zai-mcp-server/src/multiAgentCollaborator.js

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/**
 * Multi-Agent Collaboration System
 * Advanced AI agent coordination and collaboration for enhanced AI-to-AI loops
 */

export class MultiAgentCollaborator {
    constructor() {
        this.agentTypes = new Map();
        this.activeAgents = new Map();
        this.collaborationPatterns = new Map();
        this.consensusEngine = new ConsensusEngine();
        this.knowledgeSharing = new KnowledgeSharing();
        
        this.initializeAgentTypes();
        console.log('🤝 Multi-Agent Collaboration System initialized with specialized AI agents');
    }

    /**
     * Initialize different types of AI agents
     */
    initializeAgentTypes() {
        this.agentTypes.set('researcher', {
            name: 'researcher',
            role: 'Research and Analysis',
            capabilities: ['information_gathering', 'pattern_analysis', 'trend_identification'],
            specialization: 'deep_research',
            prompt_style: 'analytical',
            temperature: 0.3,
            max_tokens: 800,
            description: 'Focuses on thorough research and analysis'
        });

        this.agentTypes.set('implementer', {
            name: 'implementer',
            role: 'Implementation and Execution',
            capabilities: ['solution_design', 'implementation_planning', 'execution_strategy'],
            specialization: 'practical_solutions',
            prompt_style: 'action_oriented',
            temperature: 0.5,
            max_tokens: 600,
            description: 'Focuses on practical implementation and execution'
        });

        this.agentTypes.set('reviewer', {
            name: 'reviewer',
            role: 'Quality Assurance and Review',
            capabilities: ['quality_assessment', 'error_detection', 'improvement_suggestions'],
            specialization: 'quality_control',
            prompt_style: 'critical_analysis',
            temperature: 0.2,
            max_tokens: 500,
            description: 'Focuses on quality assurance and critical review'
        });

        this.agentTypes.set('optimizer', {
            name: 'optimizer',
            role: 'Performance Optimization',
            capabilities: ['performance_analysis', 'efficiency_improvement', 'resource_optimization'],
            specialization: 'optimization',
            prompt_style: 'efficiency_focused',
            temperature: 0.4,
            max_tokens: 600,
            description: 'Focuses on performance and efficiency optimization'
        });

        this.agentTypes.set('innovator', {
            name: 'innovator',
            role: 'Creative Innovation',
            capabilities: ['creative_thinking', 'novel_approaches', 'breakthrough_solutions'],
            specialization: 'innovation',
            prompt_style: 'creative',
            temperature: 0.8,
            max_tokens: 700,
            description: 'Focuses on creative and innovative solutions'
        });

        this.agentTypes.set('coordinator', {
            name: 'coordinator',
            role: 'Coordination and Integration',
            capabilities: ['task_coordination', 'integration_planning', 'workflow_management'],
            specialization: 'coordination',
            prompt_style: 'systematic',
            temperature: 0.3,
            max_tokens: 500,
            description: 'Focuses on coordination and integration of efforts'
        });
    }

    /**
     * Initialize agents for a specific loop
     */
    async initializeAgents(loopConfig) {
        const agents = [];
        const requiredAgents = this.determineRequiredAgents(loopConfig);
        
        for (const agentType of requiredAgents) {
            const agentConfig = this.agentTypes.get(agentType);
            if (agentConfig) {
                const agent = await this.createAgent(agentConfig, loopConfig);
                agents.push(agent);
            }
        }
        
        console.log(`🤖 [${loopConfig.id}] Initialized ${agents.length} specialized agents: ${agents.map(a => a.type).join(', ')}`);
        return agents;
    }

    /**
     * Determine which agents are needed based on loop configuration
     */
    determineRequiredAgents(loopConfig) {
        const agents = ['coordinator']; // Always include coordinator
        
        // Analyze topic to determine needed agents
        const topic = loopConfig.topic.toLowerCase();
        
        if (topic.includes('research') || topic.includes('analyze') || topic.includes('study')) {
            agents.push('researcher');
        }
        
        if (topic.includes('implement') || topic.includes('build') || topic.includes('create')) {
            agents.push('implementer');
        }
        
        if (topic.includes('review') || topic.includes('quality') || topic.includes('test')) {
            agents.push('reviewer');
        }
        
        if (topic.includes('optimize') || topic.includes('improve') || topic.includes('performance')) {
            agents.push('optimizer');
        }
        
        if (topic.includes('innovative') || topic.includes('creative') || topic.includes('novel')) {
            agents.push('innovator');
        }
        
        // Ensure minimum viable team
        if (agents.length < 3) {
            agents.push('researcher', 'implementer');
        }
        
        // Limit maximum team size
        return agents.slice(0, 6);
    }

    /**
     * Create an individual agent
     */
    async createAgent(agentConfig, loopConfig) {
        const agent = {
            id: `${agentConfig.name}_${loopConfig.id}_${Date.now()}`,
            type: agentConfig.name,
            role: agentConfig.role,
            capabilities: agentConfig.capabilities,
            specialization: agentConfig.specialization,
            config: agentConfig,
            loopId: loopConfig.id,
            performance: new AgentPerformanceTracker(),
            knowledge: new AgentKnowledge(),
            state: 'initialized',
            createdAt: Date.now()
        };
        
        this.activeAgents.set(agent.id, agent);
        return agent;
    }

    /**
     * Orchestrate collaboration between agents
     */
    async collaborate(loop, strategy) {
        const agents = loop.agents;
        const collaborationResult = {
            contributions: [],
            consensus: null,
            conflicts: [],
            insights: [],
            performance: {},
            duration: 0
        };
        
        const startTime = Date.now();
        
        try {
            // Phase 1: Individual agent contributions
            const contributions = await this.gatherIndividualContributions(agents, loop, strategy);
            collaborationResult.contributions = contributions;
            
            // Phase 2: Knowledge sharing between agents
            await this.facilitateKnowledgeSharing(agents, contributions);
            
            // Phase 3: Consensus building
            const consensus = await this.buildConsensus(agents, contributions, loop);
            collaborationResult.consensus = consensus;
            
            // Phase 4: Conflict resolution
            const conflicts = await this.resolveConflicts(agents, contributions, consensus);
            collaborationResult.conflicts = conflicts;
            
            // Phase 5: Insight synthesis
            const insights = await this.synthesizeInsights(contributions, consensus);
            collaborationResult.insights = insights;
            
            // Phase 6: Performance tracking
            collaborationResult.performance = await this.trackAgentPerformance(agents, contributions);
            
            collaborationResult.duration = Date.now() - startTime;
            
            console.log(`🤝 [${loop.id}] Collaboration completed: ${contributions.length} contributions, ${insights.length} insights, ${conflicts.length} conflicts resolved`);
            
        } catch (error) {
            console.error(`❌ [${loop.id}] Collaboration error:`, error);
            collaborationResult.error = error.message;
        }
        
        return collaborationResult;
    }

    /**
     * Gather individual contributions from each agent
     */
    async gatherIndividualContributions(agents, loop, strategy) {
        const contributions = [];
        
        for (const agent of agents) {
            try {
                const contribution = await this.getAgentContribution(agent, loop, strategy);
                contributions.push(contribution);
                
                // Update agent performance
                agent.performance.recordContribution(contribution);
                
            } catch (error) {
                console.error(`❌ Agent ${agent.id} contribution failed:`, error);
                contributions.push({
                    agentId: agent.id,
                    type: agent.type,
                    error: error.message,
                    timestamp: Date.now()
                });
            }
        }
        
        return contributions;
    }

    /**
     * Get contribution from a specific agent
     */
    async getAgentContribution(agent, loop, strategy) {
        const prompt = await this.generateAgentPrompt(agent, loop, strategy);
        
        // Simulate AI agent response (in real implementation, this would call actual AI)
        const response = await this.simulateAgentResponse(agent, prompt);
        
        const contribution = {
            agentId: agent.id,
            agentType: agent.type,
            role: agent.role,
            prompt: prompt,
            response: response,
            quality: this.assessContributionQuality(response, agent),
            relevance: this.assessContributionRelevance(response, loop),
            timestamp: Date.now(),
            iteration: loop.currentIteration
        };
        
        // Update agent knowledge
        agent.knowledge.addContribution(contribution);
        
        return contribution;
    }

    /**
     * Generate specialized prompt for each agent type
     */
    async generateAgentPrompt(agent, loop, strategy) {
        const baseContext = `
Topic: ${loop.topic}
Iteration: ${loop.currentIteration}
Strategy: ${strategy.name}
Your Role: ${agent.role}
Your Specialization: ${agent.specialization}
`;
        
        const roleSpecificPrompt = this.generateRoleSpecificPrompt(agent, loop);
        const collaborationContext = await this.getCollaborationContext(agent, loop);
        
        return `${baseContext}

${roleSpecificPrompt}

${collaborationContext}

Please provide your specialized contribution focusing on your role as ${agent.role}.
Be specific, actionable, and consider how your contribution integrates with other agents' work.`;
    }

    /**
     * Generate role-specific prompts
     */
    generateRoleSpecificPrompt(agent, loop) {
        const prompts = {
            researcher: `As a Research Agent, analyze the topic thoroughly and provide:
1. Key research findings and insights
2. Relevant patterns and trends
3. Data-driven recommendations
4. Areas requiring further investigation`,

            implementer: `As an Implementation Agent, focus on practical execution:
1. Concrete implementation steps
2. Technical requirements and dependencies
3. Resource allocation and timeline
4. Risk mitigation strategies`,

            reviewer: `As a Review Agent, provide critical analysis:
1. Quality assessment of current approach
2. Potential issues and vulnerabilities
3. Improvement recommendations
4. Compliance and standards verification`,

            optimizer: `As an Optimization Agent, focus on efficiency:
1. Performance bottlenecks identification
2. Resource optimization opportunities
3. Efficiency improvement strategies
4. Cost-benefit analysis`,

            innovator: `As an Innovation Agent, think creatively:
1. Novel approaches and breakthrough ideas
2. Creative solutions to challenges
3. Emerging trends and opportunities
4. Disruptive innovation potential`,

            coordinator: `As a Coordination Agent, manage integration:
1. Task coordination and sequencing
2. Resource allocation optimization
3. Timeline and milestone management
4. Integration strategy for all contributions`
        };
        
        return prompts[agent.type] || prompts.coordinator;
    }

    /**
     * Simulate AI agent response (placeholder for actual AI integration)
     */
    async simulateAgentResponse(agent, prompt) {
        // Simulate different response styles based on agent type
        const responses = {
            researcher: `Based on thorough analysis of "${prompt.split('Topic: ')[1]?.split('\n')[0]}", I've identified key patterns and research insights that suggest a systematic approach focusing on data-driven methodologies and evidence-based recommendations.`,
            implementer: `For practical implementation of this topic, I recommend a phased approach with clear milestones, resource allocation, and risk mitigation strategies to ensure successful execution.`,
            reviewer: `Quality assessment reveals several areas for improvement including enhanced error handling, better validation mechanisms, and more robust testing procedures to ensure reliability.`,
            optimizer: `Performance analysis indicates optimization opportunities in resource utilization, processing efficiency, and cost reduction through strategic improvements.`,
            innovator: `Creative analysis suggests innovative approaches including novel methodologies, breakthrough techniques, and disruptive solutions that could transform the current paradigm.`,
            coordinator: `Coordination analysis shows optimal task sequencing, resource allocation, and integration strategies to maximize overall effectiveness and minimize conflicts.`
        };
        
        return responses[agent.type] || responses.coordinator;
    }

    /**
     * Assess contribution quality
     */
    assessContributionQuality(response, agent) {
        // Simple quality assessment based on response length and agent type
        const baseQuality = Math.min(1.0, response.length / 200);
        const typeBonus = agent.type === 'researcher' ? 0.1 : 0.05;
        return Math.min(1.0, baseQuality + typeBonus);
    }

    /**
     * Assess contribution relevance
     */
    assessContributionRelevance(response, loop) {
        // Simple relevance assessment based on topic keywords
        const topicWords = loop.topic.toLowerCase().split(' ');
        const responseWords = response.toLowerCase().split(' ');
        const matches = topicWords.filter(word => responseWords.includes(word));
        return Math.min(1.0, matches.length / topicWords.length);
    }

    /**
     * Facilitate knowledge sharing between agents
     */
    async facilitateKnowledgeSharing(agents, contributions) {
        for (const agent of agents) {
            // Share relevant knowledge from other agents
            const relevantKnowledge = this.knowledgeSharing.findRelevantKnowledge(agent, contributions);
            agent.knowledge.addSharedKnowledge(relevantKnowledge);
        }

        console.log(`🧠 Knowledge sharing completed between ${agents.length} agents`);
    }

    /**
     * Build consensus from agent contributions
     */
    async buildConsensus(agents, contributions, loop) {
        return await this.consensusEngine.buildConsensus(contributions, {
            method: 'weighted_voting',
            weights: this.calculateAgentWeights(agents),
            threshold: 0.7,
            maxIterations: 3
        });
    }

    /**
     * Calculate weights for agents based on performance and relevance
     */
    calculateAgentWeights(agents) {
        const weights = {};

        for (const agent of agents) {
            const performance = agent.performance.getOverallScore();
            const experience = agent.knowledge.getExperienceLevel();
            const relevance = this.calculateAgentRelevance(agent);

            weights[agent.id] = (performance * 0.4 + experience * 0.3 + relevance * 0.3);
        }

        return weights;
    }

    /**
     * Resolve conflicts between agent contributions
     */
    async resolveConflicts(agents, contributions, consensus) {
        const conflicts = this.identifyConflicts(contributions);
        const resolutions = [];

        for (const conflict of conflicts) {
            const resolution = await this.resolveConflict(conflict, agents, consensus);
            resolutions.push(resolution);
        }

        return resolutions;
    }

    /**
     * Synthesize insights from all contributions
     */
    async synthesizeInsights(contributions, consensus) {
        const insights = [];

        // Extract key insights from each contribution
        for (const contribution of contributions) {
            const contributionInsights = this.extractInsights(contribution);
            insights.push(...contributionInsights);
        }

        // Add consensus-based insights
        if (consensus && consensus.insights) {
            insights.push(...consensus.insights);
        }

        // Remove duplicates and rank by importance
        const uniqueInsights = this.deduplicateInsights(insights);
        const rankedInsights = this.rankInsights(uniqueInsights);

        return rankedInsights.slice(0, 10); // Top 10 insights
    }

    /**
     * Track performance of all agents
     */
    async trackAgentPerformance(agents, contributions) {
        const performance = {};

        for (const agent of agents) {
            const agentContribution = contributions.find(c => c.agentId === agent.id);
            if (agentContribution) {
                performance[agent.id] = {
                    quality: agentContribution.quality,
                    relevance: agentContribution.relevance,
                    responseTime: agentContribution.responseTime || 0,
                    overallScore: agent.performance.getOverallScore()
                };
            }
        }

        return performance;
    }

    /**
     * Get contributions summary for reporting
     */
    async getContributions(loop) {
        const agents = loop.agents || [];
        const contributions = [];

        for (const agent of agents) {
            const agentContributions = agent.knowledge.getContributions();
            contributions.push({
                agentId: agent.id,
                agentType: agent.type,
                role: agent.role,
                totalContributions: agentContributions.length,
                averageQuality: this.calculateAverageQuality(agentContributions),
                specializations: agent.capabilities
            });
        }

        return contributions;
    }

    /**
     * Utility methods
     */
    async getCollaborationContext(agent, loop) {
        return `Previous iterations: ${loop.currentIteration}
Agent team: ${loop.agents?.map(a => a.type).join(', ') || 'solo'}
Current focus: Collaborative improvement and optimization`;
    }

    calculateAgentRelevance(agent) {
        // Calculate how relevant this agent type is for current context
        return 0.8; // Placeholder
    }

    identifyConflicts(contributions) {
        // Identify conflicts between agent contributions
        return []; // Placeholder
    }

    async resolveConflict(conflict, agents, consensus) {
        // Resolve specific conflict
        return { conflict, resolution: 'resolved', method: 'consensus' };
    }

    extractInsights(contribution) {
        // Extract insights from contribution
        return [{
            type: 'agent_insight',
            content: `Insight from ${contribution.agentType}`,
            quality: contribution.quality,
            source: contribution.agentId
        }];
    }

    deduplicateInsights(insights) {
        // Remove duplicate insights
        const unique = new Map();
        for (const insight of insights) {
            const content = insight.content || insight.toString();
            const key = content.substring(0, 50);
            if (!unique.has(key)) {
                unique.set(key, insight);
            }
        }
        return Array.from(unique.values());
    }

    rankInsights(insights) {
        // Rank insights by importance
        return insights.sort((a, b) => (b.quality || 0) - (a.quality || 0));
    }

    calculateAverageQuality(contributions) {
        if (contributions.length === 0) return 0;
        const total = contributions.reduce((sum, c) => sum + (c.quality || 0), 0);
        return total / contributions.length;
    }
}

/**
 * Agent Performance Tracker
 */
class AgentPerformanceTracker {
    constructor() {
        this.contributions = [];
        this.metrics = {
            totalContributions: 0,
            averageQuality: 0,
            averageRelevance: 0,
            responseTime: 0
        };
    }

    recordContribution(contribution) {
        this.contributions.push(contribution);
        this.updateMetrics();
    }

    updateMetrics() {
        const total = this.contributions.length;
        if (total === 0) return;

        this.metrics.totalContributions = total;
        this.metrics.averageQuality = this.contributions.reduce((sum, c) => sum + (c.quality || 0), 0) / total;
        this.metrics.averageRelevance = this.contributions.reduce((sum, c) => sum + (c.relevance || 0), 0) / total;
        this.metrics.responseTime = this.contributions.reduce((sum, c) => sum + (c.responseTime || 0), 0) / total;
    }

    getOverallScore() {
        return (this.metrics.averageQuality * 0.4 + this.metrics.averageRelevance * 0.4 +
                (this.metrics.responseTime > 0 ? Math.min(1, 5000 / this.metrics.responseTime) : 0.5) * 0.2);
    }
}

/**
 * Agent Knowledge System
 */
class AgentKnowledge {
    constructor() {
        this.contributions = [];
        this.sharedKnowledge = [];
        this.experienceLevel = 0;
    }

    addContribution(contribution) {
        this.contributions.push(contribution);
        this.experienceLevel += 0.1;
    }

    addSharedKnowledge(knowledge) {
        this.sharedKnowledge.push(...knowledge);
    }

    getContributions() {
        return this.contributions;
    }

    getExperienceLevel() {
        return Math.min(1.0, this.experienceLevel);
    }
}

/**
 * Consensus Engine
 */
class ConsensusEngine {
    async buildConsensus(contributions, options) {
        // Simple consensus building - in real implementation, this would be more sophisticated
        const validContributions = contributions.filter(c => !c.error);

        if (validContributions.length === 0) {
            return { consensus: 'no_valid_contributions', confidence: 0 };
        }

        const averageQuality = validContributions.reduce((sum, c) => sum + (c.quality || 0), 0) / validContributions.length;

        return {
            consensus: 'collaborative_agreement',
            confidence: averageQuality,
            contributions: validContributions.length,
            insights: ['Consensus reached through collaborative analysis'],
            method: options.method
        };
    }
}

/**
 * Knowledge Sharing System
 */
class KnowledgeSharing {
    findRelevantKnowledge(agent, contributions) {
        // Find knowledge relevant to this agent from other contributions
        return contributions
            .filter(c => c.agentId !== agent.id && !c.error)
            .map(c => ({
                source: c.agentType,
                knowledge: c.response.substring(0, 100),
                relevance: 0.7
            }));
    }
}