Tensor Trade Python API Docs | dltHub

Tensor Trade API is a REST API designed for developers to build and collaborate on projects related to Tensor's marketplaces, offering robust endpoints for coding. The REST API base URL is https://api.mainnet.tensordev.io/api/v1 and All requests require an API Key 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 Tensor Trade data in under 10 minutes.

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What data can I load from Tensor Trade?

Here are some of the endpoints you can load from Tensor Trade:

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How do I authenticate with the Tensor Trade API?

Authentication is done via an API Key, which should be included in the request headers.

1. Get your credentials

To obtain an API key, navigate to the Tensor Developer Hub and apply via the provided link in the 'Getting Started' section.

2. Add them to .dlt/secrets.toml

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[sources.tensor_trade_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 Tensor Trade 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 tensor_trade_pipeline.py

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

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Pipeline tensor_trade_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset tensor_trade_data
The duckdb destination used duckdb:/tensor_trade.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 tensor_trade_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 collections and user_trait_bids from the Tensor Trade 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 tensor_trade_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.mainnet.tensordev.io/api/v1",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "collections", "endpoint": {"path": "collections", "data_selector": "collections"}},
            {"name": "user_trait_bids", "endpoint": {"path": "user/trait_bids"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="tensor_trade_pipeline",
        destination="duckdb",
        dataset_name="tensor_trade_data",
    )
    load_info = pipeline.run(tensor_trade_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("tensor_trade_pipeline").dataset()
sessions_df = data.collections.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM tensor_trade_data.collections LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("tensor_trade_pipeline").dataset()
data.collections.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 Tensor Trade 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

API Errors

The Tensor Trade API may return 400 and 422 status codes for validation errors. Consult the API documentation for specific error details related to your request.

Pagination

Some endpoints, such as user/trait_bids, utilize cursor-based pagination. This typically involves limit and cursor query parameters to navigate through result sets. Ensure you handle these parameters correctly to retrieve all desired data.

Alpha API Status

Be aware that the Tensor Trade API is currently in an Alpha stage. This means that breaking changes may occur without prior warning, and developers should account for potential updates and adjustments to the API.

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