Genesys Cloud Python API Docs | dltHub

Genesys Cloud is a cloud contact center platform exposing a comprehensive REST API for provisioning, routing, conversations, analytics, and integrations. The REST API base URL is https://api.{region}.mypurecloud.com/api/v2 (region-specific host, replace {region} with your org region, e.g. api.usw2.mypurecloud.com) and All requests require an OAuth 2.0 Bearer access token (Client Credentials or Authorization Code grants)..

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 Genesys Cloud data in under 10 minutes.

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What data can I load from Genesys Cloud?

Here are some of the endpoints you can load from Genesys Cloud:

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How do I authenticate with the Genesys Cloud API?

Create an OAuth client in Genesys Cloud (Integrations → OAuth) to obtain client_id and client_secret; request a token from the tenant's auth server (region‑specific) using POST /oauth/token and include 'Authorization: Basic <client_id:client_secret>' for the token exchange. Include 'Authorization: Bearer <access_token>' on API requests. Some Authentication API flows require the 'x-api-key' header.

1. Get your credentials

1. Sign into Genesys Cloud Admin (your tenant). 2) Go to Admin → Integrations → OAuth or Admin → Integrations → OAuth Clients. 3) Click 'Add Client' and choose grant types (Client Credentials for server‑to‑server or Authorization Code for user flows). 4) Configure allowed redirect URIs and scopes. 5) Save to get Client ID and Client Secret. 6) Exchange credentials at your tenant's auth endpoint (/oauth/token) to receive access_token.

2. Add them to .dlt/secrets.toml

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[sources.genesys_cloud_source]
client_id = "your_client_id"
client_secret = "your_client_secret"
region = "usw2"

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 Genesys Cloud 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 genesys_cloud_pipeline.py

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

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Pipeline genesys_cloud_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset genesys_cloud_data
The duckdb destination used duckdb:/genesys_cloud.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 genesys_cloud_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 users and conversations from the Genesys Cloud 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 genesys_cloud_source(client_secret=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.{region}.mypurecloud.com/api/v2 (region-specific host, replace {region} with your org region, e.g. api.usw2.mypurecloud.com)",
            "auth": {
                "type": "bearer",
                "access_token": client_secret,
            },
        },
        "resources": [
            {"name": "users", "endpoint": {"path": "api/v2/users", "data_selector": "entities"}},
            {"name": "conversations", "endpoint": {"path": "api/v2/conversations"}}
        ],
    }
    yield from rest_api_resources(config)


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

SQL (DuckDB example):

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SELECT * FROM genesys_cloud_data.conversations LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("genesys_cloud_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 Genesys Cloud 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: access_token is missing, expired, or invalid — exchange client credentials for a new token. 403 Forbidden: missing x-api-key (for some auth flows) or insufficient scopes/permissions. Check OAuth client scopes and user roles.

Rate limiting

Genesys returns 429 Too Many Requests when rate limits are exceeded. Response headers include RateLimit-Limit, RateLimit-Remaining, RateLimit-Reset and sometimes Retry-After. Back off and retry after the reset interval.

Pagination and async analytics jobs

List endpoints use pageSize and pageNumber (and return pageCount/total). Large analytics queries use async jobs: POST to start job returns jobId; poll GET /api/v2/analytics/conversations/details/jobs/{jobId} until status='COMPLETED', then fetch pages at /.../results which contain 'entities' arrays. Ensure pageSize is respected and iterate pages until you have retrieved total results.

Common errors

400 Bad Request — invalid parameters. 401 Unauthorized — invalid/expired token. 403 Forbidden — invalid x-api-key or insufficient permissions. 404 Not Found — resource id invalid. 429 Too Many Requests — rate limit exceeded. 500/502/503 — transient server errors, retry with backoff.

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