Figma Python API Docs | dltHub

Figma is a collaborative interface design platform with a REST API for accessing files, images, comments, users, projects, teams, components, versions, webhooks and analytics. The REST API base URL is https://api.figma.com and All requests require an access token (personal access token or OAuth2) in the Authorization header as a Bearer token..

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 Figma data in under 10 minutes.

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What data can I load from Figma?

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

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How do I authenticate with the Figma API?

Figma supports personal access tokens and OAuth2. Include the token in the Authorization header: Authorization: Bearer . Scopes control access (e.g., file_content:read).

1. Get your credentials

1. Sign in to your Figma account. 2) To create a personal access token, go to Account Settings → Personal Access Tokens (or My Apps) and click “Create a new token”. Copy the generated token. 3) For OAuth, navigate to My Apps, register a new application, note the client_id and client_secret, and implement the OAuth2 authorization code flow to exchange a code for an access token with the required scopes.

2. Add them to .dlt/secrets.toml

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[sources.figma_source]
access_token = "your_personal_access_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 Figma 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 figma_pipeline.py

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

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Pipeline figma_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset figma_data
The duckdb destination used duckdb:/figma.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 figma_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 files and images from the Figma 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 figma_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.figma.com",
            "auth": {
                "type": "bearer",
                "token": access_token,
            },
        },
        "resources": [
            {"name": "files", "endpoint": {"path": "v1/files/:key", "data_selector": "document"}},
            {"name": "images", "endpoint": {"path": "v1/images/:key", "data_selector": "images"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="figma_pipeline",
        destination="duckdb",
        dataset_name="figma_data",
    )
    load_info = pipeline.run(figma_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("figma_pipeline").dataset()
sessions_df = data.files.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM figma_data.files LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("figma_pipeline").dataset()
data.files.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 Figma 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, verify the Authorization header is present and the token is valid. Ensure the token includes required scopes (e.g., file_content:read) and has not expired or been revoked.

Rate limits and throttling

Figma enforces rate limits; when rate limited, the API returns 429. Implement exponential backoff and retries. Consider caching file JSON since file content can be large and change infrequently.

Pagination and large responses

Many endpoints return full lists or maps (e.g., nodes, images). File JSON can be large; use query parameters (ids, depth, version) to limit returned nodes. Images endpoints return maps keyed by node id; some maps may contain null values for nodes that failed to render.

Common error responses

• 403 — invalid or expired token
• 404 — file or resource not found
• 400 — invalid parameters (some endpoints include err in response with details)
• 500 — server/rendering errors for images.

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