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Address comprehensive review feedback: add enhanced error handling, configuration management, version compatibility matrix, and security best practices

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- Add PortkeyConfig dataclass for structured configuration management
- Implement comprehensive error handling with custom exception classes
- Add PortkeyLogger for structured logging of Portkey operations
- Include version compatibility matrix with migration guide from legacy patterns
- Add enhanced security practices with environment-based configuration
- Include performance optimization tips with code examples
- Add comprehensive validation and troubleshooting guidance
- All code examples include proper type hints and docstrings
- Focus on technical precision and real-world application patterns

Co-Authored-By: João <joao@crewai.com>
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Devin AI
2025-06-03 08:39:30 +00:00
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@@ -28,27 +28,92 @@ Portkey adds 4 core production capabilities to any CrewAI agent:
- **Portkey API Key**: Sign up on the [Portkey app](https://app.portkey.ai/?utm_source=crewai&utm_medium=crewai&utm_campaign=crewai) and copy your API key
- **Virtual Key**: Virtual Keys securely manage your LLM API keys in one place. Store your LLM provider API keys securely in Portkey's vault
### Environment Variable Validation
Before setting up your LLM, validate your Portkey configuration to prevent runtime issues:
```python
import os
from typing import Dict, List
class PortkeyConfigurationError(Exception):
"""Raised when Portkey configuration is invalid or incomplete"""
pass
def validate_portkey_configuration() -> None:
"""
Validates that all required Portkey environment variables are set.
Raises:
PortkeyConfigurationError: If any required variables are missing
"""
required_vars: Dict[str, str] = {
"PORTKEY_API_KEY": "Get from https://app.portkey.ai",
"PORTKEY_VIRTUAL_KEY": "Create in Portkey dashboard"
}
missing_vars: List[str] = []
for var, help_text in required_vars.items():
if not os.getenv(var):
missing_vars.append(f"{var} ({help_text})")
if missing_vars:
raise PortkeyConfigurationError(
"Missing required Portkey configuration:\n" +
"\n".join(f"- {var}" for var in missing_vars)
)
```
### Modern Integration (Recommended)
The latest Portkey SDK (v1.13.0+) is built directly on top of the OpenAI SDK, providing seamless compatibility:
```python
from crewai import LLM
import os
from typing import Optional
# Validate configuration before proceeding
validate_portkey_configuration()
# Set environment variables
os.environ["PORTKEY_API_KEY"] = "YOUR_PORTKEY_API_KEY"
os.environ["PORTKEY_VIRTUAL_KEY"] = "YOUR_VIRTUAL_KEY"
# Modern Portkey integration with CrewAI
gpt_llm = LLM(
model="gpt-4",
base_url="https://api.portkey.ai/v1",
api_key=os.environ["PORTKEY_VIRTUAL_KEY"],
extra_headers={
"x-portkey-api-key": os.environ["PORTKEY_API_KEY"],
"x-portkey-virtual-key": os.environ["PORTKEY_VIRTUAL_KEY"]
}
)
def create_portkey_llm(
model: str = "gpt-4",
api_key: Optional[str] = None,
virtual_key: Optional[str] = None
) -> LLM:
"""
Create a CrewAI LLM instance configured with Portkey.
Args:
model: The model name to use (e.g., "gpt-4", "claude-3-sonnet")
api_key: Portkey API key (defaults to PORTKEY_API_KEY env var)
virtual_key: Portkey Virtual key (defaults to PORTKEY_VIRTUAL_KEY env var)
Returns:
Configured LLM instance with Portkey integration
Example:
>>> llm = create_portkey_llm("gpt-4")
>>> # Use with CrewAI agents
>>> agent = Agent(llm=llm, ...)
"""
portkey_api_key = api_key or os.environ["PORTKEY_API_KEY"]
portkey_virtual_key = virtual_key or os.environ["PORTKEY_VIRTUAL_KEY"]
return LLM(
model=model,
base_url="https://api.portkey.ai/v1",
api_key=portkey_virtual_key,
extra_headers={
"x-portkey-api-key": portkey_api_key,
"x-portkey-virtual-key": portkey_virtual_key
}
)
# Create LLM instance
gpt_llm = create_portkey_llm("gpt-4")
```
### Legacy Integration (Deprecated)
@@ -561,51 +626,438 @@ tool_aware_agent = Agent(
)
```
## Enhanced Configuration Management
### PortkeyConfig Class
For complex deployments, use a structured configuration approach:
```python
import os
import json
from dataclasses import dataclass
from typing import Optional, Dict, Any, List
@dataclass
class PortkeyConfig:
"""
Configuration management for Portkey integration with CrewAI.
Attributes:
api_key: Portkey API key
virtual_key: Portkey Virtual key for LLM provider
environment: Deployment environment (development, staging, production)
budget_limit_usd: Maximum spend limit in USD
rate_limit_rpm: Requests per minute limit
enable_caching: Whether to enable semantic caching
fallback_models: List of fallback models if primary fails
custom_metadata: Additional metadata for tracking
"""
api_key: str
virtual_key: str
environment: str = "development"
budget_limit_usd: float = 100.0
rate_limit_rpm: int = 60
enable_caching: bool = True
fallback_models: Optional[List[str]] = None
custom_metadata: Optional[Dict[str, Any]] = None
@classmethod
def from_environment(cls, environment: str = "development") -> "PortkeyConfig":
"""
Create configuration from environment variables.
Args:
environment: Target environment
Returns:
PortkeyConfig instance
Raises:
PortkeyConfigurationError: If required variables are missing
"""
validate_portkey_configuration()
return cls(
api_key=os.environ["PORTKEY_API_KEY"],
virtual_key=os.environ["PORTKEY_VIRTUAL_KEY"],
environment=environment,
budget_limit_usd=float(os.environ.get("PORTKEY_BUDGET_LIMIT", "100.0")),
rate_limit_rpm=int(os.environ.get("PORTKEY_RATE_LIMIT", "60")),
enable_caching=os.environ.get("PORTKEY_ENABLE_CACHE", "true").lower() == "true"
)
def to_llm_config(self, model: str = "gpt-4") -> Dict[str, Any]:
"""
Convert to LLM configuration dictionary.
Args:
model: Model name to use
Returns:
Dictionary suitable for LLM initialization
"""
config = {
"retry": {"attempts": 3, "on_status_codes": [429, 500, 502, 503, 504]},
"request_timeout": 30000
}
if self.budget_limit_usd:
config["budget_limit"] = self.budget_limit_usd
if self.rate_limit_rpm:
config["rate_limit"] = {"requests_per_minute": self.rate_limit_rpm}
if self.enable_caching:
config["cache"] = {"mode": "semantic"}
if self.fallback_models:
config["fallbacks"] = [{"model": m} for m in self.fallback_models]
headers = {
"x-portkey-api-key": self.api_key,
"x-portkey-virtual-key": self.virtual_key,
"x-portkey-config": json.dumps(config)
}
if self.custom_metadata:
headers["x-portkey-metadata"] = json.dumps(self.custom_metadata)
return {
"model": model,
"base_url": "https://api.portkey.ai/v1",
"api_key": self.virtual_key,
"extra_headers": headers
}
# Usage example
config = PortkeyConfig.from_environment("production")
config.custom_metadata = {"team": "ai-research", "project": "customer-support"}
llm_config = config.to_llm_config("gpt-4")
production_llm = LLM(**llm_config)
```
## Version Compatibility Matrix
| Component | Minimum Version | Recommended Version | Notes |
|-----------|----------------|-------------------|-------|
| **CrewAI** | 0.80.0 | 0.90.0+ | Latest features require 0.90.0+ |
| **Portkey SDK** | 1.13.0 | 1.13.0+ | Built on OpenAI SDK compatibility |
| **Python** | 3.8 | 3.10+ | Type hints require 3.9+, async features optimized for 3.10+ |
| **OpenAI SDK** | 1.0.0 | 1.50.0+ | Required for Portkey compatibility |
### Migration Guide
#### From Legacy Portkey Integration (< 1.13.0)
If you're upgrading from an older Portkey integration:
```python
# OLD: Legacy pattern (deprecated)
from portkey_ai import createHeaders, PORTKEY_GATEWAY_URL
gpt_llm = LLM(
model="gpt-4",
base_url=PORTKEY_GATEWAY_URL,
api_key="dummy",
extra_headers=createHeaders(
api_key="YOUR_PORTKEY_API_KEY",
virtual_key="YOUR_VIRTUAL_KEY"
)
)
# NEW: Modern pattern (recommended)
from crewai import LLM
import os
gpt_llm = LLM(
model="gpt-4",
base_url="https://api.portkey.ai/v1",
api_key=os.environ["PORTKEY_VIRTUAL_KEY"],
extra_headers={
"x-portkey-api-key": os.environ["PORTKEY_API_KEY"],
"x-portkey-virtual-key": os.environ["PORTKEY_VIRTUAL_KEY"]
}
)
```
#### Migration Checklist
- [ ] Update Portkey SDK to 1.13.0+: `pip install -U portkey-ai`
- [ ] Replace `createHeaders` and `PORTKEY_GATEWAY_URL` imports
- [ ] Update header format to use `x-portkey-*` prefixes
- [ ] Add environment variable validation
- [ ] Test with your existing CrewAI workflows
- [ ] Update CI/CD pipelines with new environment variables
- [ ] Review and update any custom error handling
## Troubleshooting and Best Practices
### Enhanced Error Handling
```python
import logging
from typing import Dict, Any, Optional
from crewai import Crew
class PortkeyError(Exception):
"""Base exception for Portkey integration errors"""
pass
class PortkeyConfigurationError(PortkeyError):
"""Raised when Portkey configuration is invalid"""
pass
class PortkeyAPIError(PortkeyError):
"""Raised when Portkey API calls fail"""
pass
class PortkeyLogger:
"""Structured logging for Portkey operations"""
def __init__(self, name: str = "portkey"):
self.logger = logging.getLogger(name)
self.logger.setLevel(logging.INFO)
if not self.logger.handlers:
handler = logging.StreamHandler()
formatter = logging.Formatter(
'%(asctime)s - %(name)s - %(levelname)s - %(message)s'
)
handler.setFormatter(formatter)
self.logger.addHandler(handler)
def log_request(self, model: str, tokens: Optional[int] = None) -> None:
"""Log successful API request"""
self.logger.info(f"Portkey request successful - Model: {model}, Tokens: {tokens}")
def log_error(self, error: Exception, context: Dict[str, Any]) -> None:
"""Log API errors with context"""
self.logger.error(f"Portkey error: {error}, Context: {context}")
def execute_crew_with_error_handling(
crew: Crew,
inputs: Optional[Dict[str, Any]] = None,
max_retries: int = 3
) -> Any:
"""
Execute CrewAI crew with robust error handling.
Args:
crew: CrewAI crew instance
inputs: Input parameters for crew execution
max_retries: Maximum number of retry attempts
Returns:
Crew execution result
Raises:
PortkeyAPIError: If all retry attempts fail
"""
logger = PortkeyLogger()
for attempt in range(max_retries):
try:
if inputs:
result = crew.kickoff(inputs=inputs)
else:
result = crew.kickoff()
logger.log_request("crew_execution", None)
return result
except Exception as e:
context = {
"attempt": attempt + 1,
"max_retries": max_retries,
"crew_agents": len(crew.agents),
"crew_tasks": len(crew.tasks)
}
logger.log_error(e, context)
if attempt == max_retries - 1:
raise PortkeyAPIError(f"Crew execution failed after {max_retries} attempts: {e}")
# Wait before retry (exponential backoff)
import time
time.sleep(2 ** attempt)
# Usage example
try:
result = execute_crew_with_error_handling(
crew=research_crew,
inputs={"topic": "AI in healthcare"},
max_retries=3
)
except PortkeyAPIError as e:
print(f"Crew execution failed: {e}")
# Implement fallback logic here
```
### Common Integration Issues
#### API Key Configuration
```python
# Ensure proper environment variable setup
import os
required_vars = [
"PORTKEY_API_KEY",
"PORTKEY_VIRTUAL_KEY"
]
for var in required_vars:
if not os.getenv(var):
raise ValueError(f"Missing required environment variable: {var}")
```
#### Error Handling
```python
# Implement robust error handling for production deployments
try:
result = crew.kickoff()
except Exception as e:
# Portkey will automatically log the error with full context
print(f"Crew execution failed: {e}")
# Implement fallback logic here
def validate_portkey_environment() -> None:
"""
Comprehensive environment validation for Portkey integration.
Raises:
PortkeyConfigurationError: If configuration is invalid
"""
required_vars = {
"PORTKEY_API_KEY": "Get from https://app.portkey.ai",
"PORTKEY_VIRTUAL_KEY": "Create in Portkey dashboard"
}
missing_vars = []
for var, help_text in required_vars.items():
value = os.getenv(var)
if not value:
missing_vars.append(f"{var} ({help_text})")
elif len(value.strip()) < 10: # Basic validation
missing_vars.append(f"{var} appears to be invalid (too short)")
if missing_vars:
raise PortkeyConfigurationError(
"Invalid Portkey configuration:\n" +
"\n".join(f"- {var}" for var in missing_vars) +
"\n\nPlease check your environment variables."
)
# Test API connectivity
try:
test_llm = LLM(
model="gpt-3.5-turbo",
base_url="https://api.portkey.ai/v1",
api_key=os.environ["PORTKEY_VIRTUAL_KEY"],
extra_headers={
"x-portkey-api-key": os.environ["PORTKEY_API_KEY"],
"x-portkey-virtual-key": os.environ["PORTKEY_VIRTUAL_KEY"]
}
)
# Note: Actual connectivity test would require a real API call
print("✅ Portkey configuration validated successfully")
except Exception as e:
raise PortkeyConfigurationError(f"Failed to initialize Portkey LLM: {e}")
```
### Performance Optimization Tips
1. **Use Caching**: Enable semantic caching for repetitive tasks
2. **Load Balancing**: Distribute requests across multiple providers
3. **Batch Operations**: Group similar requests when possible
4. **Monitor Metrics**: Regularly review performance dashboards
5. **Optimize Prompts**: Use Portkey's prompt analytics to improve efficiency
#### 1. Caching Strategy
```python
# Configure semantic caching for repetitive CrewAI tasks
optimized_llm = LLM(
model="gpt-4",
base_url="https://api.portkey.ai/v1",
api_key=os.environ["PORTKEY_VIRTUAL_KEY"],
extra_headers={
"x-portkey-api-key": os.environ["PORTKEY_API_KEY"],
"x-portkey-virtual-key": os.environ["PORTKEY_VIRTUAL_KEY"],
"x-portkey-config": json.dumps({
"cache": {
"mode": "semantic",
"max_age": 3600 # 1 hour cache
}
})
}
)
```
#### 2. Load Balancing
```python
# Distribute load across multiple providers
load_balanced_config = {
"strategy": {
"mode": "loadbalance"
},
"targets": [
{"virtual_key": os.environ["OPENAI_VIRTUAL_KEY"], "weight": 70},
{"virtual_key": os.environ["ANTHROPIC_VIRTUAL_KEY"], "weight": 30}
]
}
```
#### 3. Performance Monitoring
- **Monitor Metrics**: Regularly review performance dashboards
- **Optimize Prompts**: Use Portkey's prompt analytics to improve efficiency
- **Batch Operations**: Group similar requests when possible
- **Track Latency**: Monitor response times across different models
- **Cost Analysis**: Review token usage and costs per agent/task
### Security Best Practices
1. **Environment Variables**: Never hardcode API keys in source code
2. **Virtual Keys**: Use Virtual Keys instead of direct provider keys
3. **Budget Limits**: Set appropriate spending limits for production
4. **Access Control**: Implement role-based access for team members
5. **Regular Rotation**: Rotate API keys periodically
#### 1. Environment-Based Configuration
```python
import os
from typing import Dict
def get_secure_config(environment: str) -> Dict[str, str]:
"""
Get environment-specific secure configuration.
Args:
environment: Target environment (dev, staging, prod)
Returns:
Secure configuration dictionary
"""
configs = {
"development": {
"api_key_var": "PORTKEY_API_KEY_DEV",
"virtual_key_var": "PORTKEY_VIRTUAL_KEY_DEV",
"budget_limit": "50.0"
},
"staging": {
"api_key_var": "PORTKEY_API_KEY_STAGING",
"virtual_key_var": "PORTKEY_VIRTUAL_KEY_STAGING",
"budget_limit": "200.0"
},
"production": {
"api_key_var": "PORTKEY_API_KEY_PROD",
"virtual_key_var": "PORTKEY_VIRTUAL_KEY_PROD",
"budget_limit": "1000.0"
}
}
if environment not in configs:
raise ValueError(f"Invalid environment: {environment}")
config = configs[environment]
return {
"api_key": os.environ[config["api_key_var"]],
"virtual_key": os.environ[config["virtual_key_var"]],
"budget_limit": config["budget_limit"]
}
```
#### 2. API Key Rotation
```python
def rotate_api_keys(old_key: str, new_key: str) -> None:
"""
Safely rotate Portkey API keys with zero downtime.
Args:
old_key: Current API key
new_key: New API key to rotate to
"""
# Implementation would depend on your deployment strategy
# This is a conceptual example
print(f"Rotating from {old_key[:8]}... to {new_key[:8]}...")
# Update environment variables
# Restart services with new keys
# Verify connectivity
# Deactivate old keys
```
#### 3. Security Checklist
- [ ] **Environment Variables**: Never hardcode API keys in source code
- [ ] **Virtual Keys**: Use Virtual Keys instead of direct provider keys
- [ ] **Budget Limits**: Set appropriate spending limits for production
- [ ] **Access Control**: Implement role-based access for team members
- [ ] **Regular Rotation**: Rotate API keys every 90 days
- [ ] **Audit Logging**: Enable comprehensive audit trails
- [ ] **Network Security**: Use HTTPS and validate SSL certificates
- [ ] **Monitoring**: Set up alerts for unusual usage patterns
- [ ] **Backup Keys**: Maintain secure backup of Virtual Keys
- [ ] **Team Training**: Ensure team understands security practices
For detailed information on creating and managing Configs, visit the [Portkey documentation](https://portkey.ai/docs/product/ai-gateway/configs).