Cometchat Python API Docs | dltHub

CometChat is a real‑time communication platform providing REST APIs for chat, messaging, user and group management, data import and multi‑tenant management. The REST API base URL is https://{appId}.api-{region}.cometchat.io/v3 and all requests require an API key in a request header (apikey).

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 Cometchat data in under 10 minutes.

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What data can I load from Cometchat?

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

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How do I authenticate with the Cometchat API?

CometChat uses an API key sent in request headers (header name: apikey or apiKey depending on doc examples). For multi‑tenancy management APIs, requests require key & secret headers (key and secret).

1. Get your credentials

1. Sign in to https://app.cometchat.com/ 2) Select your App and go to ‘API Keys’ / ‘REST API Keys’ in the Dashboard 3) Copy the Rest API Key (fullAccess scope) to use as apikey on requests. For multi‑tenancy contact Sales to enable feature and obtain key & secret.

2. Add them to .dlt/secrets.toml

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[sources.cometchat_source]
api_key = "YOUR_COMETCHAT_REST_API_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 Cometchat 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 cometchat_pipeline.py

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

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Pipeline cometchat_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset cometchat_data
The duckdb destination used duckdb:/cometchat.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 cometchat_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 messages from the Cometchat 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 cometchat_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://{appId}.api-{region}.cometchat.io/v3",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "users", "endpoint": {"path": "users", "data_selector": "data"}},
            {"name": "messages", "endpoint": {"path": "messages", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


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

SQL (DuckDB example):

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SELECT * FROM cometchat_data.users LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("cometchat_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 Cometchat 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 you receive 401/403 ensure you are sending the REST API Key in the request header named apikey (header value = your Rest API Key). For multi‑tenancy endpoints use the provided key and secret headers.

Rate limits and errors

CometChat enforces rate limits (region and plan dependent). On rate limit responses check headers and back off; the docs provide an errors section with common error codes — inspect the /constraints, rate‑limits and errors doc for exact codes and recommended retry/backoff.

Pagination

List endpoints return paginated responses. Examine the response object (data) for pagination fields (offset/limit/page/total) and use query params (limit, page, etc.) supported by the endpoint.

Common API errors: 400 Bad Request (invalid payload), 401 Unauthorized (missing/invalid apikey), 403 Forbidden (insufficient scope), 404 Not Found (resource missing), 429 Too Many Requests (rate limit).

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