Jivochat Python API Docs | dltHub

JivoChat is a live chat and messaging platform that provides webhook‑based APIs for integrating chat functionality. The REST API base URL is https://wh.jivosite.com/ and Requests are authorized via the JIVO_PUBLIC_ID embedded in the endpoint URL; no extra headers are required..

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 Jivochat data in under 10 minutes.

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What data can I load from Jivochat?

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

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How do I authenticate with the Jivochat API?

The API uses the channel's public ID as part of the URL to identify and authorize the request; include it exactly as shown in the dashboard‑generated webhook URL.

1. Get your credentials

1. Log in to your JivoChat account.
2. Navigate to Settings → Integrations → API / Webhooks.
3. Locate the Channel URL field; it contains a URL like https://wh.jivosite.com//<JIVO_PUBLIC_ID>.
4. Copy the <JIVO_PUBLIC_ID> portion (the string after the last slash) – this is the credential you will use in the dlt source.
5. Optionally copy the full webhook URL for testing.

2. Add them to .dlt/secrets.toml

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[sources.jivochat_source]
jivo_public_id = "your_jivo_public_id_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 Jivochat 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 jivochat_pipeline.py

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

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Pipeline jivochat_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset jivochat_data
The duckdb destination used duckdb:/jivochat.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 jivochat_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 status and channel_info from the Jivochat 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 jivochat_source(jivo_public_id=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://wh.jivosite.com/",
            "auth": {
                "type": "api_key",
                "api_key": jivo_public_id,
            },
        },
        "resources": [
            {"name": "status", "endpoint": {"path": "<random>/<jivo_public_id>/status"}},
            {"name": "channel_info", "endpoint": {"path": "<random>/<jivo_public_id>"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="jivochat_pipeline",
        destination="duckdb",
        dataset_name="jivochat_data",
    )
    load_info = pipeline.run(jivochat_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("jivochat_pipeline").dataset()
sessions_df = data.status.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM jivochat_data.status LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("jivochat_pipeline").dataset()
data.status.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 Jivochat 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 / Channel Errors

The API does not use header‑based auth. If the JIVO_PUBLIC_ID is missing or incorrect, the service returns a 404 Not Found response:

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{ "error": { "code": 404, "message": "Channel not found" } }

Ensure the public ID copied from the JivoChat dashboard matches the one in the URL.

Request Errors

All errors are returned as a JSON payload with an error object containing code and message fields. Example:

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{ "error": { "code": 400, "message": "Invalid payload" } }

Check that POST bodies conform to the documented schema.

Rate Limits

The current documentation does not specify rate‑limit headers. If you encounter HTTP 429 responses, implement exponential back‑off and retry.

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