Chatra Python API Docs | dltHub

Chatra is a live chat platform and REST API for managing chats, visitors, messages and sending programmatic (pushed) messages. The REST API base URL is https://app.chatra.io/api and all requests require API keys in a Chatra.Simple Authorization header (or Basic auth alternative)..

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 Chatra data in under 10 minutes.

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What data can I load from Chatra?

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

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How do I authenticate with the Chatra API?

Chatra uses public and private API keys. Include both keys in the Authorization header like: Authorization: Chatra.Simple YOUR_PUBLIC_API_KEY:YOUR_PRIVATE_API_KEY. You may also use HTTP Basic authentication as an alternative.

1. Get your credentials

1. Log in to https://app.chatra.io
2. Go to Settings → Preferences (or Settings → API / Preferences)
3. Locate Public and Secret API keys under the API section
4. Copy the Public key and Private (secret) key to your secrets store.

2. Add them to .dlt/secrets.toml

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[sources.chatra_source]
api_key_pair = "PUBLIC_KEY:PRIVATE_KEY"

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 Chatra 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 chatra_pipeline.py

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

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Pipeline chatra_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset chatra_data
The duckdb destination used duckdb:/chatra.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 chatra_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 messages and pushedMessages from the Chatra 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 chatra_source(api_key_pair=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://app.chatra.io/api",
            "auth": {
                "type": "api_key",
                "token_pair": api_key_pair,
            },
        },
        "resources": [
            {"name": "messages", "endpoint": {"path": "messages/"}},
            {"name": "pushed_messages", "endpoint": {"path": "pushedMessages/"}}
        ],
    }
    yield from rest_api_resources(config)


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

SQL (DuckDB example):

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SELECT * FROM chatra_data.messages LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("chatra_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 Chatra 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 Authorization header is missing or keys are invalid the API returns a 401/403 with a JSON error body such as {"status":401,"message":"..."}. Ensure you supply both public and private keys in the header as: Authorization: Chatra.Simple PUBLIC:PRIVATE or use Basic auth.

Rate limits

Chatra enforces 60 requests per minute per account. When limit is exceeded the API returns HTTP 429 Too Many Requests and supplies a Retry-After header (seconds to wait). Implement retry/backoff using Retry-After header.

Error responses and formats

Errors return standard HTTP status codes and usually a JSON body like {"status":400,"message":"Invalid data format"}. Inspect the status and message fields for details. For POST/PUT validate Content-Type: application/json and required fields (e.g. clientId and text for pushedMessages POST).

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