Parallel Agents

Parallel agents unleash the power of concurrent processing, running multiple agents simultaneously to tackle independent tasks. Think of it as your AI team working in parallel - while one agent analyzes sentiment, another extracts topics, and a third generates summaries, all at the same time.

Unlike sequential agents that process step-by-step, parallel agents maximize speed and efficiency by utilizing multiple processing streams. Perfect when you have independent tasks that don't need to wait for each other.

Quick Start Example

Here's a content analysis system that simultaneously examines multiple aspects of user feedback:

import { LlmAgent, ParallelAgent, WebSearchTool } from "@iqai/adk";

// Parallel analysis: Multiple perspectives on the same content
const sentimentAnalyzer = new LlmAgent({
  name: "sentiment-analyzer",
  model: "gemini-2.5-flash",
  instruction: `
    Analyze the sentiment of the given text. Provide:
    - Overall sentiment (positive/negative/neutral)
    - Confidence score (0-1)
    - Key emotional indicators
    - Tone analysis (formal, casual, frustrated, etc.)
  `,
  outputKey: "sentiment_analysis",
});

const topicExtractor = new LlmAgent({
  name: "topic-extractor",
  model: "gemini-2.5-flash",
  instruction: `
    Extract key topics and themes from the text:
    - Main topics discussed
    - Product/feature mentions
    - Categories and tags
    - Intent classification (complaint, praise, question, etc.)
  `,
  outputKey: "topics",
});

const priorityAssessor = new LlmAgent({
  name: "priority-assessor",
  model: "gemini-2.5-flash",
  instruction: `
    Assess the urgency and priority of this feedback:
    - Priority level (high/medium/low)
    - Urgency indicators
    - Business impact assessment
    - Recommended response timeframe
  `,
  outputKey: "priority_assessment",
});

const keywordAnalyzer = new LlmAgent({
  name: "keyword-analyzer",
  model: "gemini-2.5-flash",
  instruction: `
    Extract important keywords and phrases:
    - Key terms and phrases
    - Brand/competitor mentions
    - Technical terms
    - Action items mentioned
  `,
  outputKey: "keywords",
});

// All agents run simultaneously on the same input
const feedbackAnalyzer = new ParallelAgent({
  name: "feedback-analyzer",
  description: "Multi-perspective analysis of customer feedback",
  subAgents: [
    sentimentAnalyzer,
    topicExtractor,
    priorityAssessor,
    keywordAnalyzer,
  ],
});

// Usage: Get comprehensive analysis in parallel
const analysis = await feedbackAnalyzer.run({
  message:
    "The new checkout process is confusing and I can't complete my purchase!",
});
// Results available at: analysis.sentiment_analysis, analysis.topics, analysis.priority_assessment, analysis.keywords

Visual Flow

How Parallel Processing Works

Parallel agents maximize efficiency by running all sub-agents simultaneously:

⚡ Concurrent Execution

1. All agents start together - No waiting for previous agents to finish
2. Independent processing - Each agent works on the same input independently
3. Simultaneous completion - Results become available as each agent finishes
4. Non-deterministic order - Faster agents finish first, slower ones catch up

📊 Performance Benefits

• Time savings: N agents running for T seconds = T total time (not N×T)
• Resource utilization: Maximizes CPU/GPU usage across multiple agents
• Scalability: Add more agents without increasing total processing time

🔄 Result Aggregation Each agent writes to unique keys in shared session state. Later agents (or your application) can access all results together for final processing.

Real-World Use Cases

📝 Multi-Perspective Content Analysis
Sentiment + Topics + Keywords + Priority Assessment (all simultaneously)

🔍 Comprehensive Research
Web Search + Academic Papers + News + Social Media (parallel data gathering)

📊 Financial Analysis
Technical Analysis + Fundamental Analysis + News Sentiment + Risk Assessment

🛠️ Quality Assurance
Grammar Check + Fact Verification + Style Review + Compliance Check

🌍 Multi-Language Processing
Translation + Sentiment + Cultural Context + Localization (per language)

📊 Data Validation
Format Check + Business Rules + Data Quality + Duplicate Detection

When to Choose Parallel Agents

✅ Choose Parallel When:

• Independent analysis - Multiple perspectives on the same data
• Speed is critical - Need results as fast as possible
• Resource optimization - Want to maximize processing power
• Multi-source data - Gathering from different sources simultaneously
• Comparative analysis - Need multiple approaches to the same problem

❌ Don't Use Parallel When:

• Tasks depend on each other's intermediate results
• You need guaranteed execution order
• Sequential processing is part of the business logic
• Resource constraints limit concurrent execution
• Results from one agent affect how another should process

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