0x Python API Docs - with dltHub

The 0x API is a professional-grade REST API for decentralized exchange aggregation and smart order routing. It provides access to aggregated liquidity and allows users to find the best prices. The latest version is v2. The REST API base URL is https://api.0x.org and Requests require an API key sent in the 0x-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 0x data in under 10 minutes.

---

What data can I load from 0x?

Here are some of the endpoints you can load from 0x:

---

How do I authenticate with the 0x API?

Include the header '0x-api-key: YOUR_API_KEY' in each request; optionally add '0x-version: v2' to specify the API version.

1. Get your credentials

1. Open https://go.0x.org/create-account-txrelay-z (the 0x Dashboard). 2) Sign in or create an account. 3) Navigate to the Apps section and create a new app. 4) In the app settings, generate or view the API key. 5) Copy the key and use it as the value for the 0x-api-key header.

2. Add them to .dlt/secrets.toml

Copy
[sources._0x_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.

---

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 Workbench:

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 0x 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 _0x_pipeline.py

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

Copy
Pipeline _0x_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset _0x_data
The duckdb destination used duckdb:/_0x.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 swap/allowance-holder/quote and sources from the 0x 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 _0x_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.0x.org",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "swap_allowance_holder_quote", "endpoint": {"path": "swap/allowance-holder/quote"}},
            {"name": "sources", "endpoint": {"path": "sources"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="_0x_pipeline",
        destination="duckdb",
        dataset_name="_0x_data",
    )
    load_info = pipeline.run(_0x_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("_0x_pipeline").dataset()
sessions_df = data.swap_allowance_holder_quote.df()
print(sessions_df.head())

SQL (DuckDB example):

Copy
SELECT * FROM _0x_data.swap_allowance_holder_quote LIMIT 10;

In a marimo or Jupyter notebook:

Copy
import dlt

data = dlt.pipeline("_0x_pipeline").dataset()
data.swap_allowance_holder_quote.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 0x 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 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
uv run dlthub ai toolkit install data-exploration
uv run dlthub ai toolkit install dlthub-runtime

• 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