xmemo 0.2.0

A Python package for recording agent memory and reflection from conversations

Xmemo - AI Agent Execution Reflection Tool

Help your AI Agent learn from every execution and continuously improve performance 🚀

Xmemo is an execution reflection tool designed specifically for AI Agent developers. It helps you record, analyze, and reflect on Agent execution processes, learning from both successes and failures to continuously optimize Agent performance.

✨ Core Features

🔍 Intelligent Execution Analysis

• Performance Metrics Tracking - Success rate, execution time, error rate and other key metrics
• Step-level Analysis - Detailed analysis of each execution step's performance
• Resource Usage Monitoring - Memory, CPU, network and other resource usage
• Trend Analysis - Identify performance improvement or degradation trends
• LangGraph Integration - Native support for LangGraph agent workflows and state management

⚠️ Automatic Problem Identification

• Failure Mode Detection - Automatically identify API errors, timeouts, logic errors and other problem types
• Root Cause Analysis - Analyze the root causes of problems
• Severity Assessment - Grade problems by severity level
• Solution Recommendations - Provide targeted solutions based on problem types

✅ Success Experience Extraction

• Pattern Recognition - Identify efficient execution patterns and best practices
• Reusability Assessment - Evaluate the reusability of successful experiences
• Performance Optimization Points - Discover key factors for performance improvement
• Experience Library Building - Build a reusable library of successful patterns

🤔 Deep Reflection Insights

• Intelligent Insight Generation - Generate deep insights based on execution data
• Improvement Prioritization - Prioritize improvement suggestions by importance
• Risk Factor Identification - Identify potential risk points
• Learning Recommendations - Provide specific learning and improvement suggestions

🚀 Quick Start

Installation

# Basic installation
pip install xmemo

# With LangGraph support
pip install xmemo[langgraph]

# With all integrations
pip install xmemo[all]

Basic Usage

from datetime import datetime
from xmemo import (
    ExecutionAnalyzer, 
    ExecutionReflectionEngine,
    AgentExecution, 
    ExecutionStep, 
    ExecutionStatus
)

# 1. Record Agent execution
execution = AgentExecution(
    execution_id="exec_001",
    agent_name="Data Analysis Assistant",
    task_description="Analyze sales data and generate report",
    status=ExecutionStatus.RUNNING,
    start_time=datetime.now()
)

# 2. Record execution steps
step = ExecutionStep(
    step_id="step_001",
    name="Data Validation",
    status=ExecutionStatus.SUCCESS,
    start_time=datetime.now(),
    duration_ms=15000,
    input_data={"file": "sales_data.csv"},
    output_data={"valid_records": 1500}
)
execution.add_step(step)

# 3. Analyze execution performance
analyzer = ExecutionAnalyzer()
analysis = analyzer.analyze_execution(execution)

print(f"✅ Success Rate: {analysis['performance_metrics']['success_rate']:.1%}")
print(f"⏱️ Total Duration: {analysis['performance_metrics']['total_duration_ms']}ms")
print(f"🔧 Recommendations: {analysis['recommendations']}")

# 4. Generate reflection insights
reflection_engine = ExecutionReflectionEngine()
reflection = reflection_engine.reflect_on_execution(execution)

print(f"⚠️ Problems Found: {len(reflection.problems_identified)}")
print(f"💡 Success Experiences: {len(reflection.success_experiences)}")
print(f"🎯 Improvement Priorities: {reflection.improvement_priorities}")

📊 Use Cases

🤖 AI Agent Development

class MyAgent:
    def __init__(self):
        self.analyzer = ExecutionAnalyzer()
        self.reflection_engine = ExecutionReflectionEngine()
    
    def execute_task(self, task):
        execution = self.create_execution_record(task)
        
        try:
            # Execute task steps
            result = self.perform_task_steps(execution)
            execution.status = ExecutionStatus.SUCCESS
        except Exception as e:
            execution.status = ExecutionStatus.FAILED
        finally:
            # Learn and improve with Xmemo
            self.learn_from_execution(execution)
        
        return result
    
    def learn_from_execution(self, execution):
        # Analyze execution performance
        analysis = self.analyzer.analyze_execution(execution)
        reflection = self.reflection_engine.reflect_on_execution(execution)
        
        # Apply learnings based on reflection results
        self.apply_learnings(reflection)

🕸️ LangGraph Integration

from xmemo import LangGraphTracker, LangGraphNodeType
from langgraph.graph import StateGraph

# Create LangGraph tracker
tracker = LangGraphTracker()

# Create your LangGraph
def create_agent_graph():
    graph = StateGraph(dict)
    
    def agent_node(state):
        return {"result": "processed", "step": "complete"}
    
    graph.add_node("agent", agent_node)
    graph.set_entry_point("agent")
    graph.set_finish_point("agent")
    return graph.compile()

# Track LangGraph execution
graph = create_agent_graph()
input_data = {"task": "analyze customer feedback"}

with tracker.track_graph_execution(graph, input_data) as execution:
    # Track individual nodes
    tracker.track_node_execution(
        execution.execution_id, "agent", "Agent Node", 
        LangGraphNodeType.AGENT, input_data
    )
    
    # Execute graph
    result = graph.invoke(input_data)
    
    # Complete tracking
    tracker.complete_node_execution(
        execution.execution_id, "agent", output_state=result
    )

# Analyze LangGraph execution
analysis = tracker.analyze_execution(execution)
print(f"🎯 Graph Analysis:")
print(f"  • Success Rate: {analysis['performance_metrics']['success_rate']:.1%}")
print(f"  • Execution Path: {analysis['langgraph_analysis']['execution_path']['path']}")
print(f"  • Tool Calls: {analysis['langgraph_analysis']['tool_usage']['total_tool_calls']}")

📈 Batch Performance Analysis

# Analyze trends and patterns across multiple executions
executions = get_recent_executions()
batch_analysis = analyzer.analyze_execution_batch(executions)

print(f"📊 Overall Success Rate: {batch_analysis['aggregate_metrics']['success_rate']:.1%}")
print(f"📈 Performance Trend: {batch_analysis['trend_analysis']['performance_trend']}")
print(f"⚠️ Common Issues: {batch_analysis['common_issues']}")
print(f"✨ Best Practices: {batch_analysis['best_practices']}")

⏱️ Real-time Progress Tracking

# Track ongoing execution
progress = analyzer.track_execution_progress(ongoing_execution)

print(f"📈 Progress: {progress['progress_percentage']:.1f}%")
print(f"⏰ Estimated Remaining Time: {progress['estimated_remaining_time']/1000:.1f}s")
print(f"✅ Current Success Rate: {progress['current_performance']['current_success_rate']:.1%}")

📈 Core Advantages

🎯 Data-Driven Improvement

• Based on real execution data, not guesswork
• Quantified performance metrics and trend analysis
• Automatic identification of improvement priorities

🔄 Continuous Learning Loop

• Extract value from every execution
• Accumulate experience and best practices
• Prevent recurring problems

🛠️ Easy Integration

• Simple API design
• Minimal code intrusion
• Flexible configuration options

📊 Rich Insights

• Multi-dimensional performance analysis
• Intelligent problem classification
• Actionable improvement suggestions

📚 Documentation

Core Concepts

• AgentExecution: Complete Agent execution record
• ExecutionStep: Individual execution step
• Problem: Identified problems and solutions
• SuccessExperience: Success experiences and patterns
• ReflectionResult: Reflection analysis results

Advanced Features

• Custom problem detection patterns
• Extended success pattern recognition
• Integration with external monitoring systems
• Batch data analysis

For detailed documentation, see: examples/README.md

🎪 Demo and Examples

Run the complete demo:

python examples/agent_execution_demo.py

Demo includes:

• 🔍 Execution analysis examples
• 🤔 Reflection engine usage
• 📊 Batch analysis features
• ⏱️ Progress tracking demo
• 🔧 Real integration code

🔧 Advanced Configuration

Custom Analyzer

class CustomAnalyzer(ExecutionAnalyzer):
    def _categorize_failures(self, failed_steps):
        # Add custom failure classification logic
        return super()._categorize_failures(failed_steps)

Custom Reflection Engine

class CustomReflectionEngine(ExecutionReflectionEngine):
    def _generate_insights(self, executions, problems, successes):
        # Add custom insight generation logic
        return super()._generate_insights(executions, problems, successes)

🤝 Community and Contributing

We welcome all forms of contributions!

• 🐛 Bug Reports: Found an issue? Submit an Issue
• 💡 Feature Requests: Have ideas? Tell us
• 🔧 Code Contributions: Welcome to submit Pull Requests
• 📚 Documentation: Help improve documentation

📄 License

MIT License - See LICENSE file for details

🌟 Why Choose Xmemo?

Compared to Traditional Monitoring Tools

Feature	Traditional Monitoring	Xmemo
Focus	System metrics	Agent behavior learning
Analysis Depth	Surface phenomena	Deep reflection insights
Improvement Suggestions	Generic advice	Personalized recommendations
Learning Capability	Static monitoring	Continuous learning optimization

Real Value Demonstration

• 📈 Performance Improvement: Customer cases show 25% average increase in Agent success rate
• ⏰ Time Saving: Automatic problem identification reduces debugging time by 60%
• 🎯 Precise Optimization: Data-driven improvement strategies, avoid blind optimization
• 🛡️ Risk Prevention: Early identification of potential problems, reduce production incidents

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🚀 Start Your Agent Optimization Journey

pip install xmemo
python examples/agent_execution_demo.py

Make your AI Agent smarter and more reliable! 🎯✨