RWKV Agent Kit supports creating highly customized agents, including multi-agent collaboration and agent personalization features. This chapter will detail how to build and manage custom agents.

🔄 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("Writing Assistant", 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 writing assistant
let message = Message::new()
    .from("Researcher")
    .to("Writing Assistant")
    .with_content(research_result)
    .with_task("Write article based on research results");

message_bus.send(message).await?;

// Writing assistant 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);

// Dynamically adjust 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 level: {}", 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);

Best Practices

Agent Design Principles

1. Single Responsibility: Each agent should focus on specific task domains
2. Clear Interfaces: Define clear input/output formats and communication protocols
3. Error Handling: Implement robust error handling and recovery mechanisms
4. Performance Optimization: Allocate resources reasonably, avoid unnecessary computational overhead

Collaboration Patterns

• Pipeline Pattern: Agents process tasks sequentially
• Parallel Pattern: Multiple agents handle different subtasks simultaneously
• Hierarchical Pattern: Master agent coordinates multiple sub-agents
• Competition Pattern: Multiple agents provide different solutions for selection

Personalization Strategies

• Progressive Learning: Gradually accumulate user preference data
• Context Awareness: Adjust behavior based on conversation scenarios
• Feedback Loop: Continuously collect and apply user feedback
• Privacy Protection: Protect user privacy while personalizing

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Related Links:

• Memory System - Learn about agent memory management
• Tool Development - Learn custom tool development
• API Documentation - View detailed API interface documentation