Jan AI Python API Docs | dltHub

Jan AI is a platform that provides a local OpenAI-compatible REST API server and integrations to run LLMs locally or via remote providers. The REST API base URL is http://127.0.0.1:1337 and all requests require a Bearer API key for 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 pip install "dlt[workspace]" and start loading Jan AI data in under 10 minutes.

---

What data can I load from Jan AI?

Here are some of the endpoints you can load from Jan AI:

---

How do I authenticate with the Jan AI API?

Jan's local API server requires a user‑defined API key presented in the Authorization header as: Authorization: Bearer YOUR_API_KEY.

1. Get your credentials

1. Open the Jan desktop app. 2) Navigate to Settings → Local API Server. 3) Enter a custom API Key (any string) in the API Key field. 4) Click Start Server. 5) Use that value as the Bearer token in the Authorization header for requests.

2. Add them to .dlt/secrets.toml

Copy
[sources.jan_ai_source]
api_key = "your_jan_local_api_key_here"

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.

---

How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

Copy
uv venv && source .venv/bin/activate
uv pip install "dlt[workspace]"

1. Install the dlt AI Workbench:

Copy
dlt 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
dlt ai toolkit rest-api-pipeline install

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 Jan AI 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
python jan_ai_pipeline.py

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

Copy
Pipeline jan_ai_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset jan_ai_data
The duckdb destination used duckdb:/jan_ai.duckdb location to store data
Load package 1749667187.541553 is LOADED and contains no failed jobs

Inspect your pipeline and data:

Copy
dlt pipeline jan_ai_pipeline 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 models and chat_completions from the Jan AI 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 jan_ai_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "http://127.0.0.1:1337",
            "auth": {
                "type": "bearer",
                "token": api_key,
            },
        },
        "resources": [
            {"name": "models", "endpoint": {"path": "v1/models", "data_selector": "data"}},
            {"name": "chat_completions", "endpoint": {"path": "v1/chat/completions", "data_selector": "choices"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> 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)

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("jan_ai_pipeline").dataset()
sessions_df = data.chat_completions.df()
print(sessions_df.head())

SQL (DuckDB example):

Copy
SELECT * FROM jan_ai_data.chat_completions LIMIT 10;

In a marimo or Jupyter notebook:

Copy
import dlt

data = dlt.pipeline("jan_ai_pipeline").dataset()
data.chat_completions.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 Jan AI 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.

---

Troubleshooting

Authentication failures

Ensure the Authorization header is set: Authorization: Bearer YOUR_API_KEY. Jan returns 401 Unauthorized when the key is missing or incorrect.

Connection refused / wrong host or port

If the server isn’t running or is bound to a different host/port (default 127.0.0.1:1337), requests will fail with connection refused. Verify Jan's Local API Server settings and that logs show JAN API listening at http://127.0.0.1:1337.

404 Not Found and invalid model IDs

Requests referencing a non‑existent model ID return 404 Not Found. Ensure the model ID used matches one listed by GET /v1/models.

CORS in browser clients

If calling the local server from a browser, ensure CORS is enabled in Jan settings (enabled by default) or adjust the settings; otherwise the browser may block the request.

Ensure that the API key is valid to avoid 401 Unauthorized errors. Also, verify endpoint paths and parameters to avoid 404 Not Found errors.

---

Next steps

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

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

Copy
dlt ai toolkit data-exploration install
dlt ai toolkit dlthub-runtime install

• 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