2. Adding Personality to Your Agent

3. Database Integration for Message History

4. Tool Integration for Enhanced Capabilities

5. Blockchain Integration: Ethereum Wallet Support

1. Add your Anthropic API key to .env
2. Run the program cargo run Example interaction: Welcome to Agent Friend!Type 'exit' to quit.You: Hello, who are you?Agent is thinking...Agent: I'm an AI assistant designed to be helpful, harmless, and honest. I'm designed to have conversations, answer questions, and assist with various tasks. How can I help you today? That’s our minimal working agent. From here, we can start layering in personality, memory, tools, and blockchain logic. Adding Personality to Your Agent Right now, our agent is functional but… flat. Every response comes from the same generic assistant. That’s fine for testing, but when you want your agent to feel engaging or to fit a specific use case, you need to give it personality. By adding a simple configuration system, we can shape how the agent speaks, behaves, and even introduces itself. Think of this like writing your agent’s “character sheet.” Step 1: Creating a Personality Module We’ll define a Personalitystruct and load it from a JSON file: // src/personality.rsuse serde::{Deserialize, Serialize};use std::fs;use std::path::Path;#[derive(Serialize, Deserialize, Clone, Debug)]pub struct Personality { pub name: String, pub description: String, pub system_prompt: String,}pub fn load_personality() -> anyhow::Result<Personality> { // Check if personality file exists, otherwise use default let personality_path = Path::new("assets/personality.json"); if personality_path.exists() { let personality_json = fs::read_to_string(personality_path)?; let personality: Personality = serde_json::from_str(&personality_json)?; println!("Loaded personality: {} - {}", personality.name, personality.description); Ok(personality) } else { // Default personality Ok(Personality { name: "Assistant".to_string(), description: "Helpful AI assistant".to_string(), system_prompt: "You are a helpful AI assistant.".to_string(), }) }} Step 2: Define Your Agent’s Personality Create a JSON file under assets/ to define how your agent should behave. mkdir -p assets Create assets/personality.json: "name": "Aero", "description": "AI research companion", "system_prompt": "You are Aero, an AI research companion specializing in helping with academic research, data analysis, and scientific exploration. You have a curious, analytical personality and enjoy diving deep into complex topics. Provide thoughtful, well-structured responses that help advance the user's research goals. When appropriate, suggest research directions or methodologies that might be helpful."} Step 3: Update the Anthropic Integration We’ll let the agent use the loaded personality instead of a hardcoded system prompt: / src/anthropic.rsuse serde::{Deserialize, Serialize};use std::env;use crate::personality::Personality;// ... existing code ...// Rename the call_anthropic to call_anthropic_with_personality function to accept a personalitypub async fn call_anthropic_with_personality(prompt: &str, personality: Option<&Personality>) -> anyhow::Result<String> { let api_key = env::var("ANTHROPIC_API_KEY") .expect("ANTHROPIC_API_KEY must be set"); let client = reqwest::Client::new(); let user_message = Message { role: "user".to_string(), content: vec![ContentBlock::Text { text: prompt.to_string(), }], }; // Use the provided personality or a default system prompt let system_prompt = match personality { Some(p) => &p.system_prompt, None => "You are a helpful AI assistant.", }; let request_body = serde_json::json!({ "model": "claude-3-opus-20240229", "max_tokens": 1024, "messages": [user_message], "system": system_prompt, }); let response = client .post("https://api.anthropic.com/v1/messages") .header("x-api-key", api_key) .header("anthropic-version", "2023-06-01") .header("content-type", "application/json") .json(&request_body) .send() .await?; let response_body: AnthropicResponse = response.json().await?; // Extract text from the response let response_text = response_body.content .iter() .filter_map(|block| { match block { ContentBlock::Text { text } => Some(text.clone()), } }) .collect::<Vec<String>>() .join(""); Ok(response_text)} Step 4: Update the Main Loop Load the personality when starting the agent and include it in the conversation: // src/main.rsmod anthropic;mod personality;use std::io::{self, Write};use dotenv::dotenv;use anthropic::call_anthropic_with_personality;use personality::load_personality;#[tokio::main]async fn main() -> anyhow::Result<()> { // Load environment variables dotenv().ok(); // Load personality let personality = load_personality()?; println!("Welcome to Agent Friend! I'm {}, your {}.", personality.name, personality.description); println!("Type 'exit' to quit."); loop { print!("You: "); io::stdout().flush()?; let mut user_input = String::new(); io::stdin().read_line(&mut user_input)?; let user_input = user_input.trim(); if user_input.to_lowercase() == "exit" { break; } // Get response from Claude with personality print!("{} is thinking...", personality.name); io::stdout().flush()?; let reply = call_anthropic_with_personality(user_input, Some(&personality)).await?; println!("\r"); // Clear the "thinking" message println!("{}: {}", personality.name, reply); } Ok()} Running the Agent with Personality Now, when you run the agent, it will use the personality defined in the JSON file: cargo run Example interaction with the new personality: Loaded personality: Aero - AI research companionWelcome to Agent Friend! I'm Aero, your AI research companion.Type 'exit' to quit.You: What's your approach to helping with research?Aero is thinking...Aero: My approach to helping with research is multifaceted and adaptive to your specific needs. Here's how I typically assist:1. Understanding your research goals: I start by clarifying your research questions, objectives, and the context of your work to ensure my assistance is properly aligned.2. Literature exploration: I can discuss relevant theories, methodologies, and existing research in your field, helping you identify gaps or connections you might explore.3. Methodological guidance: I can suggest appropriate research methods, experimental designs, or analytical approaches based on your research questions.4. Critical analysis: I can help you think through the strengths and limitations of different approaches, identify potential biases, and consider alternative interpretations of data or findings.5. Structured thinking: I excel at organizing complex information into coherent frameworks, helping you map out research directions or structure your arguments logically.6. Interdisciplinary connections: I can help identify relevant insights from adjacent fields that might inform your research.7. Ethical considerations: I can highlight potential ethical implications or considerations relevant to your research.Rather than simply providing answers, I aim to be a thought partner who helps you refine your thinking, consider different perspectives, and develop robust research approaches. I'm particularly focused on helping you develop your own insights and research capabilities rather than simply executing tasks.What specific aspect of research are you currently working on that I might help with? With just one JSON file, you can now completely reshape how your agent behaves — turning it into a researcher, financial assistant, game character, or anything else. But still doesn’t manage the context quite well if the conversation is long, that's why we would need some database integration Database Integration for Message History So far, our agent has short-term memory only. It responds to your latest input, but forgets everything the moment you restart. That’s fine for quick demos, but real agents need persistent memory:

Add these to your existing dependenciessqlx = { version = "0.7", features = ["runtime-tokio", "tls-rustls", "postgres", "chrono", "uuid"] }chrono = { version = "0.4", features = ["serde"] }uuid = { version = "1.4", features = ["v4", "serde"] }

• “Send 0.1 ETH to Alice” → calls the Ethereum wallet tool

Add these to your existing dependenciesethers = { version = "2.0", features = ["legacy"] }regex = "1.10.2"