Mewe Python API Docs | dltHub

Mewe is a social networking platform that offers a REST API for accessing user profiles, posts, groups, and other community data. The REST API base URL is https://api.mewe.com/v1 and All requests require a Bearer token 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 Mewe data in under 10 minutes.

---

What data can I load from Mewe?

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

---

How do I authenticate with the Mewe API?

Authentication is performed by sending the access token in the HTTP Authorization header as "Bearer {token}".

1. Get your credentials

1. Log in to your Mewe account.
2. Navigate to Settings → Developer Tools (or API Access) in the Mewe web interface.
3. Create a new application or API client.
4. Copy the generated access token (or client secret) provided for the application.
5. Store the token securely; it will be used as the Bearer token for API calls.

2. Add them to .dlt/secrets.toml

Copy
[sources.mewe_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.

---

How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

Copy
uv venv && source .venv/bin/activate
uv pip install "dlt[workspace]"

1. Install the dlt AI Workbench:

Copy
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:

Copy
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 Mewe 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:

Copy
python mewe_pipeline.py

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

Copy
Pipeline mewe_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset mewe_data
The duckdb destination used duckdb:/mewe.duckdb location to store data
Load package 1749667187.541553 is LOADED and contains no failed jobs

Inspect your pipeline and data:

Copy
dlt pipeline mewe_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.

---

Python pipeline example

This example loads profile and feed from the Mewe API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:

Copy
import dlt
from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources

@dlt.source
def mewe_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.mewe.com/v1",
            "auth": {
                "type": "bearer",
                "token": access_token,
            },
        },
        "resources": [
            {"name": "profile", "endpoint": {"path": "api/v1/profile", "data_selector": "data"}},
            {"name": "feed", "endpoint": {"path": "api/v1/feed", "data_selector": "items"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="mewe_pipeline",
        destination="duckdb",
        dataset_name="mewe_data",
    )
    load_info = pipeline.run(mewe_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.

---

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):

Copy
import dlt

data = dlt.pipeline("mewe_pipeline").dataset()
sessions_df = data.profile.df()
print(sessions_df.head())

SQL (DuckDB example):

Copy
SELECT * FROM mewe_data.profile LIMIT 10;

In a marimo or Jupyter notebook:

Copy
import dlt

data = dlt.pipeline("mewe_pipeline").dataset()
data.profile.df().head()

See how to explore your data in marimo Notebooks and how to query your data in Python with dataset.

---

What destinations can I load Mewe 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.

---

Troubleshooting

Authentication failures

• Error 401 Unauthorized – The Bearer token is missing, expired, or invalid. Verify that the token in secrets.toml is correct and has not expired.

Rate limiting

• Error 429 Too Many Requests – The API limits the number of requests per minute. Implement exponential backoff and respect the Retry-After header.

Pagination quirks

• Many Mewe endpoints return a next_page cursor in the response. Use this cursor to retrieve subsequent pages. If the cursor is absent, the result set is complete.

Ensure that the API key is valid to avoid 401 Unauthorized errors. Also, verify endpoint paths and parameters to avoid 404 Not Found errors.

---

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.

Copy
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