langwatch x nebius: builder hour

agents/customer_explorer_agent.py

@@ -6,9 +6,11 @@
 import dotenv
 from agno.agent import Agent
 from agno.models.openai import OpenAIChat
+from agno.models.nebius import Nebius
 from pydantic import BaseModel
 from datetime import datetime, timedelta
 import json
+import agent_config
 
 dotenv.load_dotenv()
 
@@ -116,12 +118,9 @@ def create_customer_explorer_agent() -> Agent:
     """Create and return the customer explorer agent"""
     return Agent(
         name="CustomerExplorerAgent",
-        model=OpenAIChat(
-            id="gpt-4o-mini",
-            api_key=os.getenv("OPENAI_API_KEY"),
-        ),
+        model=agent_config.get_model(),
         description=CUSTOMER_EXPLORER_SYSTEM_PROMPT,
-        add_history_to_context=False,
+        add_history_to_context=True,
     )
 
 def explore_customer_context(

agents/next_message_agent.py

@@ -6,7 +6,9 @@
 import dotenv
 from agno.agent import Agent
 from agno.models.openai import OpenAIChat
+from agno.models.nebius import Nebius
 from pydantic import BaseModel
+import agent_config
 
 dotenv.load_dotenv()
 
@@ -131,12 +133,9 @@ def create_next_message_agent() -> Agent:
     """Create and return the next message agent"""
     return Agent(
         name="NextMessageAgent",
-        model=OpenAIChat(
-            id="gpt-4o-mini",
-            api_key=os.getenv("OPENAI_API_KEY"),
-        ),
+        model=agent_config.get_model(),
         description=NEXT_MESSAGE_SYSTEM_PROMPT,
-        add_history_to_context=False,
+        add_history_to_context=True,
     )
 
 def suggest_next_message(

agents/summary_agent.py

@@ -6,7 +6,9 @@
 import dotenv
 from agno.agent import Agent
 from agno.models.openai import OpenAIChat
+from agno.models.nebius import Nebius
 from pydantic import BaseModel
+import agent_config
 
 dotenv.load_dotenv()
 
@@ -46,12 +48,9 @@ def create_summary_agent() -> Agent:
     """Create and return the summary agent"""
     return Agent(
         name="SummaryAgent",
-        model=OpenAIChat(
-            id="gpt-4o-mini",
-            api_key=os.getenv("OPENAI_API_KEY"),
-        ),
+        model=agent_config.get_model(),
         description=SUMMARY_SYSTEM_PROMPT,
-        add_history_to_context=False,  # Each call is independent
+        add_history_to_context=True,
     )
 
 def summarize_conversation(messages: List[Dict[str, Any]]) -> MessageSummary:

main_support_agent.py

@@ -10,6 +10,7 @@
 import dotenv
 from agno.agent import Agent
 from agno.models.openai import OpenAIChat
+from agno.models.nebius import Nebius
 
 # Import our specialized agents as tools
 from agents.summary_agent import summarize_conversation
@@ -179,9 +180,13 @@ def escalate_to_human(reason: str, urgency: str = "medium") -> str:
 # Create the main support agent
 support_agent = Agent(
     name="BankCustomerSupportAgent",
-    model=OpenAIChat(
-        id="gpt-4o-mini",
-        api_key=os.getenv("OPENAI_API_KEY"),
+    # model=OpenAIChat(
+    #     id="gpt-4o-mini",
+    #     api_key=os.getenv("OPENAI_API_KEY"),
+    # ),
+    model=Nebius(
+        id="openai/gpt-oss-120b",
+        api_key=os.getenv("NEBIUS_API_KEY"),
     ),
     tools=[
         get_conversation_summary,

main_support_agent_claude.py

@@ -0,0 +1,230 @@
+"""
+Main Bank Customer Support Agent - Claude Sonnet 4.5 Model
+
+This is the production code - kept very simple. One agent with tools, Agno handles memory.
+"""
+
+import os
+import json
+from typing import Dict, Any
+import dotenv
+from agno.agent import Agent
+from agno.models.anthropic import Claude
+
+import agent_config
+
+dotenv.load_dotenv()
+
+agent_config.set_model(Claude(id="claude-opus-4-6", api_key=os.getenv("ANTHROPIC_API_KEY")))
+
+# Import our specialized agents as tools
+from agents.summary_agent import summarize_conversation
+from agents.next_message_agent import suggest_next_message
+from agents.customer_explorer_agent import (
+    explore_customer_context,
+    analyze_customer_behavior,
+)
+
+import langwatch
+from openinference.instrumentation.agno import AgnoInstrumentor
+
+langwatch.setup(instrumentors=[AgnoInstrumentor()])
+
+SYSTEM_PROMPT = """
+You are a customer support agent for SecureBank, a modern digital banking platform.
+
+Your role is to help customers with their banking needs professionally and efficiently. You have access to specialized tools that MUST be used in specific situations:
+
+TOOL USAGE REQUIREMENTS:
+
+1. **explore_customer_account** - ALWAYS use when:
+   - Customer mentions fraud, unauthorized transactions, or security concerns
+   - Customer asks about spending patterns, budgeting, or financial analysis
+   - Customer needs account-specific insights or personalized recommendations
+   - Any urgent business account issues that need immediate investigation
+
+2. **get_message_suggestion** - ALWAYS use when:
+   - Customer has complex, multi-part problems (locked accounts + fees + missing deposits)
+   - You need guidance on complex banking regulations or procedures
+   - Customer issue involves multiple interconnected banking services
+
+3. **escalate_to_human** - ALWAYS use when:
+   - Customer explicitly demands to speak with a manager, supervisor, or human agent
+   - Customer expresses extreme frustration or dissatisfaction
+   - Business customer has urgent issues affecting operations (payroll, employee payments)
+   - Set urgency to "high" for business-critical issues
+
+4. **get_conversation_summary** - Use when:
+   - Customer asks you to summarize the conversation
+   - You need to analyze conversation patterns or sentiment
+
+CRITICAL: For simple questions like service hours, do NOT use unnecessary tools. Respond directly.
+
+Guidelines:
+- Be helpful, professional, and empathetic
+- Use tools proactively based on the requirements above
+- Provide clear, actionable solutions
+- Always prioritize customer security and privacy
+
+Remember: Tool usage is not optional when the situation matches the requirements above.
+"""
+
+
+def get_conversation_summary(conversation_context: str = "recent messages") -> str:
+    """
+    Analyze the conversation for patterns, sentiment, and key issues
+
+    Args:
+        conversation_context: Context about what to analyze
+
+    Returns:
+        JSON string with conversation analysis
+    """
+    langwatch.get_current_trace().update(
+        metadata={"labels": ["tool_get_conversation_summary"]}
+    )
+    # In a real implementation, this would get the actual conversation history
+    # For now, we'll simulate with a basic response
+    return json.dumps(
+        {
+            "summary": "Conversation analysis requested",
+            "sentiment": "neutral",
+            "key_issues": ["general inquiry"],
+            "suggested_actions": ["continue conversation"],
+        }
+    )
+
+
+def get_message_suggestion(customer_query: str, context: str = "") -> str:
+    """
+    Get suggestions for responding to customer queries using knowledge base
+
+    Args:
+        customer_query: The customer's question or concern
+        context: Additional context about the conversation
+
+    Returns:
+        JSON string with response suggestions
+    """
+    langwatch.get_current_trace().update(
+        metadata={"labels": ["tool_get_message_suggestion"]}
+    )
+    # Simulate knowledge base lookup
+    suggestion_data = {
+        "suggested_response": f"I understand your concern about: {customer_query}. Let me help you with that.",
+        "confidence": "medium",
+        "knowledge_sources": ["general_banking_guide"],
+        "alternatives": ["Ask for more details", "Escalate to specialist"],
+    }
+    return json.dumps(suggestion_data)
+
+
+def explore_customer_account(customer_id: str, query: str) -> str:
+    """
+    Explore customer account data and provide rich insights
+
+    Args:
+        customer_id: Customer identifier (e.g., CUST_001)
+        query: What to explore about the customer
+
+    Returns:
+        JSON string with customer insights and rich experiences
+    """
+    # Get customer behavior analysis
+    behavior = analyze_customer_behavior(customer_id)
+
+    # Get rich experiences based on query
+    rich_experiences = explore_customer_context(customer_id, query)
+
+    langwatch.get_current_trace().update(
+        metadata={"labels": ["tool_explore_customer_account"]}
+    )
+
+    return json.dumps(
+        {
+            "customer_behavior": behavior,
+            "rich_experiences": [
+                {
+                    "type": exp.component_type,
+                    "title": exp.title,
+                    "data": exp.data,
+                    "actions": exp.actions,
+                    "priority": exp.priority,
+                }
+                for exp in rich_experiences
+            ],
+        }
+    )
+
+
+def escalate_to_human(reason: str, urgency: str = "medium") -> str:
+    """
+    Escalate the conversation to a human agent
+
+    Args:
+        reason: Why the escalation is needed
+        urgency: Priority level (low, medium, high)
+
+    Returns:
+        JSON string with escalation details
+    """
+    langwatch.get_current_trace().update(metadata={"labels": ["escalation"]})
+
+    escalation_data = {
+        "escalated": True,
+        "reason": reason,
+        "urgency": urgency,
+        "estimated_wait": "5-10 minutes" if urgency == "high" else "10-15 minutes",
+        "message": "I'm connecting you with a specialist who can provide additional assistance.",
+    }
+    return json.dumps(escalation_data)
+
+
+# Create the main support agent
+support_agent = Agent(
+    name="BankCustomerSupportAgent",
+    model=Claude(
+        id="claude-opus-4-6",
+        api_key=os.getenv("ANTHROPIC_API_KEY"),
+    ),
+    tools=[
+        get_conversation_summary,
+        get_message_suggestion,
+        explore_customer_account,
+        escalate_to_human,
+    ],
+    description=SYSTEM_PROMPT,
+    add_history_to_context=True,
+    num_history_runs=100,
+)
+
+
+# Simple interface for testing
+def chat_with_agent(message: str) -> str:
+    """Simple interface to chat with the agent"""
+    response = support_agent.run(message)
+    return response.content
+
+
+# Example usage
+if __name__ == "__main__":
+    print("=== Bank Customer Support Agent ===")
+    print(
+        "Agent: Hello! I'm here to help with your banking needs. How can I assist you today?"
+    )
+
+    # Simulate a conversation
+    customer_message = "Hi, I'm seeing some transactions on my account that I don't recognize. I'm worried about fraud."
+    print(f"\nCustomer: {customer_message}")
+
+    response = chat_with_agent(customer_message)
+    print(f"Agent: {response}")
+
+    # Continue conversation
+    customer_message2 = "Yes, there's an $85 charge from Amazon and a $45 gas station charge. Can you help me freeze my card?"
+    print(f"\nCustomer: {customer_message2}")
+
+    response2 = chat_with_agent(customer_message2)
+    print(f"Agent: {response2}")
+
+    print("\n=== Conversation Complete ===")