Load Software Heritage data to DuckDB

Software Heritage is a universal archive of software source code that provides a REST API for non-bulk read access to the archive and management of archival requests. Everything needed to build a working Software Heritage → DuckDB pipeline is on this page: the API's base URL, authentication, endpoints, pagination and incremental field — plus a prompt that hands the whole job to your coding agent.

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Build your Software Heritage to DuckDB pipeline

Paste this prompt into Claude, Codex, or Cursor. The agent does the rest.

PromptCopy prompt
Run uvx dlthub-init@latest to build a pipeline from Software Heritage to DuckDB and run it on dltHub

That scaffolds a dltHub workspace and installs the dltHub AI harness — the project rules, the secrets-management skill, and the dlt MCP server your agent needs to work safely. From there it reads the Software Heritage API, proposes the endpoints to load, then writes, runs and validates the pipeline while you review rather than type. Credentials are inspected through MCP tools, so your agent never reads secrets.toml itself. How the LLM-native workflow works →

Prefer to write it yourself? Every fact the agent uses is below.

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Software Heritage API at a glance

These values come from the Software Heritage API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the Software Heritage API?

Authenticated requests require an Authorization header with the value 'Bearer '. The token is obtained via the Software Heritage CLI tool as an OpenID Connect bearer token.

1. Get your credentials

To obtain credentials for the Software Heritage (SWH) API, you must first create an account at the Software Heritage archive (https://archive.softwareheritage.org/). Authentication relies on OpenID Connect (OIDC) bearer tokens. You can obtain a long-lived offline token using the 'swh.web.client' CLI tool: 1. Install the tool using 'pip install swh.web.client'. 2. Run 'swh auth generate-token '. 3. Enter your password when prompted; the tool will output your bearer token.

2. Add them to .dlt/secrets.toml

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[sources.software_heritage_source]
swh_bearer_token = "your_generated_bearer_token_here"

dlt reads this file automatically at runtime. With the harness, the setup-secrets skill prompts you for the values and never handles the raw credential in chat. For production, see setting up credentials with dlt.

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What Software Heritage data can I load into DuckDB?

These are the Software Heritage endpoints dlt can load into DuckDB:

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How do I load only new Software Heritage records?

Software Heritage exposes None on api/1/origins/, so dlt can request only the records that changed since the last run. Set it as the cursor_path and dlt tracks the high-water mark for you between runs.

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{"name": "origins", "endpoint": {
    "path": "api/1/origins/",
    "data_selector": "None",
    "incremental": {"cursor_path": "None", "initial_value": "2024-01-01T00:00:00Z"},
}}

On the first run dlt loads everything from initial_value; on every run after that it requests only what changed and appends with write_disposition="merge" if you set a primary key. See incremental loading.

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What does the generated Software Heritage pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading /api/1/origin/ and /api/1/snapshot/ from the Software Heritage API into DuckDB:

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import dlt
from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources

@dlt.source
def software_heritage_source(bearer_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://archive.softwareheritage.org/api/1/",
            "auth": {"type": "bearer", "token": bearer_token},
        },
        "resources": [
            {"name": "origins", "endpoint": {"path": "api/1/origins/", "data_selector": "None"}},
            {"name": "origin_visits", "endpoint": {"path": "api/1/origin/(origin_url)/visits/", "data_selector": "None"}}
        ],
    }
    yield from rest_api_resources(config)


def load_software_heritage_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="software_heritage_pipeline",
        destination="duckdb",
        dataset_name="software_heritage_data",
    )
    load_info = pipeline.run(software_heritage_source())
    print(load_info)


if __name__ == "__main__":
    load_software_heritage_to_duckdb()

Run it with python software_heritage_pipeline.py. The agent iterates on this until it loads cleanly — you review and approve, rather than write it from scratch.

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How do I query Software Heritage data in DuckDB?

dlt creates one table per resource. Query the loaded data with Python or SQL — or ask your agent to, through the MCP server's execute_sql_query tool.

Python (pandas DataFrame):

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import dlt

data = dlt.pipeline("software_heritage_pipeline").dataset()
df = data.origins.df()
print(df.head())

SQL:

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SELECT * FROM software_heritage_data.origins LIMIT 10;

See querying your data with dataset and exploring it in marimo notebooks.

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How do I deploy the Software Heritage to DuckDB pipeline in production?

The pipeline runs locally, which is ideal for prototyping and one-off analysis. When you need it on a schedule, monitored on every load, and shared with your team, deploy the same dlt code on the dltHub platform — no infrastructure to maintain. The prompt above already ends with "run it on dltHub", so your agent can take it there directly.

• Deploy & schedule — run the pipeline as a managed job with automatic retries.
• Monitor — observable job queues, alerting, and load metrics for every run.
• Transform — promote raw Software Heritage loads into governed, documented models.
• Visualize & share — explore data in notebooks and publish live dashboards instead of static screenshots.

Book a demo →

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What other destinations can I load Software Heritage data to?

dlt loads into any of these — only the destination argument changes:

Set dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. On the dltHub platform the same pipeline runs against a managed Iceberg lakehouse. See the full destinations list.

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Next steps

• Ready for production? Book a demo of the dltHub platform →
• dlt LLM-native workflow docs
• REST API source reference
• Join the dlt community on Slack