Mindmeister Python API Docs | dltHub

MindMeister is a web-based mind mapping platform that exposes REST APIs (API v2) to programmatically access maps, users, and related resources. The REST API base URL is https://www.mindmeister.com/api/v2 and all requests require a Bearer access token (OAuth 2.0) or a personal access 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 Mindmeister data in under 10 minutes.

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What data can I load from Mindmeister?

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

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How do I authenticate with the Mindmeister API?

Authorization uses OAuth 2.0 access tokens. Send the token in the Authorization header as a Bearer token (Authorization: Bearer ACCESS_TOKEN). Personal access tokens are also supported for user-level access. (Access token can also be passed in query string but this is not recommended.)

1. Get your credentials

1. Register an OAuth2 client application in the MindMeister developer dashboard to obtain a client_id and client_secret. 2) Use the appropriate OAuth2 flow (authorization code for web apps, implicit for client-side, client_credentials for machine-to-machine) to obtain an access token from the MindMeister authorization server. 3) Alternatively create a personal access token in your MindMeister account (for accessing resources on your own behalf).

2. Add them to .dlt/secrets.toml

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[sources.mindmeister_source]
access_token = "your_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 Mindmeister 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 mindmeister_pipeline.py

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

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Pipeline mindmeister_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset mindmeister_data
The duckdb destination used duckdb:/mindmeister.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 mindmeister_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 maps and users_me from the Mindmeister 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 mindmeister_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://www.mindmeister.com/api/v2",
            "auth": {
                "type": "bearer",
                "access_token": access_token,
            },
        },
        "resources": [
            {"name": "maps", "endpoint": {"path": "api/v2/maps"}},
            {"name": "users_me", "endpoint": {"path": "api/v2/users/me"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="mindmeister_pipeline",
        destination="duckdb",
        dataset_name="mindmeister_data",
    )
    load_info = pipeline.run(mindmeister_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("mindmeister_pipeline").dataset()
sessions_df = data.maps.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM mindmeister_data.maps LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("mindmeister_pipeline").dataset()
data.maps.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 Mindmeister 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 Unauthorized, confirm your Authorization header uses a valid, unexpired Bearer access token. For OAuth apps, refresh or re-obtain tokens via the configured OAuth flow. If using a personal access token, verify it was created and has not been revoked.

Rate limiting and throttling

The public docs do not publish explicit rate limit values. If you receive 429 Too Many Requests, back off and retry after the Retry-After header if provided. Implement exponential backoff.

Pagination

API v2 uses standard paginated list semantics in endpoints returning multiple items. Check endpoint query parameters (limit/offset or page) in the specific endpoint docs. If you receive partial results, iterate according to page/next links where present.

Legacy v1 REST RPC quirks

The v1 REST RPC endpoint (/services/rest/) returns XML and requires the method and api_key parameters. v1 is deprecated; migrate to API v2.

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