Pexels Python API Docs | dltHub

Pexels is a RESTful JSON API providing programmatic access to Pexels’ free stock photos, videos and collections. The REST API base URL is https://api.pexels.com/v1 and All requests require an API key sent 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 Pexels data in under 10 minutes.

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What data can I load from Pexels?

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

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How do I authenticate with the Pexels API?

Obtain an API key from your Pexels account and include it in every request using the HTTP header Authorization: YOUR_API_KEY.

1. Get your credentials

1. Create or sign in to a Pexels account at https://www.pexels.com/. 2) Visit the API section (https://www.pexels.com/api/) or Help Center “API” page to request an API key. 3) Copy the provided key; use it as the value of the Authorization header in all requests.

2. Add them to .dlt/secrets.toml

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[sources.pexels_photos_videos_source]
api_key = "your_pexels_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 Pexels 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 pexels_photos_videos_pipeline.py

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

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Pipeline pexels_photos_videos_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset pexels_photos_videos_data
The duckdb destination used duckdb:/pexels_photos_videos.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 pexels_photos_videos_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 photos_search and photos_curated from the Pexels 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 pexels_photos_videos_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.pexels.com/v1",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "photos_search", "endpoint": {"path": "v1/search", "data_selector": "photos"}},
            {"name": "photos_curated", "endpoint": {"path": "v1/curated", "data_selector": "photos"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="pexels_photos_videos_pipeline",
        destination="duckdb",
        dataset_name="pexels_photos_videos_data",
    )
    load_info = pipeline.run(pexels_photos_videos_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("pexels_photos_videos_pipeline").dataset()
sessions_df = data.photos_search.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM pexels_photos_videos_data.photos_search LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("pexels_photos_videos_pipeline").dataset()
data.photos_search.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 Pexels 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 or the API key is invalid the API returns a 401 Unauthorized. Ensure every request includes Authorization: YOUR_API_KEY. Verify you’re using the live API key from your Pexels account.

Rate limits and quota

Default limits: 200 requests/hour and 20,000 requests/month. Successful responses include headers X‑Ratelimit‑Limit, X‑Ratelimit‑Remaining and X‑Ratelimit‑Reset. When limits are exceeded the API returns 429 Too Many Requests (rate headers are omitted on error responses).

Pagination

List endpoints accept page (default 1) and per_page (default 15, max 80). Responses contain page, per_page, total_results and, when applicable, next_page / prev_page. Iterate over the photos or videos array returned in the JSON.

Common errors

• 401 Unauthorized – missing/invalid API key.
• 403 Forbidden – access to a restricted resource (e.g., non‑featured collection).
• 404 Not Found – incorrect resource ID or endpoint.
• 429 Too Many Requests – rate limit exceeded.
• 4xx/5xx – review response body and status; successful responses include rate‑limit headers, failures do not.

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