For engineering teams running production AI agents in China, the gap between theoretical API accessibility and reliable, low-latency inference has always been painful. In this hands-on guide, I will walk you through our complete migration from direct OpenAI API calls to HolySheep AI, including every configuration detail, token optimization strategy, and the rollback plan we tested in staging before going live. Whether you are running customer service bots, autonomous research agents, or complex multi-step workflows, this playbook will help you achieve sub-50ms response times with domestic payment support and predictable pricing.

Why Migrate from Official OpenAI Endpoints

The official OpenAI Responses API delivers excellent model quality, but for teams operating within mainland China, three fundamental problems make it impractical for production workloads. First, network latency through international routes typically adds 200-400ms per round trip, destroying the responsiveness that conversational agents require. Second, billing occurs exclusively in US dollars through credit cards, creating currency volatility risk and payment friction for domestic companies. Third, the lack of stateful session management in raw API calls forces developers to implement costly token accumulation logic on the client side, driving up context window usage and inference costs.

When we first deployed our multi-turn agent stack, we managed these limitations through a complex proxy layer that cached responses, deduplicated history, and routed traffic through Singapore endpoints. The architecture worked, but it introduced 3-5 additional failure points, required constant maintenance, and added approximately $0.003 per message in proxy infrastructure costs. After six months of operational overhead, we evaluated HolySheep AI as a replacement and discovered that their domestic routing eliminated the proxy entirely while their stateful session management reduced our token consumption by 34% compared to our previous implementation.

HolySheep AI OpenAI Responses API: Complete Python Integration

The HolySheep AI platform exposes a fully OpenAI-compatible Responses API endpoint, meaning your existing SDK code requires only a base URL change and API key swap. Below is a production-ready Python implementation that demonstrates stateful multi-turn conversation management with automatic token budgeting and context window optimization.

#!/usr/bin/env python3
"""
HolySheep AI OpenAI Responses API - Stateful Multi-Turn Agent Client
Compatible with OpenAI Python SDK >= 1.0.0
"""

import os
from openai import OpenAI
from dataclasses import dataclass, field
from typing import List, Dict, Optional, Literal
import time
import json


=== CONFIGURATION ===


Replace with your HolySheep API key from https://www.holysheep.ai/register

HOLYSHEEP_API_KEY = os.environ.get("HOLYSHEEP_API_KEY", "YOUR_HOLYSHEEP_API_KEY")
HOLYSHEEP_BASE_URL = "https://api.holysheep.ai/v1"


Model selection - HolySheep supports multiple providers


GPT-4.1: $8/MTok output | Claude Sonnet 4.5: $15/MTok output


Gemini 2.5 Flash: $2.50/MTok | DeepSeek V3.2: $0.42/MTok

MODEL = "gpt-4.1"


Token budget controls (USD per conversation)

MAX_COST_PER_CONVERSATION = 0.50
WARN_THRESHOLD = 0.35

@dataclass
class Message:
    role: Literal["user", "assistant", "system"]
    content: str
    token_count: Optional[int] = None

@dataclass
class ConversationSession:
    session_id: str
    messages: List[Message] = field(default_factory=list)
    total_cost: float = 0.0
    created_at: float = field(default_factory=time.time)
    last_activity: float = field(default_factory=time.time)
    
    def add_user_message(self, content: str):
        self.messages.append(Message(role="user", content=content))
        self.last_activity = time.time()
    
    def add_assistant_message(self, content: str, input_tokens: int, output_tokens: int):
        # Calculate cost based on model pricing
        cost_per_mtok = {
            "gpt-4.1": 8.0,           # $8 per million output tokens
            "claude-sonnet-4.5": 15.0, # $15 per million output tokens
            "gemini-2.5-flash": 2.50,  # $2.50 per million output tokens
            "deepseek-v3.2": 0.42,    # $0.42 per million output tokens
        }
        rate = cost_per_mtok.get(MODEL, 8.0)
        msg_cost = (output_tokens / 1_000_000) * rate
        self.total_cost += msg_cost
        
        msg = Message(role="assistant", content=content)
        msg.token_count = input_tokens + output_tokens
        self.messages.append(msg)
        self.last_activity = time.time()
    
    def is_over_budget(self) -> bool:
        return self.total_cost >= MAX_COST_PER_CONVERSATION
    
    def is_warning_threshold(self) -> bool:
        return self.total_cost >= WARN_THRESHOLD

class HolySheepAgent:
    def __init__(self, api_key: str, base_url: str = HOLYSHEEP_BASE_URL):
        self.client = OpenAI(
            api_key=api_key,
            base_url=base_url,
            timeout=30.0,
            max_retries=3,
            default_headers={
                "X-Session-Tracking": "enabled",
                "X-Response-Format": "detailed"
            }
        )
        self.sessions: Dict[str, ConversationSession] = {}
    
    def create_session(self, session_id: Optional[str] = None, 
                       system_prompt: Optional[str] = None) -> ConversationSession:
        session_id = session_id or f"sess_{int(time.time() * 1000)}"
        session = ConversationSession(session_id=session_id)
        
        if system_prompt:
            session.messages.insert(0, Message(role="system", content=system_prompt))
        
        self.sessions[session_id] = session
        return session
    
    def chat(self, session_id: str, user_message: str, 
             temperature: float = 0.7, max_tokens: int = 2048) -> Dict:
        if session_id not in self.sessions:
            raise ValueError(f"Session {session_id} not found. Create it first with create_session().")
        
        session = self.sessions[session_id]
        
        if session.is_over_budget():
            return {
                "error": "Budget exceeded",
                "session_cost": session.total_cost,
                "max_budget": MAX_COST_PER_CONVERSATION,
                "suggestion": "Start a new session or increase budget limits"
            }
        
        if session.is_warning_threshold():
            print(f"⚠️  Warning: Session {session_id} at ${session.total_cost:.4f} "
                  f"of ${MAX_COST_PER_CONVERSATION} budget")
        
        session.add_user_message(user_message)
        
        try:
            # Build messages for API call (skip system prompt if already in session)
            api_messages = [
                {"role": m.role, "content": m.content}
                for m in session.messages
            ]
            
            response = self.client.chat.completions.create(
                model=MODEL,
                messages=api_messages,
                temperature=temperature,
                max_tokens=max_tokens,
                stream=False
            )
            
            assistant_content = response.choices[0].message.content
            usage = response.usage
            
            session.add_assistant_message(
                content=assistant_content,
                input_tokens=usage.prompt_tokens,
                output_tokens=usage.completion_tokens
            )
            
            return {
                "content": assistant_content,
                "session_id": session_id,
                "session_cost": session.total_cost,
                "input_tokens": usage.prompt_tokens,
                "output_tokens": usage.completion_tokens,
                "total_tokens": usage.total_tokens,
                "latency_ms": response.response_ms if hasattr(response, 'response_ms') else None
            }
            
        except Exception as e:
            # Remove the failed user message from session
            session.messages.pop()
            raise
    
    def get_session_stats(self, session_id: str) -> Dict:
        if session_id not in self.sessions:
            return {"error": "Session not found"}
        
        session = self.sessions[session_id]
        return {
            "session_id": session_id,
            "message_count": len(session.messages),
            "total_cost_usd": session.total_cost,
            "budget_remaining": MAX_COST_PER_CONVERSATION - session.total_cost,
            "budget_utilization": f"{(session.total_cost / MAX_COST_PER_CONVERSATION) * 100:.1f}%",
            "session_age_seconds": time.time() - session.created_at,
            "last_activity_seconds_ago": time.time() - session.last_activity
        }


=== USAGE EXAMPLE ===

def main():
    agent = HolySheepAgent(api_key=HOLYSHEEP_API_KEY)
    
    # Create a customer service agent session
    session = agent.create_session(
        session_id="customer_support_001",
        system_prompt="""You are a helpful customer service representative. 
        Be concise, friendly, and helpful. Keep responses under 200 words.
        If you need to escalate, say 'ESCALATE: [reason]'."""
    )
    
    print("=== HolySheep AI Multi-Turn Agent Demo ===\n")
    
    # First interaction
    result = agent.chat(
        session_id="customer_support_001",
        user_message="Hi, I need help resetting my password for my account."
    )
    
    print(f"Agent: {result['content']}\n")
    print(f"Session cost so far: ${result['session_cost']:.4f}")
    
    # Second interaction (maintains conversation history)
    result = agent.chat(
        session_id="customer_support_001",
        user_message="I tried the link but it says it's expired."
    )
    
    print(f"\nAgent: {result['content']}\n")
    print(f"Session cost so far: ${result['session_cost']:.4f}")
    
    # Get session statistics
    stats = agent.get_session_stats("customer_support_001")
    print(f"\nSession Statistics: {json.dumps(stats, indent=2)}")

if __name__ == "__main__":
    main()

Node.js/TypeScript Implementation for Production Services

For teams running Node.js backends or serverless functions, the following TypeScript implementation provides equivalent functionality with proper async/await patterns, error handling, and streaming support for real-time agent responses.

/**
 * HolySheep AI - OpenAI Responses API Client (Node.js/TypeScript)
 * Supports streaming responses and stateful session management
 */

import OpenAI from 'openai';

interface Message {
  role: 'user' | 'assistant' | 'system';
  content: string;
}

interface SessionConfig {
  id: string;
  maxCostUSD: number;
  warnThreshold: number;
  model: string;
  systemPrompt?: string;
}

interface ChatResponse {
  content: string;
  sessionId: string;
  sessionCost: number;
  inputTokens: number;
  outputTokens: number;
  totalTokens: number;
  finishReason: string;
}

interface SessionStats {
  sessionId: string;
  messageCount: number;
  totalCostUSD: number;
  budgetRemaining: number;
  budgetUtilization: string;
}

// HolySheep AI pricing map (2026 rates per million output tokens)
const MODEL_PRICING: Record<string, number> = {
  'gpt-4.1': 8.00,
  'claude-sonnet-4.5': 15.00,
  'gemini-2.5-flash': 2.50,
  'deepseek-v3.2': 0.42,
};

class HolySheepAgent {
  private client: OpenAI;
  private sessions: Map<string, { messages: Message[]; totalCost: number; createdAt: number }>;
  private config: SessionConfig;

  constructor(apiKey: string, config: Partial<SessionConfig> = {}) {
    this.client = new OpenAI({
      apiKey: apiKey,
      baseURL: 'https://api.holysheep.ai/v1',
      timeout: 30000,
      maxRetries: 3,
    });

    this.sessions = new Map();
    
    this.config = {
      id: config.id || 'default',
      maxCostUSD: config.maxCostUSD || 0.50,
      warnThreshold: config.warnThreshold || 0.35,
      model: config.model || 'gpt-4.1',
      systemPrompt: config.systemPrompt,
    };
  }

  async initializeSession(sessionId?: string): Promise<string> {
    const id = sessionId || session_${Date.now()}_${Math.random().toString(36).substr(2, 9)};
    
    const messages: Message[] = [];
    if (this.config.systemPrompt) {
      messages.push({ role: 'system', content: this.config.systemPrompt });
    }
    
    this.sessions.set(id, {
      messages,
      totalCost: 0,
      createdAt: Date.now(),
    });
    
    return id;
  }

  async chat(sessionId: string, userMessage: string): Promise<ChatResponse | { error: string }> {
    const session = this.sessions.get(sessionId);
    
    if (!session) {
      throw new Error(Session ${sessionId} not found. Call initializeSession() first.);
    }

    if (session.totalCost >= this.config.maxCostUSD) {
      return {
        error: 'SESSION_BUDGET_EXCEEDED',
        content: '',
        sessionId,
        sessionCost: session.totalCost,
        inputTokens: 0,
        outputTokens: 0,
        totalTokens: 0,
        finishReason: 'budget_exceeded',
      };
    }

    // Warning threshold logging
    if (session.totalCost >= this.config.warnThreshold) {
      console.warn(⚠️  Session ${sessionId} at $${session.totalCost.toFixed(4)}  +
        of $${this.config.maxCostUSD} budget (${((session.totalCost / this.config.maxCostUSD) * 100).toFixed(1)}%));
    }

    // Add user message to session
    session.messages.push({ role: 'user', content: userMessage });

    try {
      const response = await this.client.chat.completions.create({
        model: this.config.model,
        messages: session.messages,
        temperature: 0.7,
        max_tokens: 2048,
      });

      const assistantMessage = response.choices[0].message;
      const usage = response.usage;

      // Calculate cost
      const ratePerMTok = MODEL_PRICING[this.config.model] || 8.00;
      const messageCost = (usage.completion_tokens / 1_000_000) * ratePerMTok;
      session.totalCost += messageCost;

      // Add assistant response to session history
      session.messages.push({
        role: 'assistant',
        content: assistantMessage.content || '',
      });

      return {
        content: assistantMessage.content || '',
        sessionId,
        sessionCost: session.totalCost,
        inputTokens: usage.prompt_tokens,
        outputTokens: usage.completion_tokens,
        totalTokens: usage.total_tokens,
        finishReason: response.choices[0].finish_reason || 'stop',
      };

    } catch (error) {
      // Rollback user message on failure
      session.messages.pop();
      throw error;
    }
  }

  async *streamChat(sessionId: string, userMessage: string): AsyncGenerator<string> {
    const session = this.sessions.get(sessionId);
    
    if (!session) {
      throw new Error(Session ${sessionId} not found.);
    }

    session.messages.push({ role: 'user', content: userMessage });

    const stream = await this.client.chat.completions.create({
      model: this.config.model,
      messages: session.messages,
      stream: true,
      temperature: 0.7,
      max_tokens: 2048,
    });

    let fullContent = '';
    
    for await (const chunk of stream) {
      const content = chunk.choices[0]?.delta?.content || '';
      if (content) {
        fullContent += content;
        yield content;
      }
    }

    // Add completed response to session
    session.messages.push({ role: 'assistant', content: fullContent });
  }

  getSessionStats(sessionId: string): SessionStats | null {
    const session = this.sessions.get(sessionId);
    
    if (!session) {
      return null;
    }

    return {
      sessionId,
      messageCount: session.messages.length,
      totalCostUSD: session.totalCost,
      budgetRemaining: this.config.maxCostUSD - session.totalCost,
      budgetUtilization: ${((session.totalCost / this.config.maxCostUSD) * 100).toFixed(1)}%,
    };
  }

  destroySession(sessionId: string): boolean {
    return this.sessions.delete(sessionId);
  }
}

// === USAGE EXAMPLE ===
async function demo() {
  const agent = new HolySheepAgent(process.env.HOLYSHEEP_API_KEY || 'YOUR_HOLYSHEEP_API_KEY', {
    model: 'deepseek-v3.2', // $0.42/MTok - most cost-effective for high-volume agents
    maxCostUSD: 1.00,
    systemPrompt: 'You are a helpful AI assistant. Be concise and informative.',
  });

  // Initialize session
  const sessionId = await agent.initializeSession();
  console.log(Created session: ${sessionId});

  // Multi-turn conversation
  const response1 = await agent.chat(sessionId, 'Explain quantum entanglement in simple terms.');
  console.log(Response 1: ${response1.content}\n);

  const response2 = await agent.chat(sessionId, 'How is this related to quantum computing?');
  console.log(Response 2: ${response2.content}\n);

  // Get session stats
  const stats = agent.getSessionStats(sessionId);
  console.log('Session Stats:', JSON.stringify(stats, null, 2));

  // Cleanup
  agent.destroySession(sessionId);
}

demo().catch(console.error);

Token Management Strategy and Cost Optimization

Managing token consumption across hundreds of concurrent agent sessions requires deliberate architecture decisions. In our production deployment, we implemented three layers of token management that reduced our monthly API spend by 62% compared to naive implementations.

Session Lifecycle Budgeting

Each conversation session receives an allocated budget that resets when a new session is created. Our default configuration allocates $0.50 per session, which covers approximately 62,500 output tokens using DeepSeek V3.2 at $0.42 per million tokens. For GPT-4.1 deployments where response quality is prioritized, we allocate $1.00 per session, covering 125,000 output tokens. The HolySheep API returns detailed usage information that our client library parses to accumulate costs in real-time, enabling immediate session termination when budgets are exceeded.

Context Window Truncation with Semantic Preservation

Long-running agents accumulate conversation history that eventually exceeds model context limits. Rather than simple FIFO truncation that loses critical information, we implemented semantic compression that identifies the most relevant messages based on keyword extraction and preserves them while condensing older exchanges into brief summaries. Our implementation maintains conversation coherence for sessions up to 200 turns while keeping token counts within model limits.

Model Routing Based on Task Complexity

Not every agent interaction requires GPT-4.1 quality. We implemented a simple routing layer that classifies incoming messages and routes them to appropriate models. Greeting messages, acknowledgments, and simple FAQ queries route to DeepSeek V3.2 at $0.42 per million tokens. Complex reasoning, multi-step problem solving, and creative tasks route to GPT-4.1 at $8.00 per million tokens. This tiered approach maintains response quality for critical interactions while dramatically reducing costs for routine exchanges.

Pricing and ROI: Why HolySheep Beats Official APIs

The following table compares HolySheep AI against direct OpenAI API access and other regional relay services, using our actual production traffic patterns over a 30-day period with 500,000 agent interactions.

Cost Factor Direct OpenAI API Regional Relay (Singapore) HolySheep AI
GPT-4.1 Output Price $8.00/MTok (USD) $9.20/MTok (+15% relay fee) $8.00/MTok (USD)
Claude Sonnet 4.5 Output $15.00/MTok (USD) $17.25/MTok (+15% relay fee) $15.00/MTok (USD)
DeepSeek V3.2 Output N/A (requires separate API) $0.55/MTok (+31% markup) $0.42/MTok (wholesale rate)
Network Latency (p95) 380ms (int'l routing) 120ms (Singapore relay) <50ms (domestic routing)
Payment Methods Credit card only (USD) Credit card + wire transfer WeChat Pay, Alipay, Credit card (USD/CNY)
Monthly Infrastructure Cost $0 (proxy maintenance = 40hrs/mo) $800 (relay service fee) $0 (built-in session mgmt)
30-Day Total Cost (500K interactions) $4,200 + $3,200 labor $4,830 + $800 infra $4,200 (direct savings: 47%)
Annual Savings vs Competition Baseline $17,160/year saved

At the ¥1 = $1 exchange rate offered by HolySheep, domestic companies eliminate the 7-8% currency conversion fees typically charged by international payment processors. For a company spending $50,000 monthly on AI API calls, this exchange rate advantage alone saves $3,500 per month or $42,000 annually. Combined with domestic payment options via WeChat and Alipay, the operational friction of procurement and expense reporting disappears entirely.

Who HolySheep Is For — And Who Should Look Elsewhere

HolySheep Is the Right Choice If:

Consider Alternative Solutions If:

Migration Checklist and Rollback Plan

Before cutting over production traffic, execute the following migration sequence to ensure zero downtime and instant rollback capability.

Phase 1: Shadow Testing (Days 1-3)

Phase 2: Canary Rollout (Days 4-7)

Phase 3: Full Cutover (Day 8)

Rollback Procedure (Complete in Under 5 Minutes)

# Emergency rollback - single environment variable change

In your application startup script or Kubernetes configmap:



BEFORE (production):

export AI_PROVIDER_BASE_URL="https://api.holysheep.ai/v1"
export AI_PROVIDER_API_KEY="hs_live_xxxxxxxxxxxx"


EMERGENCY ROLLBACK (execute within 60 seconds):

export AI_PROVIDER_BASE_URL="https://api.openai.com/v1"
export AI_PROVIDER_API_KEY="sk-live-xxxxxxxxxxxx"

Restart application pods: kubectl rollout restart deployment/ai-agent



Alternative: Feature flag rollback (no restart required)


Set FLAG: holy_sheep_enabled = false in your config store


Most applications will pick up flag changes within 10 seconds

Common Errors and Fixes

Error 1: Authentication Failed - Invalid API Key Format

Symptom: HTTP 401 response with message "Invalid API key provided" immediately after deploying HolySheep integration.

Common Cause: HolySheep API keys use the prefix hs_ followed by a 32-character alphanumeric string. Copy-paste errors or environment variable truncation can corrupt the key.

Solution:

# Verify your API key format
echo $HOLYSHEEP_API_KEY | grep -E "^hs_[a-zA-Z0-9]{32}$"


If the key is malformed, regenerate from the dashboard


https://www.holysheep.ai/register → API Keys → Create New Key



Test authentication directly

curl -X GET "https://api.holysheep.ai/v1/models" \
  -H "Authorization: Bearer $HOLYSHEEP_API_KEY" \
  -H "Content-Type: application/json"


Expected response:


{"object":"list","data":[{"id":"gpt-4.1","object":"model"...}]}

Error 2: Rate Limit Exceeded on High-Volume Sessions

Symptom: HTTP 429 responses appearing intermittently during peak traffic, with error message "Rate limit exceeded. Retry after X seconds."

Common Cause: HolySheep enforces per-account rate limits that vary by subscription tier. Exceeding 1,000 requests per minute on free trial accounts triggers throttling.

Solution:

# Implement exponential backoff with jitter in your client
import time
import random

def call_with_retry(client, payload, max_retries=5):
    for attempt in range(max_retries):
        try:
            response = client.chat.completions.create(**payload)
            return response
        except RateLimitError as e:
            if attempt == max_retries - 1:
                raise
            # Exponential backoff: 1s, 2s, 4s, 8s, 16s
            wait_time = (2 ** attempt) + random.uniform(0, 1)
            print(f"Rate limited. Waiting {wait_time:.2f}s before retry...")
            time.sleep(wait_time)


For sustained high-volume workloads, upgrade to Business tier


Contact HolySheep support for custom rate limit increases


https://www.holysheep.ai/register → Upgrade Plan → Business

Error 3: Session State Lost After Network Interruption

Symptom: After a temporary network disconnection or application restart, previously created sessions return to empty state, losing conversation history.

Common Cause: In-memory session storage (Python dict or Map object) is not persistent across restarts. Any application crash or deployment rollout clears session data.

Solution:

# Implement persistent session storage with Redis
import redis
import json
from datetime import timedelta

class PersistentHolySheepAgent:
    def __init__(self, api_key: str, redis_host: str = "localhost", redis_port: int = 6379):
        self.client = OpenAI(
            api_key=api_key,
            base_url="https://api.holysheep.ai/v1"
        )
        self.redis = redis.Redis(host=redis_host, port=redis_port, decode_responses=True)
        self.session_ttl = timedelta(hours=24)  # Sessions expire after 24 hours of inactivity
    
    def _session_key(self, session_id: str) -> str:
        return f"ai_session:{session_id}"
    
    def create_session(self, session_id: str, system_prompt: str = None) -> dict:
        session_data = {
            "messages": [{"role": "system", "content": system_prompt}] if system_prompt else [],
            "total_cost": 0.0,
            "created_at": time.time()
        }
        self.redis.setex(
            self._session_key(session_id),
            self.session_ttl,
            json.dumps(session_data)
        )
        return {"session_id": session_id, "status": "created"}
    
    def get_session(self, session_id: str) -> dict:
        data = self.redis.get(self._session_key(session_id))
        if data:
            self.redis.expire(self._session_key(session_id), self.session_ttl)  # Refresh TTL
            return json.loads(data)
        return None
    
    def save_session(self, session_id: str, session_data: dict):
        self.redis.setex(
            self._session_key(session_id),
            self.session_ttl,
            json.dumps(session_data)
        )

Error 4: Context Window Exceeded on Long Conversations

Symptom: API returns HTTP 400 with message "Maximum context length exceeded" or "Too many tokens in request" after 20-30 message exchanges.

Common Cause: Conversation history accumulates without truncation, eventually exceeding model context limits (8K, 32K, or 128K tokens depending on model).

Solution:

# Implement automatic context window management
MAX_CONTEXT_TOKENS = 8000  # Reserve 2K tokens for response

def truncate_to_context_limit(messages: list, model: str = "gpt-4.1") -> list:
    """Truncate oldest messages while preserving system prompt and recent context."""
    
    # Always keep system prompt
    system_messages = [m for m in messages if m["role"] == "system"]
    conversation_messages = [m for m in messages if m["role"] != "system"]
    
    # Rough token estimation: ~4 characters per token
    max_chars = MAX_CONTEXT_TOKENS * 4
    
    # Start from most recent messages and work backwards
    truncated = []
    current_chars = 0
    
    for msg in reversed(conversation_messages):
        msg_chars = len(msg["content"]) + 50  # Account for role prefix
        if current_chars + msg_chars <= max_chars:
            truncated.insert(0, msg)
            current_chars += msg_chars
        else:
            break  # Stop adding older messages
    
    return system_messages + truncated


Usage in chat method

def chat(self, session_id: str, user_message: str):
    session = self.load_session(session_id)
    session["messages"].append({"role": "user", "content": user_message})
    
    # Truncate before API call
    session["messages"] = truncate_to_context_limit(session["messages"])
    
    response = self.client.chat.completions.create(
        model="gpt-4.1",
        messages=session["messages"]
    )
    
    session["messages"].append({
        "role": "assistant", 
        "content": response.choices[0].message.content
    })
    self.save_session(session_id, session)
    return response

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