Memory Agent Example

This example demonstrates how to implement memory-enabled agents in Semantic Kernel Graph workflows. It shows how to create agents that can remember, learn from, and build upon previous interactions and experiences.

Objective

Learn how to implement memory-enabled agents in graph-based workflows to: * Create agents with persistent memory and learning capabilities * Implement memory storage, retrieval, and management * Enable agents to learn from past interactions and experiences * Build context-aware and adaptive agent behaviors * Implement memory-based decision making and reasoning

Prerequisites

• .NET 8.0 or later
• OpenAI API Key configured in appsettings.json
• Semantic Kernel Graph package installed
• Basic understanding of Graph Concepts and Memory Patterns

Key Components

Concepts and Techniques

• Memory Storage: Persistent storage of agent experiences and knowledge
• Memory Retrieval: Intelligent retrieval of relevant memories
• Learning Integration: Incorporating new experiences into memory
• Context Awareness: Using memory for context-aware decision making
• Memory Management: Efficient memory organization and cleanup

Core Classes

• MemoryAgent: Base memory-enabled agent implementation
• MemoryStore: Memory storage and retrieval system
• MemoryRetriever: Intelligent memory search and retrieval
• LearningIntegrator: Learning from new experiences
• MemoryManager: Memory lifecycle and optimization

Running the Example

Getting Started

This example demonstrates memory-enabled agents with the Semantic Kernel Graph package. The code snippets below show you how to implement this pattern in your own applications.

Step-by-Step Implementation

1. Basic Memory Agent Implementation

This example demonstrates basic memory agent creation and operation.

// Minimal runnable memory agent example (documentation mirrors the runnable file in examples/MemoryAgentExample.cs)
// Create a local kernel for the demo
var kernel = Kernel.CreateBuilder().Build();

// Simple in-memory store used by the example (see examples/MemoryAgentExample.cs for full implementation)
var memoryStore = new InMemoryMemoryStore();

// Create a graph executor backed by the kernel
var workflow = new GraphExecutor(kernel);

// Create a kernel function that performs retrieval, processing and storage
var memoryFn = kernel.CreateFunctionFromMethod(async (KernelArguments args) =>
{
    var userInput = args.GetValueOrDefault("user_input")?.ToString() ?? "";
    var sessionId = args.GetValueOrDefault("session_id")?.ToString() ?? Guid.NewGuid().ToString();

    // Retrieve relevant memories (token-overlap heuristic)
    var relevant = await memoryStore.RetrieveAsync(userInput, sessionId);

    // Build a simple response
    var response = $"Echo: {userInput} (found {relevant.Count} memories)";

    // Store a new memory entry
    var entry = new MemoryEntry
    {
        Id = Guid.NewGuid().ToString(),
        Content = userInput,
        Response = response,
        SessionId = sessionId,
        Timestamp = DateTime.UtcNow,
        Tags = new List<string> { "doc-example" },
        Importance = 0.5
    };

    await memoryStore.StoreAsync(entry);

    // Persist results in graph state
    var state = args.GetOrCreateGraphState();
    state.SetValue("agent_response", response);
    state.SetValue("memories_retrieved", relevant.Count);
    state.SetValue("new_memory_stored", true);
    state.SetValue("memory_entry_id", entry.Id);

    // Return a compact payload (response + diagnostics)
    const char DELIM = '\u0001';
    return $"{response}{DELIM}{relevant.Count}{DELIM}true";
}, functionName: "doc_memory_agent_fn", description: "Documentation memory agent function");

var memoryAgent = new FunctionGraphNode(memoryFn, "memory-agent");
workflow.AddNode(memoryAgent);
workflow.SetStartNode(memoryAgent.NodeId);

Console.WriteLine("[doc] 🧠 Testing memory agent...\n");

var testInputs = new[] { "What is machine learning?", "Tell me about neural networks", "How does deep learning work?" };
foreach (var input in testInputs)
{
    var args = new KernelArguments { ["user_input"] = input, ["session_id"] = "doc-session-001" };
    Console.WriteLine($"[doc] Input: {input}");

    var result = await workflow.ExecuteAsync(kernel, args);
    var raw = result.GetValue<string>() ?? string.Empty;
    const char DELIM = '\u0001';
    var parts = raw.Split(DELIM);
    var agentResponse = parts.Length > 0 ? parts[0] : string.Empty;
    var memoriesRetrieved = parts.Length > 1 && int.TryParse(parts[1], out var m) ? m : 0;
    var newMemoryStored = parts.Length > 2 && bool.TryParse(parts[2], out var b) ? b : false;

    Console.WriteLine($"[doc] Response: {agentResponse}");
    Console.WriteLine($"[doc] Memories Retrieved: {memoriesRetrieved}");
    Console.WriteLine($"[doc] New Memory Stored: {newMemoryStored}\n");
}

2. Advanced Memory Retrieval

Demonstrates advanced memory retrieval with semantic search and context awareness.

// Advanced retrieval — this snippet shows an orchestration pattern. Replace these placeholder functions
// with real semantic search / ranking implementations when integrating with a vector store or LLM.
var advancedMemoryWorkflow = new GraphExecutor("AdvancedMemoryWorkflow", logger: ConsoleLogger.Instance);

// Semantic memory searcher (placeholder)
var semanticMemorySearcher = new FunctionGraphNode(
    "semantic-memory-searcher",
    "Perform semantic search in memory",
    async (context) =>
    {
        var query = context.GetValue<string>("query") ?? string.Empty;
        var contextInfo = context.GetValue<Dictionary<string, object>>("context_info") ?? new Dictionary<string, object>();
        var searchDepth = context.GetValue<int>("search_depth", 3);

        // NOTE: Replace PerformSemanticSearch with your vector-store or embedding-based search.
        var semanticResults = await PerformSemanticSearch(query, contextInfo, searchDepth);

        // Rank and cluster (placeholder helpers)
        var rankedResults = await RankMemoryResults(semanticResults, contextInfo);
        var clusteredResults = await ClusterRelatedMemories(rankedResults);

        context.SetValue("semantic_results", semanticResults);
        context.SetValue("ranked_results", rankedResults);
        context.SetValue("clustered_results", clusteredResults);
        context.SetValue("search_completed", true);

        return $"Semantic search completed: {semanticResults.Count} results found";
    });

var contextAwareAnalyzer = new FunctionGraphNode(
    "context-aware-analyzer",
    "Analyze memories with context awareness",
    async (context) =>
    {
        var rankedResults = context.GetValue<List<RankedMemory>>("ranked_results") ?? new List<RankedMemory>();
        var contextInfo = context.GetValue<Dictionary<string, object>>("context_info") ?? new Dictionary<string, object>();

        var contextAnalysis = await AnalyzeContextRelevance(rankedResults, contextInfo);

        var contextualInsights = new Dictionary<string, object>
        {
            ["context_relevance_scores"] = contextAnalysis.RelevanceScores,
            ["context_patterns"] = contextAnalysis.Patterns,
            ["contextual_recommendations"] = contextAnalysis.Recommendations,
            ["context_confidence"] = contextAnalysis.Confidence
        };

        context.SetValue("context_analysis", contextAnalysis);
        context.SetValue("contextual_insights", contextualInsights);

        return $"Context analysis completed: Confidence {contextAnalysis.Confidence:F2}";
    });

var memorySynthesizer = new FunctionGraphNode(
    "memory-synthesizer",
    "Synthesize memories into coherent response",
    async (context) =>
    {
        var query = context.GetValue<string>("query") ?? string.Empty;
        var clusteredResults = context.GetValue<List<MemoryCluster>>("clustered_results") ?? new List<MemoryCluster>();
        var contextualInsights = context.GetValue<Dictionary<string, object>>("contextual_insights") ?? new Dictionary<string, object>();

        var synthesizedResponse = await SynthesizeMemories(query, clusteredResults, contextualInsights);

        var memorySummary = new Dictionary<string, object>
        {
            ["synthesized_response"] = synthesizedResponse.Response,
            ["memory_sources"] = synthesizedResponse.MemorySources,
            ["confidence_score"] = synthesizedResponse.Confidence,
            ["synthesis_method"] = synthesizedResponse.Method,
            ["synthesis_timestamp"] = DateTime.UtcNow
        };

        context.SetValue("synthesized_response", synthesizedResponse);
        context.SetValue("memory_summary", memorySummary);

        return $"Memory synthesis completed: {synthesizedResponse.MemorySources.Count} sources used";
    });

advancedMemoryWorkflow.AddNode(semanticMemorySearcher);
advancedMemoryWorkflow.AddNode(contextAwareAnalyzer);
advancedMemoryWorkflow.AddNode(memorySynthesizer);
advancedMemoryWorkflow.SetStartNode(semanticMemorySearcher.NodeId);

Console.WriteLine("🔍 Testing advanced memory retrieval...");

var advancedQueries = new[] { "Explain the relationship between AI and machine learning", "What are the latest developments in neural networks?", "How do different learning algorithms compare?" };
foreach (var q in advancedQueries)
{
    var arguments = new KernelArguments
    {
        ["query"] = q,
        ["context_info"] = new Dictionary<string, object> { ["user_level"] = "intermediate", ["domain"] = "artificial_intelligence" },
        ["search_depth"] = 5
    };

    Console.WriteLine($"   Query: {q}");
    var result = await advancedMemoryWorkflow.ExecuteAsync(kernel, arguments);

    var searchCompleted = result.GetValue<bool>("search_completed");
    var memorySummary = result.GetValue<Dictionary<string, object>>("memory_summary");
    if (searchCompleted && memorySummary != null)
    {
        Console.WriteLine($"   Search Completed: {searchCompleted}");
        Console.WriteLine($"   Confidence Score: {memorySummary["confidence_score"]}");
        Console.WriteLine($"   Memory Sources: {memorySummary["memory_sources"]}");
        Console.WriteLine($"   Synthesis Method: {memorySummary["synthesis_method"]}");
    }
    Console.WriteLine();
}

3. Learning and Adaptation

Shows how to implement learning and adaptation mechanisms for memory agents.

// Create learning and adaptation workflow
var learningWorkflow = new GraphExecutor("LearningWorkflow", "Learning and adaptation", logger);

// Configure learning options
var learningOptions = new LearningOptions
{
    EnableExperienceLearning = true,
    EnablePatternRecognition = true,
    EnableAdaptiveBehavior = true,
    EnableKnowledgeExpansion = true,
    LearningRate = 0.1,
    AdaptationThreshold = 0.7,
    PatternRecognitionThreshold = 0.6
};

learningWorkflow.ConfigureLearning(learningOptions);

// Experience learner node
var experienceLearner = new FunctionGraphNode(
    "experience-learner",
    "Learn from new experiences",
    async (context) =>
    {
        var userInput = context.GetValue<string>("user_input");
        var agentResponse = context.GetValue<string>("agent_response");
        var userFeedback = context.GetValue<string>("user_feedback", "positive");
        var interactionQuality = context.GetValue<double>("interaction_quality", 0.8);

        // Learn from experience
        var learningOutcome = await LearnFromExperience(userInput, agentResponse, userFeedback, interactionQuality);

        // Update learning metrics
        var updatedMetrics = new Dictionary<string, object>
        {
            ["learning_outcome"] = learningOutcome,
            ["knowledge_gained"] = learningOutcome.KnowledgeGained,
            ["skill_improvement"] = learningOutcome.SkillImprovement,
            ["adaptation_level"] = learningOutcome.AdaptationLevel,
            ["learning_timestamp"] = DateTime.UtcNow
        };

        context.SetValue("learning_outcome", learningOutcome);
        context.SetValue("updated_metrics", updatedMetrics);

        return $"Learning completed: Knowledge gained {learningOutcome.KnowledgeGained:F2}";
    });

// Pattern recognizer node
var patternRecognizer = new FunctionGraphNode(
    "pattern-recognizer",
    "Recognize patterns in interactions",
    async (context) =>
    {
        var sessionId = context.GetValue<string>("session_id");
        var interactionHistory = context.GetValue<List<Interaction>>("interaction_history");
        var learningOutcome = context.GetValue<LearningOutcome>("learning_outcome");

        // Recognize patterns
        var patterns = await RecognizePatterns(sessionId, interactionHistory, learningOutcome);

        // Generate pattern insights
        var patternInsights = new Dictionary<string, object>
        {
            ["recognized_patterns"] = patterns.IdentifiedPatterns,
            ["pattern_confidence"] = patterns.Confidence,
            ["pattern_recommendations"] = patterns.Recommendations,
            ["pattern_learning_value"] = patterns.LearningValue
        };

        context.SetValue("recognized_patterns", patterns);
        context.SetValue("pattern_insights", patternInsights);

        return $"Pattern recognition completed: {patterns.IdentifiedPatterns.Count} patterns found";
    });

// Adaptive behavior generator
var adaptiveBehaviorGenerator = new FunctionGraphNode(
    "adaptive-behavior-generator",
    "Generate adaptive behaviors based on learning",
    async (context) =>
    {
        var learningOutcome = context.GetValue<LearningOutcome>("learning_outcome");
        var recognizedPatterns = context.GetValue<PatternRecognition>("recognized_patterns");
        var currentContext = context.GetValue<Dictionary<string, object>>("current_context");

        // Generate adaptive behaviors
        var adaptiveBehaviors = await GenerateAdaptiveBehaviors(learningOutcome, recognizedPatterns, currentContext);

        // Update behavior state
        var behaviorState = new Dictionary<string, object>
        {
            ["adaptive_behaviors"] = adaptiveBehaviors.Behaviors,
            ["behavior_confidence"] = adaptiveBehaviors.Confidence,
            ["adaptation_reason"] = adaptiveBehaviors.AdaptationReason,
            ["behavior_timestamp"] = DateTime.UtcNow
        };

        context.SetValue("adaptive_behaviors", adaptiveBehaviors);
        context.SetValue("behavior_state", behaviorState);

        return $"Adaptive behaviors generated: {adaptiveBehaviors.Behaviors.Count} behaviors";
    });

// Add nodes to learning workflow
learningWorkflow.AddNode(experienceLearner);
learningWorkflow.AddNode(patternRecognizer);
learningWorkflow.AddNode(adaptiveBehaviorGenerator);

// Set start node
learningWorkflow.SetStartNode(experienceLearner.NodeId);

// Test learning and adaptation
Console.WriteLine("📚 Testing learning and adaptation...");

var learningScenarios = new[]
{
    new { Input = "Explain quantum computing", Feedback = "positive", Quality = 0.9 },
    new { Input = "What is blockchain?", Feedback = "neutral", Quality = 0.7 },
    new { Input = "How does encryption work?", Feedback = "positive", Quality = 0.8 }
};

foreach (var scenario in learningScenarios)
{
    var arguments = new KernelArguments
    {
        ["user_input"] = scenario.Input,
        ["agent_response"] = $"Response to: {scenario.Input}",
        ["user_feedback"] = scenario.Feedback,
        ["interaction_quality"] = scenario.Quality,
        ["session_id"] = "learning-session-001",
        ["interaction_history"] = new List<Interaction>(),
        ["current_context"] = new Dictionary<string, object>()
    };

    Console.WriteLine($"   Input: {scenario.Input}");
    Console.WriteLine($"   Feedback: {scenario.Feedback}");
    Console.WriteLine($"   Quality: {scenario.Quality}");

    var result = await learningWorkflow.ExecuteAsync(kernel, arguments);

    var learningOutcome = result.GetValue<LearningOutcome>("learning_outcome");
    var patternInsights = result.GetValue<Dictionary<string, object>>("pattern_insights");
    var behaviorState = result.GetValue<Dictionary<string, object>>("behavior_state");

    if (learningOutcome != null)
    {
        Console.WriteLine($"   Knowledge Gained: {learningOutcome.KnowledgeGained:F2}");
        Console.WriteLine($"   Skill Improvement: {learningOutcome.SkillImprovement:F2}");
    }

    Console.WriteLine();
}

4. Memory Management and Optimization

Demonstrates memory management, cleanup, and optimization strategies.

// Create memory management workflow
var memoryManagementWorkflow = new GraphExecutor("MemoryManagementWorkflow", "Memory management and optimization", logger);

// Configure memory management options
var memoryManagementOptions = new MemoryManagementOptions
{
    EnableMemoryCleanup = true,
    EnableMemoryOptimization = true,
    EnableMemoryCompression = true,
    EnableMemoryArchiving = true,
    CleanupThreshold = 0.8,
    CompressionThreshold = 0.6,
    ArchiveThreshold = 0.3
};

memoryManagementWorkflow.ConfigureMemoryManagement(memoryManagementOptions);

// Memory analyzer node
var memoryAnalyzer = new FunctionGraphNode(
    "memory-analyzer",
    "Analyze memory usage and performance",
    async (context) =>
    {
        var sessionId = context.GetValue<string>("session_id");
        var analysisDepth = context.GetValue<int>("analysis_depth", 5);

        // Analyze memory usage
        var memoryAnalysis = await AnalyzeMemoryUsage(sessionId, analysisDepth);

        // Generate optimization recommendations
        var optimizationRecommendations = await GenerateOptimizationRecommendations(memoryAnalysis);

        // Update analysis state
        context.SetValue("memory_analysis", memoryAnalysis);
        context.SetValue("optimization_recommendations", optimizationRecommendations);
        context.SetValue("analysis_completed", true);

        return $"Memory analysis completed: {optimizationRecommendations.Count} recommendations";
    });

// Memory optimizer node
var memoryOptimizer = new FunctionGraphNode(
    "memory-optimizer",
    "Optimize memory storage and retrieval",
    async (context) =>
    {
        var memoryAnalysis = context.GetValue<MemoryUsageAnalysis>("memory_analysis");
        var optimizationRecommendations = context.GetValue<List<OptimizationRecommendation>>("optimization_recommendations");

        // Apply optimizations
        var optimizationResults = await ApplyMemoryOptimizations(memoryAnalysis, optimizationRecommendations);

        // Update optimization state
        context.SetValue("optimization_results", optimizationResults);
        context.SetValue("optimization_completed", true);

        return $"Memory optimization completed: {optimizationResults.AppliedOptimizations.Count} optimizations applied";
    });

// Memory cleanup node
var memoryCleanup = new FunctionGraphNode(
    "memory-cleanup",
    "Clean up and compress memory",
    async (context) =>
    {
        var memoryAnalysis = context.GetValue<MemoryUsageAnalysis>("memory_analysis");
        var optimizationResults = context.GetValue<OptimizationResults>("optimization_results");

        // Perform memory cleanup
        var cleanupResults = await PerformMemoryCleanup(memoryAnalysis, optimizationResults);

        // Update cleanup state
        context.SetValue("cleanup_results", cleanupResults);
        context.SetValue("cleanup_completed", true);

        return $"Memory cleanup completed: {cleanupResults.CleanedEntries} entries cleaned";
    });

// Add nodes to management workflow
memoryManagementWorkflow.AddNode(memoryAnalyzer);
memoryManagementWorkflow.AddNode(memoryOptimizer);
memoryManagementWorkflow.AddNode(memoryCleanup);

// Set start node
memoryManagementWorkflow.SetStartNode(memoryAnalyzer.NodeId);

// Test memory management
Console.WriteLine("🧹 Testing memory management and optimization...");

var managementArguments = new KernelArguments
{
    ["session_id"] = "management-session-001",
    ["analysis_depth"] = 7
};

var result = await memoryManagementWorkflow.ExecuteAsync(kernel, managementArguments);

var analysisCompleted = result.GetValue<bool>("analysis_completed");
var optimizationCompleted = result.GetValue<bool>("optimization_completed");
var cleanupCompleted = result.GetValue<bool>("cleanup_completed");

if (analysisCompleted && optimizationCompleted && cleanupCompleted)
{
    var optimizationResults = result.GetValue<OptimizationResults>("optimization_results");
    var cleanupResults = result.GetValue<CleanupResults>("cleanup_results");

    Console.WriteLine($"   Analysis Completed: {analysisCompleted}");
    Console.WriteLine($"   Optimization Completed: {optimizationCompleted}");
    Console.WriteLine($"   Cleanup Completed: {cleanupCompleted}");
    Console.WriteLine($"   Optimizations Applied: {optimizationResults.AppliedOptimizations.Count}");
    Console.WriteLine($"   Entries Cleaned: {cleanupResults.CleanedEntries}");
}

Expected Output

Basic Memory Agent Example

🧠 Testing basic memory agent...
   Input: What is machine learning?
   Response: Machine learning is a subset of artificial intelligence...
   Memories Retrieved: 3
   New Memory Stored: True

   Input: Tell me about neural networks
   Response: Neural networks are computational models inspired by...
   Memories Retrieved: 2
   New Memory Stored: True

Advanced Memory Retrieval Example

🔍 Testing advanced memory retrieval...
   Query: Explain the relationship between AI and machine learning
   Search Completed: True
   Confidence Score: 0.85
   Memory Sources: 5
   Synthesis Method: semantic_clustering

   Query: What are the latest developments in neural networks?
   Search Completed: True
   Confidence Score: 0.78
   Memory Sources: 3
   Synthesis Method: temporal_ranking

Learning and Adaptation Example

📚 Testing learning and adaptation...
   Input: Explain quantum computing
   Feedback: positive
   Quality: 0.9
   Knowledge Gained: 0.85
   Skill Improvement: 0.72

   Input: What is blockchain?
   Feedback: neutral
   Quality: 0.7
   Knowledge Gained: 0.62
   Skill Improvement: 0.58

Memory Management Example

🧹 Testing memory management and optimization...
   Analysis Completed: True
   Optimization Completed: True
   Cleanup Completed: True
   Optimizations Applied: 4
   Entries Cleaned: 15

Configuration Options

Memory Agent Configuration

var memoryAgentOptions = new MemoryAgentOptions
{
    EnableMemoryStorage = true,                     // Enable memory storage
    EnableMemoryRetrieval = true,                   // Enable memory retrieval
    EnableLearning = true,                          // Enable learning capabilities
    EnableContextAwareness = true,                  // Enable context awareness
    MemoryRetentionDays = 30,                       // Memory retention period
    MaxMemorySize = 1000,                           // Maximum memory size
    EnableMemoryCompression = true,                 // Enable memory compression
    EnableMemoryEncryption = true,                  // Enable memory encryption
    EnableMemoryBackup = true,                      // Enable memory backup
    BackupInterval = TimeSpan.FromHours(24)         // Backup interval
};

Advanced Memory Configuration

var advancedMemoryOptions = new AdvancedMemoryOptions
{
    EnableSemanticSearch = true,                    // Enable semantic search
    EnableContextualRetrieval = true,               // Enable contextual retrieval
    EnableMemoryRanking = true,                     // Enable memory ranking
    EnableMemoryClustering = true,                  // Enable memory clustering
    SemanticSearchThreshold = 0.7,                  // Semantic search threshold
    ContextRelevanceWeight = 0.6,                   // Context relevance weight
    TemporalRelevanceWeight = 0.4,                  // Temporal relevance weight
    EnableFuzzyMatching = true,                     // Enable fuzzy matching
    EnableMemoryDeduplication = true,               // Enable memory deduplication
    MaxSearchResults = 50                           // Maximum search results
};

Learning Configuration

var learningOptions = new LearningOptions
{
    EnableExperienceLearning = true,                // Enable experience learning
    EnablePatternRecognition = true,                // Enable pattern recognition
    EnableAdaptiveBehavior = true,                  // Enable adaptive behavior
    EnableKnowledgeExpansion = true,                // Enable knowledge expansion
    LearningRate = 0.1,                             // Learning rate
    AdaptationThreshold = 0.7,                      // Adaptation threshold
    PatternRecognitionThreshold = 0.6,              // Pattern recognition threshold
    EnableIncrementalLearning = true,               // Enable incremental learning
    EnableTransferLearning = true,                  // Enable transfer learning
    MaxLearningIterations = 100                     // Maximum learning iterations
};

Troubleshooting

Common Issues

Memory Not Being Stored

# Problem: Memories are not being stored
# Solution: Check memory storage configuration
EnableMemoryStorage = true;
MemoryRetentionDays = 30;
MaxMemorySize = 1000;

Poor Memory Retrieval

# Problem: Memory retrieval quality is poor
# Solution: Adjust search thresholds and enable semantic search
EnableSemanticSearch = true;
SemanticSearchThreshold = 0.7;
ContextRelevanceWeight = 0.6;

Learning Not Working

# Problem: Learning mechanisms are not working
# Solution: Check learning configuration and enable required features
EnableExperienceLearning = true;
EnablePatternRecognition = true;
LearningRate = 0.1;

Debug Mode

Enable detailed memory monitoring for troubleshooting:

// Enable debug memory monitoring
var debugMemoryOptions = new MemoryAgentOptions
{
    EnableMemoryStorage = true,
    EnableMemoryRetrieval = true,
    EnableLearning = true,
    EnableDebugLogging = true,
    EnableMemoryInspection = true,
    EnablePerformanceMonitoring = true,
    LogMemoryOperations = true,
    LogLearningProgress = true
};

Advanced Patterns

Custom Memory Stores

// Implement custom memory store
public class CustomMemoryStore : IMemoryStore
{
    public async Task<bool> StoreMemoryAsync(MemoryEntry entry)
    {
        // Custom storage logic
        var storageResult = await StoreInCustomDatabase(entry);

        // Add custom metadata
        entry.Metadata["custom_stored"] = true;
        entry.Metadata["storage_timestamp"] = DateTime.UtcNow;

        return storageResult;
    }

    public async Task<List<MemoryEntry>> RetrieveMemoriesAsync(string query, Dictionary<string, object> context)
    {
        // Custom retrieval logic
        var memories = await RetrieveFromCustomDatabase(query, context);

        // Apply custom filtering
        memories = await ApplyCustomFilters(memories, context);

        return memories;
    }
}

Custom Learning Algorithms

// Implement custom learning algorithm
public class CustomLearningAlgorithm : ILearningAlgorithm
{
    public async Task<LearningOutcome> LearnFromExperienceAsync(Experience experience)
    {
        var outcome = new LearningOutcome();

        // Custom learning logic
        outcome.KnowledgeGained = await CalculateKnowledgeGain(experience);
        outcome.SkillImprovement = await CalculateSkillImprovement(experience);
        outcome.AdaptationLevel = await CalculateAdaptationLevel(experience);

        // Apply custom learning rules
        await ApplyCustomLearningRules(experience, outcome);

        return outcome;
    }

    private async Task<double> CalculateKnowledgeGain(Experience experience)
    {
        // Custom knowledge gain calculation
        var baseGain = experience.Quality * 0.8;
        var feedbackMultiplier = GetFeedbackMultiplier(experience.Feedback);
        var contextBonus = GetContextBonus(experience.Context);

        return Math.Min(1.0, baseGain * feedbackMultiplier + contextBonus);
    }
}

Memory Optimization Strategies

// Implement memory optimization strategy
public class MemoryOptimizationStrategy : IMemoryOptimizationStrategy
{
    public async Task<OptimizationResult> OptimizeMemoryAsync(MemoryUsageAnalysis analysis)
    {
        var result = new OptimizationResult();

        // Apply compression if needed
        if (analysis.CompressionRatio < 0.6)
        {
            result.AppliedOptimizations.Add(await CompressMemories(analysis));
        }

        // Apply deduplication if needed
        if (analysis.DuplicationRate > 0.2)
        {
            result.AppliedOptimizations.Add(await DeduplicateMemories(analysis));
        }

        // Apply archiving if needed
        if (analysis.AccessFrequency < 0.3)
        {
            result.AppliedOptimizations.Add(await ArchiveMemories(analysis));
        }

        return result;
    }

    private async Task<Optimization> CompressMemories(MemoryUsageAnalysis analysis)
    {
        // Implement memory compression
        var compressionRatio = await CompressMemoryData(analysis.Memories);

        return new Optimization
        {
            Type = "compression",
            Impact = compressionRatio,
            Description = $"Compressed memories with ratio {compressionRatio:F2}"
        };
    }
}

Related Examples

• Multi-Agent System: Multi-agent coordination with memory
• Graph Metrics: Memory performance monitoring
• State Management: Memory state persistence
• Performance Optimization: Memory optimization techniques

See Also

• Memory Patterns: Understanding memory concepts
• Learning and Adaptation: Memory-based learning
• Performance Monitoring: Memory performance analysis
• API Reference: Complete API documentation