Load Meta Llama data to DuckDB

Meta Llama API provides access to Llama models for chat completion and other AI tasks via a RESTful interface. Everything needed to build a working Meta Llama → 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 Meta Llama 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 Meta Llama 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 Meta Llama 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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Meta Llama API at a glance

These values come from the Meta Llama API documentation. Check them against the vendor's current reference before relying on them in production.

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How do I authenticate with the Meta Llama API?

Authentication is performed by passing a bearer token in the 'Authorization' header of every request. The required format is 'Bearer <api_key>'.

1. Get your credentials

1. Navigate to the Meta API platform dashboard at https://llama.developer.meta.com/ (or the equivalent portal provided in your specific account documentation). 2. Locate and open the 'API keys' tab. 3. Click 'Create API key', assign it a memorable name, and confirm creation. 4. Copy the API key immediately upon display, as it will not be visible again. Store this key securely.

2. Add them to .dlt/secrets.toml

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[sources.meta_llama_source]
model_api_key = "LLM|your_key_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 Meta Llama data can I load into DuckDB?

These are the Meta Llama endpoints dlt can load into DuckDB:

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How do I load only new Meta Llama records?

The Meta Llama API reference does not document a timestamp or sequence field for these endpoints, so there is nothing to advertise here as verified. Pick a field from the endpoints table above that increases with every write, then set it as the cursor_path.

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{"name": "models", "endpoint": {
    "path": "v1/models",
    # Replace with a field that increases on every write.
    "incremental": {"cursor_path": "REPLACE_ME", "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 Meta Llama pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading /chat/completions and /completions from the Meta Llama API into DuckDB:

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

@dlt.source
def meta_llama_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.llama.com/v1",
            "auth": {"type": "bearer", "token": api_key},
        },
        "resources": [
            {"name": "models", "endpoint": {"path": "v1/models"}},
            {"name": "batches", "endpoint": {"path": "v1/batches"}}
        ],
    }
    yield from rest_api_resources(config)


def load_meta_llama_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="meta_llama_pipeline",
        destination="duckdb",
        dataset_name="meta_llama_data",
    )
    load_info = pipeline.run(meta_llama_source())
    print(load_info)


if __name__ == "__main__":
    load_meta_llama_to_duckdb()

Run it with python meta_llama_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 Meta Llama 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("meta_llama_pipeline").dataset()
df = data.models.df()
print(df.head())

SQL:

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SELECT * FROM meta_llama_data.models LIMIT 10;

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

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How do I deploy the Meta Llama 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 Meta Llama 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 Meta Llama 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