Load Jan AI data to DuckDB

Jan is an open-source AI platform providing a built-in, OpenAI-compatible local API server for private, offline-capable AI applications. Everything needed to build a working Jan AI → 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 Jan AI 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 Jan AI 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 Jan AI 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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Jan AI API at a glance

These values come from the Jan AI API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the Jan AI API?

Authentication is performed by including an API key in the 'Authorization' header using the Bearer token format (e.g., 'Authorization: Bearer YOUR_API_KEY').

1. Get your credentials

To obtain an API key for Jan AI's local server, navigate to the Jan application, go to 'Settings' > 'Local API Server', and manually define any string in the 'API Key' field (e.g., 'your-secure-api-key'). This string will serve as your bearer token for authentication. Click 'Start Server' to apply the settings.

2. Add them to .dlt/secrets.toml

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[sources.jan_ai_source]
api_key = "your-defined-api-key"
base_url = "http://127.0.0.1:1337/v1"

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 Jan AI data can I load into DuckDB?

These are the Jan AI endpoints dlt can load into DuckDB:

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How do I load only new Jan AI records?

Jan AI exposes after on v1/conversations, 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": "conversations", "endpoint": {
    "path": "v1/conversations",
    "data_selector": "data",
    "incremental": {"cursor_path": "after", "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 Jan AI pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading /v1/chat/completions and /v1/models from the Jan AI API into DuckDB:

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

@dlt.source
def jan_ai_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "http://127.0.0.1:1337/v1",
            "auth": {"type": "bearer", "token": api_key},
        },
        "resources": [
            {"name": "conversations", "endpoint": {"path": "v1/conversations", "data_selector": "data"}},
            {"name": "conversation_items", "endpoint": {"path": "v1/conversations/{conv_public_id}/items", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def load_jan_ai_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="jan_ai_pipeline",
        destination="duckdb",
        dataset_name="jan_ai_data",
    )
    load_info = pipeline.run(jan_ai_source())
    print(load_info)


if __name__ == "__main__":
    load_jan_ai_to_duckdb()

Run it with python jan_ai_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 Jan AI 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("jan_ai_pipeline").dataset()
df = data.conversations.df()
print(df.head())

SQL:

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SELECT * FROM jan_ai_data.conversations LIMIT 10;

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

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How do I deploy the Jan AI 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 Jan AI 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 Jan AI 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