DFHack Python API Docs | dltHub

DFHack provides a remote access interface for interacting with Dwarf Fortress using Google Protocol Buffer messages over a TCP socket. The REST API base URL is tcp://127.0.0.1:5000 and uses a custom binary TCP protocol instead of traditional HTTP authentication.

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 DFHack data in under 10 minutes.

---

What data can I load from DFHack?

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

---

How do I authenticate with the DFHack API?

The DFHack remote interface does not use HTTP or standard REST authentication. It is a custom binary TCP protocol using Google Protocol Buffers, requiring a handshake with a specific magic string ('DFHack?\n') to initiate communication.

No credentials required. The DFHack API is public, so there is nothing to obtain and nothing to add to .dlt/secrets.toml — the pipeline above runs as written.

---

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 harness:

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 DFHack 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 dfhack_pipeline.py

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

Copy
Pipeline dfhack_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset dfhack_data
The duckdb destination used duckdb:/dfhack.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 BindMethod and dfhack-run from the DFHack 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 dfhack_source():
    config: RESTAPIConfig = {
        "client": {
            "base_url": "tcp://127.0.0.1:5000",
        },
        "resources": [
            {"name": "chronicle", "endpoint": {"path": "chronicle", "data_selector": "data"}},
            {"name": "site_history", "endpoint": {"path": "site_history"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="dfhack_pipeline",
        destination="duckdb",
        dataset_name="dfhack_data",
    )
    load_info = pipeline.run(dfhack_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("dfhack_pipeline").dataset()
sessions_df = data.bind_method.df()
print(sessions_df.head())

SQL (DuckDB example):

Copy
SELECT * FROM dfhack_data.bind_method LIMIT 10;

In a marimo or Jupyter notebook:

Copy
import dlt

data = dlt.pipeline("dfhack_pipeline").dataset()
data.bind_method.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 DFHack 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 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.

Copy
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