Filemaker Python API Docs | dltHub

FileMaker Data API is a RESTful interface that allows external applications to access and manipulate FileMaker database records. The REST API base URL is https://{host}/fmi/data/v1/ and All requests require a Bearer token obtained via the /sessions endpoint..

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 Filemaker data in under 10 minutes.

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What data can I load from Filemaker?

Here are some of the endpoints you can load from Filemaker:

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How do I authenticate with the Filemaker API?

Obtain a session token via a POST to /fmi/data/v1/databases//sessions and include it in the Authorization: Bearer header on all subsequent requests.

1. Get your credentials

1. Log into the FileMaker Server Admin Console (or FileMaker Cloud admin portal).
2. Ensure the FileMaker Data API is enabled for the desired database.
3. Create or identify a FileMaker account that has "Data API" privileges.
4. Record the username and password for that account; these will be used to request a session token via the /sessions endpoint.
5. Optionally, generate an API key if your deployment supports API‑Key authentication (not typical for Data API).

2. Add them to .dlt/secrets.toml

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[sources.filemaker_source]
username = "your_username_here"
password = "your_password_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 Filemaker 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 filemaker_pipeline.py

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

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Pipeline filemaker_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset filemaker_data
The duckdb destination used duckdb:/filemaker.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 filemaker_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 layouts and records from the Filemaker 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 filemaker_source(username, password=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://{host}/fmi/data/v1/",
            "auth": {
                "type": "bearer",
                "token": username, password,
            },
        },
        "resources": [
            {"name": "layouts", "endpoint": {"path": "fmi/data/v1/databases/{db}/layouts", "data_selector": "layouts"}},
            {"name": "records", "endpoint": {"path": "fmi/data/v1/databases/{db}/layouts/{layout}/records", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="filemaker_pipeline",
        destination="duckdb",
        dataset_name="filemaker_data",
    )
    load_info = pipeline.run(filemaker_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("filemaker_pipeline").dataset()
sessions_df = data.records.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM filemaker_data.records LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("filemaker_pipeline").dataset()
data.records.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 Filemaker 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

• 401 Unauthorized: Occurs when the session token is missing, expired, or invalid. Obtain a fresh token via the /sessions endpoint.

Rate limits

• 429 Too Many Requests: The Data API enforces a limit on the number of requests per minute per account. Respect the Retry-After header and implement exponential back‑off.

Pagination

• The Data API returns up to 100 records per request. Use the _offset and _limit query parameters to page through larger result sets. The response includes a response object with data and metadata fields indicating the total record count.

Container field access

• Accessing container data requires a valid token and the correct container endpoint. A missing token returns 401, and an invalid file ID returns 404.

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