Rocket.Chat Python API Docs | dltHub

Rocket.Chat is an open‑source team communication platform that provides a REST API for managing users, channels, and other resources. The REST API base URL is https://{your-instance}.rocket.chat/api/v1 and All requests require X‑Auth‑Token and X‑User‑Id headers containing a user session token..

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 Rocket.Chat data in under 10 minutes.

---

What data can I load from Rocket.Chat?

Here are some of the endpoints you can load from Rocket.Chat:

---

How do I authenticate with the Rocket.Chat API?

Obtain an auth token via the login endpoint and include it as X‑Auth‑Token along with X‑User‑Id in all API calls.

1. Get your credentials

1. Log in to your Rocket.Chat instance with an admin account.
2. Open the Login API endpoint (POST /api/v1/login) in a tool like curl or Postman.
3. Provide JSON body { "user": "<username>", "password": "<password>" }.
4. The response returns authToken and userId.
5. Record these two values; they will be used as X‑Auth‑Token and X‑User‑Id for subsequent calls.
6. Store the values securely (e.g., in the dlt secrets.toml file).

2. Add them to .dlt/secrets.toml

Copy
[sources.rocket_chat_source]
auth_token = "your_auth_token_here"
user_id = "your_user_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.

---

How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

Copy
uv venv && source .venv/bin/activate
uv pip install "dlt[workspace]"

1. Install the dlt AI Workbench:

Copy
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:

Copy
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 Rocket.Chat 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:

Copy
python rocket_chat_pipeline.py

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

Copy
Pipeline rocket_chat_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset rocket_chat_data
The duckdb destination used duckdb:/rocket_chat.duckdb location to store data
Load package 1749667187.541553 is LOADED and contains no failed jobs

Inspect your pipeline and data:

Copy
dlt pipeline rocket_chat_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.

---

Python pipeline example

This example loads users and channels from the Rocket.Chat API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:

Copy
import dlt
from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources

@dlt.source
def rocket_chat_source(auth_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://{your-instance}.rocket.chat/api/v1",
            "auth": {
                "type": "api_key",
                "auth_token": auth_token,
            },
        },
        "resources": [
            {"name": "users", "endpoint": {"path": "users.list", "data_selector": "users"}},
            {"name": "channels", "endpoint": {"path": "channels.list", "data_selector": "channels"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="rocket_chat_pipeline",
        destination="duckdb",
        dataset_name="rocket_chat_data",
    )
    load_info = pipeline.run(rocket_chat_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.

---

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):

Copy
import dlt

data = dlt.pipeline("rocket_chat_pipeline").dataset()
sessions_df = data.users.df()
print(sessions_df.head())

SQL (DuckDB example):

Copy
SELECT * FROM rocket_chat_data.users LIMIT 10;

In a marimo or Jupyter notebook:

Copy
import dlt

data = dlt.pipeline("rocket_chat_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.

---

What destinations can I load Rocket.Chat 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.

---

Troubleshooting

Authentication Errors

If X‑Auth‑Token or X‑User‑Id are missing or invalid, the API returns a 401 Unauthorized response.

Rate Limiting

Rocket.Chat enables rate limiting by default; exceeding the allowed request rate results in a 429 Too Many Requests response. Adjust your request frequency or request higher limits from the server admin.

Pagination Limits

The REST API enforces a maximum record count per request (configurable in Settings > General > REST API). Setting count=0 may return all records if the "Enabled" option is turned on; otherwise, respect the max value returned in the response.

Ensure that the API key is valid to avoid 401 Unauthorized errors. Also, verify endpoint paths and parameters to avoid 404 Not Found errors.

---

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.

Copy
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