Chatforma Python API Docs | dltHub

Chatforma is a conversational marketing platform and messaging API for creating bots, managing dialogs, sending messages and broadcasts. The REST API base URL is https://api.pro.chatforma.com/public/v1/ and All requests require an API key passed 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 Chatforma data in under 10 minutes.

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What data can I load from Chatforma?

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

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How do I authenticate with the Chatforma API?

Chatforma uses an API key; include it in the Authorization header (or the specific header required) with each request.

1. Get your credentials

1. Log in to your Chatforma account at https://chatforma.com.
2. Open Settings / Integrations or API section.
3. Copy the displayed API key.
4. Use this key in your Authorization header when calling the API or when creating a connection in integrations (Make, Pipedream, n8n).

2. Add them to .dlt/secrets.toml

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[sources.chatforma_source]
api_key = "your_chatforma_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 Chatforma 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 chatforma_pipeline.py

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

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Pipeline chatforma_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset chatforma_data
The duckdb destination used duckdb:/chatforma.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 chatforma_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 bots and messages from the Chatforma 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 chatforma_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.pro.chatforma.com/public/v1/",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "bots", "endpoint": {"path": "bots", "data_selector": "data"}},
            {"name": "messages", "endpoint": {"path": "bots/{botId}/messages", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="chatforma_pipeline",
        destination="duckdb",
        dataset_name="chatforma_data",
    )
    load_info = pipeline.run(chatforma_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("chatforma_pipeline").dataset()
sessions_df = data.messages.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM chatforma_data.messages LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("chatforma_pipeline").dataset()
data.messages.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 Chatforma 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 requests return 401/403, verify the API key value and that it is sent in the correct header (Authorization or the specific header required). Regenerate the API key in the Chatforma dashboard if needed.

Rate limits and 429 responses

The API may enforce rate limits; on 429 responses, implement exponential backoff and retry. Check provider docs or support for specific limits.

Pagination quirks

Many list endpoints return a wrapper object with a "data" key containing the array of records (and may include pagination metadata). Inspect the response for pagination fields (page, per_page, total) and follow the provided links or parameters.

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