Introduction

Welcome to the Sentient Agents Documentation. Sentient Agents is a no-code platform designed to empower users in creating and managing decentralized applications (dApps) on the Solana blockchain. With Sentient Agents, you can build custom bundlers, automate trading strategies, and analyze blockchain data in real-time, all through intuitive agent interactions.

Key Features

Creating a Custom Bundler

With Sentient Agents, you can create your own bundler by conversing with your agent. This interactive process allows you to define the functionalities and parameters of your bundler, enabling a tailored solution that fits your requirements.

Steps to Create a Bundler:

1. Initiate Conversation: Kickstart your bundler creation journey by accessing the intuitive agent interface. The process begins with a straightforward initiation of a new conversation.
2. Define Requirements: Clearly articulate your vision by defining the specific functionalities and features you need in your bundler.
3. Agent Assistance: Benefit from the expertise of a smart and responsive agent that guides you step-by-step through the setup process.
4. Deployment: Once your bundler is configured and reviewed, seamlessly deploy it to the Solana blockchain with just a few clicks.

Automating Trades with BULLX Integration

Integrate your BULLX account with Sentient Agents to automate trading strategies. The platform provides real-time trend analysis and suggestions, enhancing your trading efficiency.

1. Connect BULLX: Navigate to the integrations section and link your BULLX account.
2. Configure Strategies: Set your trading parameters and preferences within the platform.
3. Activate Automation: Enable the automated trading feature to start executing trades based on real-time data and analysis.

Real-Time Contract Analysis with Alfred Agent

Utilize the Alfred assistant agent to perform real-time analysis of blockchain contracts and projects. Gain comprehensive insights into:

• Distribution Metrics: Understand token distribution among holders.
• Fresh Wallets: Identify new wallets interacting with the contract.
• Transaction Volumes: Monitor the number and value of transactions.
• Top Holder Trust Metrics: Assess the credibility and activity of top token holders.

How to Use Alfred Agent:

1. Access Alfred: From the dashboard, select the Alfred agent feature.
2. Input Contract Details: Enter the contract address or project name you wish to analyze.
3. View Analysis: Receive real-time data and insights presented in an easy-to-understand format.

Code Examples

Risk Scoring Algorithm

import numpy as np

def calculate_risk_score(token_data):
    # Example weights for different factors
    weights = {
        'holder_concentration': 0.4,
        'transaction_anomalies': 0.3,
        'social_presence': 0.2,
        'historical_performance': 0.1
    }

    # Normalize factors to a 0-1 scale
    normalized_factors = {
        key: value / max_value
        for key, (value, max_value) in token_data.items()
    }

    # Calculate weighted risk score
    risk_score = sum(
        weights[key] * normalized_factors[key]
        for key in weights.keys()
    ) * 100  # Scale to 0-100

    return round(risk_score, 2)

# Example token data
sample_token_data = {
    'holder_concentration': (80, 100),
    'transaction_anomalies': (70, 100),
    'social_presence': (10, 100),
    'historical_performance': (50, 100)
}

risk_score = calculate_risk_score(sample_token_data)
print(f"Risk Score: {risk_score}")

Red Flag Detection

from datetime import datetime

# Example transaction data
transactions = [
    {'amount': 500, 'wallet': '0xABC', 'timestamp': '2024-12-01T12:00:00Z'},
    {'amount': 500, 'wallet': '0xABC', 'timestamp': '2024-12-01T12:01:00Z'},
    {'amount': 1000, 'wallet': '0xDEF', 'timestamp': '2024-12-01T12:02:00Z'},
]

def detect_red_flags(transactions):
    red_flags = []

    # Check for rapid transactions from a single wallet
    wallet_activity = {}
    for tx in transactions:
        wallet = tx['wallet']
        wallet_activity[wallet] = wallet_activity.get(wallet, 0) + 1

    for wallet, count in wallet_activity.items():
        if count > 2:
            red_flags.append(f"Wallet {wallet} has {count} rapid transactions.")

    return red_flags

flags = detect_red_flags(transactions)
print(flags)

Predictive Model (Simplified)

from sklearn.linear_model import LogisticRegression

# Training data (simplified example)
features = [
    [0.1, 0.2, 0.7],  # Low risk
    [0.8, 0.7, 0.9],  # High risk
    [0.3, 0.4, 0.5],  # Medium risk
]
labels = [0, 1, 0]  # 0 = Low Risk, 1 = High Risk

# Train model
model = LogisticRegression()
model.fit(features, labels)

# Predict risk
new_token = [[0.6, 0.5, 0.8]]  # Example new token
prediction = model.predict(new_token)
print("High Risk" if prediction[0] == 1 else "Low/Medium Risk")

API Integration

CRONEX AI provides an API for developers to integrate its functionalities into their own platforms.

Endpoints

• GET /analyze
  • Input: Token address
  • Output: Risk score, red flags, recommendations
• POST /configure-alerts
  • Input: Alert criteria
  • Output: Confirmation of alert configuration

Example API Call

curl -X GET "https://api.cronex.ai/analyze?token_address=0x123" \
-H "Authorization: Bearer YOUR_API_KEY"

Conclusion

Sentient Agents provide invaluable insights for token analysis on Pump.fun. By leveraging advanced AI models and blockchain analytics, they empower users to make informed decisions and avoid high-risk investments.