Performance Optimization¶

Maximize speed, efficiency, and scalability

From microseconds to enterprise scale with 400+ optimized modules

Enterprise Performance

Part of 237 enterprise modules with production-optimized patterns. See Enterprise Documentation.

Performance Metrics¶

Latency

P50, P95, P99 optimization

Reduce Latency
Memory

Efficient resource usage

Optimize Memory
Throughput

Requests per second

Increase Throughput
Scalability

Horizontal and vertical

Scale Better

Performance Architecture¶

Optimized System Design

Performance-oriented architecture pattern Guide

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Overview¶

This guide provides comprehensive strategies for optimizing the performance of your AgenticAI Framework applications. Learn how to maximize throughput, minimize latency, and efficiently scale your agent-based systems.

Performance Metrics¶

Key Performance Indicators (KPIs)¶

graph TB
    subgraph "Latency Metrics"
        P50[P50 Latency<br/>Median Response Time]
        P95[P95 Latency<br/>95th Percentile]
        P99[P99 Latency<br/>99th Percentile]
    end

    subgraph "Throughput Metrics"
        TPS[Transactions/Second<br/>Request Rate]
        QPS[Queries/Second<br/>LLM Calls]
        BATCH[Batch Size<br/>Processing Volume]
    end

    subgraph "Resource Metrics"
        CPU[CPU Usage<br/>Compute Utilization]
        MEM[Memory Usage<br/>RAM/Heap Size]
        IO[I/O Operations<br/>Disk/Network]
    end

    subgraph "Quality Metrics"
        SUCCESS[Success Rate<br/>Completion %]
        ERROR[Error Rate<br/>Failure %]
        TIMEOUT[Timeout Rate<br/>Expiration %]
    end

    style P50 fill:#4caf50
    style P95 fill:#ffc107
    style P99 fill:#ff9800
    style TPS fill:#2196f3
    style SUCCESS fill:#4caf50

Target Performance Benchmarks¶

Metric	Target	Excellent	Action Required
P50 Latency	< 100ms	< 50ms	> 200ms
P95 Latency	< 500ms	< 200ms	> 1s
P99 Latency	< 1s	< 500ms	> 2s
Throughput	> 1000 TPS	> 5000 TPS	< 500 TPS
CPU Usage	< 70%	< 50%	> 85%
Memory Usage	< 80%	< 60%	> 90%
Success Rate	> 99%	> 99.9%	< 98%
Error Rate	< 1%	< 0.1%	> 2%

Agent Performance Optimization¶

1. Agent Pool Management¶

Python
from agenticaiframework.agents import AgentManager, Agent
from concurrent.futures import ThreadPoolExecutor

# Configure optimal pool size
agent_manager = AgentManager(
    max_agents=50, # Based on CPU cores × 2-4
    min_idle_agents=5,
    pool_timeout=30,
    enable_warmup=True
)

# Pre-warm agent pool
def warmup_agents():
    """Initialize agent pool before traffic"""
    for i in range(agent_manager.min_idle_agents):
        agent = Agent(
            name=f"warmup_agent_{i}",
            role="general",
            capabilities=["task_execution"]
        )
        agent_manager.register_agent(agent)

warmup_agents()

2. Concurrent Task Execution¶

Python
from agenticaiframework.tasks import Task, TaskQueue
import asyncio

# Use async/await for concurrent operations
async def execute_tasks_concurrently(tasks: list[Task]):
    """Execute multiple tasks concurrently"""

    # Create task coroutines
    coroutines = [agent.execute_task_async(task) 
        for task in tasks
    ]

    # Execute with timeout
    results = await asyncio.gather(
        *coroutines,
        return_exceptions=True
    )

    return results

# Batch processing for high throughput
def batch_execute(tasks: list[Task], batch_size: int = 10):
    """Process tasks in optimized batches"""

    for i in range(0, len(tasks), batch_size):
        batch = tasks[i:i + batch_size]
        results = asyncio.run(execute_tasks_concurrently(batch))
        yield results

3. Agent Context Optimization¶

Python
from agenticaiframework.agents import ContextManager

# Optimize context window management
context_mgr = ContextManager(
    max_tokens=4000, # Reduce for faster processing
    compression_threshold=0.8,
    enable_summarization=True
)

# Priority-based context retention
context_mgr.add_context(
    "Critical system info",
    importance=1.0 # High priority, always kept
)

context_mgr.add_context(
    "Optional details",
    importance=0.3 # Low priority, dropped first
)

# Get optimized context
optimized = context_mgr.get_context_summary()

LLM Performance Optimization¶

1. Response Caching¶

Python
from agenticaiframework.llms import LLMManager
from functools import lru_cache
import hashlib

class CachedLLMManager(LLMManager):
    """LLM manager with intelligent caching"""

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.cache = {}
        self.cache_hits = 0
        self.cache_misses = 0

    def generate_cache_key(self, prompt: str, model: str) -> str:
        """Generate cache key from prompt and model"""
        content = f"{model}:{prompt}"
        return hashlib.sha256(content.encode()).hexdigest()

    def generate_with_cache(self, prompt: str, model: str = "gpt-4"):
        """Generate with caching"""
        cache_key = self.generate_cache_key(prompt, model)

        # Check cache
        if cache_key in self.cache:
            self.cache_hits += 1
            return self.cache[cache_key]

        # Cache miss - call LLM
        self.cache_misses += 1
        response = self.generate(prompt, model=model)

        # Store in cache
        self.cache[cache_key] = response
        return response

    def get_cache_stats(self):
        """Get cache performance metrics"""
        total = self.cache_hits + self.cache_misses
        hit_rate = self.cache_hits / total if total > 0 else 0
        return {
            "hits": self.cache_hits,
            "misses": self.cache_misses,
            "hit_rate": f"{hit_rate:.2%}",
            "cache_size": len(self.cache)
        }

2. Prompt Optimization¶

Python
# Inefficient - verbose prompt
inefficient_prompt = """
Please analyze the following data and provide a comprehensive report 
including all details, explanations, and recommendations. Be thorough
and include examples for every point you make.

Data: {data}
"""

# Efficient - concise prompt
efficient_prompt = """
Analyze data and list: 1) Key findings 2) Recommendations

Data: {data}
"""

# Token reduction: ~60% fewer tokens
# Latency improvement: ~50% faster
# Cost reduction: ~60% cheaper

3. Model Selection Strategy¶

Python
from agenticaiframework.llms import LLMManager

llm_manager = LLMManager()

# Use appropriate model based on task complexity
def select_optimal_model(task_complexity: str) -> str:
    """Select most cost-effective model"""

    model_strategy = {
        "simple": "gpt-3.5-turbo", # Fast, cheap
        "moderate": "gpt-4-turbo", # Balanced
        "complex": "gpt-4", # Powerful
        "reasoning": "o1-preview" # Specialized
    }

    return model_strategy.get(task_complexity, "gpt-3.5-turbo")

# Example usage
response = llm_manager.generate(
    prompt="Simple classification task",
    model=select_optimal_model("simple")
)

4. Streaming Responses¶

Python
import logging

logger = logging.getLogger(__name__)

async def stream_llm_response(prompt: str):
    """Stream LLM response for better UX"""

    async for chunk in llm_manager.generate_stream(prompt):
        # Process chunk immediately
        yield chunk

        # Update UI in real-time
        # Lower perceived latency

# Usage
async for token in stream_llm_response("Explain quantum computing"):
    logger.info(token, end="", flush=True)

Memory System Optimization¶

1. Efficient Storage Strategy¶

Python
from agenticaiframework.memory import MemoryManager

# Configure memory tiers
memory_config = {
    "hot_tier": {
        "backend": "redis",
        "ttl": 300, # 5 minutes
        "max_size_mb": 100
    },
    "warm_tier": {
        "backend": "sqlite",
        "ttl": 3600, # 1 hour
        "max_size_mb": 1000
    },
    "cold_tier": {
        "backend": "disk",
        "ttl": 86400, # 24 hours
        "compression": True
    }
}

memory_manager = MemoryManager(config=memory_config)

2. Memory Retrieval Optimization¶

Python
# Use indexed lookups
memory_manager.create_index("agent_id")
memory_manager.create_index("timestamp")

# Efficient retrieval
results = memory_manager.retrieve(
    filter={"agent_id": "agent_001"},
    limit=10, # Limit results
    use_index=True, # Use index
    projection=["id", "data"] # Return only needed fields
)

# Avoid full table scans
# Slow - full scan
all_memories = memory_manager.retrieve_all()

# Fast - filtered query
recent_memories = memory_manager.retrieve(
    filter={"timestamp": {"$gte": timestamp_24h_ago}},
    limit=100
)

3. Memory Cleanup Strategy¶

Python
import logging

logger = logging.getLogger(__name__)

import schedule
import time

def cleanup_stale_memories():
    """Periodic cleanup of old memories"""

    # Remove memories older than 7 days
    cutoff_time = time.time() - (7 * 24 * 3600)

    deleted = memory_manager.delete_where(
        filter={"timestamp": {"$lt": cutoff_time}}
    )

    logger.info(f"Cleaned up {deleted} stale memories")

# Schedule cleanup
schedule.every().day.at("02:00").do(cleanup_stale_memories)

Knowledge Base Optimization¶

1. Vector Search Performance¶

Python
from agenticaiframework.knowledge import KnowledgeBase

# Optimize vector dimensions
knowledge_base = KnowledgeBase(
    vector_dimension=384, # Use smaller models (MiniLM vs BERT)
    index_type="hnsw", # Hierarchical NSW for speed
    ef_construction=200, # Balance speed/recall
    m=16 # Connections per node
)

# Batch embeddings for efficiency
def batch_embed_documents(documents: list[str], batch_size: int = 32):
    """Batch embedding generation"""

    embeddings = []
    for i in range(0, len(documents), batch_size):
        batch = documents[i:i + batch_size]
        batch_embeddings = knowledge_base.embed_batch(batch)
        embeddings.extend(batch_embeddings)

    return embeddings

2. Semantic Search Optimization¶

Python
# Use approximate search for speed
results = knowledge_base.search(
    query="What is machine learning?",
    top_k=10, # Limit results
    ef_search=50, # Lower for speed, higher for accuracy
    threshold=0.7 # Minimum similarity
)

# Pre-filter before search
results = knowledge_base.search(
    query="ML concepts",
    filter={"category": "machine_learning"}, # Reduce search space
    top_k=5
)

System-Level Optimizations¶

1. Database Connection Pooling¶

Python
from sqlalchemy import create_engine
from sqlalchemy.pool import QueuePool

# Configure connection pool
engine = create_engine(
    "postgresql://user:pass@localhost/db",
    poolclass=QueuePool,
    pool_size=20, # Maintain 20 connections
    max_overflow=10, # Allow 10 additional connections
    pool_pre_ping=True, # Verify connections before use
    pool_recycle=3600 # Recycle connections hourly
)

2. Async I/O Operations¶

Python
import aiohttp
import asyncio

async def fetch_external_data(urls: list[str]):
    """Fetch multiple URLs concurrently"""

    async with aiohttp.ClientSession() as session:
        tasks = [session.get(url) for url in urls]
        responses = await asyncio.gather(*tasks)
        return responses

# 10x faster than sequential requests
results = asyncio.run(fetch_external_data(["https://api1.com/data",
    "https://api2.com/data",
    "https://api3.com/data"
]))

3. CPU-Bound Task Optimization¶

Python
from multiprocessing import Pool
import numpy as np

def cpu_intensive_task(data):
    """CPU-bound processing"""
    # Example: embedding generation, data transformation
    return np.array(data).mean()

# Use process pool for CPU-bound tasks
with Pool(processes=8) as pool:
    results = pool.map(cpu_intensive_task, large_dataset)

Monitoring & Profiling¶

1. Performance Monitoring¶

Python
from agenticaiframework.monitoring import MonitoringManager
import time

monitor = MonitoringManager()

def track_performance(func):
    """Decorator to track function performance"""
    def wrapper(*args, **kwargs):
        start = time.time()

        try:
            result = func(*args, **kwargs)
            duration = time.time() - start

            monitor.record_metric(
                metric="function_latency",
                value=duration,
                tags={"function": func.__name__}
            )

            return result
        except Exception as e:
            monitor.record_error(
                error=str(e),
                context={"function": func.__name__}
            )
            raise

    return wrapper

@track_performance
def process_request(data):
    # Your code here
    pass

2. Profiling Code¶

Python
import cProfile
import pstats
from io import StringIO

def profile_function(func, *args, **kwargs):
    """Profile function execution"""

    profiler = cProfile.Profile()
    profiler.enable()

    result = func(*args, **kwargs)

    profiler.disable()

    # Print stats
    s = StringIO()
    ps = pstats.Stats(profiler, stream=s).sort_stats('cumulative')
    ps.print_stats(20) # Top 20 functions

    logger.info(s.getvalue())
    return result

# Usage
profile_function(expensive_operation, data)

3. Memory Profiling¶

Python
from memory_profiler import profile

@profile
def memory_intensive_function():
    """Function to profile for memory usage"""
    large_list = [i for i in range(1000000)]
    large_dict = {i: i*2 for i in range(1000000)}
    return large_list, large_dict

# Run to see line-by-line memory usage
memory_intensive_function()