Reply Python API Docs | dltHub

Reply is a sales engagement platform that provides a programmable API for managing conversations and contacts. The REST API base URL is https://api.reply.io and All requests require an API key that must be included in the Authorization header or as a query parameter..

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 Reply data in under 10 minutes.

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What data can I load from Reply?

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

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How do I authenticate with the Reply API?

Authentication is performed by sending the API key in the Authorization header as Bearer <api_key> or as a api_key query parameter.

1. Get your credentials

1. Log in to your Reply account at https://app.reply.io.
2. Click on your avatar in the top‑right corner and select Settings.
3. In the Settings menu, choose API or Integrations.
4. Locate the API Key field and click Copy (or manually copy the key).
5. Store the key securely; you will use it in the Authorization header for API calls.

2. Add them to .dlt/secrets.toml

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[sources.reply_sales_engagement_source]
api_key = "your_api_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 Reply 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 reply_sales_engagement_pipeline.py

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

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Pipeline reply_sales_engagement_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset reply_sales_engagement_data
The duckdb destination used duckdb:/reply_sales_engagement.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 reply_sales_engagement_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 contacts and conversations from the Reply 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 reply_sales_engagement_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.reply.io",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "contacts", "endpoint": {"path": "v1/contacts", "data_selector": "contacts"}},
            {"name": "conversations", "endpoint": {"path": "v1/conversations", "data_selector": "conversations"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="reply_sales_engagement_pipeline",
        destination="duckdb",
        dataset_name="reply_sales_engagement_data",
    )
    load_info = pipeline.run(reply_sales_engagement_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("reply_sales_engagement_pipeline").dataset()
sessions_df = data.conversations.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM reply_sales_engagement_data.conversations LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("reply_sales_engagement_pipeline").dataset()
data.conversations.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 Reply 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 API key is missing, malformed, or does not correspond to an existing user. Verify that the key is correct and included in the Authorization header.
• 403 Forbidden – The API key lacks permissions for the requested resource. Ensure the key has the required scopes.

Rate limiting

• The API imposes limits on request volume. If you receive 429 Too Many Requests, pause and retry after the time indicated in the Retry-After header.

Pagination quirks

• Endpoints that return large result sets use cursor‑based pagination. Check the response for a next_cursor field and include it as a query parameter (cursor=...) in subsequent calls to retrieve the next page.

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