AI Agents

curl -X POST http://localhost:8010/api/agents \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_TOKEN" \
  -d '{
    "name": "Customer Support Agent",
    "initial_message": "Hello! How can I help you today?",
    "ai_provider": "gemini",
    "system_prompt": "You are a helpful customer support representative...",
    "voice_provider": "twilio",
    "temperature": 0.7,
    "max_tokens": 1024
  }'

from Service.AgentService import AgentService
from models.models import AgentConfig

service = AgentService()

config = AgentConfig(
    name="Customer Support Agent",
    initial_message="Hello! How can I help you today?",
    ai_provider="gemini",
    system_prompt="You are a helpful customer support representative...",
    temperature=0.7,
    max_tokens=1024
)

agent = service.create_agent(config)
print(f"Agent created: {agent.id}")

You are a helpful customer support representative for TechCorp.

Your responsibilities:
1. Answer customer questions about our products
2. Help with troubleshooting issues
3. Process refund requests
4. Maintain a professional and friendly tone

Guidelines:
- Always be polite and patient
- Ask clarifying questions if needed
- Apologize for any inconvenience
- Offer solutions or escalate if necessary

Available tools:
- Look up order history
- Process refunds
- Schedule callbacks
- Create support tickets

agent_config = {
    "ai_provider": "gemini",
    "model": "gemini-2.0-flash-exp",
    "temperature": 0.7,
}

agent_config = {
    "ai_provider": "openai",
    "model": "gpt-4-turbo",
    "temperature": 0.7,
}

agent_config = {
    "ai_provider": "hume",
    "model": "hume-standard",
    "temperature": 0.7,
    "enable_emotion_recognition": True,
}

1. Caller connects
2. Agent plays initial_message
3. Agent listens to caller input
4. Input processed by AI
5. Agent generates response
6. Response converted to speech
7. Audio played to caller
8. Loop until call ends

context = ConversationContext(
    agent_id=agent_id,
    call_id=call_id,
    conversation_history=[
        {"role": "user", "content": "Hello"},
        {"role": "assistant", "content": "Hi there!"},
    ],
    metadata={
        "customer_id": "123",
        "order_id": "456",
    }
)

# Define available tools
tools = [
    {
        "name": "lookup_order",
        "description": "Look up customer order information",
        "parameters": {
            "type": "object",
            "properties": {
                "order_id": {"type": "string"}
            }
        }
    },
    {
        "name": "process_refund",
        "description": "Process a refund for an order",
        "parameters": {
            "type": "object",
            "properties": {
                "order_id": {"type": "string"},
                "reason": {"type": "string"}
            }
        }
    }
]

# Agent calls tools as needed
result = agent.execute_tool("lookup_order", {"order_id": "ORD-123"})

curl -X GET http://localhost:8010/api/agents \
  -H "Authorization: Bearer YOUR_TOKEN"

curl -X GET http://localhost:8010/api/agents/{agent_id} \
  -H "Authorization: Bearer YOUR_TOKEN"

curl -X PUT http://localhost:8010/api/agents/{agent_id} \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_TOKEN" \
  -d '{
    "system_prompt": "Updated prompt...",
    "temperature": 0.5
  }'

curl -X DELETE http://localhost:8010/api/agents/{agent_id} \
  -H "Authorization: Bearer YOUR_TOKEN"

agent_config = {
    "language": "es",  # Spanish
    "timezone": "America/Mexico_City",
}

agent_config = {
    "voice_provider": "elevenlabs",
    "voice_id": "EXAVITQu4vr4xnSDxMaL",
    "voice_name": "Sarah",
}

agent_config = {
    "ai_provider": "hume",
    "enable_emotion_recognition": True,
    "emotion_response_mapping": {
        "frustration": "escalate_to_human",
        "happiness": "provide_upsell",
    }
}

agent_config = {
    "knowledge_base_id": "kb-123",
    "knowledge_base_instructions": """
    Use the knowledge base to answer questions about:
    - Product features
    - Pricing information
    - Company policies
    """,
}

CUSTOMER_SUPPORT_PROMPT = """
You are a friendly and professional customer support agent.

Your goals:
1. Help customers with product questions
2. Troubleshoot technical issues
3. Process orders and refunds
4. Escalate complex issues to a human agent

Always:
- Be empathetic and understanding
- Listen carefully to customer concerns
- Provide clear solutions
- Ask permission before actions
"""

SALES_PROMPT = """
You are an enthusiastic sales representative.

Your goals:
1. Understand customer needs
2. Present relevant products
3. Overcome objections
4. Close sales

Techniques:
- Ask discovery questions
- Highlight benefits, not features
- Use social proof
- Create urgency appropriately
"""

TECHNICAL_SUPPORT_PROMPT = """
You are a knowledgeable technical support specialist.

Your approach:
1. Understand the technical problem
2. Ask diagnostic questions
3. Provide step-by-step solutions
4. Verify the issue is resolved

Remember:
- Be patient with non-technical users
- Avoid jargon when possible
- Escalate if beyond your scope
- Document the issue
"""

SCHEDULING_PROMPT = """
You are a professional scheduling assistant.

Your responsibilities:
1. Understand customer needs
2. Check availability
3. Confirm appointment details
4. Send confirmation

Available tools:
- check_availability
- create_appointment
- send_confirmation
"""

from utils.logger import logger

# Log key metrics
logger.info(f"Agent {agent_id} - Response time: {response_time}ms")
logger.info(f"Agent {agent_id} - Token usage: {tokens_used}")
logger.info(f"Agent {agent_id} - Call duration: {duration}s")

# Use streaming for real-time feedback
agent_config = {
    "use_streaming": True,  # Reduces token count
    "response_cache": True,  # Cache repeated questions
}

# Test agent response
from Service.AgentService import AgentService

service = AgentService()
agent = service.get_agent("agent-id")

response = agent.process_message(
    "What are your business hours?"
)
print(response)

# Make a test call to the agent
curl -X POST http://localhost:8010/api/calls \
  -H "Content-Type: application/json" \
  -d '{
    "agent_id": "agent-id",
    "to_phone": "+1234567890",
    "from_phone": "+0987654321",
    "test_mode": true
  }'

# Check agent status
agent = service.get_agent("agent-id")
if not agent.is_active:
    print("Agent is disabled")

# Check AI provider connectivity
try:
    response = agent.test_connection()
    print(f"Connection OK: {response}")
except Exception as e:
    print(f"Connection failed: {e}")

agent_config = {
    "max_tokens": 256,  # Reduce response length
    "summarize_context": True,  # Compress history
}