Alli LLM App Market Python API Docs | dltHub

Alli LLM App Market API is a RESTful service that provides access to a marketplace of LLM applications. The REST API base URL is https://backend-ja.alli.ai/webapi and All requests require an API-KEY header containing a REST API key..

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 Alli LLM App Market data in under 10 minutes.

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What data can I load from Alli LLM App Market?

Here are some of the endpoints you can load from Alli LLM App Market:

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How do I authenticate with the Alli LLM App Market API?

Include an API-KEY header with your REST API key on every request.

1. Get your credentials

1. Log in to your Allganize account at https://app.alli.ai.
2. Click on your profile icon and select Settings.
3. In the Settings page, locate the REST API Key section.
4. Copy the displayed key; this is the value to use for the API-KEY header.
5. Store the key securely (e.g., in secrets.toml).

2. Add them to .dlt/secrets.toml

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[sources.alli_llm_app_market_source]
api_key = "your_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.

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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 Alli LLM App Market 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 alli_llm_app_market_pipeline.py

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

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Pipeline alli_llm_app_market_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset alli_llm_app_market_data
The duckdb destination used duckdb:/alli_llm_app_market.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 alli_llm_app_market_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 apps and run_app from the Alli LLM App Market 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 alli_llm_app_market_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://backend-ja.alli.ai/webapi",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "apps", "endpoint": {"path": "apps", "data_selector": "result"}},
            {"name": "run_app", "endpoint": {"path": "apps/{app_id}/run", "data_selector": "result"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="alli_llm_app_market_pipeline",
        destination="duckdb",
        dataset_name="alli_llm_app_market_data",
    )
    load_info = pipeline.run(alli_llm_app_market_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("alli_llm_app_market_pipeline").dataset()
sessions_df = data.apps.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM alli_llm_app_market_data.apps LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("alli_llm_app_market_pipeline").dataset()
data.apps.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 Alli LLM App Market 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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Troubleshooting

Authentication Errors

• 403 7001 Invalid API Key: Returned when the API-KEY header is missing or contains an incorrect value. Ensure you are using the REST API key from your account settings.

Generic API Errors

• Error format: { "type": "APIError", "code": <numeric_code>, "message": "..." }. Check the code and message to determine the cause (e.g., rate limiting, malformed request).

Pagination

• The API currently does not provide built‑in pagination for list endpoints; retrieve all records in a single response or implement client‑side paging if needed.

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

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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