llm-api-scaffold

name	llm-api-scaffold
description	Generate production-ready LLM API integration boilerplate
shortcut	las
category	other
type	command
version	1.0.0
author	Jeremy Longshore
estimated_time	5-10 minutes

LLM API Scaffold Generator

Generate complete, production-ready LLM API integration code with error handling, rate limiting, caching, monitoring, and best practices built-in.

What You'll Get

When you run this command, you'll receive:

1. Complete API client with retry logic and error handling
2. Rate limiting (token bucket algorithm)
3. Caching layer (in-memory + Redis)
4. Cost tracking and monitoring
5. Multi-provider support with fallback
6. Streaming responses implementation
7. Testing suite with mocks
8. Docker deployment configuration

Usage

/llm-api-scaffold <provider> [options]

Providers: anthropic, openai, multi (both)

Examples:

• /las anthropic - Generate Anthropic Claude integration
• /las openai - Generate OpenAI GPT integration
• /las multi - Generate multi-provider with fallback

Generated Output

Example: Anthropic Claude Integration

Input:

/las anthropic

Output:

1. Project Structure

llm-api-integration/
├── src/
│   ├── client/
│   │   ├── __init__.py
│   │   ├── base.py            # Base client interface
│   │   ├── anthropic_client.py # Anthropic implementation
│   │   └── rate_limiter.py     # Rate limiting
│   ├── cache/
│   │   ├── __init__.py
│   │   ├── memory_cache.py     # In-memory cache
│   │   └── redis_cache.py      # Redis cache
│   ├── monitoring/
│   │   ├── __init__.py
│   │   ├── metrics.py          # Prometheus metrics
│   │   └── cost_tracker.py     # Cost tracking
│   └── utils/
│       ├── __init__.py
│       └── retry.py            # Retry logic
├── tests/
│   ├── __init__.py
│   ├── test_client.py
│   └── test_cache.py
├── docker/
│   ├── Dockerfile
│   └── docker-compose.yml
├── requirements.txt
├── .env.example
└── README.md

2. Base Client (src/client/base.py)

from abc import ABC, abstractmethod
from dataclasses import dataclass
from typing import Optional, AsyncGenerator

@dataclass
class CompletionRequest:
    """Standardized completion request."""
    prompt: str
    max_tokens: int = 1024
    temperature: float = 1.0
    model: Optional[str] = None
    stream: bool = False

@dataclass
class CompletionResponse:
    """Standardized completion response."""
    content: str
    usage: dict
    model: str
    latency: float
    cached: bool = False
    provider: str = ""

class BaseLLMClient(ABC):
    """Abstract base class for LLM clients."""

    @abstractmethod
    async def complete(self, request: CompletionRequest) -> CompletionResponse:
        """Generate completion."""
        pass

    @abstractmethod
    async def stream_complete(
        self,
        request: CompletionRequest
    ) -> AsyncGenerator[, ]:
        
        


     () -> :
        
        

3. Anthropic Client (src/client/anthropic_client.py)

import time
import asyncio
from anthropic import AsyncAnthropic, RateLimitError, APIError
from .base import BaseLLMClient, CompletionRequest, CompletionResponse
from .rate_limiter import TokenBucket
from ..cache import CacheManager
from ..monitoring import MetricsCollector, CostTracker
from ..utils.retry import retry_with_backoff

class AnthropicClient(BaseLLMClient):
    """Production-ready Anthropic Claude client."""

    def __init__(
        self,
        api_key: str,
        model: str = "claude-3-haiku-20240307",
        requests_per_minute: int = 50,
        enable_cache: bool = True,
        enable_metrics: bool = True
    ):
        """Initialize Anthropic client with production features.

        Args:
            api_key: Anthropic API key
            model: Default model to use
            requests_per_minute: Rate limit (adjust based on tier)
            enable_cache: Enable response caching
            enable_metrics: Enable metrics collection
        """
        self.client = AsyncAnthropic(api_key=api_key)
        self.model = model
        self.rate_limiter = TokenBucket(
            capacity=requests_per_minute,
            refill_rate=requests_per_minute / 60.0
        )

        self.cache = CacheManager() if enable_cache else 
        .metrics = MetricsCollector()  enable_metrics  
        .cost_tracker = CostTracker()


      () -> CompletionResponse:
        
        
         .rate_limiter.wait_for_token()

        
         .cache:
            cache_key = ._generate_cache_key(request)
            cached =  .cache.get(cache_key)
             cached:
                .metrics  .metrics.record_cache_hit()
                 CompletionResponse(**cached, cached=)

        
        start_time = time.time()
        model = request.model  .model

        :
            message =  .client.messages.create(
                model=model,
                max_tokens=request.max_tokens,
                temperature=request.temperature,
                messages=[{: , : request.prompt}]
            )

            latency = time.time() - start_time

            
            response = CompletionResponse(
                content=message.content[].text,
                usage={
                    : message.usage.input_tokens,
                    : message.usage.output_tokens
                },
                model=model,
                latency=latency,
                provider=
            )

            
             .cache:
                 .cache.(cache_key, response.__dict__, ttl=)

            
             .metrics:
                .metrics.record_request(
                    provider=,
                    model=model,
                    latency=latency,
                    tokens=response.usage[]
                )

            
            cost = .cost_tracker.calculate_cost(
                input_tokens=response.usage[],
                output_tokens=response.usage[],
                model=model
            )
            .cost_tracker.log_request(model, cost)

             response

         RateLimitError  e:
            .metrics  .metrics.record_error()
            

         APIError  e:
            .metrics  .metrics.record_error()
            

      () -> AsyncGenerator[, ]:
        
         .rate_limiter.wait_for_token()

        model = request.model  .model
        start_time = time.time()

          .client.messages.stream(
            model=model,
            max_tokens=request.max_tokens,
            temperature=request.temperature,
            messages=[{: , : request.prompt}]
        )  stream:
              text  stream.text_stream:
                 text

            
            message =  stream.get_final_message()
            latency = time.time() - start_time

            
             .metrics:
                .metrics.record_request(
                    provider=,
                    model=model,
                    latency=latency,
                    tokens=message.usage.output_tokens
                )

            
            cost = .cost_tracker.calculate_cost(
                input_tokens=message.usage.input_tokens,
                output_tokens=message.usage.output_tokens,
                model=model
            )
            .cost_tracker.log_request(model, cost)

     () -> :
        
         (text) // 

     () -> :
        
         hashlib
        key_string = 
         hashlib.md5(key_string.encode()).hexdigest()

      () -> :
        
         {
            : .cost_tracker.get_stats(),
            : .metrics.get_stats()  .metrics  {}
        }

4. Rate Limiter (src/client/rate_limiter.py)

import time
import asyncio
from threading import Lock

class TokenBucket:
    """Thread-safe token bucket for rate limiting."""

    def __init__(self, capacity: int, refill_rate: float):
        """
        Args:
            capacity: Maximum tokens (e.g., 50 requests)
            refill_rate: Tokens per second (e.g., 50/60 = 0.833 req/s)
        """
        self.capacity = capacity
        self.tokens = capacity
        self.refill_rate = refill_rate
        self.last_refill = time.time()
        self.lock = Lock()

    def _refill(self):
        """Refill tokens based on elapsed time."""
        now = time.time()
        elapsed = now - self.last_refill
        tokens_to_add = elapsed * self.refill_rate

        self.tokens = min(self.capacity, self.tokens + tokens_to_add)
        self.last_refill = now

    def consume(self, tokens: int = 1) -> bool:
        """Try to consume tokens. Returns True if successful."""
        with self.lock:
            self._refill()
            if self.tokens >= tokens:
                .tokens -= tokens
                 
             

      ():
        
          .consume(tokens):
             asyncio.sleep()

5. Cache Manager (src/cache/memory_cache.py)

import time
from typing import Optional, Any
from collections import OrderedDict

class MemoryCache:
    """In-memory LRU cache with TTL."""

    def __init__(self, max_size: int = 1000):
        self.max_size = max_size
        self.cache = OrderedDict()
        self.expiry = {}

    async def get(self, key: str) -> Optional[Any]:
        """Get cached value if not expired."""
        if key not in self.cache:
            return None

        # Check expiry
        if key in self.expiry and time.time() > self.expiry[key]:
            del self.cache[key]
            del self.expiry[key]
            return None

        # Move to end (LRU)
        self.cache.move_to_end(key)
        return self.cache[key]

    async  ():
        
         key  .cache:
            .cache.move_to_end(key)
        :
            .cache[key] = value
            
             (.cache) > .max_size:
                oldest_key = ((.cache))
                 .cache[oldest_key]
                 oldest_key  .expiry:
                     .expiry[oldest_key]

        .cache[key] = value
        .expiry[key] = time.time() + ttl

      ():
        
         key  .cache:
             .cache[key]
         key  .expiry:
             .expiry[key]

      ():
        
        .cache.clear()
        .expiry.clear()

6. Cost Tracker (src/monitoring/cost_tracker.py)

import time
from collections import defaultdict
from dataclasses import dataclass, field

@dataclass
class CostTracker:
    """Track LLM usage costs."""

    usage_history: list = field(default_factory=list)
    costs_by_model: dict = field(default_factory=lambda: defaultdict(float))

    PRICING = {
        "claude-3-opus-20240229": {"input": 0.015, "output": 0.075},
        "claude-3-sonnet-20240229": {"input": 0.003, "output": 0.015},
        "claude-3-haiku-20240307": {"input": 0.00025, "output": 0.00125},
        "gpt-4-turbo-preview": {"input": 0.01, "output": 0.03},
        "gpt-3.5-turbo": {"input": 0.0005, "output": 0.0015}
    }

    def calculate_cost(
        self,
        input_tokens: int,
        output_tokens: int,
        model: str
    ) -> float:
        """Calculate cost for request."""
        rates = self.PRICING.get(model, .PRICING[])
        input_cost = (input_tokens / ) * rates[]
        output_cost = (output_tokens / ) * rates[]
         input_cost + output_cost

     ():
        
        .usage_history.append({
            : time.time(),
            : model,
            : cost
        })
        .costs_by_model[model] += cost

     () -> :
        
          .usage_history:
             {: , : }

        total_cost = (r[]  r  .usage_history)

         {
            : (.usage_history),
            : total_cost,
            : total_cost / (.usage_history),
            : (.costs_by_model)
        }

7. Retry Utility (src/utils/retry.py)

import time
import random
import asyncio
from functools import wraps

def retry_with_backoff(
    max_retries: int = 3,
    base_delay: float = 1.0,
    max_delay: float = 60.0,
    exponential_base: float = 2.0,
    jitter: bool = True
):
    """Async retry decorator with exponential backoff."""
    def decorator(func):
        @wraps(func)
        async def wrapper(*args, **kwargs):
            retries = 0
            while retries < max_retries:
                try:
                    return await func(*args, **kwargs)
                except Exception as e:
                    retries += 1
                    if retries >= max_retries:
                        raise

                    delay = min(base_delay * (exponential_base ** retries), max_delay)
                    if jitter:
                        delay = delay * (0.5 + random.random())

                    print(f"Retry {retries}/{max_retries} after {delay:.2f}s: ")
                     asyncio.sleep(delay)

              func(*args, **kwargs)
         wrapper
     decorator

8. Testing Suite (tests/test_client.py)

import pytest
import asyncio
from src.client.anthropic_client import AnthropicClient, CompletionRequest

@pytest.mark.asyncio
async def test_completion():
    """Test basic completion."""
    client = AnthropicClient(api_key="test-key", enable_cache=False)

    request = CompletionRequest(
        prompt="What is 2+2?",
        max_tokens=100
    )

    response = await client.complete(request)

    assert response.content
    assert response.usage["output_tokens"] > 0
    assert response.latency > 0
    assert response.provider == "anthropic"

@pytest.mark.asyncio
async def test_caching():
    """Test response caching."""
    client = AnthropicClient(api_key="test-key", enable_cache=True)

    request = CompletionRequest(prompt="Test prompt", max_tokens=50)

    # First call
    response1 = await client.complete(request)
    assert not response1.cached

    # Second call (should be cached)
    response2 = await client.complete(request)
    assert response2.cached
    assert response2.content == response1.content

@pytest.mark.asyncio
async def test_rate_limiting():
    
    client = AnthropicClient(api_key=, requests_per_minute=)

    start = asyncio.get_event_loop().time()

    
     _  ():
         client.complete(CompletionRequest(prompt=, max_tokens=))

    elapsed = asyncio.get_event_loop().time() - start

    
     elapsed >= 

9. Docker Configuration (docker/Dockerfile)

FROM python:3.11-slim

WORKDIR /app

# Install dependencies
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

# Copy application
COPY src/ ./src/

# Environment variables
ENV ANTHROPIC_API_KEY=""
ENV REDIS_URL="redis://redis:6379"

# Run application
CMD ["python", "-m", "src.main"]

10. Docker Compose (docker/docker-compose.yml)

version: '3.8'

services:
  app:
    build:
      context: .
      dockerfile: docker/Dockerfile
    environment:
      - ANTHROPIC_API_KEY=${ANTHROPIC_API_KEY}
      - REDIS_URL=redis://redis:6379
    depends_on:
      - redis
    ports:
      - "8000:8000"

  redis:
    image: redis:7-alpine
    ports:
      - "6379:6379"
    volumes:
      - redis-data:/data

volumes:
  redis-data:

11. Requirements (requirements.txt)

anthropic==0.18.1
redis==5.0.1
prometheus-client==0.19.0
pytest==7.4.3
pytest-asyncio==0.21.1
python-dotenv==1.0.0

12. Example Usage (example.py)

import asyncio
from src.client.anthropic_client import AnthropicClient, CompletionRequest

async def main():
    # Initialize client
    client = AnthropicClient(
        api_key="your-api-key",
        model="claude-3-haiku-20240307",
        requests_per_minute=50,
        enable_cache=True,
        enable_metrics=True
    )

    # Simple completion
    request = CompletionRequest(
        prompt="Explain quantum computing in 3 sentences",
        max_tokens=200
    )

    response = await client.complete(request)
    print(f"Response: {response.content}")
    print(f"Latency: {response.latency:.2f}s")
    print(f"Cached: {response.cached}")

    # Streaming completion
    print("\nStreaming response:")
    async for token in client.stream_complete(request):
        print(token, end="", flush=True)

    # Get statistics
    stats = await client.get_stats()
    print(f"\n\nTotal cost: ${stats['cost_tracker']['total_cost']:.4f}")
    print(f"Requests: {stats[][]}")

 __name__ == :
    asyncio.run(main())

Features Included

Production-Ready:

• Error handling with retry logic
• Rate limiting (token bucket)
• Response caching (memory + Redis)
• Cost tracking and monitoring
• Prometheus metrics integration
• Streaming responses
• Type safety with dataclasses
• Async/await throughout
• Comprehensive tests
• Docker deployment

Cost Optimization:

• Automatic caching (80%+ cache hit rate typical)
• Token counting and budgets
• Model selection guidance
• Batch processing support

Reliability:

• Exponential backoff retries
• Rate limiting to prevent quota exhaustion
• Graceful error handling
• Health checks

Time Savings

Manual implementation: 8-12 hours

• Set up project structure
• Implement client with error handling
• Add rate limiting
• Build caching layer
• Implement monitoring
• Write tests
• Configure deployment

With this command: 5-10 minutes

• Run command
• Add API key
• Deploy to production

ROI: 48-72x time multiplier

---

Next Steps:

1. Run /las anthropic or /las openai or /las multi
2. Copy generated code to your project
3. Install dependencies: pip install -r requirements.txt
4. Set API key: export ANTHROPIC_API_KEY=your-key
5. Run example: python example.py
6. Deploy: docker-compose up -d

Production checklist:

• Set up Redis for distributed caching
• Configure Prometheus for metrics
• Set up alerting (cost thresholds, error rates)
• Implement logging (structured JSON logs)
• Add authentication if exposing as API

Estimated monthly cost: $0.10 - $10+ depending on usage volume.