---

9. Environment-Specific Configurations

You can maintain separate configuration files for different environments:

• config_dev.py for development
• config_staging.py for staging
• config_prod.py for production

Load them dynamically based on an environment variable:

import os
from agenticaiframework.configurations import set_config

env = os.getenv("AGENTICAI_ENV", "dev")
if env == "prod":
    from config_prod import CONFIG
elif env == "staging":
    from config_staging import CONFIG
else:
    from config_dev import CONFIG

for key, value in CONFIG.items():
    set_config(key, value)

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10. Dynamic Configuration Updates

You can update configuration values at runtime without restarting the application:

from agenticaiframework.configurations import set_config, get_config

set_config("log_level", "DEBUG")
print(get_config("log_level"))  # Output: DEBUG

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11. Configuration Validation

Implement validation to ensure configuration values are correct:

def validate_config():
    from agenticaiframework.configurations import get_config
    if not get_config("api_key"):
        raise ValueError("API key is missing!")

---

12. Secrets Management

For sensitive values like API keys, use a secrets manager:

• AWS Secrets Manager
• HashiCorp Vault
• Azure Key Vault
• Google Secret Manager

Example with AWS Secrets Manager:

import boto3
import json

def get_secret(secret_name):
    client = boto3.client("secretsmanager")
    response = client.get_secret_value(SecretId=secret_name)
    return json.loads(response["SecretString"])

---

13. Best Practices for Configuration

• Keep configuration files out of version control if they contain secrets.
• Use .env files for local development and load them with python-dotenv.
• Document all configuration keys in CONFIGURATION.md.

AgenticAI Configuration Guide

This document explains how to configure AgenticAI for different environments and use cases.

---

1. Configuration Methods

You can configure AgenticAI in three ways:

1. Programmatically – Using set_config() from agenticaiframework.configurations.
2. Configuration File – Editing configurations.py.
3. Environment Variables – Setting variables before running your application.

---

2. Common Configuration Keys

Key	Description	Example
llm_provider	LLM provider to use	"openai"
api_key	API key for LLM provider	"sk-xxxx"
memory_backend	Memory storage backend	"in_memory"
log_level	Logging verbosity	"INFO"
default_agent	Default agent name	"default_agent"

---

3. Programmatic Configuration

from agenticaiframework.configurations import set_config

set_config("llm_provider", "openai")
set_config("api_key", "your_api_key_here")
set_config("memory_backend", "in_memory")

---

4. Using Environment Variables

export AGENTICAI_LLM_PROVIDER=openai
export AGENTICAI_API_KEY=your_api_key_here

---

5. Configuration File

Edit agenticaiframework/configurations.py:

CONFIG = {
    "llm_provider": "openai",
    "api_key": "",
    "memory_backend": "in_memory",
    "log_level": "INFO",
    "default_agent": "default_agent"
}

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6. Advanced Configuration

• Multiple LLM Providers – You can register multiple providers and switch dynamically.
• Custom Memory Backends – Implement a new backend in memory.py and set it in config.
• Logging – Adjust log_level to "DEBUG" for detailed logs.

---

7. Verifying Configuration

from agenticaiframework.configurations import get_config

print(get_config("llm_provider"))

---

8. Best Practices

• Store API keys securely (e.g., environment variables, secret managers).
• Avoid committing sensitive data to version control.
• Use different configurations for development, staging, and production.