Mendeley Python API Docs | dltHub

Mendeley is an academic reference management and research data platform exposing a REST API for accessing catalogue documents, user libraries, and datasets. The REST API base URL is https://api.mendeley.com and all requests require OAuth2 (Bearer) access tokens for authentication.

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 Mendeley data in under 10 minutes.

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What data can I load from Mendeley?

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

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How do I authenticate with the Mendeley API?

The API uses OAuth2 (Authorization Code and Client Credentials flows) and requires the access token in the Authorization header as: Authorization: Bearer <ACCESS_TOKEN>. The token may also be passed as an access_token query parameter if headers cannot be set.

1. Get your credentials

1. Register an application in the Mendeley developer portal to obtain a client_id and client_secret. 2) For user‑scoped calls, perform the OAuth2 authorization_code flow: redirect the user to the authorization URL, receive an authorization code, and exchange it at the token endpoint to receive an access_token and refresh_token. 3) For server‑to‑server calls, use the client_credentials flow to obtain an access_token from the token endpoint. 4) Use the access_token in the Authorization header for API calls.

2. Add them to .dlt/secrets.toml

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[sources.mendeley_source]
client_id = "YOUR_CLIENT_ID"
client_secret = "YOUR_CLIENT_SECRET"
access_token = "YOUR_ACCESS_TOKEN"
refresh_token = "YOUR_REFRESH_TOKEN"

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 Mendeley 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 mendeley_pipeline.py

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

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Pipeline mendeley_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset mendeley_data
The duckdb destination used duckdb:/mendeley.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 mendeley_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 documents and catalog from the Mendeley 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 mendeley_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.mendeley.com",
            "auth": {
                "type": "bearer",
                "access_token": access_token,
            },
        },
        "resources": [
            {"name": "documents", "endpoint": {"path": "documents"}},
            {"name": "catalog", "endpoint": {"path": "catalog"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="mendeley_pipeline",
        destination="duckdb",
        dataset_name="mendeley_data",
    )
    load_info = pipeline.run(mendeley_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("mendeley_pipeline").dataset()
sessions_df = data.documents.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM mendeley_data.documents LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("mendeley_pipeline").dataset()
data.documents.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 Mendeley 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 indicates missing, expired or invalid access token. Responses include WWW-Authenticate header and a JSON message such as: {"message": "Could not access resource because: Token has expired"}. Ensure token is included as Authorization: Bearer <ACCESS_TOKEN> or use access_token query parameter if necessary.

Scope and permission errors

403 Forbidden indicates the token lacks required scopes (e.g., using client_credentials token for user‑scoped endpoints). Response body may contain a plain text message like: "You do not have the required scopes [all] for this operation".

Rate limiting

429 Too Many Requests indicates rate limiting; Mendeley recommends contacting api-support@mendeley.com. Respect pagination limits (default 20, max 500) and use Link headers or marker parameter to page through results.

Pagination quirks

List endpoints are paginated. Use limit (default 20, max 500) and marker or Link headers to retrieve next pages. Some endpoints return results as top‑level JSON arrays; do not assume a wrapper object. Use the provided Link header or SDK helper pagination examples to iterate pages.

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