> ## Documentation Index
> Fetch the complete documentation index at: https://docs.praison.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Configuration Best Practices

> Best practices and guidelines for configuring PraisonAI effectively

# Configuration Best Practices

This guide provides comprehensive best practices for configuring PraisonAI components, ensuring optimal performance, reliability, and maintainability of your AI agent systems.

## General Configuration Principles

### 1. Start Simple, Iterate Gradually

```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
# ❌ Avoid: Over-engineering from the start
agent = Agent(
    name="ComplexAgent",
    max_iter=100,
    max_retry_limit=10,
    context_length=200000,
    # ... 50 more parameters
)

# ✅ Better: Start simple and add complexity as needed
agent = Agent(
    name="SimpleAgent",
    role="Assistant",
    llm="gpt-4o-mini"  # Start with efficient model
)

# Add features incrementally based on requirements
if production_environment:
    agent.max_retry_limit = 3
    agent.add_guardrails(safety_rules)
```

### 2. Use Environment-Specific Configurations

```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
# ✅ Good: Environment-aware configuration
import os

def get_agent_config():
    env = os.getenv("ENVIRONMENT", "development")
    
    base_config = {
        "name": "Assistant",
        "llm": "gpt-4o-mini",
        "temperature": 0.7
    }
    
    if env == "production":
        base_config.update({
            "max_retry_limit": 5,
            "timeout": 60,
            "guardrails": "strict",
            "logging": "info"
        })
    elif env == "staging":
        base_config.update({
            "max_retry_limit": 3,
            "timeout": 120,
            "logging": "debug"
        })
    else:  # development
        base_config.update({
            "max_retry_limit": 1,
            "timeout": 300,
            "logging": "debug",
            "verbose": True
        })
    
    return base_config
```

### 3. Centralize Configuration Management

```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
# ✅ Good: Centralized configuration
# config/agent_config.yaml
"""
defaults:
  timeout: 60
  max_retries: 3
  temperature: 0.7

agents:
  researcher:
    role: "Research Specialist"
    max_iter: 20
    tools: ["web_search", "arxiv", "wikipedia"]
    
  analyst:
    role: "Data Analyst"
    max_iter: 15
    tools: ["calculator", "data_viz", "statistics"]
"""

# config_loader.py
import yaml

class ConfigManager:
    def __init__(self, config_path="config/agent_config.yaml"):
        with open(config_path, 'r') as f:
            self.config = yaml.safe_load(f)
    
    def get_agent_config(self, agent_type):
        defaults = self.config.get("defaults", {})
        specific = self.config.get("agents", {}).get(agent_type, {})
        return {**defaults, **specific}
```

## Agent Configuration Best Practices

### 1. Optimize Iteration and Retry Limits

```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
# Task complexity guidelines
TASK_COMPLEXITY_SETTINGS = {
    "simple": {
        "max_iter": 5,
        "max_retry_limit": 1,
        "timeout": 30
    },
    "moderate": {
        "max_iter": 15,
        "max_retry_limit": 3,
        "timeout": 60
    },
    "complex": {
        "max_iter": 30,
        "max_retry_limit": 5,
        "timeout": 120
    }
}

def create_agent_for_task(task_type, complexity="moderate"):
    settings = TASK_COMPLEXITY_SETTINGS[complexity]
    return Agent(
        name=f"{task_type}_agent",
        **settings
    )
```

### 2. Configure Context Windows Wisely

```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
# ✅ Good: Dynamic context window management
def calculate_context_window(task, model="gpt-4o"):
    MODEL_LIMITS = {
        "gpt-4o": 128000,
        "gpt-3.5-turbo": 16385,
        "claude-3": 200000
    }
    
    # Reserve space for output
    output_reserve = 2000
    
    # Calculate based on task needs
    if task.requires_long_context:
        return MODEL_LIMITS[model] - output_reserve
    else:
        # Use smaller window for efficiency
        return min(50000, MODEL_LIMITS[model])
```

### 3. Implement Graceful Degradation

```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
# ✅ Good: Fallback configuration
fallback_config = {
    "primary_model": "gpt-4o",
    "fallback_chain": [
        {"model": "gpt-4-turbo", "on_error": ["rate_limit"]},
        {"model": "gpt-3.5-turbo", "on_error": ["any"]},
        {"model": "local_llm", "on_error": ["api_down"]}
    ],
    "degradation_strategy": {
        "reduce_context": True,
        "simplify_prompts": True,
        "disable_tools": ["expensive_api_tool"]
    }
}
```

## Task Configuration Best Practices

### 1. Design Clear Task Dependencies

```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
# ✅ Good: Explicit task flow
task_flow = {
    "data_collection": {
        "type": "normal",
        "next_tasks": ["data_validation"],
        "timeout": 60
    },
    "data_validation": {
        "type": "decision",
        "condition": {
            "field": "data_quality",
            "operator": ">=",
            "value": 0.8
        },
        "next_tasks": {
            "true": "data_analysis",
            "false": "data_cleaning"
        }
    },
    "data_cleaning": {
        "type": "normal",
        "next_tasks": ["data_validation"],  # Loop back
        "max_attempts": 3
    },
    "data_analysis": {
        "type": "normal",
        "is_terminal": True
    }
}
```

### 2. Use Conditional Logic Sparingly

```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
# ❌ Avoid: Overly complex conditions
complex_condition = {
    "type": "compound",
    "operator": "AND",
    "conditions": [
        {"type": "compound", "operator": "OR", "conditions": [...]},
        {"type": "compound", "operator": "AND", "conditions": [...]},
        # ... nested 5 levels deep
    ]
}

# ✅ Better: Simple, readable conditions
def evaluate_task_condition(task_output):
    """Custom evaluator for complex logic"""
    if task_output.quality_score < 0.8:
        return "needs_improvement"
    elif task_output.completeness < 0.9:
        return "needs_completion"
    else:
        return "ready_for_review"

simple_condition = {
    "type": "function",
    "function": evaluate_task_condition
}
```

## Memory Configuration Best Practices

### 1. Choose the Right Memory Provider

```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
# Decision matrix for memory providers
def select_memory_provider(requirements):
    if requirements.get("graph_relationships"):
        return {
            "provider": "neo4j",
            "graph_enabled": True
        }
    elif requirements.get("simple_storage"):
        return {
            "provider": "rag",
            "use_embedding": False
        }
    elif requirements.get("cloud_sync"):
        return {
            "provider": "mem0",
            "sync_enabled": True
        }
    else:
        return {
            "provider": "rag",
            "use_embedding": True
        }
```

### 2. Optimize Quality Thresholds

```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
# ✅ Good: Adaptive quality thresholds
def get_quality_threshold(context):
    base_threshold = 0.7
    
    # Adjust based on context
    adjustments = {
        "critical_task": 0.15,
        "high_precision": 0.10,
        "exploratory": -0.20,
        "speed_priority": -0.15
    }
    
    for key, adjustment in adjustments.items():
        if context.get(key):
            base_threshold += adjustment
    
    return max(0.3, min(0.95, base_threshold))
```

### 3. Implement Memory Lifecycle Management

```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
# ✅ Good: Memory lifecycle configuration
memory_lifecycle = {
    "retention_policy": {
        "short_term": {
            "ttl": 86400,  # 1 day
            "cleanup": "hourly"
        },
        "long_term": {
            "ttl": 2592000,  # 30 days
            "archive_after": 604800,  # 7 days
            "compression": True
        },
        "permanent": {
            "criteria": ["high_value", "reference"],
            "backup": True
        }
    },
    "cleanup_strategy": {
        "method": "quality_based",
        "keep_top_percent": 20,
        "min_quality_score": 0.5
    }
}
```

## LLM Configuration Best Practices

### 1. Implement Smart Retry Logic

```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
# ✅ Good: Intelligent retry configuration
import random

class SmartRetryConfig:
    def __init__(self):
        self.base_delay = 1.0
        self.max_delay = 60.0
        self.error_strategies = {
            "RateLimitError": self.handle_rate_limit,
            "TimeoutError": self.handle_timeout,
            "APIError": self.handle_api_error
        }
    
    def handle_rate_limit(self, error, attempt):
        # Respect rate limit headers
        retry_after = error.headers.get("Retry-After", 60)
        return min(retry_after, self.max_delay)
    
    def handle_timeout(self, error, attempt):
        # Exponential backoff with jitter
        delay = self.base_delay * (2 ** attempt)
        jitter = random.uniform(0, delay * 0.1)
        return min(delay + jitter, self.max_delay)
    
    def handle_api_error(self, error, attempt):
        # Linear backoff for API errors
        return min(self.base_delay * attempt, self.max_delay)
```

### 2. Optimize Token Usage

```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
# ✅ Good: Token optimization strategies
token_optimization = {
    "strategies": {
        "compression": {
            "enabled": True,
            "method": "semantic",
            "target_reduction": 0.3
        },
        "caching": {
            "cache_prompts": True,
            "cache_completions": True,
            "similarity_threshold": 0.95
        },
        "truncation": {
            "strategy": "sliding_window",
            "preserve": ["recent", "relevant"],
            "max_history": 10
        }
    },
    "monitoring": {
        "track_usage": True,
        "alert_threshold": 0.8,
        "optimize_on_high_usage": True
    }
}
```

## Tool Configuration Best Practices

### 1. Set Appropriate Timeouts

```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
# ✅ Good: Tool-specific timeout configuration
TOOL_TIMEOUT_PROFILES = {
    "instant": {
        "timeout": 5,
        "operations": ["cache_lookup", "validation"]
    },
    "fast": {
        "timeout": 15,
        "operations": ["calculation", "local_search"]
    },
    "standard": {
        "timeout": 30,
        "operations": ["api_call", "web_search"]
    },
    "slow": {
        "timeout": 120,
        "operations": ["ml_inference", "large_download"]
    }
}

def get_tool_timeout(tool_name, operation_type):
    for profile_name, profile in TOOL_TIMEOUT_PROFILES.items():
        if operation_type in profile["operations"]:
            return profile["timeout"]
    return 30  # Default
```

### 2. Implement Resource Pooling

```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
# ✅ Good: Efficient resource pooling
resource_pool_config = {
    "connection_pools": {
        "http": {
            "min_size": 2,
            "max_size": 20,
            "timeout": 30,
            "recycle_after": 3600,
            "health_check": True
        },
        "database": {
            "min_size": 1,
            "max_size": 10,
            "timeout": 60,
            "retry_on_disconnect": True
        }
    },
    "thread_pools": {
        "io_bound": {
            "workers": "2 * cpu_count",
            "queue_size": 100
        },
        "cpu_bound": {
            "workers": "cpu_count",
            "queue_size": 50
        }
    }
}
```

## Performance Optimization Checklist

### 1. Caching Strategy

```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
# ✅ Comprehensive caching configuration
caching_strategy = {
    "levels": {
        "l1_memory": {
            "size": 100,
            "ttl": 300,
            "eviction": "lru"
        },
        "l2_disk": {
            "size": 1000,
            "ttl": 3600,
            "compression": True
        },
        "l3_distributed": {
            "provider": "redis",
            "ttl": 86400,
            "sharding": True
        }
    },
    "key_strategy": "semantic_hash",
    "invalidation": {
        "on_update": True,
        "scheduled": "hourly"
    }
}
```

### 2. Monitoring and Alerting

```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
# ✅ Comprehensive monitoring setup
monitoring_config = {
    "metrics": {
        "system": ["cpu", "memory", "disk", "network"],
        "application": ["request_rate", "error_rate", "latency"],
        "business": ["task_completion", "quality_scores"]
    },
    "alerts": {
        "critical": {
            "error_rate": {"threshold": 0.05, "window": 300},
            "latency_p99": {"threshold": 5000, "window": 300}
        },
        "warning": {
            "memory_usage": {"threshold": 0.8, "window": 600},
            "queue_depth": {"threshold": 1000, "window": 300}
        }
    },
    "dashboards": {
        "update_frequency": 60,
        "retention": 30  # days
    }
}
```

## Security Best Practices

### 1. Secure Configuration Storage

```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
# ✅ Good: Secure configuration management
import os
from cryptography.fernet import Fernet

class SecureConfigManager:
    def __init__(self):
        self.key = os.environ.get("CONFIG_ENCRYPTION_KEY")
        self.cipher = Fernet(self.key.encode()) if self.key else None
    
    def get_sensitive_config(self, key):
        """Retrieve and decrypt sensitive configuration"""
        encrypted = os.environ.get(f"ENCRYPTED_{key}")
        if encrypted and self.cipher:
            return self.cipher.decrypt(encrypted.encode()).decode()
        return os.environ.get(key)  # Fallback to plain env var
    
    def validate_config(self, config):
        """Validate configuration security"""
        issues = []
        
        # Check for hardcoded secrets
        for key, value in config.items():
            if isinstance(value, str):
                if "password" in key.lower() and value != "***":
                    issues.append(f"Potential hardcoded password in {key}")
                if "key" in key.lower() and len(value) > 20:
                    issues.append(f"Potential hardcoded API key in {key}")
        
        return issues
```

### 2. Implement Least Privilege

```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
# ✅ Good: Role-based configuration
role_based_config = {
    "roles": {
        "viewer": {
            "max_iter": 5,
            "allowed_tools": ["read_only_search"],
            "max_tokens": 1000
        },
        "user": {
            "max_iter": 15,
            "allowed_tools": ["search", "calculate"],
            "max_tokens": 4000
        },
        "power_user": {
            "max_iter": 30,
            "allowed_tools": ["all_except_admin"],
            "max_tokens": 8000
        },
        "admin": {
            "max_iter": 50,
            "allowed_tools": ["all"],
            "max_tokens": 16000,
            "override_guardrails": True
        }
    }
}
```

## Configuration Testing

### 1. Validate Configurations

```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
# ✅ Good: Configuration validation
class ConfigValidator:
    def __init__(self, schema):
        self.schema = schema
    
    def validate(self, config):
        errors = []
        
        # Type validation
        for key, expected_type in self.schema.items():
            if key in config:
                if not isinstance(config[key], expected_type):
                    errors.append(f"{key} must be {expected_type}")
        
        # Range validation
        if "max_iter" in config:
            if not 1 <= config["max_iter"] <= 100:
                errors.append("max_iter must be between 1 and 100")
        
        # Dependency validation
        if config.get("use_graph") and not config.get("graph_uri"):
            errors.append("graph_uri required when use_graph is True")
        
        return errors

# Test configurations
def test_agent_config():
    validator = ConfigValidator({
        "max_iter": int,
        "temperature": float,
        "llm": str
    })
    
    test_config = {
        "max_iter": 15,
        "temperature": 0.7,
        "llm": "gpt-4o"
    }
    
    errors = validator.validate(test_config)
    assert len(errors) == 0, f"Configuration errors: {errors}"
```

## Summary Checklist

✅ **Start Simple**: Begin with minimal configuration and add complexity as needed

✅ **Environment-Specific**: Use different configurations for dev, staging, and production

✅ **Centralize Management**: Keep configurations in one place for easy management

✅ **Validate Everything**: Implement validation for all configuration values

✅ **Monitor Performance**: Track the impact of configuration changes

✅ **Document Decisions**: Document why specific values were chosen

✅ **Regular Review**: Periodically review and optimize configurations

✅ **Security First**: Never hardcode secrets, use encryption for sensitive data

✅ **Test Configurations**: Validate configurations before deployment

✅ **Plan for Failure**: Always have fallback configurations ready

## See Also

* [Agent Configuration](/configuration/agent-config) - Detailed agent parameters
* [Task Configuration](/configuration/task-config) - Task workflow settings
* [Memory Configuration](/configuration/memory-config) - Memory system setup
* [Examples](/examples) - Real-world configuration examples