add

agentgraph/shared/models/reference_based/failure.py

@@ -20,4 +20,4 @@ class Failure(BaseModel):
     description: str = Field(..., description="One-sentence explanation of the failure")
     raw_text: str = Field("", description="Exact snippet of trace text that evidences the failure (can be left blank and recovered via raw_text_ref)")
     raw_text_ref: List[ContentReference] = Field(..., description="List of references to every occurrence of the failure evidence in the trace")
-    affected_id: Optional[str] = Field(None, description="ID of related Entity or Relation responsible for or impacted by the failure") 
+    affected_id: str = Field(..., description="ID of related Entity or Relation responsible for or impacted by the failure") 

agentgraph/shared/models/reference_based/optimization_recommendation.py

@@ -19,4 +19,4 @@ class OptimizationRecommendation(BaseModel):
     recommendation_type: RecommendationType = Field(..., description="The category of optimization being suggested")
     description: str = Field(..., description="A detailed, human-readable explanation of the recommendation, including the observed pattern and the justification for the change.")
     affected_ids: List[str] = Field(default_factory=list, description="A list of Entity or Relation IDs that are the focus of this recommendation")
-    raw_text_ref: Optional[List[ContentReference]] = Field(default=None, description="Optional list of references to the exact trace locations related to this recommendation")
+    raw_text_ref: List[ContentReference] = Field(default_factory=list, description="List of references to the exact trace locations related to this recommendation")

agentgraph/shared/models/reference_based/report.py

@@ -12,5 +12,5 @@ class KnowledgeGraph(BaseModel):
     system_summary: str = Field("", description="A short 2-3 sentence summary of the agent system's purpose and structure")
     entities: List[Entity] = Field(default_factory=list, description="List of entities in the knowledge graph")
     relations: List[Relation] = Field(default_factory=list, description="List of relations in the knowledge graph")
-    failures: Optional[List['Failure']] = Field(default=None, description="Optional list of detected risk or failure events across the trace")
-    optimizations: Optional[List[OptimizationRecommendation]] = Field(default=None, description="Optional list of recommendations for optimizing the agent system")
+    failures: List[Failure] = Field(default_factory=list, description="List of detected risk or failure events across the trace")
+    optimizations: List[OptimizationRecommendation] = Field(default_factory=list, description="List of recommendations for optimizing the agent system")

simple_test.py

@@ -1,72 +0,0 @@
-#!/usr/bin/env python3
-"""
-Simple test to verify OpenAI structured outputs functionality
-"""
-
-import os
-from dotenv import load_dotenv
-from openai import OpenAI
-from pydantic import BaseModel
-from typing import List
-
-# Load environment variables
-load_dotenv('/Users/zekunwu/Desktop/agent_monitoring/.env')
-
-class SimpleEntity(BaseModel):
-    name: str
-    type: str
-
-class SimpleKG(BaseModel):
-    entities: List[SimpleEntity]
-    system_name: str
-
-def test_basic_openai():
-    """Test basic OpenAI structured outputs"""
-    
-    # Check if API key exists
-    api_key = os.getenv("OPENAI_API_KEY")
-    if not api_key:
-        print("❌ OPENAI_API_KEY not found in environment")
-        return False
-    
-    if api_key == "your_openai_api_key_here":
-        print("❌ Please set a real OpenAI API key in .env file")
-        return False
-    
-    print(f"✅ API key found: {api_key[:10]}...")
-    
-    try:
-        client = OpenAI(api_key=api_key)
-        
-        response = client.responses.parse(
-            model="gpt-4o-2024-08-06",
-            input=[
-                {"role": "system", "content": "Extract entities from text."},
-                {"role": "user", "content": "Alice the manager uses Excel tool to analyze data."}
-            ],
-            text_format=SimpleKG,
-        )
-        
-        result = response.output_parsed
-        print(f"✅ OpenAI API call successful!")
-        print(f"System: {result.system_name}")
-        print(f"Entities: {len(result.entities)}")
-        for entity in result.entities:
-            print(f"  - {entity.type}: {entity.name}")
-        
-        return True
-        
-    except Exception as e:
-        print(f"❌ OpenAI API call failed: {e}")
-        return False
-
-if __name__ == "__main__":
-    print("🧪 Testing Basic OpenAI Structured Outputs")
-    print("=" * 50)
-    
-    success = test_basic_openai()
-    
-    if success:
-        print("\n🎉 Basic test passed! Ready to use OpenAI structured outputs.")
-    else:
-        print("\n💥 Basic test failed. Please check your OpenAI API key.")

test_simple_kg.py

@@ -1,110 +0,0 @@
-#!/usr/bin/env python3
-"""
-Test with simplified KnowledgeGraph model
-"""
-
-import os
-import sys
-from dotenv import load_dotenv
-from openai import OpenAI
-from pydantic import BaseModel, Field
-from typing import List, Optional
-
-# Load environment variables
-load_dotenv('/Users/zekunwu/Desktop/agent_monitoring/.env')
-
-# Simplified models
-class SimpleEntity(BaseModel):
-    id: str
-    type: str  # Agent, Task, Tool, Input, Output, Human
-    name: str
-    importance: str  # HIGH, MEDIUM, LOW
-
-class SimpleRelation(BaseModel):
-    id: str
-    source: str
-    target: str
-    type: str  # PERFORMS, USES, etc.
-    importance: str
-
-class SimpleKnowledgeGraph(BaseModel):
-    system_name: str = Field("", description="Name of the system")
-    system_summary: str = Field("", description="Summary of the system")
-    entities: List[SimpleEntity] = Field(default_factory=list)
-    relations: List[SimpleRelation] = Field(default_factory=list)
-
-def test_simple_kg():
-    """Test with simplified KG model"""
-    
-    client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
-    
-    test_input = """
-    Assistant: I'll help you analyze the customer data to find purchasing patterns.
-
-    Action: load_data
-    Action Input: {"dataset": "customer_purchases.csv"}
-    Observation: Data loaded successfully. Found 10,000 customer records.
-
-    Action: analyze_patterns  
-    Action Input: {"columns": ["purchase_amount", "product_category", "customer_age"]}
-    Observation: Analysis complete. Found strong correlation between age and product preferences.
-
-    Final Answer: Based on the analysis, customers aged 25-35 prefer electronics.
-    """
-    
-    system_prompt = """Extract a knowledge graph with these entity types:
-- Agent: AI agents 
-- Task: Specific tasks
-- Tool: Tools or functions
-- Input: Data inputs
-- Output: Data outputs
-- Human: Human users
-
-Use these relationship types:
-- PERFORMS: Agent→Task
-- USES: Agent→Tool
-- PRODUCES: Task→Output
-
-Create entities with IDs like agent_001, task_001, etc."""
-
-    try:
-        print("🧪 Testing Simplified Knowledge Graph Extraction")
-        print("=" * 60)
-        
-        response = client.responses.parse(
-            model="gpt-4o-2024-08-06",
-            input=[
-                {"role": "system", "content": system_prompt},
-                {"role": "user", "content": f"Extract knowledge graph from: {test_input}"}
-            ],
-            text_format=SimpleKnowledgeGraph,
-        )
-        
-        kg = response.output_parsed
-        
-        print(f"✅ Extraction successful!")
-        print(f"📊 System: {kg.system_name}")
-        print(f"📝 Summary: {kg.system_summary}")
-        print(f"🔢 Entities: {len(kg.entities)}")
-        print(f"🔗 Relations: {len(kg.relations)}")
-        
-        print("\n📋 Entities:")
-        for entity in kg.entities:
-            print(f"  - {entity.id}: {entity.type} - {entity.name} ({entity.importance})")
-        
-        print("\n🔗 Relations:")
-        for relation in kg.relations:
-            print(f"  - {relation.id}: {relation.source} → {relation.target} ({relation.type})")
-        
-        return True
-        
-    except Exception as e:
-        print(f"❌ Test failed: {e}")
-        return False
-
-if __name__ == "__main__":
-    success = test_simple_kg()
-    if success:
-        print("\n🎉 Simplified KG test passed!")
-    else:
-        print("\n💥 Simplified KG test failed.")