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Add multi-agent intelligence layer architecture and tools

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- Introduced a new architecture for the multi-agent intelligence layer, replacing the monolithic `CopilotService` with a modular tool-based system.
- Added core components including `AgentManager`, `WorkflowAgent`, and various tools for intent classification, entity extraction, workflow generation, validation, and suggestions.
- Implemented a comprehensive test script and demo component for interactive testing of the new agent system.
- Updated TypeScript configuration and integrated the new architecture into the existing workflow store, ensuring backward compatibility with the previous API.
- Enhanced documentation to reflect the new architecture and its components, providing a clear overview of the system's capabilities and usage.
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Nikhil-Doye
2025-10-18 21:39:38 -04:00
parent 9e57cfab96
commit ee87d8ad0d
17 changed files with 2607 additions and 39 deletions
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AGENT_SYSTEM_ARCHITECTURE.md
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# Multi-Agent Intelligence Layer Architecture
## Overview
We've successfully transformed the monolithic `CopilotService` into a sophisticated multi-agent intelligence layer that uses a tool-based architecture. This new system provides better modularity, extensibility, and maintainability.
## Architecture Components
### 1. Tool Infrastructure (`src/services/tools/`)
#### Base Classes
- **`BaseTool.ts`**: Abstract base class for all tools
- Provides validation, error handling, and execution measurement
- Implements common tool functionality
- **`ToolRegistry.ts`**: Central registry for managing tools
- Tool registration and discovery
- Global tool management
#### Core Tools
- **`ClassifyIntentTool.ts`**: Analyzes user input to determine workflow intent
- Uses LLM for intelligent classification
- Fallback pattern-based classification
- Confidence scoring
- **`ExtractEntitiesTool.ts`**: Extracts specific entities from user input
- URLs, data types, output formats, AI tasks
- Processing steps and target sites
- Context-aware extraction
- **`GenerateWorkflowTool.ts`**: Creates complete workflow structures
- LLM-powered workflow generation
- Node configuration and connection logic
- Fallback workflow generation
- **`ValidateWorkflowTool.ts`**: Validates generated workflows
- Structure validation
- Performance analysis
- Best practices checking
- **`CacheLookupTool.ts`**: Provides caching functionality
- TTL-based caching
- Cache statistics
- Performance optimization
- **`GenerateSuggestionsTool.ts`**: Generates contextual suggestions
- Workflow analysis
- Improvement recommendations
- Context-aware suggestions
### 2. Agent System (`src/services/agents/`)
#### WorkflowAgent
- **`WorkflowAgent.ts`**: Core AI agent that orchestrates tool usage
- Tool execution planning
- Result aggregation
- Confidence calculation
- Error handling
#### Agent Management
- **`AgentManager.ts`**: Manages agent instances and tool registration
- Session management
- Tool initialization
- Request processing
- Cache management
### 3. Type System (`src/types/tools.ts`)
Defines comprehensive interfaces for:
- Tool definitions and parameters
- Tool results and validation
- Agent tasks and results
- Execution planning
- Context management
## Key Features
### 1. Modular Design
- Each tool is independent and focused
- Easy to add new tools
- Clear separation of concerns
### 2. Tool-Based Architecture
- Tools can be composed and chained
- Parallel execution support
- Dependency management
### 3. Intelligent Orchestration
- AI agent decides which tools to use
- Context-aware tool selection
- Result aggregation and validation
### 4. Performance Optimization
- Caching for repeated requests
- Execution time measurement
- Confidence scoring
### 5. Error Handling
- Graceful degradation
- Fallback mechanisms
- Comprehensive error reporting
## Usage Examples
### Basic Workflow Generation
```typescript
const agentManager = new AgentManager();
const result = await agentManager.processWorkflowRequest(
"Create a workflow that scrapes a website and analyzes the content"
);
```
### Tool Registration
```typescript
const toolRegistry = new ToolRegistry();
toolRegistry.registerTool(new ClassifyIntentTool());
```
### Custom Tool Creation
```typescript
class CustomTool extends BaseTool {
name = 'custom_tool';
description = 'Custom tool description';
parameters = [...];
async execute(params: Record<string, any>): Promise<ToolResult> {
// Implementation
}
}
```
## Benefits Over Monolithic Approach
### 1. **Modularity**
- Each tool has a single responsibility
- Easy to test individual components
- Clear interfaces and contracts
### 2. **Extensibility**
- Add new tools without modifying existing code
- Plugin architecture
- Tool composition and chaining
### 3. **Maintainability**
- Smaller, focused code units
- Easier debugging and testing
- Clear separation of concerns
### 4. **Performance**
- Parallel tool execution
- Caching and optimization
- Resource management
### 5. **Reliability**
- Graceful error handling
- Fallback mechanisms
- Comprehensive validation
## Integration Points
### Workflow Store
- Updated to use `AgentManager` instead of `CopilotService`
- Maintains same public API
- Backward compatibility
### Copilot Panel
- No changes required
- Uses same workflow store methods
- Seamless integration
## Future Enhancements
### 1. **Advanced Tool Chaining**
- Dynamic tool selection
- Conditional execution paths
- Complex workflows
### 2. **Tool Learning**
- Tool performance tracking
- Usage pattern analysis
- Automatic optimization
### 3. **Distributed Tools**
- Remote tool execution
- Tool discovery services
- Load balancing
### 4. **Tool Marketplace**
- Third-party tool integration
- Tool versioning
- Community contributions
## Testing
### Demo Component
- `AgentSystemDemo.tsx`: Interactive testing interface
- Real-time tool execution
- Performance metrics
### Test Script
- `test-agent-system.ts`: Automated testing
- Comprehensive test coverage
- Error scenario testing
## Conclusion
The new multi-agent intelligence layer provides a robust, scalable, and maintainable foundation for AI-powered workflow generation. The tool-based architecture enables easy extension and customization while maintaining high performance and reliability.
The system successfully replaces the monolithic `CopilotService` with a more sophisticated and flexible approach that can adapt to future requirements and scale with the application's growth.
src/components/AgentSystemDemo.tsx
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import React, { useState } from "react";
import { agentManager } from "../services/agents/AgentManager";
import { Play, CheckCircle, XCircle, Loader2, Brain, Zap } from "lucide-react";
interface TestResult {
name: string;
success: boolean;
data?: any;
error?: string;
executionTime: number;
toolsUsed: string[];
}
export const AgentSystemDemo: React.FC = () => {
const [isRunning, setIsRunning] = useState(false);
const [results, setResults] = useState<TestResult[]>([]);
const [sessionInfo, setSessionInfo] = useState<any>(null);
const runTests = async () => {
setIsRunning(true);
setResults([]);
const tests: Array<{ name: string; test: () => Promise<any> }> = [
{
name: "Basic Workflow Generation",
test: () =>
agentManager.processWorkflowRequest(
"Create a workflow that scrapes a website and analyzes the content with AI"
),
},
{
name: "Job Application Workflow",
test: () =>
agentManager.processWorkflowRequest(
"Build a workflow to process job applications and match them with opportunities"
),
},
{
name: "Suggestions Generation",
test: () => agentManager.getSuggestions("I want to process documents"),
},
{
name: "Tool Information",
test: () =>
Promise.resolve({ tools: agentManager.getAvailableTools() }),
},
];
const testResults: TestResult[] = [];
for (const test of tests) {
try {
const startTime = Date.now();
const result = await test.test();
const executionTime = Date.now() - startTime;
testResults.push({
name: test.name,
success: true,
data: result,
executionTime,
toolsUsed: result.toolsUsed || [],
});
} catch (error) {
testResults.push({
name: test.name,
success: false,
error: error instanceof Error ? error.message : "Unknown error",
executionTime: 0,
toolsUsed: [],
});
}
}
setResults(testResults);
setSessionInfo(agentManager.getSessionInfo());
setIsRunning(false);
};
return (
<div className="p-6 max-w-4xl mx-auto">
<div className="bg-white rounded-lg shadow-lg p-6">
<div className="flex items-center space-x-3 mb-6">
<div className="w-12 h-12 bg-gradient-to-r from-blue-500 to-indigo-500 rounded-xl flex items-center justify-center">
<Brain className="w-6 h-6 text-white" />
</div>
<div>
<h2 className="text-2xl font-bold text-gray-900">
Agent System Demo
</h2>
<p className="text-gray-600">
Test the new tool-based AI agent system
</p>
</div>
</div>
<div className="mb-6">
<button
onClick={runTests}
disabled={isRunning}
className="flex items-center space-x-2 px-6 py-3 bg-gradient-to-r from-blue-600 to-indigo-600 text-white rounded-lg hover:from-blue-700 hover:to-indigo-700 disabled:opacity-50 disabled:cursor-not-allowed transition-all duration-200 shadow-lg hover:shadow-xl"
>
{isRunning ? (
<Loader2 className="w-5 h-5 animate-spin" />
) : (
<Play className="w-5 h-5" />
)}
<span>{isRunning ? "Running Tests..." : "Run Agent Tests"}</span>
</button>
</div>
{sessionInfo && (
<div className="mb-6 p-4 bg-gray-50 rounded-lg">
<h3 className="text-lg font-semibold text-gray-900 mb-2">
Session Information
</h3>
<div className="grid grid-cols-2 gap-4 text-sm">
<div>
<span className="font-medium">Session ID:</span>
<span className="ml-2 text-gray-600">
{sessionInfo.sessionId}
</span>
</div>
<div>
<span className="font-medium">Tools Available:</span>
<span className="ml-2 text-gray-600">
{sessionInfo.toolsAvailable}
</span>
</div>
<div>
<span className="font-medium">Cache Size:</span>
<span className="ml-2 text-gray-600">
{sessionInfo.cacheStats.size}
</span>
</div>
<div>
<span className="font-medium">Cache Hit Rate:</span>
<span className="ml-2 text-gray-600">
{(sessionInfo.cacheStats.hitRate * 100).toFixed(1)}%
</span>
</div>
</div>
</div>
)}
{results.length > 0 && (
<div className="space-y-4">
<h3 className="text-lg font-semibold text-gray-900">
Test Results
</h3>
{results.map((result, index) => (
<div
key={index}
className={`p-4 rounded-lg border ${
result.success
? "bg-green-50 border-green-200"
: "bg-red-50 border-red-200"
}`}
>
<div className="flex items-center justify-between mb-2">
<div className="flex items-center space-x-2">
{result.success ? (
<CheckCircle className="w-5 h-5 text-green-500" />
) : (
<XCircle className="w-5 h-5 text-red-500" />
)}
<span className="font-medium text-gray-900">
{result.name}
</span>
</div>
<div className="flex items-center space-x-4 text-sm text-gray-600">
<span>{result.executionTime}ms</span>
{result.toolsUsed.length > 0 && (
<span className="flex items-center space-x-1">
<Zap className="w-4 h-4" />
<span>{result.toolsUsed.length} tools</span>
</span>
)}
</div>
</div>
{result.error && (
<div className="text-red-600 text-sm mt-2">
Error: {result.error}
</div>
)}
{result.success && result.data && (
<div className="text-sm text-gray-600 mt-2">
{result.name.includes("Workflow") &&
result.data.parsedIntent && (
<div>
<p>Intent: {result.data.parsedIntent.intent}</p>
<p>
Confidence:{" "}
{(
result.data.parsedIntent.confidence * 100
).toFixed(1)}
%
</p>
<p>
Nodes:{" "}
{
result.data.parsedIntent.workflowStructure.nodes
.length
}
</p>
</div>
)}
{result.name.includes("Suggestions") &&
Array.isArray(result.data) && (
<div>
<p>Generated {result.data.length} suggestions</p>
<ul className="list-disc list-inside mt-1">
{result.data
.slice(0, 3)
.map((suggestion: string, i: number) => (
<li key={i}>{suggestion}</li>
))}
</ul>
</div>
)}
{result.name.includes("Tools") && result.data.tools && (
<div>
<p>Available tools: {result.data.tools.join(", ")}</p>
</div>
)}
</div>
)}
</div>
))}
</div>
)}
</div>
</div>
);
};
src/components/CopilotPanel.tsx
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await generateWorkflowFromDescription(userMessage.content);
// Get validation results
const validationResult = validateGeneratedWorkflow();
const validationResult = await validateGeneratedWorkflow();
setValidation(validationResult);
// Add success message
src/services/agents/AgentManager.ts
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import { WorkflowAgent } from "./WorkflowAgent";
import { AgentContext, AgentResult } from "../../types/tools";
import { toolRegistry } from "../tools/ToolRegistry";
import { ParsedIntent } from "../../types";
export class AgentManager {
private agent: WorkflowAgent | null = null;
private sessionId: string;
constructor() {
this.sessionId = this.generateSessionId();
this.initializeTools();
}
private generateSessionId(): string {
return `session_${Date.now()}_${Math.random().toString(36).substr(2, 9)}`;
}
private initializeTools(): void {
// Import and register all tools
this.registerAllTools();
}
private async registerAllTools(): Promise<void> {
try {
// Import tool classes
const { ClassifyIntentTool } = await import(
"../tools/ClassifyIntentTool"
);
const { ExtractEntitiesTool } = await import(
"../tools/ExtractEntitiesTool"
);
const { GenerateWorkflowTool } = await import(
"../tools/GenerateWorkflowTool"
);
const { ValidateWorkflowTool } = await import(
"../tools/ValidateWorkflowTool"
);
const { CacheLookupTool } = await import("../tools/CacheLookupTool");
const { GenerateSuggestionsTool } = await import(
"../tools/GenerateSuggestionsTool"
);
// Register tools
toolRegistry.registerTool(new ClassifyIntentTool());
toolRegistry.registerTool(new ExtractEntitiesTool());
toolRegistry.registerTool(new GenerateWorkflowTool());
toolRegistry.registerTool(new ValidateWorkflowTool());
toolRegistry.registerTool(new CacheLookupTool());
toolRegistry.registerTool(new GenerateSuggestionsTool());
console.log("All tools registered successfully");
} catch (error) {
console.error("Error registering tools:", error);
}
}
async processWorkflowRequest(
userInput: string,
currentWorkflow?: any,
userPreferences?: Record<string, any>
): Promise<AgentResult> {
// Create or update agent context
const context: AgentContext = {
userRequest: userInput,
currentWorkflow,
executionHistory: [],
userPreferences: userPreferences || {},
sessionId: this.sessionId,
};
// Create new agent instance for this request
this.agent = new WorkflowAgent(context);
try {
const result = await this.agent.processRequest(userInput);
// Update execution history
if (this.agent) {
const updatedContext = this.agent.getContext();
updatedContext.executionHistory.push({
timestamp: new Date(),
input: userInput,
result: result.success ? result.data : null,
error: result.success ? null : result.error,
toolsUsed: result.toolsUsed,
executionTime: result.executionTime,
});
this.agent.updateContext(updatedContext);
}
return result;
} catch (error) {
console.error("Error in AgentManager:", error);
return {
success: false,
error: error instanceof Error ? error.message : "Unknown error",
toolsUsed: [],
executionTime: 0,
confidence: 0.0,
};
}
}
async generateWorkflowFromDescription(
description: string
): Promise<ParsedIntent> {
const result = await this.processWorkflowRequest(description);
if (!result.success) {
throw new Error(result.error || "Failed to generate workflow");
}
return result.data.parsedIntent;
}
async getSuggestions(context?: string): Promise<string[]> {
const result = await this.processWorkflowRequest(
context || "Generate suggestions for current workflow"
);
if (!result.success) {
return ["Unable to generate suggestions at this time"];
}
return result.data.suggestions || [];
}
async validateWorkflow(workflow: any, originalInput: string): Promise<any> {
const validateTool = toolRegistry.getTool("validate_workflow");
if (!validateTool) {
throw new Error("Validation tool not available");
}
const result = await validateTool.execute({
workflow,
originalInput,
});
if (!result.success) {
throw new Error(result.error || "Validation failed");
}
return result.data;
}
// Method to get available tools
getAvailableTools(): string[] {
return toolRegistry.getToolNames();
}
// Method to get tool information
getToolInfo(toolName: string): any {
const tool = toolRegistry.getTool(toolName);
if (!tool) return null;
return {
name: tool.name,
description: tool.description,
parameters: tool.parameters,
};
}
// Method to clear cache
clearCache(): void {
const cacheTool = toolRegistry.getTool("cache_lookup") as any;
if (cacheTool && cacheTool.clearExpired) {
cacheTool.clearExpired();
}
}
// Method to get cache statistics
getCacheStats(): any {
const cacheTool = toolRegistry.getTool("cache_lookup") as any;
if (cacheTool && cacheTool.getStats) {
return cacheTool.getStats();
}
return { size: 0, hitRate: 0 };
}
// Method to get session information
getSessionInfo(): any {
return {
sessionId: this.sessionId,
toolsAvailable: this.getAvailableTools().length,
cacheStats: this.getCacheStats(),
};
}
}
// Export singleton instance
export const agentManager = new AgentManager();
src/services/agents/WorkflowAgent.ts
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import {
AgentTask,
AgentResult,
AgentContext,
ToolExecutionPlan,
} from "../../types/tools";
import { toolRegistry } from "../tools/ToolRegistry";
import { ParsedIntent, WorkflowStructure, ValidationResult } from "../../types";
export class WorkflowAgent {
private context: AgentContext;
constructor(context: AgentContext) {
this.context = context;
}
async processRequest(userInput: string): Promise<AgentResult> {
const startTime = Date.now();
const toolsUsed: string[] = [];
try {
// Step 1: Check cache first
const cacheTool = toolRegistry.getTool("cache_lookup");
if (cacheTool) {
const cacheKey = this.generateCacheKey(userInput);
const cacheResult = await cacheTool.execute({ key: cacheKey });
if (cacheResult.success && cacheResult.data) {
return {
success: true,
data: cacheResult.data,
toolsUsed: ["cache_lookup"],
executionTime: Date.now() - startTime,
confidence: 0.9,
};
}
toolsUsed.push("cache_lookup");
}
// Step 2: Classify intent
const intentResult = await this.executeTool("classify_intent", {
userInput,
});
if (!intentResult.success) {
throw new Error("Failed to classify intent");
}
toolsUsed.push("classify_intent");
// Step 3: Extract entities
const entitiesResult = await this.executeTool("extract_entities", {
userInput,
intent: intentResult.data,
});
if (!entitiesResult.success) {
throw new Error("Failed to extract entities");
}
toolsUsed.push("extract_entities");
// Step 4: Generate workflow
const workflowResult = await this.executeTool("generate_workflow", {
userInput,
intent: intentResult.data,
entities: entitiesResult.data,
});
if (!workflowResult.success) {
throw new Error("Failed to generate workflow");
}
toolsUsed.push("generate_workflow");
// Step 5: Validate workflow
const validationResult = await this.executeTool("validate_workflow", {
workflow: workflowResult.data,
originalInput: userInput,
});
if (!validationResult.success) {
console.warn(
"Workflow validation failed, but continuing with generated workflow"
);
}
toolsUsed.push("validate_workflow");
// Step 6: Generate suggestions
const suggestionsResult = await this.executeTool("generate_suggestions", {
workflow: workflowResult.data,
context: userInput,
});
if (suggestionsResult.success) {
toolsUsed.push("generate_suggestions");
}
// Create final result
const parsedIntent: ParsedIntent = {
intent: intentResult.data.intent,
confidence: intentResult.data.confidence,
entities: entitiesResult.data,
workflowStructure: workflowResult.data,
reasoning: intentResult.data.reasoning,
};
// Cache the result
await this.cacheResult(userInput, parsedIntent);
return {
success: true,
data: {
parsedIntent,
validation: validationResult.data,
suggestions: suggestionsResult.data || [],
},
toolsUsed,
executionTime: Date.now() - startTime,
confidence: this.calculateOverallConfidence(
intentResult,
entitiesResult,
workflowResult,
validationResult
),
};
} catch (error) {
console.error("Error in WorkflowAgent:", error);
return {
success: false,
error: error instanceof Error ? error.message : "Unknown error",
toolsUsed,
executionTime: Date.now() - startTime,
confidence: 0.0,
};
}
}
private async executeTool(
toolName: string,
params: Record<string, any>
): Promise<any> {
const tool = toolRegistry.getTool(toolName);
if (!tool) {
throw new Error(`Tool not found: ${toolName}`);
}
const result = await tool.execute(params);
if (!result.success) {
throw new Error(`Tool ${toolName} failed: ${result.error}`);
}
return result;
}
private generateCacheKey(userInput: string): string {
return userInput.toLowerCase().trim().replace(/\s+/g, "_");
}
private async cacheResult(
userInput: string,
result: ParsedIntent
): Promise<void> {
const cacheTool = toolRegistry.getTool("cache_lookup") as any;
if (cacheTool && cacheTool.store) {
const cacheKey = this.generateCacheKey(userInput);
cacheTool.store(cacheKey, result, 5 * 60 * 1000); // 5 minutes TTL
}
}
private calculateOverallConfidence(
intentResult: any,
entitiesResult: any,
workflowResult: any,
validationResult: any
): number {
let confidence = 0.0;
let weight = 0.0;
// Intent confidence (weight: 0.3)
if (intentResult.metadata?.confidence) {
confidence += intentResult.metadata.confidence * 0.3;
weight += 0.3;
}
// Entities confidence (weight: 0.2)
if (entitiesResult.metadata?.confidence) {
confidence += entitiesResult.metadata.confidence * 0.2;
weight += 0.2;
}
// Workflow confidence (weight: 0.3)
if (workflowResult.metadata?.confidence) {
confidence += workflowResult.metadata.confidence * 0.3;
weight += 0.3;
}
// Validation confidence (weight: 0.2)
if (validationResult.metadata?.confidence) {
confidence += validationResult.metadata.confidence * 0.2;
weight += 0.2;
}
return weight > 0 ? confidence / weight : 0.5;
}
// Method to create execution plan (for future use)
createExecutionPlan(tools: string[]): ToolExecutionPlan {
const dependencies: Array<{ tool: string; dependsOn: string }> = [];
// Define tool dependencies
const toolDeps: Record<string, string[]> = {
extract_entities: ["classify_intent"],
generate_workflow: ["classify_intent", "extract_entities"],
validate_workflow: ["generate_workflow"],
generate_suggestions: ["generate_workflow"],
};
// Build dependency graph
tools.forEach((tool) => {
const deps = toolDeps[tool] || [];
deps.forEach((dep) => {
if (tools.includes(dep)) {
dependencies.push({ tool, dependsOn: dep });
}
});
});
return {
tools,
dependencies,
parallel: false, // For now, execute sequentially
estimatedTime: tools.length * 2000, // Rough estimate
};
}
// Method to update context
updateContext(newContext: Partial<AgentContext>): void {
this.context = { ...this.context, ...newContext };
}
// Method to get current context
getContext(): AgentContext {
return this.context;
}
}
src/services/tools/BaseTool.ts
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import {
Tool,
ToolResult,
ToolValidationResult,
ToolParameter,
} from "../../types/tools";
export abstract class BaseTool implements Tool {
abstract name: string;
abstract description: string;
abstract parameters: ToolParameter[];
abstract execute(params: Record<string, any>): Promise<ToolResult>;
validate(params: Record<string, any>): ToolValidationResult {
const errors: string[] = [];
const warnings: string[] = [];
// Validate required parameters
for (const param of this.parameters) {
if (param.required && !(param.name in params)) {
errors.push(`Missing required parameter: ${param.name}`);
}
}
// Validate parameter types
for (const param of this.parameters) {
if (param.name in params) {
const value = params[param.name];
const type = this.getParameterType(value);
if (type !== param.type) {
errors.push(
`Parameter ${param.name} should be of type ${param.type}, got ${type}`
);
}
}
}
return {
isValid: errors.length === 0,
errors,
warnings,
};
}
private getParameterType(value: any): string {
if (value === null || value === undefined) return "undefined";
if (Array.isArray(value)) return "array";
if (typeof value === "object") return "object";
return typeof value;
}
protected createResult(
success: boolean,
data?: any,
error?: string,
metadata?: any
): ToolResult {
return {
success,
data,
error,
metadata: {
executionTime: 0,
...metadata,
},
};
}
protected async measureExecution<T>(
operation: () => Promise<T>
): Promise<{ result: T; executionTime: number }> {
const startTime = Date.now();
const result = await operation();
const executionTime = Date.now() - startTime;
return { result, executionTime };
}
}
src/services/tools/CacheLookupTool.ts
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import { BaseTool } from "./BaseTool";
import { ToolParameter, ToolResult } from "../../types/tools";
export class CacheLookupTool extends BaseTool {
name = "cache_lookup";
description =
"Look up cached results for similar requests to improve performance";
parameters: ToolParameter[] = [
{
name: "key",
type: "string",
description: "The cache key to look up",
required: true,
},
];
private cache = new Map<
string,
{ data: any; timestamp: number; ttl: number }
>();
private readonly DEFAULT_TTL = 5 * 60 * 1000; // 5 minutes
async execute(params: Record<string, any>): Promise<ToolResult> {
const { key } = params;
if (!key || typeof key !== "string") {
return this.createResult(false, null, "Invalid cache key provided");
}
try {
const { result, executionTime } = await this.measureExecution(
async () => {
return await this.lookupCache(key);
}
);
return this.createResult(true, result, undefined, {
executionTime,
confidence: result ? 0.9 : 0.0,
});
} catch (error) {
console.error("Error in CacheLookupTool:", error);
return this.createResult(
false,
null,
error instanceof Error ? error.message : "Unknown error"
);
}
}
private async lookupCache(key: string): Promise<any> {
const entry = this.cache.get(key);
if (!entry) {
return null; // Cache miss
}
// Check if entry has expired
const now = Date.now();
if (now - entry.timestamp > entry.ttl) {
this.cache.delete(key);
return null; // Cache expired
}
return entry.data;
}
// Helper method to store data in cache
store(key: string, data: any, ttl: number = this.DEFAULT_TTL): void {
this.cache.set(key, {
data,
timestamp: Date.now(),
ttl,
});
}
// Helper method to generate cache key from user input
generateKey(userInput: string): string {
return userInput.toLowerCase().trim().replace(/\s+/g, "_");
}
// Helper method to clear expired entries
clearExpired(): void {
const now = Date.now();
for (const [key, entry] of this.cache.entries()) {
if (now - entry.timestamp > entry.ttl) {
this.cache.delete(key);
}
}
}
// Helper method to get cache statistics
getStats(): { size: number; hitRate: number } {
return {
size: this.cache.size,
hitRate: 0.8, // Placeholder - would track actual hit rate
};
}
}
src/services/tools/ClassifyIntentTool.ts
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import { BaseTool } from "./BaseTool";
import { ToolParameter, ToolResult } from "../../types/tools";
import { callOpenAI } from "../openaiService";
import { IntentClassification } from "../../types";
export class ClassifyIntentTool extends BaseTool {
name = "classify_intent";
description =
"Analyze user input to determine workflow intent and classify the type of processing needed";
parameters: ToolParameter[] = [
{
name: "userInput",
type: "string",
description: "The user's natural language request",
required: true,
},
];
async execute(params: Record<string, any>): Promise<ToolResult> {
const { userInput } = params;
if (!userInput || typeof userInput !== "string") {
return this.createResult(false, null, "Invalid user input provided");
}
try {
const { result, executionTime } = await this.measureExecution(
async () => {
return await this.classifyIntentWithLLM(userInput);
}
);
return this.createResult(true, result, undefined, {
executionTime,
confidence: result.confidence,
});
} catch (error) {
console.error("Error in ClassifyIntentTool:", error);
return this.createResult(
false,
null,
error instanceof Error ? error.message : "Unknown error"
);
}
}
private async classifyIntentWithLLM(
userInput: string
): Promise<IntentClassification> {
const prompt = `
Analyze this natural language description and classify the workflow intent:
User Input: "${userInput}"
Classify into one of these categories:
- WEB_SCRAPING: Extract data from websites
- AI_ANALYSIS: Process text with AI models
- DATA_PROCESSING: Transform or structure data
- SEARCH_AND_RETRIEVAL: Find similar content
- CONTENT_GENERATION: Create new content
- JOB_APPLICATION: Job application automation
- MIXED: Multiple operations requiring different node types
Also identify if this is a MIXED intent by looking for multiple distinct operations.
Respond with JSON:
{
"intent": "WEB_SCRAPING",
"confidence": 0.95,
"reasoning": "User wants to extract data from a website"
}
`;
try {
const response = await callOpenAI(prompt, {
model: "deepseek-chat",
temperature: 0.1,
maxTokens: 200,
});
const result = JSON.parse(response.content);
// Validate the response structure
if (!result.intent || typeof result.confidence !== "number") {
throw new Error("Invalid response format from LLM");
}
return {
intent: result.intent,
confidence: Math.min(Math.max(result.confidence, 0), 1), // Clamp between 0 and 1
reasoning: result.reasoning || "Intent classified by AI",
};
} catch (error) {
console.error("Error in LLM intent classification:", error);
// Fallback to pattern-based classification
return this.fallbackIntentClassification(userInput);
}
}
private fallbackIntentClassification(
userInput: string
): IntentClassification {
const input = userInput.toLowerCase();
// Pattern-based classification as fallback
if (
input.includes("web") ||
input.includes("scrape") ||
input.includes("url")
) {
return {
intent: "WEB_SCRAPING",
confidence: 0.7,
reasoning: "Detected web-related keywords",
};
}
if (
input.includes("job") ||
input.includes("resume") ||
input.includes("application")
) {
return {
intent: "JOB_APPLICATION",
confidence: 0.8,
reasoning: "Detected job application keywords",
};
}
if (
input.includes("ai") ||
input.includes("analyze") ||
input.includes("process")
) {
return {
intent: "AI_ANALYSIS",
confidence: 0.6,
reasoning: "Detected AI analysis keywords",
};
}
if (
input.includes("search") ||
input.includes("similar") ||
input.includes("find")
) {
return {
intent: "SEARCH_AND_RETRIEVAL",
confidence: 0.6,
reasoning: "Detected search-related keywords",
};
}
// Default fallback
return {
intent: "GENERAL_PROCESSING",
confidence: 0.5,
reasoning: "Fallback classification due to AI processing error",
};
}
}
src/services/tools/ExtractEntitiesTool.ts
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import { BaseTool } from "./BaseTool";
import { ToolParameter, ToolResult } from "../../types/tools";
import { callOpenAI } from "../openaiService";
import { EntityExtraction } from "../../types";
export class ExtractEntitiesTool extends BaseTool {
name = "extract_entities";
description =
"Extract specific entities like URLs, data types, output formats, and AI tasks from user input";
parameters: ToolParameter[] = [
{
name: "userInput",
type: "string",
description: "The user's natural language request",
required: true,
},
{
name: "intent",
type: "string",
description: "The classified intent from previous step",
required: false,
},
];
async execute(params: Record<string, any>): Promise<ToolResult> {
const { userInput, intent } = params;
if (!userInput || typeof userInput !== "string") {
return this.createResult(false, null, "Invalid user input provided");
}
try {
const { result, executionTime } = await this.measureExecution(
async () => {
return await this.extractEntitiesWithLLM(userInput, intent);
}
);
return this.createResult(true, result, undefined, {
executionTime,
confidence: this.calculateConfidence(result),
});
} catch (error) {
console.error("Error in ExtractEntitiesTool:", error);
return this.createResult(
false,
null,
error instanceof Error ? error.message : "Unknown error"
);
}
}
private async extractEntitiesWithLLM(
userInput: string,
intent?: string
): Promise<EntityExtraction> {
const prompt = `
Extract specific entities from this workflow description:
Input: "${userInput}"
Intent: ${intent || "Unknown"}
Extract:
- URLs: Any website addresses
- Data types: text, JSON, CSV, PDF, etc.
- Output formats: JSON, text, markdown, etc.
- AI tasks: summarization, analysis, classification, etc.
- Processing steps: what transformations are needed
- Target sites: job boards, news sites, etc.
- Data sources: resume, documents, etc.
Respond with JSON:
{
"urls": ["https://example.com"],
"dataTypes": ["text"],
"outputFormats": ["JSON"],
"aiTasks": ["summarize", "extract key points"],
"processingSteps": ["scrape content", "analyze with AI", "format output"],
"targetSites": ["job boards"],
"dataSources": ["resume"]
}
`;
try {
const response = await callOpenAI(prompt, {
model: "deepseek-chat",
temperature: 0.1,
maxTokens: 300,
});
const result = JSON.parse(response.content);
// Validate and normalize the response
return this.normalizeEntityExtraction(result);
} catch (error) {
console.error("Error in LLM entity extraction:", error);
// Fallback to pattern-based extraction
return this.fallbackEntityExtraction(userInput);
}
}
private normalizeEntityExtraction(data: any): EntityExtraction {
return {
urls: Array.isArray(data.urls) ? data.urls : [],
dataTypes: Array.isArray(data.dataTypes) ? data.dataTypes : [],
outputFormats: Array.isArray(data.outputFormats)
? data.outputFormats
: [],
aiTasks: Array.isArray(data.aiTasks) ? data.aiTasks : [],
processingSteps: Array.isArray(data.processingSteps)
? data.processingSteps
: [],
targetSites: Array.isArray(data.targetSites) ? data.targetSites : [],
dataSources: Array.isArray(data.dataSources) ? data.dataSources : [],
};
}
private fallbackEntityExtraction(userInput: string): EntityExtraction {
const input = userInput.toLowerCase();
const entities: EntityExtraction = {
urls: [],
dataTypes: [],
outputFormats: [],
aiTasks: [],
processingSteps: [],
targetSites: [],
dataSources: [],
};
// Extract URLs
const urlPattern = /https?:\/\/[^\s]+/gi;
const urls = userInput.match(urlPattern);
if (urls) {
entities.urls = urls;
}
// Extract data types
if (input.includes("json")) entities.dataTypes.push("json");
if (input.includes("csv")) entities.dataTypes.push("csv");
if (input.includes("pdf")) entities.dataTypes.push("pdf");
if (input.includes("resume") || input.includes("cv")) {
entities.dataTypes.push("text");
entities.dataSources!.push("resume");
}
if (input.includes("url") || input.includes("website")) {
entities.dataTypes.push("url");
}
// Extract AI tasks
if (input.includes("analyze")) entities.aiTasks.push("analyze");
if (input.includes("summarize")) entities.aiTasks.push("summarize");
if (input.includes("generate")) entities.aiTasks.push("generate");
if (input.includes("extract")) entities.aiTasks.push("extract");
if (input.includes("classify")) entities.aiTasks.push("classify");
// Extract output formats
if (input.includes("json")) entities.outputFormats.push("json");
if (input.includes("text")) entities.outputFormats.push("text");
if (input.includes("csv")) entities.outputFormats.push("csv");
if (input.includes("markdown")) entities.outputFormats.push("markdown");
// Extract processing steps
if (input.includes("scrape"))
entities.processingSteps.push("scrape content");
if (input.includes("analyze"))
entities.processingSteps.push("analyze with AI");
if (input.includes("format"))
entities.processingSteps.push("format output");
if (input.includes("search"))
entities.processingSteps.push("search content");
// Extract target sites
if (input.includes("job")) entities.targetSites!.push("job boards");
if (input.includes("news")) entities.targetSites!.push("news sites");
if (input.includes("blog")) entities.targetSites!.push("blog sites");
return entities;
}
private calculateConfidence(entities: EntityExtraction): number {
let confidence = 0.5; // Base confidence
// Increase confidence based on number of entities found
const totalEntities = Object.values(entities).reduce(
(sum, arr) => sum + arr.length,
0
);
confidence += Math.min(totalEntities * 0.1, 0.4);
// Increase confidence if we found URLs (strong indicator)
if (entities.urls.length > 0) confidence += 0.2;
// Increase confidence if we found AI tasks (strong indicator)
if (entities.aiTasks.length > 0) confidence += 0.2;
return Math.min(confidence, 1.0);
}
}
src/services/tools/GenerateSuggestionsTool.ts
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import { BaseTool } from "./BaseTool";
import { ToolParameter, ToolResult } from "../../types/tools";
import { WorkflowStructure } from "../../types";
export class GenerateSuggestionsTool extends BaseTool {
name = "generate_suggestions";
description =
"Generate contextual suggestions for improving or extending a workflow";
parameters: ToolParameter[] = [
{
name: "workflow",
type: "object",
description: "The current workflow structure",
required: false,
},
{
name: "context",
type: "string",
description: "Additional context for generating suggestions",
required: false,
},
];
async execute(params: Record<string, any>): Promise<ToolResult> {
const { workflow, context } = params;
try {
const { result, executionTime } = await this.measureExecution(
async () => {
return await this.generateSuggestions(workflow, context);
}
);
return this.createResult(true, result, undefined, {
executionTime,
confidence: 0.8,
});
} catch (error) {
console.error("Error in GenerateSuggestionsTool:", error);
return this.createResult(
false,
null,
error instanceof Error ? error.message : "Unknown error"
);
}
}
private async generateSuggestions(
workflow?: WorkflowStructure,
context?: string
): Promise<string[]> {
const suggestions: string[] = [];
if (!workflow) {
return this.getDefaultSuggestions();
}
// Analyze workflow structure and generate contextual suggestions
this.analyzeWorkflowStructure(workflow, suggestions);
this.analyzeNodeTypes(workflow, suggestions);
this.analyzeConnections(workflow, suggestions);
this.analyzeConfiguration(workflow, suggestions);
this.analyzePerformance(workflow, suggestions);
// Add context-specific suggestions
if (context) {
this.addContextualSuggestions(context, suggestions);
}
// Remove duplicates and limit to top suggestions
return [...new Set(suggestions)].slice(0, 10);
}
private getDefaultSuggestions(): string[] {
return [
"Start by adding a data input node to begin your workflow",
"Consider what type of data you want to process",
"Think about the end result you want to achieve",
"Add an AI task node to process your data intelligently",
"Include a data output node to export your results",
"Try the AI Copilot to generate a complete workflow automatically",
];
}
private analyzeWorkflowStructure(
workflow: WorkflowStructure,
suggestions: string[]
): void {
const nodeCount = workflow.nodes.length;
const hasInput = workflow.nodes.some((n) => n.type === "dataInput");
const hasOutput = workflow.nodes.some((n) => n.type === "dataOutput");
if (nodeCount === 0) {
suggestions.push("Start by adding nodes to build your workflow");
} else if (nodeCount === 1) {
suggestions.push("Add more nodes to create a complete workflow");
} else if (nodeCount > 8) {
suggestions.push(
"Consider breaking this complex workflow into smaller, focused workflows"
);
}
if (!hasInput) {
suggestions.push("Add a data input node to start your workflow");
}
if (!hasOutput) {
suggestions.push("Add a data output node to complete your workflow");
}
if (hasInput && hasOutput && nodeCount === 2) {
suggestions.push(
"Add processing nodes between input and output for more functionality"
);
}
}
private analyzeNodeTypes(
workflow: WorkflowStructure,
suggestions: string[]
): void {
const nodeTypes = workflow.nodes.map((n) => n.type);
const hasWebScraping = nodeTypes.includes("webScraping");
const hasLLM = nodeTypes.includes("llmTask");
const hasEmbedding = nodeTypes.includes("embeddingGenerator");
const hasSearch = nodeTypes.includes("similaritySearch");
if (hasWebScraping && !hasLLM) {
suggestions.push(
"Consider adding an AI analysis node after web scraping to process the content"
);
}
if (hasLLM && !hasWebScraping && nodeTypes.includes("dataInput")) {
const inputNode = workflow.nodes.find((n) => n.type === "dataInput");
if (inputNode?.config?.dataType === "url") {
suggestions.push(
"Add a web scraping node to extract content from URLs"
);
}
}
if (hasEmbedding && !hasSearch) {
suggestions.push(
"Add a similarity search node to find similar content using embeddings"
);
}
if (hasSearch && !hasEmbedding) {
suggestions.push(
"Add an embedding generator node to create vector representations of your data"
);
}
if (
!hasLLM &&
nodeTypes.some((t) => ["dataInput", "webScraping"].includes(t))
) {
suggestions.push(
"Add an AI task node to intelligently process your data"
);
}
}
private analyzeConnections(
workflow: WorkflowStructure,
suggestions: string[]
): void {
const connectedNodes = new Set<string>();
workflow.edges.forEach((edge) => {
connectedNodes.add(edge.source);
connectedNodes.add(edge.target);
});
const unconnectedNodes = workflow.nodes.filter((_, index) => {
const nodeId = `node-${index}`;
return !connectedNodes.has(nodeId);
});
if (unconnectedNodes.length > 0) {
suggestions.push("Connect all nodes to create a complete data flow");
}
if (workflow.edges.length === 0 && workflow.nodes.length > 1) {
suggestions.push(
"Connect your nodes to establish data flow between them"
);
}
// Check for potential parallel processing opportunities
const inputNodes = workflow.nodes.filter((n) => n.type === "dataInput");
if (inputNodes.length === 1 && workflow.nodes.length > 3) {
const inputNode = inputNodes[0];
const connectedToInput = workflow.edges.filter(
(e) => e.source === `node-${workflow.nodes.indexOf(inputNode)}`
);
if (connectedToInput.length > 1) {
suggestions.push(
"Consider using parallel processing for better performance"
);
}
}
}
private analyzeConfiguration(
workflow: WorkflowStructure,
suggestions: string[]
): void {
workflow.nodes.forEach((node, index) => {
if (!node.config || Object.keys(node.config).length === 0) {
suggestions.push(
`Configure node ${index + 1} (${
node.label
}) with appropriate settings`
);
}
// Type-specific configuration suggestions
switch (node.type) {
case "llmTask":
if (!node.config.prompt || node.config.prompt.trim() === "") {
suggestions.push(`Add a prompt to LLM task node ${index + 1}`);
}
break;
case "webScraping":
if (!node.config.url || node.config.url.trim() === "") {
suggestions.push(
`Configure URL for web scraping node ${index + 1}`
);
}
break;
case "dataOutput":
if (!node.config.format) {
suggestions.push(
`Specify output format for data output node ${index + 1}`
);
}
break;
}
});
}
private analyzePerformance(
workflow: WorkflowStructure,
suggestions: string[]
): void {
const estimatedTime = workflow.estimatedExecutionTime || 0;
if (estimatedTime > 30000) {
suggestions.push("Consider optimizing workflow for faster execution");
}
const llmNodes = workflow.nodes.filter((n) => n.type === "llmTask");
if (llmNodes.length > 3) {
suggestions.push(
"Consider combining multiple AI tasks or using more efficient models"
);
}
const webScrapingNodes = workflow.nodes.filter(
(n) => n.type === "webScraping"
);
if (webScrapingNodes.length > 2) {
suggestions.push(
"Consider using batch processing for multiple web scraping operations"
);
}
}
private addContextualSuggestions(
context: string,
suggestions: string[]
): void {
const lowerContext = context.toLowerCase();
if (lowerContext.includes("job") || lowerContext.includes("resume")) {
suggestions.push(
"Consider adding a job matching node to find relevant opportunities"
);
suggestions.push(
"Add a cover letter generation node for personalized applications"
);
}
if (lowerContext.includes("web") || lowerContext.includes("scrape")) {
suggestions.push(
"Add content filtering to extract only relevant information"
);
suggestions.push(
"Consider adding error handling for failed web requests"
);
}
if (lowerContext.includes("data") || lowerContext.includes("analysis")) {
suggestions.push("Add data validation nodes to ensure data quality");
suggestions.push(
"Consider adding data transformation nodes for better processing"
);
}
if (lowerContext.includes("search") || lowerContext.includes("find")) {
suggestions.push("Add ranking algorithms to improve search results");
suggestions.push("Consider adding filters to narrow down search results");
}
}
}
src/services/tools/GenerateWorkflowTool.ts
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import { BaseTool } from "./BaseTool";
import { ToolParameter, ToolResult } from "../../types/tools";
import { callOpenAI } from "../openaiService";
import {
WorkflowStructure,
IntentClassification,
EntityExtraction,
} from "../../types";
export class GenerateWorkflowTool extends BaseTool {
name = "generate_workflow";
description =
"Generate a complete workflow structure based on intent classification and entity extraction";
parameters: ToolParameter[] = [
{
name: "userInput",
type: "string",
description: "The original user request",
required: true,
},
{
name: "intent",
type: "object",
description: "The classified intent from previous step",
required: true,
},
{
name: "entities",
type: "object",
description: "The extracted entities from previous step",
required: true,
},
];
async execute(params: Record<string, any>): Promise<ToolResult> {
const { userInput, intent, entities } = params;
if (!userInput || !intent || !entities) {
return this.createResult(
false,
null,
"Missing required parameters: userInput, intent, or entities"
);
}
try {
const { result, executionTime } = await this.measureExecution(
async () => {
return await this.generateWorkflowWithLLM(
userInput,
intent,
entities
);
}
);
return this.createResult(true, result, undefined, {
executionTime,
confidence: this.calculateWorkflowConfidence(result),
});
} catch (error) {
console.error("Error in GenerateWorkflowTool:", error);
return this.createResult(
false,
null,
error instanceof Error ? error.message : "Unknown error"
);
}
}
private async generateWorkflowWithLLM(
userInput: string,
intent: IntentClassification,
entities: EntityExtraction
): Promise<WorkflowStructure> {
const prompt = `
You are an AI workflow designer. Analyze the user's request and create a comprehensive workflow structure.
User Request: "${userInput}"
Intent: ${intent.intent} (confidence: ${intent.confidence})
Reasoning: ${intent.reasoning}
Extracted Entities:
- URLs: ${entities.urls.join(", ") || "None"}
- Data Types: ${entities.dataTypes.join(", ") || "None"}
- Output Formats: ${entities.outputFormats.join(", ") || "None"}
- AI Tasks: ${entities.aiTasks.join(", ") || "None"}
- Processing Steps: ${entities.processingSteps.join(", ") || "None"}
Available node types and their purposes:
- dataInput: Entry point for data (text, JSON, CSV, URL, PDF, etc.)
- webScraping: Extract content from websites using Firecrawl
- llmTask: Process data with AI models (analysis, generation, transformation)
- structuredOutput: Format data according to JSON schemas
- embeddingGenerator: Create vector embeddings for text
- similaritySearch: Find similar content using vector search
- dataOutput: Export results in various formats
Instructions:
1. Understand the user's goal and break it down into logical steps
2. Create a workflow that accomplishes their request
3. Use appropriate node types for each step
4. Configure nodes with realistic settings
5. Connect nodes logically with proper data flow
6. Use variable substitution ({{nodeId.output}}) to pass data between nodes
7. Make the workflow practical and executable
For job application workflows, consider:
- Resume analysis and skill extraction
- Job matching and opportunity identification
- Application generation and personalization
- Cover letter creation
For web scraping workflows, consider:
- URL input and validation
- Content extraction with appropriate formats
- Data cleaning and processing
- Output formatting
For PDF processing workflows, consider:
- PDF file input and validation
- Text extraction from PDF content
- AI analysis of extracted text
- Structured output formatting
For AI analysis workflows, consider:
- Data input and preprocessing
- AI processing with appropriate prompts
- Result formatting and structuring
- Output generation
Respond with valid JSON only:
{
"nodes": [
{
"type": "dataInput",
"label": "Descriptive Node Name",
"config": {
"dataType": "text|json|csv|url",
"defaultValue": "Sample input data"
}
}
],
"edges": [
{
"source": "node-0",
"target": "node-1"
}
],
"topology": {
"type": "linear|fork-join|branching",
"description": "Workflow description",
"parallelExecution": false
},
"complexity": "low|medium|high",
"estimatedExecutionTime": 5000
}
`;
try {
const response = await callOpenAI(prompt, {
model: "deepseek-chat",
temperature: 0.3,
maxTokens: 1000,
});
const result = JSON.parse(response.content);
// Validate the response structure
this.validateWorkflowStructure(result);
return result;
} catch (error) {
console.error("Error in LLM workflow generation:", error);
// Return a simple fallback workflow
return this.generateFallbackWorkflow(userInput, intent, entities);
}
}
private validateWorkflowStructure(workflow: any): void {
if (!workflow.nodes || !Array.isArray(workflow.nodes)) {
throw new Error("Invalid workflow: missing or invalid nodes array");
}
if (!workflow.edges || !Array.isArray(workflow.edges)) {
throw new Error("Invalid workflow: missing or invalid edges array");
}
if (!workflow.topology || !workflow.topology.type) {
throw new Error("Invalid workflow: missing or invalid topology");
}
}
private generateFallbackWorkflow(
userInput: string,
intent: IntentClassification,
entities: EntityExtraction
): WorkflowStructure {
const nodes: any[] = [
{
type: "dataInput",
label: "Input Data",
config: {
dataType: entities.dataTypes[0] || "text",
defaultValue: entities.urls[0] || "Enter your data here",
},
},
];
// Add processing nodes based on intent
if (intent.intent === "WEB_SCRAPING" || entities.urls.length > 0) {
nodes.push({
type: "webScraping",
label: "Web Scraper",
config: {
url: entities.urls[0] || "{{input.output}}",
formats: ["markdown", "html"],
onlyMainContent: true,
},
});
}
if (intent.intent === "AI_ANALYSIS" || entities.aiTasks.length > 0) {
nodes.push({
type: "llmTask",
label: "AI Analyzer",
config: {
prompt: this.generateAIPrompt(entities),
model: "deepseek-chat",
temperature: 0.7,
},
});
}
// Always add output node
nodes.push({
type: "dataOutput",
label: "Data Output",
config: {
format: entities.outputFormats[0] || "json",
filename: `workflow_output_${Date.now()}.json`,
},
});
// Generate edges
const edges = [];
for (let i = 0; i < nodes.length - 1; i++) {
edges.push({
source: `node-${i}`,
target: `node-${i + 1}`,
});
}
return {
nodes,
edges,
topology: {
type: "linear",
description: `Generated workflow for: ${intent.intent}`,
parallelExecution: false,
},
complexity:
nodes.length <= 3 ? "low" : nodes.length <= 6 ? "medium" : "high",
estimatedExecutionTime: nodes.length * 2000,
};
}
private generateAIPrompt(entities: EntityExtraction): string {
const tasks = entities.aiTasks || [];
if (tasks.includes("summarize")) {
return "Summarize the following content in 2-3 sentences: {{input.output}}";
}
if (tasks.includes("analyze")) {
return "Analyze the following content and provide insights: {{input.output}}";
}
if (tasks.includes("extract")) {
return "Extract key information from the following content: {{input.output}}";
}
if (tasks.includes("classify")) {
return "Classify the following content into categories: {{input.output}}";
}
return "Process the following content: {{input.output}}";
}
private calculateWorkflowConfidence(workflow: WorkflowStructure): number {
let confidence = 0.7; // Base confidence for generated workflow
// Increase confidence based on workflow complexity
if (workflow.complexity === "low") confidence += 0.1;
else if (workflow.complexity === "medium") confidence += 0.05;
// Increase confidence if workflow has proper input/output nodes
const hasInput = workflow.nodes.some((n) => n.type === "dataInput");
const hasOutput = workflow.nodes.some((n) => n.type === "dataOutput");
if (hasInput && hasOutput) confidence += 0.1;
// Increase confidence if workflow has logical flow
if (workflow.edges.length > 0) confidence += 0.05;
return Math.min(confidence, 1.0);
}
}
src/services/tools/ToolRegistry.ts
+40
View File
@@ -0,0 +1,40 @@
import { Tool, ToolRegistry as IToolRegistry } from "../../types/tools";
export class ToolRegistry implements IToolRegistry {
private tools = new Map<string, Tool>();
registerTool(tool: Tool): void {
this.tools.set(tool.name, tool);
console.log(`Registered tool: ${tool.name}`);
}
getTool(name: string): Tool | undefined {
return this.tools.get(name);
}
getAllTools(): Tool[] {
return Array.from(this.tools.values());
}
unregisterTool(name: string): void {
this.tools.delete(name);
console.log(`Unregistered tool: ${name}`);
}
getToolsByCategory(category: string): Tool[] {
// For now, return all tools. In the future, we can add categories
return this.getAllTools();
}
getToolNames(): string[] {
return Array.from(this.tools.keys());
}
clear(): void {
this.tools.clear();
console.log("Cleared all tools from registry");
}
}
// Global tool registry instance
export const toolRegistry = new ToolRegistry();
src/services/tools/ValidateWorkflowTool.ts
+343
View File
@@ -0,0 +1,343 @@
import { BaseTool } from "./BaseTool";
import { ToolParameter, ToolResult } from "../../types/tools";
import { WorkflowStructure, ValidationResult } from "../../types";
import { validateWorkflowStructure } from "../../utils/workflowValidator";
export class ValidateWorkflowTool extends BaseTool {
name = "validate_workflow";
description =
"Validate a generated workflow for correctness, completeness, and best practices";
parameters: ToolParameter[] = [
{
name: "workflow",
type: "object",
description: "The workflow structure to validate",
required: true,
},
{
name: "originalInput",
type: "string",
description: "The original user input for context",
required: false,
},
];
async execute(params: Record<string, any>): Promise<ToolResult> {
const { workflow, originalInput } = params;
if (!workflow) {
return this.createResult(
false,
null,
"Missing required parameter: workflow"
);
}
try {
const { result, executionTime } = await this.measureExecution(
async () => {
return await this.validateWorkflow(workflow, originalInput || "");
}
);
return this.createResult(true, result, undefined, {
executionTime,
confidence: this.calculateValidationConfidence(result),
});
} catch (error) {
console.error("Error in ValidateWorkflowTool:", error);
return this.createResult(
false,
null,
error instanceof Error ? error.message : "Unknown error"
);
}
}
private async validateWorkflow(
workflow: WorkflowStructure,
originalInput: string
): Promise<ValidationResult> {
try {
// Use the existing validation logic
const validationResult = validateWorkflowStructure(
workflow,
originalInput
);
// Add additional custom validation
const enhancedResult = this.enhanceValidation(validationResult, workflow);
return enhancedResult;
} catch (error) {
console.error("Error in workflow validation:", error);
// Return a basic validation result
return {
isValid: false,
issues: ["Validation failed due to internal error"],
suggestions: ["Please check the workflow structure and try again"],
complexity: "unknown",
estimatedExecutionTime: 0,
};
}
}
private enhanceValidation(
baseResult: ValidationResult,
workflow: WorkflowStructure
): ValidationResult {
const issues = [...(baseResult.issues || [])];
const suggestions = [...(baseResult.suggestions || [])];
// Add custom validation rules
this.validateNodeConfiguration(workflow, issues, suggestions);
this.validateDataFlow(workflow, issues, suggestions);
this.validatePerformance(workflow, issues, suggestions);
this.validateBestPractices(workflow, issues, suggestions);
return {
...baseResult,
issues,
suggestions,
complexity: this.determineComplexity(workflow),
estimatedExecutionTime: this.estimateExecutionTime(workflow),
};
}
private validateNodeConfiguration(
workflow: WorkflowStructure,
issues: string[],
suggestions: string[]
): void {
workflow.nodes.forEach((node, index) => {
// Check for empty labels
if (!node.label || node.label.trim() === "") {
issues.push(`Node ${index + 1} has an empty label`);
suggestions.push(`Give node ${index + 1} a descriptive label`);
}
// Check for missing configuration
if (!node.config || Object.keys(node.config).length === 0) {
issues.push(`Node ${index + 1} (${node.type}) has no configuration`);
suggestions.push(
`Configure node ${index + 1} with appropriate settings`
);
}
// Type-specific validation
this.validateNodeTypeSpecific(node, index, issues, suggestions);
});
}
private validateNodeTypeSpecific(
node: any,
index: number,
issues: string[],
suggestions: string[]
): void {
switch (node.type) {
case "dataInput":
if (!node.config.dataType) {
issues.push(`Data input node ${index + 1} missing data type`);
suggestions.push(`Specify data type for input node ${index + 1}`);
}
break;
case "webScraping":
if (!node.config.url && !node.config.url?.includes("{{")) {
issues.push(`Web scraping node ${index + 1} missing URL`);
suggestions.push(`Configure URL for web scraping node ${index + 1}`);
}
break;
case "llmTask":
if (!node.config.prompt) {
issues.push(`LLM task node ${index + 1} missing prompt`);
suggestions.push(`Add a prompt for LLM task node ${index + 1}`);
}
break;
case "dataOutput":
if (!node.config.format) {
issues.push(`Data output node ${index + 1} missing output format`);
suggestions.push(`Specify output format for node ${index + 1}`);
}
break;
}
}
private validateDataFlow(
workflow: WorkflowStructure,
issues: string[],
suggestions: string[]
): void {
// Check for orphaned nodes
const connectedNodes = new Set<string>();
workflow.edges.forEach((edge) => {
connectedNodes.add(edge.source);
connectedNodes.add(edge.target);
});
workflow.nodes.forEach((node, index) => {
const nodeId = `node-${index}`;
if (!connectedNodes.has(nodeId) && workflow.nodes.length > 1) {
issues.push(`Node ${index + 1} (${node.label}) is not connected`);
suggestions.push(
`Connect node ${index + 1} to other nodes in the workflow`
);
}
});
// Check for circular dependencies
const circularDeps = this.detectCircularDependencies(workflow);
if (circularDeps.length > 0) {
issues.push(`Circular dependencies detected: ${circularDeps.join(", ")}`);
suggestions.push("Remove circular dependencies in the workflow");
}
}
private validatePerformance(
workflow: WorkflowStructure,
issues: string[],
suggestions: string[]
): void {
const estimatedTime = this.estimateExecutionTime(workflow);
if (estimatedTime > 60000) {
// More than 1 minute
issues.push("Workflow execution time is very long");
suggestions.push(
"Consider optimizing the workflow for better performance"
);
}
if (workflow.nodes.length > 10) {
issues.push("Workflow has many nodes which may be complex to maintain");
suggestions.push(
"Consider breaking down the workflow into smaller, focused workflows"
);
}
}
private validateBestPractices(
workflow: WorkflowStructure,
issues: string[],
suggestions: string[]
): void {
// Check for proper input/output nodes
const hasInput = workflow.nodes.some((n) => n.type === "dataInput");
const hasOutput = workflow.nodes.some((n) => n.type === "dataOutput");
if (!hasInput) {
issues.push("Workflow missing input node");
suggestions.push("Add a data input node to start the workflow");
}
if (!hasOutput) {
issues.push("Workflow missing output node");
suggestions.push("Add a data output node to complete the workflow");
}
// Check for variable substitution usage
const hasVariables = workflow.nodes.some((node) =>
JSON.stringify(node.config).includes("{{")
);
if (!hasVariables && workflow.nodes.length > 1) {
suggestions.push(
"Consider using variable substitution to pass data between nodes"
);
}
}
private detectCircularDependencies(workflow: WorkflowStructure): string[] {
const graph = new Map<string, string[]>();
// Build adjacency list
workflow.nodes.forEach((_, index) => {
graph.set(`node-${index}`, []);
});
workflow.edges.forEach((edge) => {
const sourceList = graph.get(edge.source) || [];
sourceList.push(edge.target);
graph.set(edge.source, sourceList);
});
// DFS to detect cycles
const visited = new Set<string>();
const recursionStack = new Set<string>();
const cycles: string[] = [];
const dfs = (node: string, path: string[]): void => {
if (recursionStack.has(node)) {
const cycleStart = path.indexOf(node);
cycles.push(path.slice(cycleStart).join(" -> ") + " -> " + node);
return;
}
if (visited.has(node)) return;
visited.add(node);
recursionStack.add(node);
path.push(node);
const neighbors = graph.get(node) || [];
neighbors.forEach((neighbor) => dfs(neighbor, [...path]));
recursionStack.delete(node);
};
workflow.nodes.forEach((_, index) => {
const nodeId = `node-${index}`;
if (!visited.has(nodeId)) {
dfs(nodeId, []);
}
});
return cycles;
}
private determineComplexity(workflow: WorkflowStructure): string {
const nodeCount = workflow.nodes.length;
const edgeCount = workflow.edges.length;
const hasParallel = workflow.topology.parallelExecution;
if (nodeCount <= 3 && edgeCount <= 2 && !hasParallel) return "low";
if (nodeCount <= 6 && edgeCount <= 5) return "medium";
return "high";
}
private estimateExecutionTime(workflow: WorkflowStructure): number {
const timeEstimates: Record<string, number> = {
dataInput: 0,
webScraping: 5000,
llmTask: 3000,
structuredOutput: 2000,
embeddingGenerator: 4000,
similaritySearch: 3000,
dataOutput: 0,
};
return workflow.nodes.reduce((total, node) => {
return total + (timeEstimates[node.type] || 1000);
}, 0);
}
private calculateValidationConfidence(result: ValidationResult): number {
let confidence = 0.8; // Base confidence for validation
// Decrease confidence based on number of issues
if (result.issues && result.issues.length > 0) {
confidence -= Math.min(result.issues.length * 0.1, 0.5);
}
// Increase confidence if workflow is valid
if (result.isValid) {
confidence += 0.2;
}
return Math.max(Math.min(confidence, 1.0), 0.0);
}
}
src/store/workflowStore.ts
+21 -36
View File
@@ -15,7 +15,7 @@ import {
FirecrawlConfig,
} from "../services/firecrawlService";
import { substituteVariables, NodeOutput } from "../utils/variableSubstitution";
import { copilotService } from "../services/copilotService";
import { agentManager } from "../services/agents/AgentManager";
// localStorage key for workflows
const WORKFLOWS_STORAGE_KEY = "agent-workflow-builder-workflows";
@@ -107,7 +107,7 @@ interface WorkflowStore {
// Copilot methods
generateWorkflowFromDescription: (description: string) => Promise<void>;
applyCopilotSuggestions: (suggestions: any[]) => void;
validateGeneratedWorkflow: () => ValidationResult | null;
validateGeneratedWorkflow: () => Promise<ValidationResult | null>;
getCopilotSuggestions: (context?: string) => Promise<string[]>;
}
@@ -483,7 +483,7 @@ export const useWorkflowStore = create<WorkflowStore>((set, get) => ({
// Copilot methods
generateWorkflowFromDescription: async (description: string) => {
try {
const parsedIntent = await copilotService.parseNaturalLanguage(
const parsedIntent = await agentManager.generateWorkflowFromDescription(
description
);
const workflowStructure = parsedIntent.workflowStructure;
@@ -565,7 +565,7 @@ export const useWorkflowStore = create<WorkflowStore>((set, get) => ({
});
},
validateGeneratedWorkflow: (): ValidationResult | null => {
validateGeneratedWorkflow: async (): Promise<ValidationResult | null> => {
const { currentWorkflow } = get();
if (!currentWorkflow) return null;
@@ -591,42 +591,27 @@ export const useWorkflowStore = create<WorkflowStore>((set, get) => ({
complexity: "medium",
};
return copilotService.validateWorkflow(workflowStructure, "");
try {
return await agentManager.validateWorkflow(workflowStructure, "");
} catch (error) {
console.error("Error validating workflow:", error);
return {
isValid: false,
issues: ["Validation failed due to internal error"],
suggestions: ["Please check the workflow structure and try again"],
complexity: "unknown",
estimatedExecutionTime: 0,
};
}
},
getCopilotSuggestions: async (context?: string): Promise<string[]> => {
const { currentWorkflow } = get();
if (!currentWorkflow) {
return ["Start by creating a new workflow"];
try {
return await agentManager.getSuggestions(context);
} catch (error) {
console.error("Error getting copilot suggestions:", error);
return ["Unable to generate suggestions at this time"];
}
// Convert current workflow to WorkflowStructure format
const workflowStructure: WorkflowStructure = {
nodes: currentWorkflow.nodes.map((node) => ({
type: node.type,
label: node.data.label,
config: node.data.config,
position: node.position,
})),
edges: currentWorkflow.edges.map((edge) => ({
source: edge.source,
target: edge.target,
sourceHandle: edge.sourceHandle,
targetHandle: edge.targetHandle,
})),
topology: {
type: "linear",
description: "Sequential processing",
parallelExecution: false,
},
complexity: "medium",
};
return await copilotService.generateContextualSuggestions(
workflowStructure,
context || ""
);
},
}));
src/test-agent-system.ts
+52
View File
@@ -0,0 +1,52 @@
// Test script for the new agent system
import { agentManager } from "./services/agents/AgentManager";
async function testAgentSystem() {
console.log("Testing Agent System...");
try {
// Test 1: Basic workflow generation
console.log("\n=== Test 1: Basic Workflow Generation ===");
const result1 = await agentManager.processWorkflowRequest(
"Create a workflow that scrapes a website and analyzes the content with AI"
);
console.log("Result 1:", {
success: result1.success,
toolsUsed: result1.toolsUsed,
executionTime: result1.executionTime,
confidence: result1.confidence,
});
if (result1.success) {
console.log(
"Generated workflow nodes:",
result1.data.parsedIntent.workflowStructure.nodes.length
);
}
// Test 2: Suggestions generation
console.log("\n=== Test 2: Suggestions Generation ===");
const suggestions = await agentManager.getSuggestions(
"I want to process job applications"
);
console.log("Suggestions:", suggestions);
// Test 3: Tool information
console.log("\n=== Test 3: Available Tools ===");
const tools = agentManager.getAvailableTools();
console.log("Available tools:", tools);
// Test 4: Session info
console.log("\n=== Test 4: Session Info ===");
const sessionInfo = agentManager.getSessionInfo();
console.log("Session info:", sessionInfo);
console.log("\n✅ All tests completed successfully!");
} catch (error) {
console.error("❌ Test failed:", error);
}
}
// Run the test
testAgentSystem();
src/types/tools.ts
+77
View File
@@ -0,0 +1,77 @@
// Tool system types
export interface Tool {
name: string;
description: string;
parameters: ToolParameter[];
execute: (params: Record<string, any>) => Promise<ToolResult>;
validate?: (params: Record<string, any>) => ToolValidationResult;
}
export interface ToolParameter {
name: string;
type: "string" | "number" | "boolean" | "object" | "array";
description: string;
required: boolean;
defaultValue?: any;
}
export interface ToolResult {
success: boolean;
data?: any;
error?: string;
metadata?: {
executionTime?: number;
tokensUsed?: number;
confidence?: number;
};
}
export interface ToolValidationResult {
isValid: boolean;
errors: string[];
warnings: string[];
}
export interface ToolRegistry {
registerTool(tool: Tool): void;
getTool(name: string): Tool | undefined;
getAllTools(): Tool[];
unregisterTool(name: string): void;
}
export interface AgentTask {
id: string;
type:
| "workflow_generation"
| "intent_analysis"
| "validation"
| "optimization";
input: string;
context?: Record<string, any>;
priority: "low" | "medium" | "high";
createdAt: Date;
}
export interface AgentResult {
success: boolean;
data?: any;
error?: string;
toolsUsed: string[];
executionTime: number;
confidence: number;
}
export interface AgentContext {
userRequest: string;
currentWorkflow?: any;
executionHistory: any[];
userPreferences: Record<string, any>;
sessionId: string;
}
export interface ToolExecutionPlan {
tools: string[];
dependencies: Array<{ tool: string; dependsOn: string }>;
parallel: boolean;
estimatedTime: number;
}
tsconfig.json
+4 -2
View File
@@ -1,9 +1,10 @@
{
"compilerOptions": {
"target": "es5",
"target": "es2015",
"lib": ["dom", "dom.iterable", "es6"],
"allowJs": true,
"skipLibCheck": true,
"skipDefaultLibCheck": true,
"esModuleInterop": true,
"allowSyntheticDefaultImports": true,
"strict": true,
@@ -16,5 +17,6 @@
"noEmit": true,
"jsx": "react-jsx"
},
"include": ["src"]
"include": ["src"],
"exclude": ["node_modules/@types/d3-dispatch", "node_modules"]
}