Neon Python API Docs | dltHub

Neon is a cloud‑native PostgreSQL platform that provides a REST API for managing projects, branches, databases, and related resources. The REST API base URL is https://console.neon.tech/api/v2/ and All requests require a Bearer token in the Authorization 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 pip install "dlt[workspace]" and start loading Neon data in under 10 minutes.

---

What data can I load from Neon?

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

---

How do I authenticate with the Neon API?

Include your Neon API key in the Authorization header as a Bearer token, e.g. Authorization: Bearer $NEON_API_KEY. Requests accept and return JSON.

1. Get your credentials

1. Sign in to the Neon Console (https://console.neon.tech).
2. Navigate to Account → API Keys or Organization Settings → API Keys.
3. Click Create new API key, give it a name, and confirm.
4. Copy the generated key value.
5. Store the key securely, e.g., as an environment variable NEON_API_KEY or in secrets.toml.

2. Add them to .dlt/secrets.toml

Copy
[sources.neon_api_source]
api_key = "your_neon_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 venv && source .venv/bin/activate
uv pip install "dlt[workspace]"

1. Install the dlt AI Workbench:

Copy
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:

Copy
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 Neon 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
python neon_api_pipeline.py

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

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

Inspect your pipeline and data:

Copy
dlt pipeline neon_api_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.

---

Python pipeline example

This example loads projects and project_branches from the Neon 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 neon_api_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://console.neon.tech/api/v2/",
            "auth": {
                "type": "bearer",
                "token": api_key,
            },
        },
        "resources": [
            {"name": "projects", "endpoint": {"path": "projects", "data_selector": "projects"}},
            {"name": "project_branches", "endpoint": {"path": "projects/{project_id}/branches", "data_selector": "branches"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="neon_api_pipeline",
        destination="duckdb",
        dataset_name="neon_api_data",
    )
    load_info = pipeline.run(neon_api_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("neon_api_pipeline").dataset()
sessions_df = data.projects.df()
print(sessions_df.head())

SQL (DuckDB example):

Copy
SELECT * FROM neon_api_data.projects LIMIT 10;

In a marimo or Jupyter notebook:

Copy
import dlt

data = dlt.pipeline("neon_api_pipeline").dataset()
data.projects.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 Neon 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.

---

Troubleshooting

Authentication errors

• 401 Unauthorized – Missing or invalid API key. Ensure the Authorization: Bearer <API_KEY> header is correctly set.
• 403 Forbidden – The API key does not have permission for the requested resource.

Rate limiting

• 429 Too Many Requests – The client has exceeded the allowed request rate. Wait for the period indicated in the Retry-After header before retrying.

Pagination quirks

• List endpoints return a pagination.next cursor when more pages are available. Include this cursor as the cursor query parameter in the next request to retrieve subsequent pages.
• Missing or malformed cursor values result in a 400 Bad Request.

Server errors

• 500 Internal Server Error / 503 Service Unavailable – Transient server problems. Implement exponential back‑off and retry.

Ensure that the API key is valid to avoid 401 Unauthorized errors. Also, verify endpoint paths and parameters to avoid 404 Not Found errors.

---

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
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