatbot.com",
            "auth": {
                "type": "bearer",
                "token": api_key,
            },
        },
        "resources": [
            {"name": "users", "endpoint": {"path": "users", "data_selector": "data"}},
            {"name": "welcome_interaction", "endpoint": {"path": "stories/:storyID/interactions/welcome"}}
        ],
    }
    yield from rest_api_resources(config)


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

SQL (DuckDB example):

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SELECT * FROM chatbot_data.users LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("chatbot_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 Chatbot 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

Ensure Authorization header is present and formatted: Authorization: Bearer ${DEVELOPER_ACCESS_TOKEN}. 401 responses indicate invalid or missing token.

Rate limits and server errors

The API returns standard HTTP status codes. 4xx indicate client errors (400 bad request, 404 not found, 401 unauthorized). 5xx indicate server errors; retry with backoff. Timeout >10s may be rejected.

Pagination

List endpoints (for example GET /users) support limit and after parameters. Responses include data array and count for total matching items; use after (cursor) and limit to page through results.

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