Tavus Python API Docs | dltHub

Tavus provides an API for building conversational video interfaces and personalized video generation sessions. The REST API base URL is https://tavusapi.com and all requests require an API key passed in the x-api-key header.

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 add "dlt[hub]" and start loading Tavus data in under 10 minutes.

---

What data can I load from Tavus?

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

---

How do I authenticate with the Tavus API?

Authentication is performed by sending an API key in the x-api-key header on every request. The API key must be kept secret and should not be exposed in client-side code.

1. Get your credentials

1. Log in to the Tavus PAL Maker dashboard (https://maker.tavus.io). 2. Select 'API Key' from the sidebar navigation menu. 3. Click the 'Create New Key' button. 4. Provide a name for the key and optionally configure allowed IP addresses for security. 5. Click 'Create API Key' and ensure you copy the generated key immediately, as it cannot be recovered if lost.

2. Add them to .dlt/secrets.toml

Copy
[sources.tavus_source]
tavus_api_key = "your_api_key_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 init
uv add "dlt[hub]"

1. Install the dlt AI harness:

Copy
uv run dlthub 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
uv run dlthub ai toolkit install rest-api-pipeline

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 Tavus 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
uv run python tavus_pipeline.py

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

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

Inspect your pipeline and data:

Copy
uv run dlthub 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 POST /v2/pals and POST /v2/conversations from the Tavus 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 tavus_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://tavusapi.com",
            "auth": {"type": "api_key", "api_key": api_key, "name": "x-api-key", "location": "header"},
        },
        "resources": [
            {"name": "conversations", "endpoint": {"path": "v2/conversations", "data_selector": "data"}},
            {"name": "videos", "endpoint": {"path": "v2/videos", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="tavus_pipeline",
        destination="duckdb",
        dataset_name="tavus_data",
    )
    load_info = pipeline.run(tavus_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("tavus_pipeline").dataset()
sessions_df = data.conversations.df()
print(sessions_df.head())

SQL (DuckDB example):

Copy
SELECT * FROM tavus_data.conversations LIMIT 10;

In a marimo or Jupyter notebook:

Copy
import dlt

data = dlt.pipeline("tavus_pipeline").dataset()
data.conversations.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 Tavus 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.

---

Next steps

Continue your data engineering journey with the other toolkits of the dltHub AI harness:

• data-exploration — Build custom notebooks, charts, and dashboards for deeper analysis with marimo notebooks.
• dlthub-platform — Deploy, schedule, and monitor your pipeline in production.

Copy
uv run dlthub ai toolkit install data-exploration
uv run dlthub ai toolkit install dlthub-platform

• 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