RWKV Agent Kit provides rich advanced features to help developers build more powerful and intelligent AI agents. This chapter will dive deep into the usage methods and best practices of these advanced features.

🧠 Advanced Memory System

Semantic Memory Retrieval

The semantic memory system uses vector embedding technology to retrieve relevant memories based on semantic similarity:

use rwkv_agent_kit::memory::{SemanticMemory, MemoryQuery};
use rwkv_agent_kit::embeddings::SentenceTransformer;

// Initialize semantic memory system
let embedding_model = SentenceTransformer::new("all-MiniLM-L6-v2")?;
let mut memory = SemanticMemory::new(embedding_model);

// Store memories
memory.store("User likes coffee", "preference").await?;
memory.store("User lives in Beijing", "location").await?;
memory.store("User is a software engineer", "profession").await?;

// Semantic search
let query = MemoryQuery::new("User's dietary preferences")
    .with_similarity_threshold(0.7)
    .with_max_results(5);

let memories = memory.search(query).await?;
for memory in memories {
    println!("Related memory: {} (similarity: {:.2})", memory.content, memory.similarity);
}

Knowledge Graph Construction

Build entity relationship graphs supporting complex knowledge reasoning:

use rwkv_agent_kit::knowledge::{KnowledgeGraph, Entity, Relation};

// Create knowledge graph
let mut kg = KnowledgeGraph::new();

// Add entities
let user = kg.add_entity(Entity::new("User", "Person"));
let coffee = kg.add_entity(Entity::new("Coffee", "Beverage"));
let beijing = kg.add_entity(Entity::new("Beijing", "City"));

// Add relations
kg.add_relation(user, "likes", coffee)?;
kg.add_relation(user, "lives_in", beijing)?;

// Query relations
let preferences = kg.find_relations(user, "likes")?;
for (entity, relation) in preferences {
    println!("User {}: {}", relation, entity.name);
}

// Path query
let path = kg.find_path(user, coffee, 3)?;
if let Some(path) = path {
    println!("Relationship path: {:?}", path);
}

Memory Compression and Summarization

Automatically compress long-term memories and extract key information:

use rwkv_agent_kit::memory::{MemoryCompressor, CompressionStrategy};

// Create memory compressor
let compressor = MemoryCompressor::new(CompressionStrategy::Extractive);

// Compress memory
let long_conversation = "Very long conversation history...";
let summary = compressor.compress(long_conversation, 200).await?;

println!("Compressed summary: {}", summary);

// Hierarchical compression
let hierarchical_summary = compressor
    .hierarchical_compress(long_conversation)
    .with_levels(3)
    .with_compression_ratio(0.3)
    .execute().await?;

🛠️ Advanced Tool System

Custom Tool Development

Create custom tools to extend agent capabilities:

use rwkv_agent_kit::tools::{Tool, ToolResult, ToolError};
use serde::{Deserialize, Serialize};
use async_trait::async_trait;

#[derive(Debug, Serialize, Deserialize)]
struct WeatherQuery {
    city: String,
    date: Option<String>,
}

#[derive(Debug)]
struct WeatherTool {
    api_key: String,
}

#[async_trait]
impl Tool for WeatherTool {
    fn name(&self) -> &str {
        "weather_query"
    }
    
    fn description(&self) -> &str {
        "Query weather information for specified city"
    }
    
    fn parameters_schema(&self) -> serde_json::Value {
        serde_json::json!({
            "type": "object",
            "properties": {
                "city": {
                    "type": "string",
                    "description": "City name"
                },
                "date": {
                    "type": "string",
                    "description": "Query date (optional)"
                }
            },
            "required": ["city"]
        })
    }
    
    async fn execute(&self, params: serde_json::Value) -> Result<ToolResult, ToolError> {
        let query: WeatherQuery = serde_json::from_value(params)?;
        
        // Call weather API
        let weather_data = self.fetch_weather(&query.city, query.date.as_deref()).await?;
        
        Ok(ToolResult::success(weather_data))
    }
}

impl WeatherTool {
    async fn fetch_weather(&self, city: &str, date: Option<&str>) -> Result<serde_json::Value, ToolError> {
        // Implement weather API call logic
        // ...
        Ok(serde_json::json!({
            "city": city,
            "temperature": "22°C",
            "condition": "Sunny",
            "humidity": "65%"
        }))
    }
}

Tool Chain Composition

Combine multiple tools to create complex workflows:

use rwkv_agent_kit::tools::{ToolChain, ToolRegistry};

// Register tools
let mut registry = ToolRegistry::new();
registry.register(Box::new(WeatherTool::new(api_key)));
registry.register(Box::new(CalendarTool::new()));
registry.register(Box::new(EmailTool::new()));

// Create tool chain
let chain = ToolChain::new()
    .add_step("weather_query", json!({"city": "Beijing"}))
    .add_conditional_step(
        |result| result["condition"] == "Rainy",
        "calendar_query", 
        json!({"date": "today"})
    )
    .add_step("email_send", json!({
        "to": "user@example.com",
        "subject": "Weather Alert",
        "body": "It's raining today, remember to bring an umbrella!"
    }));

// Execute tool chain
let result = chain.execute(&registry).await?;
println!("Tool chain execution result: {:?}", result);

Tool Permission Management

Implement fine-grained tool access control:

use rwkv_agent_kit::tools::{ToolPermission, PermissionManager};

// Create permission manager
let mut perm_manager = PermissionManager::new();

// Define permission policies
perm_manager.add_policy("user_tools", vec![
    ToolPermission::Allow("weather_query"),
    ToolPermission::Allow("calendar_query"),
    ToolPermission::Deny("system_command"),
]);

perm_manager.add_policy("admin_tools", vec![
    ToolPermission::AllowAll,
]);

// Check permissions
let user_role = "user_tools";
if perm_manager.check_permission(user_role, "weather_query") {
    // Execute tool
    let result = tool.execute(params).await?;
} else {
    return Err("Insufficient permissions".into());
}

🔄 Multi-Agent Collaboration

Agent Communication

Implement message passing and collaboration between agents:

use rwkv_agent_kit::multi_agent::{Agent, Message, MessageBus};
use tokio::sync::mpsc;

// Create message bus
let message_bus = MessageBus::new();

// Create agents
let mut agent1 = Agent::new("Researcher", config.clone())
    .with_role("Responsible for information gathering and analysis")
    .with_tools(vec!["web_search", "data_analysis"])
    .connect_to_bus(&message_bus);

let mut agent2 = Agent::new("Writer", config.clone())
    .with_role("Responsible for content creation and editing")
    .with_tools(vec!["text_generation", "grammar_check"])
    .connect_to_bus(&message_bus);

// Agent collaboration workflow
let task = "Write an article about AI development trends";

// Researcher gathers information
let research_result = agent1.process(task).await?;

// Send message to writer
let message = Message::new()
    .from("Researcher")
    .to("Writer")
    .with_content(research_result)
    .with_task("Write article based on research results");

message_bus.send(message).await?;

// Writer receives message and processes
let article = agent2.receive_and_process().await?;

println!("Collaborative article: {}", article);

Task Decomposition and Assignment

Automatically decompose complex tasks and assign them to appropriate agents:

use rwkv_agent_kit::multi_agent::{TaskDecomposer, TaskScheduler};

// Create task decomposer
let decomposer = TaskDecomposer::new();

// Decompose complex task
let complex_task = "Create a complete product marketing plan";
let subtasks = decomposer.decompose(complex_task).await?;

// Create task scheduler
let mut scheduler = TaskScheduler::new();

// Register agents and their capabilities
scheduler.register_agent("Market Analyst", vec!["market_research", "competitor_analysis"]);
scheduler.register_agent("Creative Director", vec!["creative_design", "content_creation"]);
scheduler.register_agent("Data Analyst", vec!["data_analysis", "report_generation"]);

// Assign tasks
for subtask in subtasks {
    let assigned_agent = scheduler.assign_task(&subtask).await?;
    println!("Task '{}' assigned to: {}", subtask.description, assigned_agent);
}

// Execute and monitor tasks
let results = scheduler.execute_all().await?;
for (task, result) in results {
    println!("Task '{}' completed: {:?}", task, result);
}

🎯 Agent Personalization

Dynamic Personality Adjustment

Dynamically adjust agent personality based on user preferences and context:

use rwkv_agent_kit::personality::{PersonalityProfile, PersonalityAdjuster};

// Define personality profile
let mut personality = PersonalityProfile::new()
    .with_trait("friendliness", 0.8)
    .with_trait("formality", 0.3)
    .with_trait("creativity", 0.7)
    .with_trait("analytical", 0.6);

// Create personality adjuster
let adjuster = PersonalityAdjuster::new();

// Adjust personality based on user feedback
let user_feedback = "Please be more formal in responses";
let adjusted_personality = adjuster
    .adjust_based_on_feedback(&personality, user_feedback)
    .await?;

// Apply personality to agent
agent.set_personality(adjusted_personality);

// Dynamic adjustment based on context
let context = "Business meeting environment";
let context_personality = adjuster
    .adjust_for_context(&personality, context)
    .await?;

agent.set_temporary_personality(context_personality);

Learning User Preferences

Agents can learn and adapt to user preferences:

use rwkv_agent_kit::learning::{PreferenceTracker, UserProfile};

// Create user preference tracker
let mut tracker = PreferenceTracker::new();

// Record user interactions
tracker.record_interaction("User chose detailed explanation over brief answer");
tracker.record_interaction("User showed confusion with technical terms");
tracker.record_interaction("User prefers answers with examples");

// Analyze preference patterns
let user_profile = tracker.analyze_preferences().await?;

println!("User preference analysis:");
println!("- Detail preference: {}", user_profile.detail_preference);
println!("- Technical level: {}", user_profile.technical_level);
println!("- Learning style: {:?}", user_profile.learning_style);

// Adapt response style based on preferences
agent.adapt_to_user_profile(user_profile);

📊 Advanced Monitoring and Analytics

Performance Monitoring

Real-time monitoring of agent performance and resource usage:

use rwkv_agent_kit::monitoring::{PerformanceMonitor, Metrics};
use std::time::Duration;

// Create performance monitor
let monitor = PerformanceMonitor::new()
    .with_sampling_interval(Duration::from_secs(1))
    .with_metrics(vec![
        Metrics::ResponseTime,
        Metrics::MemoryUsage,
        Metrics::TokensPerSecond,
        Metrics::ToolExecutionTime,
    ]);

// Start monitoring
monitor.start();

// Collect metrics during agent processing
let start_time = std::time::Instant::now();
let response = agent.process(user_input).await?;
let response_time = start_time.elapsed();

monitor.record_metric(Metrics::ResponseTime, response_time.as_millis() as f64);

// Generate performance report
let report = monitor.generate_report().await?;
println!("Performance report: {:?}", report);

// Set performance alerts
monitor.set_alert(Metrics::ResponseTime, 5000.0, |value| {
    eprintln!("Warning: Response time too long {}ms", value);
});

Conversation Quality Analysis

Analyze conversation quality and user satisfaction:

use rwkv_agent_kit::analytics::{ConversationAnalyzer, QualityMetrics};

// Create conversation analyzer
let analyzer = ConversationAnalyzer::new();

// Analyze conversation quality
let conversation_history = vec![
    ("User", "Hello, I want to learn about machine learning"),
    ("Agent", "Hello! I'd be happy to help you learn about machine learning..."),
    // ... more conversation
];

let quality_metrics = analyzer.analyze_conversation(&conversation_history).await?;

println!("Conversation quality analysis:");
println!("- Relevance score: {:.2}", quality_metrics.relevance_score);
println!("- Coherence score: {:.2}", quality_metrics.coherence_score);
println!("- Helpfulness score: {:.2}", quality_metrics.helpfulness_score);
println!("- User engagement: {:.2}", quality_metrics.engagement_score);

// Identify improvement points
let improvement_suggestions = analyzer.suggest_improvements(&quality_metrics).await?;
for suggestion in improvement_suggestions {
    println!("Improvement suggestion: {}", suggestion);
}

🔒 Security and Privacy

Content Safety Filtering

Implement multi-layered content safety checks:

use rwkv_agent_kit::safety::{ContentFilter, SafetyLevel, FilterResult};

// Create content filter
let filter = ContentFilter::new()
    .with_safety_level(SafetyLevel::Strict)
    .with_custom_rules(vec![
        "Prohibit discussion of illegal activities",
        "Avoid providing medical advice",
        "Do not disclose personal information",
    ]);

// Filter user input
let user_input = "User input content...";
match filter.check_input(user_input).await? {
    FilterResult::Safe => {
        // Continue processing
        let response = agent.process(user_input).await?;
    }
    FilterResult::Unsafe(reason) => {
        println!("Input rejected: {}", reason);
        return Ok("Sorry, I cannot process this request.".to_string());
    }
    FilterResult::Warning(warning) => {
        println!("Input warning: {}", warning);
        // Process with caution
    }
}

// Filter agent output
let agent_response = "Agent response...";
let filtered_response = filter.filter_output(agent_response).await?;

Privacy Protection

Protect user privacy and sensitive information:

use rwkv_agent_kit::privacy::{PrivacyProtector, SensitiveDataDetector};

// Create privacy protector
let protector = PrivacyProtector::new()
    .with_encryption_key("your-encryption-key")
    .with_anonymization(true);

// Detect sensitive information
let detector = SensitiveDataDetector::new();
let text = "My phone is 13812345678, email is user@example.com";

let sensitive_data = detector.detect(text).await?;
for data in sensitive_data {
    println!("Detected sensitive info: {} (type: {:?})", data.value, data.data_type);
}

// Anonymization processing
let anonymized_text = protector.anonymize(text).await?;
println!("Anonymized: {}", anonymized_text);

// Encrypted storage
let encrypted_memory = protector.encrypt_memory("User's private information").await?;
memory.store_encrypted(encrypted_memory).await?;

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These advanced features provide powerful extension capabilities for RWKV Agent Kit. By properly using these features, you can build more intelligent, secure, and personalized AI agents.

Next Steps: Check out Example Projects to see practical applications of these advanced features, or visit the API Documentation for detailed interface descriptions.