Tavus Python API Docs | dltHub

Tavus is an API platform for building real-time, human-like multimodal video conversations (Conversational Video Interface) with AI replicas. The REST API base URL is https://tavusapi.com and all requests require an API key in a header (x-api-key).

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 Tavus data in under 10 minutes.

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What data can I load from Tavus?

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

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How do I authenticate with the Tavus API?

Authentication is via an API key sent in the x-api-key header. Some OpenAPI snippets also show an alternate header name x-api-key and older pages reference x-api-key; examples use x-api-key: <api_key>.

1. Get your credentials

1. Open the Tavus Developer Portal / Dashboard. 2) Go to "API Key" in the sidebar. 3) Click "Create New Key". 4) Name the key, optionally restrict by IPs, click Create. 5) Copy and securely store the API key (do not expose in client-side code).

2. Add them to .dlt/secrets.toml

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[sources.tavus_platform_source]
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.

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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 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:

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python tavus_platform_pipeline.py

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

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Pipeline tavus_platform_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset tavus_platform_data
The duckdb destination used duckdb:/tavus_platform.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 tavus_platform_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 personas and conversational_replicas from the Tavus 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 tavus_platform_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://tavusapi.com",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "personas", "endpoint": {"path": "v2/personas/{persona_id}", "data_selector": "data"}},
            {"name": "conversational_replicas", "endpoint": {"path": "v2/conversational-replicas", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="tavus_platform_pipeline",
        destination="duckdb",
        dataset_name="tavus_platform_data",
    )
    load_info = pipeline.run(tavus_platform_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("tavus_platform_pipeline").dataset()
sessions_df = data.personas.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM tavus_platform_data.personas LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("tavus_platform_pipeline").dataset()
data.personas.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 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.

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Troubleshooting

Authentication failures

If you receive 401 Unauthorized, verify your x-api-key header is set to your API key. Create/regenerate keys in the Developer Portal and avoid exposing keys client-side.

Rate limits and 429

A 429 Too Many Requests indicates rate limiting. Back off and retry later; implement exponential backoff.

Common HTTP errors

400 Bad Request — malformed request or invalid path params. 403 Forbidden — insufficient permissions. 404 Not Found — resource not found. 500 Server Error — retry later.

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