Running Multiple AI Agents in Parallel

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What this article covers

• How to run multiple AI agents in parallel.
• Orchestration, load balancing, and resource management.
• Specializing agents for different tasks.
• Real-world examples of multi-agent systems.
• Best practices for scaling and reliability.

Introduction: Understanding Multiple Agents

Running multiple agents means moving beyond a single agent handling everything. Instead, you deploy specialized agents for different tasks: one handles research, another analyzes, a third writes. They work in parallel or sequentially, coordinated by an orchestrator.

This article targets experienced users building multi-agent systems. For foundations, see Multi-Agent Systems and Running AI Agents Locally.

Why use multiple agents?

Imagine writing an article: one agent researches, a second analyzes, a third writes, a fourth reviews. Each agent focuses on its specialty, and they all work simultaneously. This approach is faster and produces better results than a single agent trying to do everything.

Multi-agent systems explained

Multi-agent system = Specialized agents + Orchestrator. Each agent owns a task, the orchestrator coordinates their work. Run tasks in parallel for speed, sequentially when one depends on another.

The core principle: specialization over generalization.

Who should read this?

• Advanced developers building multi-agent systems.
• Teams automating complex workflows.
• Engineers orchestrating specialized agents.
• Production teams operating many agents at scale.

Key concepts

• Multi-Agent Systems - Multiple agents working together. Use when you need parallel task execution.
• Agent Systems - System design patterns. Use when designing architecture.
• Ollama - Model server. Use for running local models.
• vLLM - High-performance inference. Use for many parallel requests.
• Docker - Containerization. Use for deployment.

Architecture

Orchestrator (coordinates)
    │
    ├─ Agent 1: Research (Ollama)
    ├─ Agent 2: Analysis (Ollama)
    ├─ Agent 3: Writing (Ollama)
    └─ Agent 4: Review (Ollama)
    │
    ▼
Combine results
    │
    ▼
Final output

Practical example: Content pipeline

import asyncio
from openai import AsyncOpenAI

class ContentPipeline:
    """Multi-agent pipeline for content creation"""

    def __init__(self):
        self.client = AsyncOpenAI(
            base_url="http://ollama:11434/v1",
            api_key="not-needed"
        )

    async def run(self, topic):
        """Execute the pipeline"""
        # Parallel: research + analysis
        research, analysis = await asyncio.gather(
            self.research_agent(topic),
            self.analysis_agent(topic)
        )

        # Sequential: writing (depends on research + analysis)
        draft = await self.writer_agent(topic, research, analysis)

        # Parallel: review + formatting
        review, formatted = await asyncio.gather(
            self.review_agent(draft),
            self.format_agent(draft)
        )

        return {
            "draft": draft,
            "review": review,
            "formatted": formatted
        }

    async def research_agent(self, topic):
        """Research agent"""
        response = await self.client.chat.completions.create(
            model="llama3.1",
            messages=[{"role": "user", "content": f"Research: {topic}"}]
        )
        return response.choices[0].message.content

    async def analysis_agent(self, topic):
        """Analysis agent"""
        response = await self.client.chat.completions.create(
            model="llama3.1",
            messages=[{"role": "user", "content": f"Analyze: {topic}"}]
        )
        return response.choices[0].message.content

Practical example: Docker for multiple agents

version: "3.8"

services:
  ollama:
    image: ollama/ollama:latest
    ports:
      - "11434:11434"
    volumes:
      - ollama_data:/root/.ollama
    networks:
      - agent_net

  agent-research:
    build: ./agents/research
    environment:
      - OLLAMA_URL=http://ollama:11434
      - AGENT_ROLE=research
    networks:
      - agent_net

  agent-analysis:
    build: ./agents/analysis
    environment:
      - OLLAMA_URL=http://ollama:11434
      - AGENT_ROLE=analysis
    networks:
      - agent_net

  agent-writer:
    build: ./agents/writer
    environment:
      - OLLAMA_URL=http://ollama:11434
      - AGENT_ROLE=writer
    networks:
      - agent_net

  orchestrator:
    build: ./orchestrator
    ports:
      - "8000:8000"
    environment:
      - OLLAMA_URL=http://ollama:11434
      - AGENTS=research,analysis,writer
    depends_on:
      - ollama
      - agent-research
      - agent-analysis
      - agent-writer
    networks:
      - agent_net

volumes:
  ollama_data:

networks:
  agent_net:
    driver: bridge

Resource management

# For many parallel agents: use vLLM instead of Ollama
# vLLM batches requests efficiently

# Or: run multiple Ollama instances
OLLAMA_INSTANCES = [
    "http://ollama-1:11434",
    "http://ollama-2:11434",
    "http://ollama-3:11434"
]

# Round-robin for load balancing
def get_ollama_url():
    import random
    return random.choice(OLLAMA_INSTANCES)

Security considerations

• Isolation: Each agent should run isolated in its own container or process.
• Permissions: Give each agent only the permissions it needs.
• Communication: Agents should communicate securely on an internal network.
• Monitoring: Monitor all agents. See Logging.

Common pitfalls

• Too many agents: More agents means more complexity. Start with 2-3.
• No orchestration: Agents need an orchestrator to coordinate their work.
• Resource bottlenecks: Many agents require substantial GPU and RAM. Size appropriately.
• Deadlocks: Agents can block each other. Set timeouts.
• No fallback: The system should continue if an agent fails.

Further reading

• Multi-Agent Systems - Core concepts.
• Running AI Agents Locally - Overview.
• vLLM - Performance optimization.
• Docker - Deployment.
• CrewAI - Multi-agent framework.
• AutoGen - Multi-agent framework.

Key takeaways:

• Multiple agents = specialized agents + orchestrator.
• Run tasks in parallel for speed, sequentially for dependencies.
• Use vLLM for many parallel requests, Docker for deployment.
• Multi-agent systems unlock powerful automation for complex workflows.
• Resource management and orchestration are critical to success.

FAQ

Sources and further reading

• CrewAI - Multi-agent framework.
• AutoGen - Multi-agent framework.
• vLLM - High-performance inference.
• Ollama - Model server.

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