kalytera 0.1.1

Real-time quality monitoring and failure detection for production AI agents

🧠 Kalytera - Production-Ready AI Agent Performance Intelligence

The complete platform for monitoring, testing, and optimizing any AI agent in production.

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🚀 What You Get

Kalytera is a finished product that agents can immediately use to build and test. No more low-level work - just integrate and get instant insights.

✅ Production-Ready SDK

• One-line integration: iq.track(user_input, agent_response)
• Non-blocking monitoring: Never slows down your agent
• Auto-batching: Efficient data transmission
• Error-safe: Agent keeps running even if Kalytera is down

✅ Autonomous Testing Framework

• Comprehensive test suites: Coding, Customer Service, Data Science, Sales
• Automated evaluation: LLM judges score every response
• Performance grading: A+ to F grades with specific recommendations
• Continuous monitoring: Real-time health checks

✅ Enterprise Dashboard

• Clear agent identification: See exactly which agents are monitored
• Evaluation coverage: Prominent display of sample percentages
• Key metrics at top: Quality scores, success rates, performance indicators
• Actionable insights: Specific developer recommendations with priorities

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📦 Complete Installation

# 1. Clone Kalytera
git clone <repo>
cd Kalytera

# 2. API is already deployed at:
# https://kalytera-api-z9it.onrender.com

# 3. Dashboard is running at:
# http://localhost:8509

That's it. Kalytera is ready for production use.

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🎯 5-Minute Quick Start

Step 1: Monitor Any Agent (2 lines of code)

from kalytera_sdk import Kalytera

# Initialize once
iq = Kalytera(agent_id="my-awesome-agent")

# Monitor any interaction (non-blocking)
iq.track(
    user_input="How do I fix this bug?", 
    agent_response="Here's how to fix it..."
)

# Get real-time insights
insights = iq.get_insights()
performance_score = iq.get_performance_score()  # 0.0 - 1.0
recommendations = iq.get_recommendations()

Step 2: Test Agent Performance (1 line)

from agent_testing_framework import AgentTester

# Test any agent function
def my_agent(user_input: str) -> str:
    return "Agent response here"

# Run comprehensive tests
tester = AgentTester("my-agent")
tester.register_agent(my_agent)
results = tester.run_full_test_suite()

# Get performance report
report = tester.generate_performance_report()
print(report)  # Detailed A+ to F grade with recommendations

Step 3: View Enterprise Dashboard

URL: http://localhost:8509

• ✅ Agent identification: See which agents are being evaluated
• ✅ Evaluation coverage: 1.8% (3 of 171 interactions evaluated)
• ✅ Key metrics: Quality scores, success rates, performance indicators
• ✅ Actionable insights: Specific developer recommendations

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🏭 Production Examples

Coding Agent Integration

class CodingAgent:
    def __init__(self):
        self.kalytera = Kalytera(agent_id="production-coding-agent")
    
    def respond(self, user_input: str) -> str:
        response = self.generate_response(user_input)
        
        # Track with Kalytera (non-blocking)
        self.kalytera.track(user_input, response)
        
        return response

Customer Service Agent

class CustomerServiceAgent:
    def __init__(self):
        self.kalytera = Kalytera(agent_id="customer-service-agent")
    
    def handle_request(self, customer_input: str) -> str:
        response = self.generate_response(customer_input)
        
        # Automatic performance monitoring
        self.kalytera.track(customer_input, response)
        
        return response

Autonomous Testing

# Test any agent automatically
tester = AgentTester("production-agent")
tester.register_agent(my_agent_function)

# Run full test suite
results = tester.run_full_test_suite()
# Output: Pass rate: 85.2% (Grade: A)

# Continuous monitoring
tester.continuous_monitoring(interval_minutes=60)

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📊 What Kalytera Monitors

Usage Analytics

• Session volumes and patterns
• Intent classification across all agent types
• Workflow completion rates
• Response times and performance

Quality Assessment

• LLM-as-a-Judge evaluation: Autonomous scoring of every response
• Quality scores by agent type and intent
• Failure pattern detection
• Root cause analysis

Performance Insights

• Real-time recommendations: Specific actions to improve agent performance
• A+ to F grading system
• Critical issue identification
• Developer action items with priorities

Loss Pattern Analysis

• Dropout detection in agent workflows
• High-impact failure identification
• Recommended fixes for common problems

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🎯 Agent Testing Framework

Comprehensive Test Suites

• Coding Agents: Debug errors, write functions, optimize code
• Customer Service: Handle complaints, billing issues, account recovery
• Data Science: Analyze data, create visualizations, generate insights
• Sales/BDR: Qualify leads, handle objections, close deals
• General: Basic reasoning, explanations, problem-solving

Automated Evaluation

# Example test results
🏆 AGENT PERFORMANCE REPORT
Agent ID: my-coding-agent

📊 OVERALL PERFORMANCE
• Tests Run: 12
• Pass Rate: 85.2% (10/12)
• Average Quality: 0.82/1.0
• Average Response Time: 1,200ms

🎯 RECOMMENDATIONS
1. 🔴 PRIORITY: Improve data_science performance (60% pass rate)
2. ⚡ Optimize response times for complex queries
3. 📈 Continue monitoring - overall performance is solid

🎓 OVERALL GRADE: A

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🏢 Enterprise Features

Multi-Agent Monitoring

• Monitor coding assistants, customer service, data science, sales, marketing agents
• Unified dashboard showing performance across all agent types
• Comparative analysis and benchmarking

Production-Safe Integration

• Non-blocking tracking: Never impacts agent performance
• Error-resilient: Agent continues working even if Kalytera is down
• Efficient batching: Minimal network overhead
• Auto-retry logic: Handles network failures gracefully

Actionable Developer Insights

• Specific recommendations: "Improve customer_service responses (quality: 0.65)"
• Priority levels: Critical, High, Medium with timelines
• Expected impact: "Could improve 1,500 interactions/month"
• Root cause analysis: Identify exactly what needs fixing

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🔗 Complete System

1. Kalytera SDK (kalytera_sdk.py)

• Production-ready Python SDK
• One-line agent integration
• Real-time performance insights
• Non-blocking monitoring

2. Testing Framework (agent_testing_framework.py)

• Autonomous agent testing
• Comprehensive test suites
• A+ to F performance grading
• Continuous monitoring

3. Enterprise Dashboard (http://localhost:8509)

• Professional monitoring interface
• Clear agent identification
• Key metrics prominently displayed
• Actionable developer insights

4. Production API (https://kalytera-api-z9it.onrender.com)

• Deployed and ready for use
• High availability monitoring
• Real-time data processing
• Secure agent data handling

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📈 Immediate Value

For Developers

• Zero setup time: Works immediately with any agent
• Clear performance metrics: Know exactly how your agent is performing
• Specific improvements: Get actionable recommendations, not vague scores
• Production confidence: Test thoroughly before deployment

For Enterprises

• Multi-agent visibility: Monitor all AI agents from one dashboard
• Performance benchmarking: Compare agents and identify top performers
• Risk mitigation: Catch performance degradation before it impacts users
• ROI measurement: Prove business impact of agent improvements

For Product Teams

• User experience insights: See where agents fail and frustrate users
• Optimization roadmap: Clear priority list of improvements
• Quality assurance: Automated testing prevents regressions
• Competitive advantage: Higher quality agents = better user experience

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🚀 Ready for Production

Kalytera is a complete, finished product. Your agents can start using it immediately:

# 1. Install (copy 3 files)
# kalytera_sdk.py, agent_testing_framework.py, complete_kalytera_example.py

# 2. Integrate (2 lines)
from kalytera_sdk import Kalytera
iq = Kalytera(agent_id="your-agent")
iq.track(user_input, agent_response)

# 3. Test (1 line)
from agent_testing_framework import AgentTester
AgentTester("your-agent").run_full_test_suite()

# 4. Monitor (dashboard)
# http://localhost:8509

No more low-level work. No more building infrastructure. Kalytera handles everything so you can focus on building great agents.

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📞 Support

• API Endpoint: https://kalytera-api-z9it.onrender.com
• Enterprise Dashboard: http://localhost:8509
• Complete Examples: python3 complete_kalytera_example.py
• Production Ready: Copy 3 files and start monitoring

Kalytera: The finished product for AI agent performance intelligence.