MLC LLM Python API Docs | dltHub

MLC LLM provides a REST API for deploying large language models. The API documentation is available at https://llm.mlc.ai/docs/deploy/rest.html. The API supports functions like getting current weather. The REST API base URL is http://127.0.0.1:8000 and No authentication required by default for the local MLC‑LLM REST server..

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 MLC LLM data in under 10 minutes.

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What data can I load from MLC LLM?

Here are some of the endpoints you can load from MLC LLM:

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How do I authenticate with the MLC LLM API?

The MLC‑LLM REST server runs locally (default host 127.0.0.1 and port 8000) and does not require API keys or Bearer tokens by default. If you place it behind an authenticated gateway, configure the gateway to add the needed Authorization header.

1. Get your credentials

1. Start the MLC‑LLM REST server locally; no credentials are needed for the default installation. 2. If you place the server behind an API gateway or reverse proxy that requires authentication, log in to the gateway's management console, create an API key or token, and configure the proxy to forward an Authorization: Bearer <token> header to the MLC server. 3. Add the obtained token to your dlt secrets.toml if the proxy expects it.

2. Add them to .dlt/secrets.toml

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[sources.mlc_llm_source]

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.

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How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

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

1. Install the dlt AI Workbench:

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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:

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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 MLC LLM 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:

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python mlc_llm_pipeline.py

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

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

Inspect your pipeline and data:

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dlt pipeline mlc_llm_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.

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Python pipeline example

This example loads models and chat_completions from the MLC LLM API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:

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

@dlt.source
def mlc_llm_source(=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "http://127.0.0.1:8000",
            "auth": {
                "type": "none",
                "": ,
            },
        },
        "resources": [
            {"name": "models", "endpoint": {"path": "v1/models"}},
            {"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="mlc_llm_pipeline",
        destination="duckdb",
        dataset_name="mlc_llm_data",
    )
    load_info = pipeline.run(mlc_llm_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.

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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):

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import dlt

data = dlt.pipeline("mlc_llm_pipeline").dataset()
sessions_df = data.models.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM mlc_llm_data.models LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("mlc_llm_pipeline").dataset()
data.models.df().head()

See how to explore your data in marimo Notebooks and how to query your data in Python with dataset.

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What destinations can I load MLC LLM 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.

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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.

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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