Scaling Swarms

from swarms import Agent
from swarms.structs.multi_agent_exec import run_agents_concurrently

# Create multiple agents
agents = [
    Agent(
        agent_name=f"Worker-{i}",
        model_name="gpt-4o-mini",
        max_loops=1,
    )
    for i in range(5)
]

# Run all agents concurrently on the same task
results = run_agents_concurrently(
    agents=agents,
    task="Analyze the potential impact of AI on healthcare",
    max_workers=None,  # Uses 95% of CPU cores by default
)

# Results is a list of outputs in completion order
for i, result in enumerate(results):
    print(f"Agent {i+1} result: {result}")

results_dict = run_agents_concurrently(
    agents=agents,
    task="Generate a market analysis report",
    return_agent_output_dict=True,  # Return as dict
)

# Results preserve agent order
for agent_name, output in results_dict.items():
    print(f"Result from {agent_name}:")
    print(output)
    print("-" * 50)

from swarms.structs.multi_agent_exec import run_agents_concurrently

# Process image across multiple agents
results = run_agents_concurrently(
    agents=vision_agents,
    task="Analyze this medical scan",
    img="path/to/scan.jpg",
    max_workers=5,
)

import asyncio
from swarms.structs.multi_agent_exec import (
    run_agent_async,
    run_agents_concurrently_async,
)

async def process_tasks():
    agents = [
        Agent(agent_name=f"Async-Agent-{i}", model_name="gpt-4o-mini")
        for i in range(10)
    ]
    
    # Run all agents asynchronously
    results = await run_agents_concurrently_async(
        agents=agents,
        task="Process this data"
    )
    
    return results

# Run the async function
results = asyncio.run(process_tasks())

from swarms.structs.multi_agent_exec import run_agents_concurrently_uvloop

# Automatically uses uvloop on Unix, winloop on Windows
results = run_agents_concurrently_uvloop(
    agents=agents,
    task="High-throughput task processing",
    max_workers=None,  # Uses 95% of CPU cores
)

# Also supports different tasks per agent
results = run_agents_with_tasks_uvloop(
    agents=agents,
    tasks=[f"Task {i}" for i in range(len(agents))],
    max_workers=10,
)

from swarms.structs.multi_agent_exec import (
    run_agents_concurrently_multiprocess
)
import os

# Process large number of agents in batches
agents = [Agent(agent_name=f"Agent-{i}") for i in range(100)]

results = run_agents_concurrently_multiprocess(
    agents=agents,
    task="Process this task",
    batch_size=os.cpu_count(),  # Process in CPU-sized batches
)

from swarms.structs.multi_agent_exec import batched_grid_agent_execution

# Create specialized agents
agents = [
    Agent(agent_name="Researcher", system_prompt="Research expert"),
    Agent(agent_name="Writer", system_prompt="Content writer"),
    Agent(agent_name="Analyst", system_prompt="Data analyst"),
]

# Different task for each agent
tasks = [
    "Research AI trends",
    "Write a blog post",
    "Analyze market data",
]

# Execute in parallel
results = batched_grid_agent_execution(
    agents=agents,
    tasks=tasks,
    max_workers=None,  # Uses 90% of CPU cores
)

# Results maintain order of input agents
for i, result in enumerate(results):
    print(f"{agents[i].agent_name} completed: {tasks[i]}")
    print(f"Result: {result}")

from swarms.structs.multi_agent_exec import run_agents_with_different_tasks

# Create agent-task pairs
agent_task_pairs = [
    (researcher, "Research quantum computing"),
    (writer, "Write about AI ethics"),
    (analyst, "Analyze stock trends"),
    (editor, "Edit research paper"),
    # ... hundreds more pairs
]

# Process in batches
results = run_agents_with_different_tasks(
    agent_task_pairs=agent_task_pairs,
    batch_size=10,  # Process 10 at a time
    max_workers=5,  # Use 5 workers per batch
)

from swarms import Agent
from swarms.structs.aop import AOP

# Create agents
agents = [
    Agent(agent_name=f"Worker-{i}", model_name="gpt-4o")
    for i in range(10)
]

# Deploy with queue-based execution
deployer = AOP(
    server_name="ScalableCluster",
    agents=agents,
    queue_enabled=True,
    max_workers_per_agent=5,  # 5 workers per agent
    max_queue_size_per_agent=1000,  # Large queue capacity
)

deployer.run()

deployer = AOP(
    server_name="HighThroughputCluster",
    queue_enabled=True,
    
    # Worker configuration
    max_workers_per_agent=10,  # More workers = more throughput
    
    # Queue configuration
    max_queue_size_per_agent=10000,  # Large queue
    processing_timeout=60,  # Adjust based on task complexity
    retry_delay=0.5,  # Quick retries
    
    # Performance tuning
    verbose=False,  # Reduce logging overhead
)

import os

# Calculate optimal worker count
num_cores = os.cpu_count()

# For I/O-bound tasks (API calls, network)
io_bound_workers = int(num_cores * 2)  # 2x cores

# For CPU-bound tasks
cpu_bound_workers = num_cores  # 1x cores

# For mixed workloads
mixed_workers = int(num_cores * 1.5)  # 1.5x cores

results = run_agents_concurrently(
    agents=agents,
    task="Task",
    max_workers=io_bound_workers,
)

agent = Agent(
    agent_name="Optimized-Agent",
    model_name="gpt-4o",
    dynamic_context_window=True,  # Auto-manage context
    context_length=8000,
)

# Truncate history to prevent memory bloat
agent = Agent(
    agent_name="Memory-Efficient-Agent",
    model_name="gpt-4o",
    context_length=4000,
)

# Manual truncation after processing
for task in large_task_list:
    result = agent.run(task)
    agent.truncate_history(max_messages=20)  # Keep only recent messages

def find_optimal_batch_size(agents, test_task):
    """
    Find optimal batch size through testing.
    """
    import time
    
    batch_sizes = [5, 10, 20, 50, 100]
    results = {}
    
    for batch_size in batch_sizes:
        start = time.time()
        run_agents_concurrently_multiprocess(
            agents[:batch_size],
            test_task,
            batch_size=batch_size,
        )
        duration = time.time() - start
        results[batch_size] = duration
        print(f"Batch size {batch_size}: {duration:.2f}s")
    
    optimal = min(results.items(), key=lambda x: x[1])
    print(f"Optimal batch size: {optimal[0]}")
    return optimal[0]

class LoadBalancer:
    def __init__(self, agents):
        self.agents = agents
        self.current_index = 0
    
    def get_next_agent(self):
        agent = self.agents[self.current_index]
        self.current_index = (self.current_index + 1) % len(self.agents)
        return agent
    
    def process_tasks(self, tasks):
        results = []
        for task in tasks:
            agent = self.get_next_agent()
            result = agent.run(task)
            results.append(result)
        return results

# Usage
balancer = LoadBalancer(agents)
results = balancer.process_tasks(large_task_list)

import heapq

class PriorityLoadBalancer:
    def __init__(self, agents):
        # Track agent load (priority queue)
        self.agent_load = [(0, agent) for agent in agents]
        heapq.heapify(self.agent_load)
    
    def assign_task(self, task, priority=0):
        # Get least loaded agent
        load, agent = heapq.heappop(self.agent_load)
        
        # Process task
        result = agent.run(task)
        
        # Update load and re-add to queue
        heapq.heappush(self.agent_load, (load + 1 - priority, agent))
        
        return result

import time
from collections import defaultdict

class ScalingMetrics:
    def __init__(self):
        self.agent_metrics = defaultdict(lambda: {
            "requests": 0,
            "successes": 0,
            "failures": 0,
            "total_duration": 0.0,
        })
    
    def record(self, agent_name, success, duration):
        metrics = self.agent_metrics[agent_name]
        metrics["requests"] += 1
        metrics["total_duration"] += duration
        if success:
            metrics["successes"] += 1
        else:
            metrics["failures"] += 1
    
    def get_summary(self):
        summary = {}
        for agent_name, metrics in self.agent_metrics.items():
            avg_duration = (
                metrics["total_duration"] / metrics["requests"]
                if metrics["requests"] > 0 else 0
            )
            success_rate = (
                metrics["successes"] / metrics["requests"]
                if metrics["requests"] > 0 else 0
            )
            summary[agent_name] = {
                "requests": metrics["requests"],
                "success_rate": f"{success_rate:.1%}",
                "avg_duration": f"{avg_duration:.2f}s",
            }
        return summary

metrics = ScalingMetrics()

def monitored_run(agent, task):
    start = time.time()
    success = False
    try:
        result = agent.run(task)
        success = True
        return result
    finally:
        duration = time.time() - start
        metrics.record(agent.agent_name, success, duration)

import threading
import time

class ThroughputMonitor:
    def __init__(self, window_size=60):
        self.window_size = window_size
        self.requests = []
        self.lock = threading.Lock()
    
    def record_request(self):
        with self.lock:
            now = time.time()
            self.requests.append(now)
            # Remove old requests outside window
            cutoff = now - self.window_size
            self.requests = [t for t in self.requests if t > cutoff]
    
    def get_throughput(self):
        with self.lock:
            return len(self.requests) / self.window_size
    
    def start_reporting(self, interval=10):
        def report():
            while True:
                throughput = self.get_throughput()
                print(f"Current throughput: {throughput:.2f} req/s")
                time.sleep(interval)
        
        thread = threading.Thread(target=report, daemon=True)
        thread.start()

monitor = ThroughputMonitor()
monitor.start_reporting()

# Record each request
for task in tasks:
    result = agent.run(task)
    monitor.record_request()

import os

# Get CPU count
num_cores = os.cpu_count()

# I/O-bound: 2x cores
max_workers = num_cores * 2

# CPU-bound: 1x cores  
max_workers = num_cores

# Conservative: 90-95% of cores
max_workers = int(num_cores * 0.9)

def resilient_concurrent_run(agents, task, max_workers=None):
    try:
        return run_agents_concurrently(
            agents=agents,
            task=task,
            max_workers=max_workers,
        )
    except Exception as e:
        logger.error(f"Concurrent execution failed: {e}")
        logger.info("Falling back to sequential execution")
        # Fallback to sequential
        return [agent.run(task) for agent in agents]

import psutil

def check_resources():
    cpu_percent = psutil.cpu_percent(interval=1)
    memory_percent = psutil.virtual_memory().percent
    
    if cpu_percent > 90:
        logger.warning(f"High CPU usage: {cpu_percent}%")
    if memory_percent > 85:
        logger.warning(f"High memory usage: {memory_percent}%")
    
    return {
        "cpu_percent": cpu_percent,
        "memory_percent": memory_percent,
    }

agent = Agent(
    agent_name="Timeout-Agent",
    model_name="gpt-4o",
    timeout=30,  # Set based on expected task duration
)