Flowlu Python API Docs | dltHub

Flowlu is an all-in-one business management platform providing CRM, project management, invoicing, time tracking and related REST API access. The REST API base URL is https://{your_company}.flowlu.com/api/v1 and API key required for most endpoints; OAuth2 (Bearer) supported for OAuth flows..

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 Flowlu data in under 10 minutes.

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What data can I load from Flowlu?

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

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How do I authenticate with the Flowlu API?

API key (server key) must be passed with each request as the query parameter api_key for API v1 endpoints (Content-Type: application/x-www-form-urlencoded). OAuth2 endpoints return a Bearer access_token to be used in Authorization header for OAuth-protected endpoints.

1. Get your credentials

1. Log in to your Flowlu account. 2) Go to Portal Settings > System Settings > API Settings (or Portal Settings → API Settings). 3) Create a new API key, configure module access rights, copy the generated key. 4) Use that key as the api_key query parameter in requests. For OAuth2: create an application in Portal Settings → Applications, follow /oauth2/authorize and /oauth2/access_token flow to obtain access_token.

2. Add them to .dlt/secrets.toml

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[sources.flowlu_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 Flowlu 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 flowlu_pipeline.py

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

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Pipeline flowlu_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset flowlu_data
The duckdb destination used duckdb:/flowlu.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 flowlu_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 crm_lead and projects_project from the Flowlu 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 flowlu_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://{your_company}.flowlu.com/api/v1",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "crm_lead", "endpoint": {"path": "module/crm/lead", "data_selector": "response.items"}},
            {"name": "projects_project", "endpoint": {"path": "module/projects/project", "data_selector": "response.items"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="flowlu_pipeline",
        destination="duckdb",
        dataset_name="flowlu_data",
    )
    load_info = pipeline.run(flowlu_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("flowlu_pipeline").dataset()
sessions_df = data.crm_lead.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM flowlu_data.crm_lead LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("flowlu_pipeline").dataset()
data.crm_lead.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 Flowlu 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 api_key is missing or invalid requests return HTTP 401 or a JSON error such as {"error":"Access token has been revoked"} or an error object with error_code; ensure api_key parameter is included (example: https://{company}.flowlu.com/api/v1/module/crm/lead/list?api_key=YOUR_KEY) or use Authorization: Bearer <access_token> for OAuth endpoints. Ensure API key has required module permissions.

Pagination and list responses

List endpoints return wrapper object response with paging fields: total, page, count and items (array of records). Use page and limit query params (default limit 50, max 100) to iterate pages. The records array selector is response.items.

Validation and record errors

Create/update/get may return JSON error objects, e.g. {"error":"validation","description":"Form filling error","details":{...}} or {"error":{"error_code":20,"error_msg":"not found"}}. Check HTTP status and examine error or error.error_msg for details.

Rate limits and timeouts

Flowlu docs don't specify public rate limits; implement exponential backoff and retries on 429/5xx and respect typical API best-practices. Monitor for HTTP 429 responses.

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