Fiddler AI Python API Docs | dltHub

Fiddler AI is an observability platform for ML/LLM models that provides APIs to manage models, baselines, events, jobs, and related resources. The REST API base URL is https://app.fiddler.ai/v3 and all requests require a Bearer token 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 Fiddler AI data in under 10 minutes.

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What data can I load from Fiddler AI?

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

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How do I authenticate with the Fiddler AI API?

The API uses HTTP Bearer authentication. Include an Authorization header with value: "Bearer <YOUR_TOKEN>" on every request.

1. Get your credentials

1. Sign in to your Fiddler account at app.fiddler.ai.
2. Open your user/organization settings or the API/Integrations section.
3. Create or copy an existing API/Service token (a string starting with fid_ or similar).
4. Use this token in the Authorization header: "Authorization: Bearer ".

2. Add them to .dlt/secrets.toml

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[sources.fiddler_ai_source]
token = "your_fiddler_bearer_token_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 Fiddler 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:

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python fiddler_ai_pipeline.py

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

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Pipeline fiddler_ai_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset fiddler_ai_data
The duckdb destination used duckdb:/fiddler_ai.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 fiddler_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.

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Python pipeline example

This example loads models and baselines from the Fiddler AI 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 fiddler_ai_source(token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://app.fiddler.ai/v3",
            "auth": {
                "type": "bearer",
                "token": token,
            },
        },
        "resources": [
            {"name": "models", "endpoint": {"path": "v3/models", "data_selector": "data.items"}},
            {"name": "baselines", "endpoint": {"path": "v3/baselines", "data_selector": "data.items"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="fiddler_ai_pipeline",
        destination="duckdb",
        dataset_name="fiddler_ai_data",
    )
    load_info = pipeline.run(fiddler_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.

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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("fiddler_ai_pipeline").dataset()
sessions_df = data.models.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM fiddler_ai_data.models LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("fiddler_ai_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 Fiddler 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.

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Troubleshooting

Authentication failures

If you receive 401/403 responses, verify the Authorization header is present and correct: "Authorization: Bearer ". Ensure the token has required scopes and has not expired or been revoked.

Rate limits and server errors

Responses can return standard HTTP codes (429, 500). For 429, implement exponential backoff and retry. For 500‑series errors, retry with backoff and contact Fiddler support if persistent.

Pagination and data selectors

List endpoints return a paginated wrapper: the response uses kind: "PAGINATED" and the records are under data.items. Use query parameters limit and offset (or page_index/page_size where supported) to paginate until data.items is empty.

Common error response format

The API returns an ErrorResponse object with fields: api_version, kind = "ERROR", and error:{code, message, errors[]} where errors[] contains reason/message/help for details. Typical HTTP statuses: 400, 401, 403, 404, 409, 429, 500.

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