Pipefy Python API Docs | dltHub

Pipefy is a workflow and process automation platform exposing a GraphQL-based API to query and manipulate pipes, cards, phases, users, tables and related workflow data. The REST API base URL is https://api.pipefy.com/ and All requests require a Bearer token (OAuth2) in the Authorization header..

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 Pipefy data in under 10 minutes.

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What data can I load from Pipefy?

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

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How do I authenticate with the Pipefy API?

Pipefy uses OAuth2 Bearer tokens. Pass the token in the HTTP header: Authorization: Bearer <YOUR_TOKEN>. Service Accounts are recommended for production; Personal Access Tokens are available for testing.

1. Get your credentials

1. Sign in to your Pipefy account as admin or super admin. 2) For Personal Access Token: visit https://app.pipefy.com/tokens and click “Generate new token”, add a description and Save; copy the token. 3) For Service Account (recommended): create a Service Account in the Pipefy admin console per Pipefy’s Service Accounts docs and retrieve the service account key.

2. Add them to .dlt/secrets.toml

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[sources.pipefy_source]
api_token = "your_pipefy_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 Pipefy 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 pipefy_pipeline.py

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

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Pipeline pipefy_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset pipefy_data
The duckdb destination used duckdb:/pipefy.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 pipefy_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 cards and pipes from the Pipefy 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 pipefy_source(api_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.pipefy.com/",
            "auth": {
                "type": "bearer",
                "token": api_token,
            },
        },
        "resources": [
            {"name": "cards", "endpoint": {"path": "graphql (use query: cards)", "data_selector": "data.cards.edges[].node"}},
            {"name": "pipes", "endpoint": {"path": "graphql (use query: pipes)", "data_selector": "data.pipes[]"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="pipefy_pipeline",
        destination="duckdb",
        dataset_name="pipefy_data",
    )
    load_info = pipeline.run(pipefy_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("pipefy_pipeline").dataset()
sessions_df = data.cards.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM pipefy_data.cards LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("pipefy_pipeline").dataset()
data.cards.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 Pipefy 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 the Authorization header is missing, invalid or expired the API returns 401 Unauthorized. Ensure requests include: Authorization: Bearer . Use Service Accounts for production and verify the token has not been revoked.

Rate limits and request quotas

Pipefy documents both monthly plan call limits and short-term request limits. Monthly limits vary by plan (Starter 20/mo, Business 500/mo, Enterprise 10,000/mo or custom). Additionally, short-term throttling: about 500 requests every 30 seconds; exceeding it may block requests for several minutes. Implement exponential backoff and respect paging to reduce calls.

Pagination and Relay edges

Many list fields use Relay-style pagination: responses return edges and node objects (e.g., data.cards.edges[].node). Queries typically return 50 items per request by default; use GraphQL pagination arguments (first/after) to page through results.

Partial responses and errors array

GraphQL responses may be HTTP 200 with a top-level "errors" array when part of the query failed. Check both HTTP status and the "errors" field; when present, the response may include partial data under "data".

Common error formats

Authentication errors: 401 Unauthorized. Schema/field errors: GraphQL errors like "Cannot query field 'X'" appear in the response errors array. Rate limit violations result in temporary blocks; follow request handling guidance and backoff.

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