Implementation Best Practices¶

Overview¶

This guide outlines best practices for implementing and using Ralph Orchestrator in production environments.

Architecture Best Practices¶

1. Modular Design¶

Keep agent implementations separate and modular
Use dependency injection for flexibility
Implement clear interfaces between components

2. Error Handling¶

# Good practice: Comprehensive error handling
try:
    response = await agent.process(prompt)
except AgentTimeoutError as e:
    logger.error(f"Agent timeout: {e}")
    return fallback_response()
except AgentAPIError as e:
    logger.error(f"API error: {e}")
    return handle_api_error(e)

3. Configuration Management¶

Use environment variables for sensitive data
Implement configuration validation
Support multiple configuration profiles

Performance Optimization¶

1. Caching Strategies¶

# Implement intelligent caching
from functools import lru_cache

@lru_cache(maxsize=128)
def get_agent_response(prompt_hash):
    return agent.process(prompt)

2. Connection Pooling¶

Reuse HTTP connections
Implement connection limits
Use async operations where possible

3. Rate Limiting¶

# Implement rate limiting
from asyncio import Semaphore

rate_limiter = Semaphore(10)  # Max 10 concurrent requests

async def make_request():
    async with rate_limiter:
        return await agent.process(prompt)

Security Best Practices¶

1. API Key Management¶

Never hardcode API keys
Use secure key storage solutions
Rotate keys regularly

2. Input Validation¶

# Always validate and sanitize inputs
def validate_prompt(prompt: str) -> str:
    if len(prompt) > MAX_PROMPT_LENGTH:
        raise ValueError("Prompt too long")

    # Remove potentially harmful content
    sanitized = sanitize_input(prompt)
    return sanitized

3. Output Filtering¶

Filter sensitive information from responses
Implement content moderation
Log security events

Monitoring and Observability¶

1. Structured Logging¶

import structlog

logger = structlog.get_logger()

logger.info("agent_request", 
    agent_type="claude",
    prompt_length=len(prompt),
    user_id=user_id,
    timestamp=datetime.utcnow()
)

2. Metrics Collection¶

Track response times
Monitor error rates
Measure token usage

3. Health Checks¶

# Implement health check endpoints
async def health_check():
    checks = {
        "database": await check_db_connection(),
        "agents": await check_agent_availability(),
        "cache": await check_cache_status()
    }
    return all(checks.values())

Testing Strategies¶

1. Unit Testing¶

# Test individual components
def test_prompt_validation():
    valid_prompt = "Calculate 2+2"
    assert validate_prompt(valid_prompt) == valid_prompt

    invalid_prompt = "x" * (MAX_PROMPT_LENGTH + 1)
    with pytest.raises(ValueError):
        validate_prompt(invalid_prompt)

2. Integration Testing¶

Test agent interactions
Verify error handling
Test edge cases

3. Load Testing¶

# Use tools like locust for load testing
locust -f load_test.py --host=http://localhost:8000

Deployment Best Practices¶

1. Container Strategy¶

# Multi-stage build for smaller images
FROM python:3.11 as builder
WORKDIR /app
COPY requirements.txt .
RUN pip install --user -r requirements.txt

FROM python:3.11-slim
COPY --from=builder /root/.local /root/.local
COPY . .
CMD ["python", "-m", "ralph_orchestrator"]

2. Scaling Considerations¶

Implement horizontal scaling
Use load balancers
Consider serverless options

3. Blue-Green Deployments¶

Minimize downtime
Enable quick rollbacks
Test in production-like environments

Common Pitfalls to Avoid¶

1. Over-Engineering¶

Start simple and iterate
Don't optimize prematurely
Focus on core functionality first

2. Ignoring Rate Limits¶

Always respect API rate limits
Implement exponential backoff
Monitor quota usage

3. Poor Error Messages¶

# Bad
except Exception:
    return "Error occurred"

# Good
except ValueError as e:
    return f"Invalid input: {e}"

Maintenance Guidelines¶

1. Regular Updates¶

Keep dependencies updated
Monitor security advisories
Test updates in staging first

2. Documentation¶

Maintain up-to-date documentation
Document configuration changes
Keep runbooks current

3. Backup and Recovery¶

Implement regular backups
Test recovery procedures
Document disaster recovery plans

Conclusion¶

Following these best practices will help ensure your Ralph Orchestrator implementation is: - Reliable and performant - Secure and maintainable - Scalable and observable

Remember to adapt these practices to your specific use case and requirements.