ai-agent-orchestrator

name	ai-agent-orchestrator
description	Orchestrates multi-agent AI systems with task delegation, agent communication, shared memory, and workflow coordination. Use when users request "multi-agent system", "agent orchestration", "AI agents", "agent coordination", or "autonomous agents".

AI Agent Orchestrator

Build coordinated multi-agent systems for complex task automation.

Core Workflow

1. Define agents: Create specialized agents
2. Design workflow: Plan agent coordination
3. Implement handoffs: Agent-to-agent communication
4. Add shared memory: Persistent context
5. Create supervisor: Orchestrate execution
6. 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<string, any>): Promise<AgentResponse> {
    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, any>): 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<string, Agent> = new Map();
  private results: Map<string, string> = new Map();

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

  async execute(workflow: WorkflowStep[]): Promise<Record<string, string>> {
    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 {
  [name: string]: {
    agent: Agent;
    description: string;
  };
}

export class SupervisorOrchestrator {
  private supervisor: ChatOpenAI;
  private agents: AgentRegistry = {};
  private sharedContext: Record<string, any> = {};
  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<string> {
    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<string, Agent> = new Map();

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

    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<string> {
    // 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<void> {
    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<T>(key: string): Promise<T | null> {
    const value = await this.redis.get(this.prefix + key);
    return value ? JSON.parse(value) : null;
  }

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

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

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

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

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

  async clear(): Promise<void> {
    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

1. Single responsibility: Each agent has one clear role
2. Clear handoffs: Explicit agent-to-agent communication
3. Shared context: Use memory for persistent state
4. Iteration limits: Prevent infinite loops
5. Error handling: Graceful degradation
6. Observability: Log all agent actions
7. Testing: Test agents individually and together
8. 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