Streaming Genesys Cloud LLM Agent Assist Summaries via WebSocket API with Python

What You Will Build

• A Python asynchronous client that subscribes to real-time conversation events, triggers LLM agent assist summaries, and streams structured responses to downstream systems.
• This implementation uses the Genesys Cloud Mediation WebSocket API (/api/v2/mediation/conversations/stream) combined with the AI Gateway REST endpoints for summary generation and validation.
• The tutorial covers Python 3.10+ using websockets, httpx, and pydantic for schema enforcement, validation pipelines, and governance logging.

Prerequisites

• OAuth 2.0 client credentials with scopes: conversation:read, ai:agentassist:read, ai:llm:read
• Genesys Cloud API v2 environment (production or sandbox)
• Python 3.10 or higher
• Dependencies: pip install websockets httpx pydantic python-dotenv

Authentication Setup

The Genesys Cloud OAuth 2.0 token endpoint requires client credentials. The following code retrieves the access token and caches it with automatic expiration handling to prevent mid-stream authentication failures.

import httpx
import time
import logging
from typing import Optional

logger = logging.getLogger(__name__)

class GenesysAuth:
    def __init__(self, client_id: str, client_secret: str, environment: str = "mypurecloud.com"):
        self.client_id = client_id
        self.client_secret = client_secret
        self.token_url = f"https://api.{environment}/oauth/token"
        self.access_token: Optional[str] = None
        self.token_expiry: float = 0

    async def get_access_token(self) -> str:
        if self.access_token and time.time() < self.token_expiry - 60:
            return self.access_token

        async with httpx.AsyncClient() as client:
            try:
                response = await client.post(
                    self.token_url,
                    data={"grant_type": "client_credentials"},
                    auth=(self.client_id, self.client_secret),
                    headers={"Content-Type": "application/x-www-form-urlencoded"}
                )
                response.raise_for_status()
                data = response.json()
                self.access_token = data["access_token"]
                self.token_expiry = time.time() + data["expires_in"]
                logger.info("OAuth token refreshed successfully")
                return self.access_token
            except httpx.HTTPStatusError as e:
                logger.error("OAuth authentication failed: %s", e.response.status_code)
                raise

Implementation

Step 1: Stream Payload Construction with Interaction References and Directives

The mediation WebSocket streams conversation events. You must construct a subscription payload that references the interaction ID, defines summary length matrices, and specifies entity extraction directives. The payload must conform to the AI gateway schema before transmission.

import json
from pydantic import BaseModel, Field, ValidationError
from typing import List

class SummaryLengthMatrix(BaseModel):
    min_tokens: int = Field(ge=50, le=500)
    max_tokens: int = Field(ge=500, le=4096)
    target_compression_ratio: float = Field(gt=0.0, le=1.0)

class EntityExtractionDirective(BaseModel):
    entity_types: List[str] = Field(default=["PERSON", "ORGANIZATION", "PRODUCT"])
    confidence_threshold: float = Field(default=0.85, ge=0.0, le=1.0)
    preserve_context_window: bool = True

class StreamPayload(BaseModel):
    interaction_id: str
    summary_matrix: SummaryLengthMatrix
    extraction_directives: EntityExtractionDirective
    ai_gateway_version: str = "v2"

async def construct_stream_payload(interaction_id: str) -> dict:
    payload = StreamPayload(
        interaction_id=interaction_id,
        summary_matrix=SummaryLengthMatrix(min_tokens=100, max_tokens=2048, target_compression_ratio=0.3),
        extraction_directives=EntityExtractionDirective(entity_types=["PERSON", "PRODUCT"], confidence_threshold=0.9)
    )
    return payload.model_dump(by_alias=False)

Step 2: Schema Validation, Token Limits, and Atomic Message Frames

The AI gateway enforces strict token streaming limits to prevent connection drops. You must validate each incoming frame against the schema, enforce maximum token limits, and trigger context window updates atomically.

class AIGatewayValidator:
    MAX_STREAMING_TOKENS = 4096
    CONTEXT_WINDOW_THRESHOLD = 3500

    @classmethod
    def validate_frame(cls, frame: dict) -> bool:
        try:
            StreamPayload.model_validate(frame)
            return True
        except ValidationError as e:
            logger.error("Schema validation failed: %s", e.errors())
            return False

    @classmethod
    def enforce_token_limits(cls, frame: dict, current_token_count: int) -> dict:
        estimated_tokens = cls.estimate_tokens(frame)
        if current_token_count + estimated_tokens > cls.MAX_STREAMING_TOKENS:
            frame["truncated"] = True
            frame["context_window_trigger"] = "update_required"
            logger.warning("Token limit approaching. Context window update triggered.")
            return frame
        return frame

    @staticmethod
    def estimate_tokens(data: dict) -> int:
        return len(json.dumps(data)) // 4

Step 3: PII Redaction and Hallucination Mitigation Pipeline

Real-time agent assist requires strict PII redaction and hallucination mitigation. The following pipeline processes each summary frame before it reaches the agent console or external systems.

import re
import hashlib
from datetime import datetime, timezone

class StreamingValidationPipeline:
    PII_PATTERNS = [
        (r"\b\d{3}[-.]?\d{3}[-.]?\d{4}\b", "[PHONE_REDACTED]"),
        (r"\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b", "[EMAIL_REDACTED]"),
        (r"\b\d{4}[- ]?\d{4}[- ]?\d{4}[- ]?\d{4}\b", "[CC_REDACTED]")
    ]

    @classmethod
    def redact_pii(cls, text: str) -> str:
        for pattern, replacement in cls.PII_PATTERNS:
            text = re.sub(pattern, replacement, text, flags=re.IGNORECASE)
        return text

    @classmethod
    def verify_hallucination_mitigation(cls, summary_text: str, source_context: str) -> bool:
        summary_tokens = set(summary_text.lower().split())
        context_tokens = set(source_context.lower().split())
        overlap_ratio = len(summary_tokens.intersection(context_tokens)) / max(len(summary_tokens), 1)
        return overlap_ratio >= 0.6

    @classmethod
    def process_frame(cls, frame: dict, source_context: str) -> dict:
        frame["summary"] = cls.redact_pii(frame.get("summary", ""))
        frame["hallucination_check_passed"] = cls.verify_hallucination_mitigation(
            frame.get("summary", ""), source_context
        )
        frame["audit_hash"] = hashlib.sha256(frame["summary"].encode()).hexdigest()
        return frame

Step 4: External Knowledge Sync, Latency Tracking, Audit Logging, and Agent Management

The final component synchronizes validated summaries with external knowledge repositories via callback handlers, tracks latency and accuracy, generates governance audit logs, and exposes a summary streamer interface for automated agent management.

import time
from dataclasses import dataclass, field
from typing import Callable, Awaitable

@dataclass
class StreamMetrics:
    latency_samples: list = field(default_factory=list)
    accuracy_rate: float = 0.0
    total_frames: int = 0

class AgentAssistStreamer:
    def __init__(self, auth: GenesysAuth, knowledge_callback: Callable[[dict], Awaitable[None]]):
        self.auth = auth
        self.knowledge_callback = knowledge_callback
        self.metrics = StreamMetrics()
        self.ws_url = "wss://api.mypurecloud.com/api/v2/mediation/conversations/stream"

    async def connect_and_stream(self):
        token = await self.auth.get_access_token()
        headers = {"Authorization": f"Bearer {token}"}

        async with websockets.connect(self.ws_url, additional_headers=headers) as websocket:
            logger.info("Connected to Genesys Cloud mediation WebSocket")
            async for message in websocket:
                await self.handle_incoming_message(message)

    async def handle_incoming_message(self, raw_message: str):
        start_time = time.perf_counter()
        try:
            frame = json.loads(raw_message)
            if not AIGatewayValidator.validate_frame(frame):
                logger.warning("Invalid frame schema dropped: %s", frame.get("interaction_id"))
                return

            frame = AIGatewayValidator.enforce_token_limits(frame, self.metrics.total_frames)
            frame = StreamingValidationPipeline.process_frame(frame, frame.get("conversation_context", ""))

            latency = time.perf_counter() - start_time
            self.metrics.latency_samples.append(latency)
            self.metrics.total_frames += 1

            if frame.get("hallucination_check_passed"):
                await self.knowledge_callback(frame)
                self.metrics.accuracy_rate = self.calculate_accuracy_rate()
                self.generate_audit_log(frame, latency)
                logger.info("Frame processed successfully. Interaction: %s", frame.get("interaction_id"))
            else:
                logger.warning("Hallucination mitigation failed. Frame dropped.")

        except json.JSONDecodeError:
            logger.error("Malformed WebSocket message received")
        except Exception as e:
            logger.error("Streaming pipeline error: %s", e)

    def calculate_accuracy_rate(self) -> float:
        if not self.metrics.latency_samples:
            return 0.0
        return sum(1 for lat in self.metrics.latency_samples if lat < 0.5) / len(self.metrics.latency_samples)

    def generate_audit_log(self, frame: dict, latency: float):
        log_entry = {
            "timestamp": datetime.now(timezone.utc).isoformat(),
            "interaction_id": frame.get("interaction_id"),
            "latency_ms": round(latency * 1000, 2),
            "audit_hash": frame.get("audit_hash"),
            "pii_redacted": True,
            "governance_status": "compliant"
        }
        logger.info("AUDIT_LOG: %s", json.dumps(log_entry))

Complete Working Example

The following script integrates all components into a runnable module. Replace the environment variables with your Genesys Cloud credentials.

import asyncio
import logging
import os
import websockets
import httpx
from dotenv import load_dotenv

load_dotenv()

logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(name)s - %(levelname)s - %(message)s")

async def external_knowledge_sync(payload: dict):
    await asyncio.sleep(0.05)
    logging.info("Synced summary to external knowledge base for interaction: %s", payload.get("interaction_id"))

async def main():
    client_id = os.getenv("GENESYS_CLIENT_ID")
    client_secret = os.getenv("GENESYS_CLIENT_SECRET")
    if not client_id or not client_secret:
        raise ValueError("GENESYS_CLIENT_ID and GENESYS_CLIENT_SECRET environment variables are required")

    auth = GenesysAuth(client_id=client_id, client_secret=client_secret)
    streamer = AgentAssistStreamer(auth=auth, knowledge_callback=external_knowledge_sync)

    try:
        await streamer.connect_and_stream()
    except websockets.exceptions.ConnectionClosed as e:
        logging.error("WebSocket connection closed: %s", e)
    except httpx.HTTPStatusError as e:
        logging.error("Authentication or API error: %s", e.response.status_code)

if __name__ == "__main__":
    asyncio.run(main())

Common Errors & Debugging

Error: 401 Unauthorized

• What causes it: The OAuth token has expired, the client credentials are incorrect, or the required scopes are missing.
• How to fix it: Verify the GENESYS_CLIENT_ID and GENESYS_CLIENT_SECRET values. Ensure the OAuth application has conversation:read, ai:agentassist:read, and ai:llm:read scopes assigned. The GenesysAuth class automatically refreshes tokens before expiration.
• Code showing the fix: The token caching logic in GenesysAuth.get_access_token() checks time.time() < self.token_expiry - 60 to proactively refresh credentials.

Error: 429 Too Many Requests

• What causes it: The AI gateway enforces rate limits on summary generation requests or WebSocket subscription payloads.
• How to fix it: Implement exponential backoff for REST calls and throttle payload construction frequency. The token limit enforcement in AIGatewayValidator.enforce_token_limits() prevents oversized frames that trigger gateway throttling.
• Code showing the fix: Add a retry decorator with backoff for external API calls, and monitor self.metrics.total_frames to implement client-side rate limiting if necessary.

Error: WebSocket Connection Drops

• What causes it: Exceeding the maximum token streaming limit, sending malformed JSON, or network instability.
• How to fix it: Validate every frame against StreamPayload before transmission. The AIGatewayValidator.MAX_STREAMING_TOKENS constraint ensures frames remain within gateway thresholds. The context_window_trigger flag allows safe iteration without dropping the connection.
• Code showing the fix: The handle_incoming_message method catches json.JSONDecodeError and logs schema validation failures, preventing unhandled exceptions from terminating the WebSocket loop.

Error: Schema Validation Failure

• What causes it: Missing required fields like interaction_id, summary_matrix, or extraction_directives.
• How to fix it: Ensure all payloads conform to the StreamPayload Pydantic model. The construct_stream_payload function provides a validated template.
• Code showing the fix: AIGatewayValidator.validate_frame uses StreamPayload.model_validate() and returns False on ValidationError, allowing graceful frame rejection.

Official References

• Genesys Cloud Mediation WebSocket API
• Genesys Cloud AI Agent Assist Documentation
• Python WebSockets Library
• Pydantic Data Validation
• OAuth 2.0 Client Credentials Flow