Groupme Python API Docs | dltHub

GroupMe is a group messaging platform and API for accessing user, group, message, bot and related resources. The REST API base URL is https://api.groupme.com/v3 and all requests require an access token passed as query param (token) or X-Access-Token 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 Groupme data in under 10 minutes.

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What data can I load from Groupme?

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

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How do I authenticate with the Groupme API?

Authentication uses an access token passed either as the query parameter token=YOUR_ACCESS_TOKEN or as the HTTP header "X-Access-Token: YOUR_ACCESS_TOKEN" on all requests.

1. Get your credentials

1. Sign in at https://dev.groupme.com/ or https://groupme.com and navigate to the Developers / Bots page.
2. Your personal access token is displayed at the top of the bots page or in your account settings.
3. Copy the token and use it as the token query parameter (e.g., ?token=YOUR_TOKEN) or set the HTTP header X-Access-Token: YOUR_TOKEN for API calls.

2. Add them to .dlt/secrets.toml

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[sources.groupme_source]
api_key = "your_groupme_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 Groupme 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 groupme_pipeline.py

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

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Pipeline groupme_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset groupme_data
The duckdb destination used duckdb:/groupme.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 groupme_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 groups and messages from the Groupme 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 groupme_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.groupme.com/v3",
            "auth": {
                "type": "api_key",
                "api_key": access_token,
            },
        },
        "resources": [
            {"name": "groups", "endpoint": {"path": "groups", "data_selector": "response"}},
            {"name": "messages", "endpoint": {"path": "groups/:group_id/messages", "data_selector": "response.messages"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="groupme_pipeline",
        destination="duckdb",
        dataset_name="groupme_data",
    )
    load_info = pipeline.run(groupme_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("groupme_pipeline").dataset()
sessions_df = data.messages.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM groupme_data.messages LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("groupme_pipeline").dataset()
data.messages.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 Groupme 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 requests return 401/403 or the meta.code indicates an auth error, verify that a valid access token is provided via the token query parameter or X-Access-Token header. Ensure the token has not been revoked.

Rate limits and 5xx errors

The API may return 5xx or 503 when under heavy load. Implement exponential backoff retries. No explicit rate‑limit headers are documented, so use conservative retry logic.

Pagination quirks

GET /groups supports page and per_page (default 10). GET /groups/:group_id/messages uses before_id, after_id, and since_id for pagination. If no messages are found with before_id, the API may return a 304.

Error envelope

Errors are returned inside a meta object, e.g., { "meta": { "code": 400, "errors": ["Name is required"] }, "response": null }. Inspect meta.code and meta.errors to determine the failure reason.

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