Chainlink Python API Docs | dltHub

Chainlink Data Streams is a REST API that provides cryptographically-signed, verifiable data reports (feed reports) for use by smart contracts and off-chain consumers. The REST API base URL is https://api.dataengine.chain.link (mainnet), https://api.testnet-dataengine.chain.link (testnet) and All requests require HMAC-based authentication using specific headers (UUID, timestamp, SHA256 signature)..

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 Chainlink data in under 10 minutes.

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What data can I load from Chainlink?

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

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How do I authenticate with the Chainlink API?

Requests must include three headers: Authorization (user UUID), X-Authorization-Timestamp (milliseconds-precision timestamp), and X-Authorization-Signature-SHA256 (HMAC-SHA256 signature). The signature is generated using a shared secret over parts of the request as documented in the Data Streams Authentication page.

1. Get your credentials

1. Contact Chainlink / Data Streams sales or console to provision access. 2) In the Data Streams dashboard or onboarding flow obtain your user UUID (to use as Authorization header) and shared secret. 3) Store the UUID and secret securely; use the secret to compute X-Authorization-Signature-SHA256 per docs.

2. Add them to .dlt/secrets.toml

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[sources.chainlink_data_feeds_source]
uuid = "your_uuid_here"
secret = "your_shared_secret_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 Chainlink 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 chainlink_data_feeds_pipeline.py

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

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Pipeline chainlink_data_feeds_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset chainlink_data_feeds_data
The duckdb destination used duckdb:/chainlink_data_feeds.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 chainlink_data_feeds_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 reports and reports/latest from the Chainlink 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 chainlink_data_feeds_source(auth=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.dataengine.chain.link (mainnet), https://api.testnet-dataengine.chain.link (testnet)",
            "auth": {
                "type": "http_hmac",
                "secret": auth,
            },
        },
        "resources": [
            {"name": "reports", "endpoint": {"path": "api/v1/reports", "data_selector": "report"}},
            {"name": "reports_latest", "endpoint": {"path": "api/v1/reports/latest", "data_selector": "report"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="chainlink_data_feeds_pipeline",
        destination="duckdb",
        dataset_name="chainlink_data_feeds_data",
    )
    load_info = pipeline.run(chainlink_data_feeds_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("chainlink_data_feeds_pipeline").dataset()
sessions_df = data.reports.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM chainlink_data_feeds_data.reports LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("chainlink_data_feeds_pipeline").dataset()
data.reports.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 Chainlink 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 the Authorization header (UUID), X-Authorization-Timestamp (must be within a few seconds of server time), and X-Authorization-Signature-SHA256 (HMAC-SHA256 with shared secret). Ensure clocks are synced and the signature is computed exactly per spec.

Rate limiting and partial data (/bulk)

The /api/v1/reports/bulk endpoint can return 206 Partial Content when data for one or more requested feedIDs is missing for the timestamp; handle 206 by processing the returned subset (key 'reports').

Bad requests and pagination

400 Bad Request indicates missing or malformed query parameters (e.g., missing feedID, timestamp, or invalid startTimestamp/limit). Use /api/v1/reports/page with startTimestamp and limit for pagination; responses include 'reports' array and obey the requested limit.

Server errors

500 Internal Server Error indicates an issue on Chainlink's side; implement retries with exponential backoff and alert on repeated 5xx responses.

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