Webex-teams Python API Docs | dltHub

Webex is a Cisco-hosted collaboration platform that provides messaging, meetings, and devices APIs to create spaces, send messages, manage people and teams. The REST API base URL is https://webexapis.com/v1 and All requests require a Bearer token for authentication.

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 Webex-teams data in under 10 minutes.

---

What data can I load from Webex-teams?

Here are some of the endpoints you can load from Webex-teams:

---

How do I authenticate with the Webex-teams API?

Webex uses OAuth2 access tokens (personal access token, Bot token, or OAuth integration tokens). Provide the token in the Authorization header as: Authorization: Bearer <ACCESS_TOKEN>.

1. Get your credentials

1. Sign in to Webex Developer Portal (https://developer.webex.com). 2) For quick testing, copy your Personal Access Token from the Developer Portal (expires after 12 hours). 3) For production, register an Integration (OAuth) or create a Bot to obtain client_id/client_secret and follow the OAuth flows to exchange an authorization code for access_token via POST https://webexapis.com/v1/access_token.

2. Add them to .dlt/secrets.toml

Copy
[sources.webex_teams_source]
access_token = "your_personal_or_oauth_access_token_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 Webex-teams 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 webex_teams_pipeline.py

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

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

Inspect your pipeline and data:

Copy
dlt pipeline webex_teams_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 messages and rooms from the Webex-teams 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 webex_teams_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://webexapis.com/v1",
            "auth": {
                "type": "bearer",
                "token": access_token,
            },
        },
        "resources": [
            {"name": "messages", "endpoint": {"path": "messages", "data_selector": "items"}},
            {"name": "rooms", "endpoint": {"path": "rooms", "data_selector": "items"}}
        ],
    }
    yield from rest_api_resources(config)


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

SQL (DuckDB example):

Copy
SELECT * FROM webex_teams_data.messages LIMIT 10;

In a marimo or Jupyter notebook:

Copy
import dlt

data = dlt.pipeline("webex_teams_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.

---

What destinations can I load Webex-teams 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 failures

Ensure the Authorization header is set to Bearer <token>. Personal access tokens expire after 12 hours; expired or invalid tokens return HTTP 401 with a JSON error indicating the token is invalid. For production use OAuth integrations or Bot tokens.

Rate limits (429 Too Many Requests)

Webex enforces rate limits (commonly ~300 req/min, higher for /people and /messages). On a 429 response, inspect the Retry-After header and back off accordingly.

Pagination & partial failures

List endpoints use RFC5988 Link headers for pagination (rel="next"). Responses include an items array. Individual records may contain an errors object while the overall response remains 200 OK.

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