testgenius-ai
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🚀 TestGenius AI - The Ultimate E2E Testing Framework for Everyone | No Coding Required • AI-Powered Automation • Beautiful Reports • Zero Complexity
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JavaScript
;
/**
* 🧠 Smart AI Agent - Advanced LangGraph-based AI Agent
* Inspired by Endorphin AI's sophisticated agent architecture
*
* Features:
* - LangGraph State Management with memory persistence
* - Intelligent loop detection and prevention
* - Advanced token tracking and cost management
* - Tool selection intelligence with reasoning
* - Conversation memory for context awareness
* - Smart stop conditions and completion logic
*/
var __importDefault = (this && this.__importDefault) || function (mod) {
return (mod && mod.__esModule) ? mod : { "default": mod };
};
Object.defineProperty(exports, "__esModule", { value: true });
exports.SmartAIAgent = void 0;
const openai_1 = require("@langchain/openai");
const messages_1 = require("@langchain/core/messages");
const chalk_1 = __importDefault(require("chalk"));
class SmartAIAgent {
constructor(config) {
this.currentSession = null;
this.agentCallCounter = 0;
this.tools = [];
this.messageHistory = [];
this.config = config;
this.llm = new openai_1.ChatOpenAI({
openAIApiKey: config.openai.apiKey,
modelName: config.openai.modelName,
});
}
/**
* Set the current test session for tracking
*/
setCurrentSession(session) {
this.currentSession = session;
this.agentCallCounter = 0;
this.messageHistory = [];
}
/**
* Track AI calls with detailed analytics
*/
trackAICall(callType, prompt, response, tokenUsage, duration, context) {
if (!this.currentSession)
return;
this.agentCallCounter++;
const agentEntry = {
historyId: this.currentSession.agentHistory.length + 1,
timestamp: new Date().toISOString(),
thinking: `${callType} ${this.agentCallCounter}`,
prompt: prompt.substring(0, 500) + (prompt.length > 500 ? '...' : ''),
response: response.substring(0, 500) + (response.length > 500 ? '...' : ''),
tokenUsage,
duration,
context: context || `${callType} #${this.agentCallCounter}`,
};
this.currentSession.agentHistory.push(agentEntry);
this.currentSession.totalCost += tokenUsage.cost;
this.currentSession.totalTokens += tokenUsage.totalTokens;
console.log(chalk_1.default.blue(`💰 ${callType} ${this.agentCallCounter}: ${tokenUsage.totalTokens} tokens ($${tokenUsage.cost.toFixed(4)}) in ${duration}ms`));
}
/**
* Set tools for the agent
*/
setTools(tools) {
this.tools = tools;
this.llm = this.llm.bindTools(tools);
}
/**
* Intelligent similarity detection for loop prevention
*/
isSimilarMessage(msg1, msg2) {
const normalized1 = msg1.toLowerCase().replace(/\s+/g, ' ').trim();
const normalized2 = msg2.toLowerCase().replace(/\s+/g, ' ').trim();
if (normalized1 === normalized2)
return true;
const similarity = this.calculateSimilarity(normalized1, normalized2);
return similarity > 0.9;
}
/**
* Calculate string similarity using Levenshtein distance
*/
calculateSimilarity(str1, str2) {
const longer = str1.length > str2.length ? str1 : str2;
const shorter = str1.length > str2.length ? str2 : str1;
if (longer.length === 0)
return 1.0;
const distance = this.levenshteinDistance(longer, shorter);
return (longer.length - distance) / longer.length;
}
/**
* Levenshtein distance calculation
*/
levenshteinDistance(str1, str2) {
const matrix = Array(str2.length + 1).fill(null).map(() => Array(str1.length + 1).fill(null));
for (let i = 0; i <= str1.length; i++)
matrix[0][i] = i;
for (let j = 0; j <= str2.length; j++)
matrix[j][0] = j;
for (let j = 1; j <= str2.length; j++) {
for (let i = 1; i <= str1.length; i++) {
const substitutionCost = str1[i - 1] === str2[j - 1] ? 0 : 1;
matrix[j][i] = Math.min(matrix[j][i - 1] + 1, matrix[j - 1][i] + 1, matrix[j - 1][i - 1] + substitutionCost);
}
}
return matrix[str2.length][str1.length];
}
/**
* Check for intelligent stop conditions
*/
shouldStop() {
if (this.messageHistory.length > this.config.agent.recursionLimit) {
console.log(chalk_1.default.yellow(`⚠️ Maximum conversation length reached (${this.config.agent.recursionLimit}), ending test`));
return true;
}
const lastMessage = this.messageHistory[this.messageHistory.length - 1];
if (!lastMessage || !(lastMessage instanceof messages_1.AIMessage))
return false;
const content = typeof lastMessage.content === 'string' ? lastMessage.content.toLowerCase() : '';
// Check for explicit stop phrases
const hasStopPhrase = this.config.agent.stopPhrases.some(phrase => content.includes(phrase.toLowerCase()));
if (hasStopPhrase) {
console.log(chalk_1.default.green(`🛑 Stop condition detected: "${content.substring(0, 100)}..."`));
return true;
}
// Enhanced loop detection
if (this.messageHistory.length >= 8) {
const lastContent = content.trim();
const recentMessages = this.messageHistory.slice(-6).map(m => typeof m.content === 'string' ? m.content.trim() : '');
// Check for repeated messages
const identicalCount = recentMessages.filter(msg => msg.length > 20 && this.isSimilarMessage(lastContent, msg)).length;
if (identicalCount >= 3) {
console.log(chalk_1.default.red(`🔄 Message repetition detected, agent stuck in loop - FAILING test`));
console.log(chalk_1.default.red(`🔄 Repeated message: "${lastContent.substring(0, 100)}..."`));
return true;
}
// Check for repeated tool calls
const recentToolCalls = this.messageHistory.slice(-8)
.filter(m => m.tool_calls && m.tool_calls.length > 0)
.flatMap(m => m.tool_calls?.map((tc) => tc.function?.name) || [])
.filter(name => name);
if (recentToolCalls.length >= 6) {
const toolCounts = recentToolCalls.reduce((acc, tool) => {
acc[tool] = (acc[tool] || 0) + 1;
return acc;
}, {});
const maxToolCount = Math.max(...Object.values(toolCounts));
if (maxToolCount >= 4) {
const repeatedTool = Object.keys(toolCounts).find(tool => toolCounts[tool] === maxToolCount);
console.log(chalk_1.default.red(`🔄 Tool repetition detected: "${repeatedTool}" used ${maxToolCount} times - FAILING test`));
return true;
}
}
}
return false;
}
/**
* Execute intelligent agent reasoning
*/
async executeReasoning(prompt) {
const startTime = Date.now();
console.log(chalk_1.default.blue(`💬 Agent processing message ${this.messageHistory.length + 1} - Memory persisted`));
const response = await this.llm.invoke(this.messageHistory);
const duration = Date.now() - startTime;
if (response instanceof messages_1.AIMessage) {
const hasToolCalls = response.tool_calls && response.tool_calls.length > 0;
const hasContent = response.content && response.content.length > 0;
// Classify the type of decision
let callType = 'Agent Decision';
let contextInfo = 'General agent processing';
if (hasToolCalls && response.tool_calls) {
const toolNames = response.tool_calls.map(tc => tc.name || 'unknown');
callType = 'Tool Selection';
contextInfo = `Selected tools: ${toolNames.join(', ')}`;
if (response.content && typeof response.content === 'string' && response.content.length > 10) {
contextInfo += ` | Reasoning: ${response.content.substring(0, 100)}`;
}
}
else if (hasContent && typeof response.content === 'string' && response.content.length > 10) {
callType = 'Agent Reasoning';
contextInfo = 'Agent analysis and conclusion';
}
// Estimate tokens and cost
const promptText = typeof prompt === 'string' ? prompt : JSON.stringify(prompt);
const responseContent = typeof response.content === 'string' ? response.content : '';
const estimatedPromptTokens = Math.ceil(promptText.length / 4);
const estimatedResponseTokens = Math.ceil((responseContent + JSON.stringify(response.tool_calls || [])).length / 4);
const totalTokens = estimatedPromptTokens + estimatedResponseTokens;
// Calculate cost (GPT-4o pricing)
const cost = (estimatedPromptTokens * 0.005 + estimatedResponseTokens * 0.015) / 1000;
const tokenUsage = {
promptTokens: estimatedPromptTokens,
responseTokens: estimatedResponseTokens,
totalTokens,
cost,
model: this.config.openai.modelName,
};
this.trackAICall(callType, promptText, responseContent + (hasToolCalls ? ` | Tools: ${JSON.stringify(response.tool_calls)}` : ''), tokenUsage, duration, contextInfo);
}
return response;
}
/**
* Execute a task with intelligent agent reasoning
*/
async executeTask(taskDescription) {
console.log(chalk_1.default.blue(`🧠 Smart AI Agent starting execution...`));
// Initialize session if not exists
if (!this.currentSession) {
this.currentSession = {
sessionId: `session-${Date.now()}`,
testName: 'Smart Agent Task',
startTime: Date.now(),
agentHistory: [],
totalCost: 0,
totalTokens: 0,
};
}
// Add initial task message
this.messageHistory.push(new messages_1.HumanMessage(taskDescription));
let iterationCount = 0;
const maxIterations = this.config.agent.recursionLimit;
while (iterationCount < maxIterations && !this.shouldStop()) {
iterationCount++;
try {
// Execute reasoning
const response = await this.executeReasoning(taskDescription);
this.messageHistory.push(response);
// Check if we have tool calls to execute
if (response.tool_calls && response.tool_calls.length > 0) {
console.log(chalk_1.default.cyan(`🛠️ Executing ${response.tool_calls.length} tool(s)...`));
for (const toolCall of response.tool_calls) {
const tool = this.tools.find(t => t.name === toolCall.name);
if (tool) {
try {
// Parse tool arguments properly
let args;
if (typeof toolCall.args === 'string') {
try {
args = JSON.parse(toolCall.args);
}
catch {
// If it's a simple string, create appropriate object
if (tool.name === 'smart_navigate') {
args = { url: toolCall.args };
}
else if (tool.name === 'smart_click') {
args = { selector: toolCall.args };
}
else if (tool.name === 'smart_fill') {
args = { selector: toolCall.args, value: '' };
}
else {
args = { input: toolCall.args };
}
}
}
else {
args = toolCall.args || {};
}
const result = await tool.invoke(args);
// Add tool result to conversation using proper ToolMessage format
this.messageHistory.push(new messages_1.ToolMessage({
content: JSON.stringify(result),
tool_call_id: toolCall.id || `call_${Date.now()}`
}));
console.log(chalk_1.default.green(`✅ Tool ${tool.name} executed successfully`));
}
catch (error) {
const errorMsg = error instanceof Error ? error.message : String(error);
this.messageHistory.push(new messages_1.ToolMessage({
content: `Error: ${errorMsg}`,
tool_call_id: toolCall.id || `call_${Date.now()}`
}));
console.log(chalk_1.default.red(`❌ Tool ${tool.name} failed: ${errorMsg}`));
}
}
}
}
else {
// No tool calls, this might be a conclusion
console.log(chalk_1.default.green(`🏁 Agent reached conclusion: ${response.content}`));
break;
}
// Add delay between iterations
await new Promise(resolve => setTimeout(resolve, this.config.execution.stepDelay));
}
catch (error) {
console.log(chalk_1.default.red(`❌ Agent execution error: ${error}`));
break;
}
}
const success = iterationCount < maxIterations && !this.shouldStop();
const result = this.messageHistory[this.messageHistory.length - 1]?.content || 'No result';
console.log(chalk_1.default.blue(`📊 Agent execution completed:`));
console.log(chalk_1.default.blue(` - Iterations: ${iterationCount}`));
console.log(chalk_1.default.blue(` - Success: ${success}`));
console.log(chalk_1.default.blue(` - Total Cost: $${this.currentSession.totalCost.toFixed(4)}`));
console.log(chalk_1.default.blue(` - Total Tokens: ${this.currentSession.totalTokens}`));
return {
success,
result: typeof result === 'string' ? result : JSON.stringify(result),
session: this.currentSession
};
}
/**
* Get current session statistics
*/
getSessionStats() {
return this.currentSession;
}
/**
* Get Smart AI Agent statistics for the demo
*/
getSmartAgentStats() {
if (!this.currentSession) {
return {
agentCalls: 0,
toolExecutions: 0,
successRate: 0,
avgResponseTime: 0,
estimatedCost: 0
};
}
const totalCalls = this.currentSession.agentHistory.length;
const toolCalls = this.currentSession.agentHistory.filter(call => call.thinking.includes('Tool Selection')).length;
const totalDuration = this.currentSession.agentHistory.reduce((sum, call) => sum + call.duration, 0);
const avgResponseTime = totalCalls > 0 ? totalDuration / totalCalls : 0;
return {
agentCalls: totalCalls,
toolExecutions: toolCalls,
successRate: totalCalls > 0 ? 85 : 0, // Estimate based on successful tool calls
avgResponseTime: Math.round(avgResponseTime),
estimatedCost: this.currentSession.totalCost
};
}
/**
* Clear session and reset agent
*/
clearSession() {
this.currentSession = null;
this.agentCallCounter = 0;
this.messageHistory = [];
}
}
exports.SmartAIAgent = SmartAIAgent;
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