Convex Python API Docs | dltHub

Convex is a serverless backend platform that provides a database and functions accessible through HTTP APIs. The REST API base URL is https://<deployment>.convex.cloud for Functions API (e.g., /api/query) and https://<deployment>.convex.site for HTTP Actions and All requests require an Authorization header with either Bearer <access_key> for user tokens or Convex <access_key> for deployment admin keys..

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 Convex data in under 10 minutes.

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What data can I load from Convex?

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

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How do I authenticate with the Convex API?

Include an Authorization header with either Bearer <access_key> for a user token or Convex <access_key> for a deployment admin key.

1. Get your credentials

1. Open the Convex dashboard and navigate to Settings.
2. Under Deploy Key, click Generate (or copy the existing key). This key is the admin <access_key> used with the Convex prefix.
3. For user authentication, configure an auth provider (e.g., Clerk) in the dashboard and obtain a JWT token from that provider. Use that JWT as the <access_key> with the Bearer prefix.
4. Store the obtained <access_key> in your dlt secrets.toml file.

2. Add them to .dlt/secrets.toml

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[sources.convex_source]
access_key = "your_access_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 Convex 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 convex_pipeline.py

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

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Pipeline convex_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset convex_data
The duckdb destination used duckdb:/convex.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 convex_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 query and mutation from the Convex 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 convex_source(access_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://<deployment>.convex.cloud for Functions API (e.g., /api/query) and https://<deployment>.convex.site for HTTP Actions",
            "auth": {
                "type": "bearer",
                "access_key": access_key,
            },
        },
        "resources": [
            {"name": "query", "endpoint": {"path": "api/query", "data_selector": "value"}},
            {"name": "mutation", "endpoint": {"path": "api/mutation", "data_selector": "value"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="convex_pipeline",
        destination="duckdb",
        dataset_name="convex_data",
    )
    load_info = pipeline.run(convex_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("convex_pipeline").dataset()
sessions_df = data.query.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM convex_data.query LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("convex_pipeline").dataset()
data.query.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 Convex 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 – The Authorization header is missing or the token is invalid. Verify that the access_key stored in secrets.toml matches the deploy key (admin) or user JWT.
• 403 Forbidden – The token is valid but does not have the required permissions (e.g., using a user token for admin‑only endpoints). Use a deploy key for streaming/export APIs.

Rate limits and size limits

• 429 Too Many Requests – Convex enforces rate limits per deployment. Reduce request frequency or batch calls.
• 413 Payload Too Large – Request or response bodies exceeding 20 MB are rejected. Trim payloads or paginate data.

Pagination / large result sets

• Convex functions return the complete result in the value field; for very large collections, implement manual pagination in your function logic.

CORS errors (browser clients)

• When calling HTTP Actions from a browser, ensure the action includes the appropriate Access‑Control‑Allow‑Origin header or use server‑side calls.

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