ai-agent-orchestrator

仓库: dengineproblem/agents-monorepo
安装量: 48
排名: #15345

安装

npx skills add https://github.com/patricio0312rev/skills --skill ai-agent-orchestrator

AI Agent Orchestrator

Build coordinated multi-agent systems for complex task automation.

Core Workflow Define agents: Create specialized agents Design workflow: Plan agent coordination Implement handoffs: Agent-to-agent communication Add shared memory: Persistent context Create supervisor: Orchestrate execution Monitor execution: Track agent activities Agent Architecture Agent Definition // agents/base.ts import { ChatOpenAI } from '@langchain/openai'; import { SystemMessage, HumanMessage, AIMessage } from '@langchain/core/messages';

export interface AgentConfig { name: string; role: string; systemPrompt: string; tools?: Tool[]; model?: string; }

export interface AgentResponse { content: string; toolCalls?: ToolCall[]; nextAgent?: string; completed?: boolean; }

export class Agent { private model: ChatOpenAI; private config: AgentConfig; private messageHistory: BaseMessage[] = [];

constructor(config: AgentConfig) { this.config = config; this.model = new ChatOpenAI({ modelName: config.model || 'gpt-4-turbo-preview', temperature: 0.7, }); }

async execute(input: string, context?: Record): Promise { const systemMessage = new SystemMessage( this.buildSystemPrompt(context) );

const messages = [
  systemMessage,
  ...this.messageHistory,
  new HumanMessage(input),
];

const response = await this.model.invoke(messages, {
  tools: this.config.tools,
});

this.messageHistory.push(new HumanMessage(input));
this.messageHistory.push(new AIMessage(response.content as string));

return this.parseResponse(response);

}

private buildSystemPrompt(context?: Record): string { let prompt = this.config.systemPrompt;

if (context) {
  prompt += `\n\nContext:\n${JSON.stringify(context, null, 2)}`;
}

return prompt;

}

private parseResponse(response: any): AgentResponse { // Parse tool calls and determine next actions return { content: response.content as string, toolCalls: response.tool_calls, completed: response.content?.includes('[TASK_COMPLETE]'), }; }

clearHistory() { this.messageHistory = []; } }

Specialized Agents // agents/specialists.ts import { Agent, AgentConfig } from './base';

export const ResearchAgent = new Agent({ name: 'researcher', role: 'Research Specialist', systemPrompt: `You are a research specialist. Your job is to: - Search for and gather relevant information - Analyze sources and extract key insights - Summarize findings clearly - Cite sources when possible

When you have gathered sufficient information, include [TASK_COMPLETE] in your response. If you need help from another agent, specify: [HANDOFF:agent_name]`, tools: [searchTool, webScrapeTool], });

export const WriterAgent = new Agent({ name: 'writer', role: 'Content Writer', systemPrompt: `You are a professional content writer. Your job is to: - Create engaging, well-structured content - Adapt tone and style to the target audience - Incorporate research and data effectively - Edit and refine for clarity

Use the research provided to create compelling content. When complete, include [TASK_COMPLETE].`, });

export const ReviewerAgent = new Agent({ name: 'reviewer', role: 'Quality Reviewer', systemPrompt: `You are a quality reviewer. Your job is to: - Review content for accuracy and clarity - Check for errors and inconsistencies - Suggest improvements - Approve or request revisions

Provide specific feedback. If approved, include [APPROVED]. If revisions needed, include [REVISIONS_NEEDED] with specific changes.`, });

export const PlannerAgent = new Agent({ name: 'planner', role: 'Task Planner', systemPrompt: `You are a task planner. Your job is to: - Break down complex tasks into subtasks - Identify which specialist agent should handle each subtask - Create an execution order - Track progress

Output a structured plan in JSON format: { "goal": "...", "steps": [ { "step": 1, "agent": "researcher", "task": "..." }, { "step": 2, "agent": "writer", "task": "..." } ] }`, });

Orchestrator Simple Sequential Orchestrator // orchestrator/sequential.ts import { Agent } from '../agents/base';

interface WorkflowStep { agent: Agent; task: string; inputFrom?: string; }

export class SequentialOrchestrator { private agents: Map = new Map(); private results: Map = new Map();

registerAgent(name: string, agent: Agent) { this.agents.set(name, agent); }

async execute(workflow: WorkflowStep[]): Promise> { for (const step of workflow) { const agent = step.agent;

  // Get input from previous step if specified
  let input = step.task;
  if (step.inputFrom && this.results.has(step.inputFrom)) {
    input = `${step.task}\n\nPrevious output:\n${this.results.get(step.inputFrom)}`;
  }

  console.log(`Executing: ${agent.name} - ${step.task}`);

  const result = await agent.execute(input);
  this.results.set(agent.name, result.content);

  console.log(`Completed: ${agent.name}`);
}

return Object.fromEntries(this.results);

} }

// Usage const orchestrator = new SequentialOrchestrator(); orchestrator.registerAgent('researcher', ResearchAgent); orchestrator.registerAgent('writer', WriterAgent); orchestrator.registerAgent('reviewer', ReviewerAgent);

const results = await orchestrator.execute([ { agent: ResearchAgent, task: 'Research the latest AI trends in 2024' }, { agent: WriterAgent, task: 'Write a blog post about AI trends', inputFrom: 'researcher' }, { agent: ReviewerAgent, task: 'Review the blog post', inputFrom: 'writer' }, ]);

Supervisor Orchestrator // orchestrator/supervisor.ts import { ChatOpenAI } from '@langchain/openai'; import { Agent } from '../agents/base';

interface AgentRegistry {

agent: Agent;
description: string;

}; }

export class SupervisorOrchestrator { private supervisor: ChatOpenAI; private agents: AgentRegistry = {}; private sharedContext: Record = {}; private maxIterations = 10;

constructor() { this.supervisor = new ChatOpenAI({ modelName: 'gpt-4-turbo-preview', temperature: 0, }); }

registerAgent(name: string, agent: Agent, description: string) { this.agents[name] = { agent, description }; }

async execute(task: string): Promise { let iteration = 0; let currentTask = task; const history: string[] = [];

while (iteration < this.maxIterations) {
  iteration++;

  // Supervisor decides next action
  const decision = await this.supervise(currentTask, history);

  if (decision.complete) {
    return decision.finalResponse!;
  }

  // Execute selected agent
  const { agent } = this.agents[decision.nextAgent!];
  const result = await agent.execute(decision.agentTask!, this.sharedContext);

  // Update shared context
  this.sharedContext[decision.nextAgent!] = result.content;
  history.push(`${decision.nextAgent}: ${result.content}`);

  // Check for handoff
  if (result.nextAgent) {
    currentTask = `Continue with: ${result.content}`;
  }
}

throw new Error('Max iterations reached');

}

private async supervise( task: string, history: string[] ): Promise<{ complete: boolean; finalResponse?: string; nextAgent?: string; agentTask?: string; }> { const agentList = Object.entries(this.agents) .map(([name, { description }]) => - ${name}: ${description}) .join('\n');

const prompt = `You are a supervisor coordinating AI agents.

Available agents: ${agentList}

Task: ${task}

History: ${history.join('\n')}

Decide the next action. Respond in JSON: { "thought": "your reasoning", "complete": false, "nextAgent": "agent_name", "agentTask": "specific task for the agent" }

Or if the task is complete: { "thought": "your reasoning", "complete": true, "finalResponse": "the final answer" }`;

const response = await this.supervisor.invoke([{ role: 'user', content: prompt }]);
return JSON.parse(response.content as string);

} }

Parallel Agent Execution // orchestrator/parallel.ts export class ParallelOrchestrator { private agents: Map = new Map();

async executeParallel( tasks: Array<{ agentName: string; task: string }> ): Promise> { const results = new Map();

await Promise.all(
  tasks.map(async ({ agentName, task }) => {
    const agent = this.agents.get(agentName);
    if (!agent) throw new Error(`Agent ${agentName} not found`);

    const result = await agent.execute(task);
    results.set(agentName, result.content);
  })
);

return results;

}

async fanOutFanIn( task: string, agentNames: string[], aggregator: Agent ): Promise { // Fan out: same task to multiple agents const parallelResults = await this.executeParallel( agentNames.map((name) => ({ agentName: name, task })) );

// Fan in: aggregate results
const aggregatedInput = Array.from(parallelResults.entries())
  .map(([name, result]) => `${name}:\n${result}`)
  .join('\n\n---\n\n');

const finalResult = await aggregator.execute(
  `Synthesize these perspectives:\n\n${aggregatedInput}`
);

return finalResult.content;

} }

Shared Memory // memory/shared.ts import { Redis } from 'ioredis';

export class SharedMemory { private redis: Redis; private prefix: string;

constructor(sessionId: string) { this.redis = new Redis(process.env.REDIS_URL!); this.prefix = agent:${sessionId}:; }

async set(key: string, value: any, ttl?: number): Promise { const serialized = JSON.stringify(value); if (ttl) { await this.redis.setex(this.prefix + key, ttl, serialized); } else { await this.redis.set(this.prefix + key, serialized); } }

async get(key: string): Promise { const value = await this.redis.get(this.prefix + key); return value ? JSON.parse(value) : null; }

async append(key: string, item: any): Promise { const list = (await this.get(key)) || []; list.push(item); await this.set(key, list); }

async getConversation(): Promise { return (await this.get('conversation')) || []; }

async addMessage(message: Message): Promise { await this.append('conversation', message); }

async getAgentOutputs(): Promise> { return (await this.get>('outputs')) || {}; }

async setAgentOutput(agent: string, output: string): Promise { const outputs = await this.getAgentOutputs(); outputs[agent] = output; await this.set('outputs', outputs); }

async clear(): Promise { const keys = await this.redis.keys(this.prefix + '*'); if (keys.length > 0) { await this.redis.del(...keys); } } }

Event-Driven Agent Communication // events/agent-events.ts import { EventEmitter } from 'events';

export class AgentEventBus extends EventEmitter { private static instance: AgentEventBus;

static getInstance(): AgentEventBus { if (!this.instance) { this.instance = new AgentEventBus(); } return this.instance; }

emitAgentMessage(from: string, to: string, message: any) { this.emit(message:${to}, { from, message, timestamp: new Date() }); }

emitAgentComplete(agent: string, result: any) { this.emit('agent:complete', { agent, result, timestamp: new Date() }); }

emitAgentError(agent: string, error: Error) { this.emit('agent:error', { agent, error, timestamp: new Date() }); }

onMessage(agentName: string, handler: (message: any) => void) { this.on(message:${agentName}, handler); }

onAnyComplete(handler: (event: any) => void) { this.on('agent:complete', handler); } }

// Usage const eventBus = AgentEventBus.getInstance();

// Agent listens for messages eventBus.onMessage('writer', async ({ from, message }) => { console.log(Writer received from ${from}:, message); const result = await WriterAgent.execute(message); eventBus.emitAgentComplete('writer', result); });

// Orchestrator listens for completions eventBus.onAnyComplete(({ agent, result }) => { console.log(${agent} completed:, result.content); });

Workflow Definition // workflows/definition.ts interface WorkflowDefinition { name: string; description: string; agents: string[]; steps: WorkflowStep[]; errorHandling: 'retry' | 'fallback' | 'abort'; }

const ContentCreationWorkflow: WorkflowDefinition = { name: 'content-creation', description: 'Create and publish content', agents: ['planner', 'researcher', 'writer', 'reviewer'], steps: [ { id: 'plan', agent: 'planner', input: '{task}', outputKey: 'plan', }, { id: 'research', agent: 'researcher', input: 'Research for: {plan.topic}', outputKey: 'research', parallel: true, }, { id: 'write', agent: 'writer', input: 'Write about {plan.topic} using research: {research}', outputKey: 'draft', dependsOn: ['research'], }, { id: 'review', agent: 'reviewer', input: 'Review: {draft}', outputKey: 'review', loop: { condition: 'review.approved === false', maxIterations: 3, backTo: 'write', }, }, ], errorHandling: 'retry', };

Best Practices Single responsibility: Each agent has one clear role Clear handoffs: Explicit agent-to-agent communication Shared context: Use memory for persistent state Iteration limits: Prevent infinite loops Error handling: Graceful degradation Observability: Log all agent actions Testing: Test agents individually and together Timeout handling: Prevent stuck agents Output Checklist

Every agent system should include:

Agent base class with common functionality Specialized agents with clear roles Orchestrator for coordination Shared memory system Event-based communication Workflow definitions Error handling and retries Iteration limits Logging and monitoring Agent handoff protocol

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