Magic Eden Python API Docs | dltHub

Magic Eden is a marketplace API providing NFT marketplace and metadata data and actions for Solana (listings, offers, collections, activities, wallet tokens). The REST API base URL is https://api-mainnet.magiceden.dev/v2 and Some endpoints require an API key; others are public but rate-limited..

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 Magic Eden data in under 10 minutes.

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What data can I load from Magic Eden?

Here are some of the endpoints you can load from Magic Eden:

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How do I authenticate with the Magic Eden API?

API keys (for gated endpoints) are issued by Magic Eden and must be sent in requests as an HTTP header (x-api-key: <YOUR_KEY>). Many marketplace/analytics endpoints are public but still rate-limited.

1. Get your credentials

1. Go to https://docs.magiceden.io/reference/solana-api-keys or the Magic Eden developer portal and sign in (or create) a Magic Eden account.
2. Navigate to API Keys / Developer Keys (or request developer access) and create a new Solana API key.
3. Copy the generated API key; store it securely.
4. Use the key in requests by setting header 'x-api-key: <YOUR_API_KEY>'.

2. Add them to .dlt/secrets.toml

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[sources.magic_eden_source]
api_key = "your_magiceden_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 Magic Eden 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 magic_eden_pipeline.py

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

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Pipeline magic_eden_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset magic_eden_data
The duckdb destination used duckdb:/magic_eden.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 magic_eden_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 nfts_by_owner and token_listings from the Magic Eden 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 magic_eden_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api-mainnet.magiceden.dev/v2",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "nfts_by_owner", "endpoint": {"path": "wallets/getNFTsByOwner/:publicKey", "data_selector": "results"}},
            {"name": "token_listings", "endpoint": {"path": "tokens/token-mint/listings", "data_selector": "results"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="magic_eden_pipeline",
        destination="duckdb",
        dataset_name="magic_eden_data",
    )
    load_info = pipeline.run(magic_eden_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("magic_eden_pipeline").dataset()
sessions_df = data.nfts_by_owner.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM magic_eden_data.nfts_by_owner LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("magic_eden_pipeline").dataset()
data.nfts_by_owner.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 Magic Eden 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 401/403 responses when calling gated endpoints, confirm you supplied the correct API key in the x-api-key header and that the key has not been revoked. Ensure no extra whitespace and that you're calling the correct cluster (dev vs mainnet).

Rate limits (429)

The API is rate‑limited; public endpoints can still return 429. Implement exponential backoff and retries, reduce parallelism, and batch queries where possible. If you need higher limits, request increased quota from Magic Eden support or apply for a developer key with higher limits.

Pagination and query format

Many query endpoints accept a single 'q' URL parameter that contains a Mongo‑style pipeline / $match/$sort/$skip/$limit JSON object (URL‑encoded). Results are returned under the 'results' key; use skip/limit for pagination. Large queries may require adjusting skip/limit and respecting rate limits to avoid timeouts.

Bad requests and schema errors (400)

If the API returns 400, verify the 'q' parameter JSON is valid and URL‑encoded, required path parameters (e.g., :publicKey or :mintAddress) are valid Solana public keys, and numeric params are numbers.

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