Chess.com Python API Docs | dltHub

Chess.com PubAPI is a read-only REST API that provides access to public data such as player profiles, game archives, and tournament information. The REST API base URL is https://api.chess.com/pub/ and no authentication required, but a custom User-Agent header is mandatory for compliance.

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 Chess.com data in under 10 minutes.

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What data can I load from Chess.com?

Here are some of the endpoints you can load from Chess.com:

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How do I authenticate with the Chess.com API?

The Chess.com PubAPI is read-only and does not require authentication for accessing public data. However, it is mandatory to provide a 'User-Agent' header containing contact information to identify your application.

1. Get your credentials

The Chess.com Published Data API (PubAPI) is a public, read-only REST API that does not require an API key for access. To use the API, you simply send HTTP requests to the base URL (https://api.chess.com/pub/). While no formal authentication dashboard exists for this public data, Chess.com requires that all clients provide a recognizable 'User-Agent' header containing contact information (e.g., 'User-Agent: MyApp/1.0 (contact@example.com)') to identify your application. For specialized integrations requiring user authentication (e.g., OAuth, connected boards), you must contact Chess.com directly through their developer support channels or specific integration request forms.

2. Add them to .dlt/secrets.toml

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[sources.chess_com_source]
user_agent = "YourAppName/1.0 (your-email@example.com)"

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 init
uv add "dlt[hub]"

1. Install the dlt AI harness:

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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:

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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 Chess.com 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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uv run python chess_com_pipeline.py

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

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Pipeline chess_com_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset chess_com_data
The duckdb destination used duckdb:/chess_com.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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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.

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Python pipeline example

This example loads player and player_stats from the Chess.com 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 chess_com_source(user_agent=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.chess.com/pub/",
            "auth": {"type": "api_key", "api_key": user_agent, "name": "User-Agent"},
        },
        "resources": [
            {"name": "player_archives", "endpoint": {"path": "player/{username}/games/archives", "data_selector": "archives"}},
            {"name": "monthly_games", "endpoint": {"path": "player/{username}/games/{year}/{month}", "data_selector": "games"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="chess_com_pipeline",
        destination="duckdb",
        dataset_name="chess_com_data",
    )
    load_info = pipeline.run(chess_com_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("chess_com_pipeline").dataset()
sessions_df = data.monthly_games.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM chess_com_data.monthly_games LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("chess_com_pipeline").dataset()
data.monthly_games.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 Chess.com 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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Next steps

Continue your data engineering journey with the other toolkits of the dltHub AI harness:

• data-exploration — Build custom notebooks, charts, and dashboards for deeper analysis with marimo notebooks.
• dlthub-platform — Deploy, schedule, and monitor your pipeline in production.

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uv run dlthub ai toolkit install data-exploration
uv run dlthub ai toolkit install dlthub-platform

• 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