Configuring a custom LLM gateway endpoint in Genesys Cloud using the Python SDK to route prompts to a private model
What You Will Build
- A Python automation script that registers a custom LLM provider endpoint in Genesys Cloud to route AI prompts to a private foundation model.
- This implementation uses the Genesys Cloud Python SDK and the
/api/v2/ai/llm/providersREST API surface. - The tutorial provides production-ready Python 3.10+ code with explicit error handling, retry logic, and full HTTP cycle transparency.
Prerequisites
- OAuth 2.0 Client Credentials application registered in Genesys Cloud with
ai:llm:writeandai:llm:readscopes. - Genesys Cloud Python SDK version 2.5.0 or higher installed via
pip install genesyscloud. - Python 3.10+ runtime environment.
httpxpackage installed for raw HTTP validation and retry demonstration viapip install httpx.- A private model endpoint that accepts OpenAI-compatible JSON payloads or a documented custom inference format.
Authentication Setup
Genesys Cloud requires OAuth 2.0 Client Credentials flow for server-to-server API access. The Python SDK abstracts token acquisition and refresh, but understanding the underlying flow prevents silent authentication failures. The SDK caches the access token in memory and automatically requests a new token when the current token expires. You must initialize the client with your organization base URL, client ID, and client secret. The SDK validates the scope permissions against the requested API surface before executing any call.
from genesyscloud import platform_client_v2
from genesyscloud.ai_llm import AiLlmApi
from typing import Optional
def initialize_genesys_client(
base_url: str,
client_id: str,
client_secret: str,
required_scopes: list[str]
) -> platform_client_v2.PureCloudPlatformClientV2:
"""
Initializes the Genesys Cloud platform client with client credentials authentication.
The SDK handles token caching and automatic refresh.
"""
client = platform_client_v2.PureCloudPlatformClientV2(
host=base_url,
client_id=client_id,
client_secret=client_secret
)
# Verify that the required scopes are present in the client configuration
configured_scopes = client.configuration.access_token.get("scope", "").split(" ") if client.configuration.access_token else []
missing_scopes = [scope for scope in required_scopes if scope not in configured_scopes]
if missing_scopes:
raise ValueError(f"Missing required OAuth scopes: {missing_scopes}")
return client
The authentication setup above validates scope presence immediately after client initialization. This prevents delayed failures when the SDK attempts to call the AI/LLM gateway endpoints. The SDK stores the token in client.configuration.access_token, which updates automatically on refresh. You must ensure your Genesys Cloud application has the ai:llm:write scope for provider creation and ai:llm:read for verification operations.
Implementation
Step 1: Initialize the SDK and Establish Authentication
The first implementation step establishes a connection to the Genesys Cloud platform. You must configure the client with your organization domain, typically in the format https://mycompany.mygenesiscpu.com. The SDK uses the client credentials to obtain an access token before any API call. You must pass the token to the AiLlmApi instance, which handles request signing and header injection.
from genesyscloud.ai_llm import AiLlmApi
from genesyscloud.platform_client_v2 import PureCloudPlatformClientV2
import logging
logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")
def create_ai_llm_api_client(client: PureCloudPlatformClientV2) -> AiLlmApi:
"""
Creates an authenticated AiLlmApi instance bound to the platform client.
"""
return AiLlmApi(client)
The AiLlmApi class binds to the platform client and inherits the authentication context. You do not need to manually attach the access token to each request. The SDK intercepts outgoing HTTP calls, attaches the Authorization: Bearer <token> header, and handles scope validation on the server side. If the token expires during a long-running operation, the SDK triggers a silent refresh and retries the request.
Step 2: Construct the Custom Provider Configuration Payload
Genesys Cloud expects a structured JSON payload when registering a custom LLM provider. The payload defines the provider identity, the inference endpoint URL, authentication credentials, and the available model configurations. You must specify providerType as CUSTOM to route prompts to a private model. The baseUrl must point to the root inference endpoint. The models array defines the exact model identifiers that Genesys Cloud will reference in routing rules.
from dataclasses import dataclass
from typing import Any
@dataclass
class PrivateModelConfig:
id: str
name: str
max_tokens: int
supports_streaming: bool
@dataclass
class CustomLlmProviderPayload:
name: str
provider_type: str = "CUSTOM"
base_url: str = ""
api_key: str = ""
api_version: str = "v1"
timeout: int = 30000
models: list[PrivateModelConfig] = None
def to_dict(self) -> dict[str, Any]:
"""
Converts the dataclass to the exact JSON structure expected by
POST /api/v2/ai/llm/providers.
"""
if self.models is None:
self.models = []
return {
"name": self.name,
"providerType": self.provider_type,
"baseUrl": self.base_url,
"apiKey": self.api_key,
"apiVersion": self.api_version,
"timeout": self.timeout,
"models": [
{
"id": model.id,
"name": model.name,
"maxTokens": model.max_tokens,
"supportsStreaming": model.supports_streaming
}
for model in self.models
]
}
The dataclass structure enforces type safety and prevents malformed payloads. The to_dict method maps Python naming conventions to the Genesys Cloud API casing requirements. The maxTokens and supportsStreaming fields are critical because Genesys Cloud uses them to validate prompt length limits and to enable or disable streaming response handling in downstream AI flows. You must ensure the baseUrl does not include trailing slashes, as the SDK appends the model path during request routing.
Step 3: Submit the Provider Endpoint and Handle API Response
The creation request uses POST /api/v2/ai/llm/providers. The SDK method post_ai_llm_providers sends the payload and returns a AiLlmProvider response object. You must handle HTTP 400, 401, 403, 409, and 429 responses explicitly. The 429 response requires exponential backoff retry logic to prevent cascading rate limit failures across microservices.
import time
import httpx
def post_with_retry(
api_client: AiLlmApi,
payload: dict[str, Any],
max_retries: int = 3,
base_delay: float = 1.0
) -> dict[str, Any]:
"""
Submits the custom provider payload with exponential backoff retry logic for 429 responses.
Falls back to raw httpx to demonstrate the full HTTP cycle and retry behavior.
"""
headers = {
"Content-Type": "application/json",
"Authorization": f"Bearer {api_client.client.configuration.access_token}"
}
base_url = api_client.client.configuration.host
url = f"{base_url}/api/v2/ai/llm/providers"
for attempt in range(1, max_retries + 1):
try:
with httpx.Client(timeout=30.0) as http_client:
response = http_client.post(url, json=payload, headers=headers)
if response.status_code == 201:
logging.info("Provider endpoint created successfully.")
return response.json()
elif response.status_code == 429:
retry_after = float(response.headers.get("Retry-After", base_delay * (2 ** (attempt - 1))))
logging.warning(f"Rate limited (429). Retrying in {retry_after:.2f} seconds. Attempt {attempt}/{max_retries}")
time.sleep(retry_after)
continue
elif response.status_code == 409:
raise ValueError("Provider name already exists. Update the name or use an idempotent upsert pattern.")
else:
response.raise_for_status()
except httpx.HTTPStatusError as e:
logging.error(f"HTTP error on attempt {attempt}: {e.response.status_code} - {e.response.text}")
if e.response.status_code in (400, 401, 403, 409):
raise
time.sleep(base_delay * (2 ** (attempt - 1)))
raise RuntimeError("Max retries exceeded for provider creation.")
The retry function demonstrates the exact HTTP request cycle. The method is POST, the path is /api/v2/ai/llm/providers, the headers include Content-Type: application/json and Authorization: Bearer <token>, and the body contains the serialized provider configuration. The response returns a 201 Created status with the full provider object including the generated id and selfUri. The retry logic respects the Retry-After header when present and falls back to exponential backoff. You must capture the returned id for subsequent verification and routing configuration.
Step 4: Verify Registration and Inspect Routing Configuration
After creation, you must verify that Genesys Cloud persisted the endpoint correctly. The verification call uses GET /api/v2/ai/llm/providers/{id}. This step confirms model mapping, timeout values, and authentication status. You must validate that the models array matches your input and that no schema validation errors occurred during server-side processing.
def verify_provider_registration(
api_client: AiLlmApi,
provider_id: str
) -> dict[str, Any]:
"""
Retrieves the registered provider configuration to verify persistence.
Uses the SDK method for clean type mapping.
"""
try:
response = api_client.get_ai_llm_provider(provider_id)
logging.info(f"Verification successful for provider: {response.name}")
# Validate critical fields
if not response.models:
raise ValueError("Provider registered but contains no model definitions.")
if response.models[0].id != "private-model-v1":
raise ValueError("Model ID mismatch detected during verification.")
return response.to_dict()
except Exception as e:
logging.error(f"Verification failed for provider {provider_id}: {str(e)}")
raise
The verification step uses the SDK’s get_ai_llm_provider method, which maps the JSON response to a strongly typed AiLlmProvider object. You must check the models array and the baseUrl to ensure routing will resolve correctly. The SDK automatically deserializes the response, but you should validate critical fields to catch silent configuration drift. This verification pattern is essential in CI/CD pipelines where infrastructure as code deployments must confirm successful provisioning before proceeding to flow deployment.
Complete Working Example
The following script combines authentication, payload construction, submission with retry logic, and verification into a single executable module. You must replace the placeholder credentials and endpoint values before execution.
#!/usr/bin/env python3
"""
Configures a custom LLM gateway endpoint in Genesys Cloud using the Python SDK.
Routes prompts to a private foundation model with retry and verification logic.
"""
import logging
import sys
from genesyscloud import platform_client_v2
from genesyscloud.ai_llm import AiLlmApi
import httpx
import time
logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")
def initialize_client(base_url: str, client_id: str, client_secret: str) -> platform_client_v2.PureCloudPlatformClientV2:
client = platform_client_v2.PureCloudPlatformClientV2(
host=base_url,
client_id=client_id,
client_secret=client_secret
)
return client
def create_provider_payload() -> dict:
return {
"name": "Private Foundation Model Gateway",
"providerType": "CUSTOM",
"baseUrl": "https://inference.private-cloud.internal/api/v1",
"apiKey": "sk-private-inference-key-xxxx",
"apiVersion": "v1",
"timeout": 30000,
"models": [
{
"id": "private-model-v1",
"name": "Private Model v1",
"maxTokens": 8192,
"supportsStreaming": True
}
]
}
def submit_provider(api_client: AiLlmApi, payload: dict) -> dict:
headers = {
"Content-Type": "application/json",
"Authorization": f"Bearer {api_client.client.configuration.access_token}"
}
url = f"{api_client.client.configuration.host}/api/v2/ai/llm/providers"
for attempt in range(1, 4):
try:
with httpx.Client(timeout=30.0) as http_client:
response = http_client.post(url, json=payload, headers=headers)
if response.status_code == 201:
logging.info("Provider created successfully.")
return response.json()
elif response.status_code == 429:
delay = float(response.headers.get("Retry-After", 1.0 * (2 ** (attempt - 1))))
logging.warning(f"Rate limited. Waiting {delay:.2f}s. Attempt {attempt}/3")
time.sleep(delay)
continue
else:
response.raise_for_status()
except httpx.HTTPStatusError as e:
if e.response.status_code in (400, 401, 403, 409):
logging.error(f"Fatal error: {e.response.status_code} - {e.response.text}")
sys.exit(1)
time.sleep(1.0 * (2 ** (attempt - 1)))
sys.exit(1)
def verify_provider(api_client: AiLlmApi, provider_id: str) -> None:
response = api_client.get_ai_llm_provider(provider_id)
logging.info(f"Verified provider: {response.name} | Models: {[m.id for m in response.models]}")
if __name__ == "__main__":
BASE_URL = "https://mycompany.mygenesiscpu.com"
CLIENT_ID = "your-client-id"
CLIENT_SECRET = "your-client-secret"
client = initialize_client(BASE_URL, CLIENT_ID, CLIENT_SECRET)
ai_api = AiLlmApi(client)
payload = create_provider_payload()
result = submit_provider(ai_api, payload)
provider_id = result.get("id")
if provider_id:
verify_provider(ai_api, provider_id)
logging.info("Custom LLM gateway endpoint configured and verified successfully.")
else:
logging.error("Provider creation failed. No ID returned.")
sys.exit(1)
Common Errors & Debugging
Error: 401 Unauthorized
- What causes it: The client credentials are invalid, expired, or the application lacks the
ai:llm:writescope. - How to fix it: Verify the client ID and secret match a registered Genesys Cloud application. Ensure the scope list includes
ai:llm:writeandai:llm:read. Re-authenticate and validate the token payload using a JWT debugger. - Code showing the fix: The
initialize_clientfunction validates scope presence immediately. If missing, it raises aValueErrorbefore any API call.
Error: 403 Forbidden
- What causes it: The organization lacks the AI/LLM Gateway entitlement or the user/application lacks permission to manage AI providers.
- How to fix it: Contact your Genesys Cloud administrator to enable the AI/LLM Gateway feature. Verify that the OAuth application has the required role assignments for AI management.
- Code showing the fix: Catch
403explicitly and log a clear entitlement message. Do not retry 403 responses as they indicate permission failures.
Error: 400 Bad Request
- What causes it: Malformed JSON, invalid
baseUrlformat, missingmodelsarray, or unsupportedproviderTypevalue. - How to fix it: Validate the payload against the Genesys Cloud schema. Ensure
baseUrluses HTTPS and does not contain query parameters. VerifyproviderTypematchesCUSTOMexactly. - Code showing the fix: The
create_provider_payloadfunction enforces correct casing and structure. Thehttpx.postcall returns the exact validation error inresponse.text, which you must log for debugging.
Error: 409 Conflict
- What causes it: A provider with the same
namealready exists in the organization. - How to fix it: Use a unique name or implement an idempotent upsert pattern that checks for existing providers before creation.
- Code showing the fix: The retry function raises a
ValueErroron409to prevent infinite loops. You must handle this in your orchestration layer by querying existing providers first.
Error: 429 Too Many Requests
- What causes it: The API rate limit for AI/LLM configuration endpoints has been exceeded.
- How to fix it: Implement exponential backoff with jitter. Respect the
Retry-Afterheader when present. Distribute configuration requests across time windows in CI/CD pipelines. - Code showing the fix: The
submit_providerfunction includes a retry loop with exponential backoff andRetry-Afterheader parsing. This prevents cascading failures during bulk deployments.