| name | llm-integrator |
| description | LLM API integration covering OpenAI, Anthropic, and Google API patterns, streaming responses, function calling and tool use, token counting, cost optimization, fallback strategies, rate limit handling, response caching, and multi-model routing.
Use when the user asks about llm integrator, llm integrator best practices, or needs guidance on llm integrator implementation.
Do NOT use when the user needs a different specialized skill or is asking about an unrelated technology domain.
|
| license | Apache-2.0 |
| metadata | {"author":"foundry-skills","version":"1.0.0","tags":"ai-ml deep-learning guide","category":"ai-machine-learning","subcategory":"llm-engineering","depends":"","disclaimer":"none","difficulty":"intermediate"} |
LLM Integrator
Overview
Integrating LLMs into production applications requires robust API handling, cost management, and reliability patterns. This skill covers practical integration with major LLM providers, including streaming, tool use, error handling, and multi-model architectures for production systems.
Provider API Patterns
OpenAI
from openai import OpenAI, AsyncOpenAI
client = OpenAI(api_key=environment-variables["OPENAI_API_KEY"])
response = client.chat.completions.create(
model="gpt-4o",
messages=[
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "Explain quantum computing."},
],
for p in prompts
]
responses = await asyncio.gather(*tasks)
return [r.choices[0].message.content for r in responses]
Anthropic
from anthropic import Anthropic, AsyncAnthropic
client = Anthropic(api_key=environment-variables["ANTHROPIC_API_KEY"])
response = client.messages.create(
model="claude-sonnet-4-20250514",
max_tokens=1024,
system="You are an expert software architect.",
messages=[
{"role": "user", "content": "Design a microservices architecture for e-commerce."},
],
if block.type == "thinking":
print(f"Thinking: {block.thinking}")
elif block.type == "text":
print(f"Answer: {block.text}")
Google Gemini
import google.generativeai as genai
genai.configure(api_key=environment-variables["GOOGLE_API_KEY"])
model = genai.GenerativeModel("gemini-2.0-flash")
response = model.generate_content("Explain neural networks.")
print(response.text)
import PIL.Image
image = PIL.Image.open("diagram.png")
response = model.generate_content(["Describe this diagram:", image])
print(response.text)
Streaming Responses
OpenAI Streaming
def stream_openai(prompt: str):
"""Stream response tokens as they are generated."""
stream = client.chat.completions.create(
model="gpt-4o",
messages=[{"role": "user", "content": prompt}],
stream=True,
)
full_response = ""
for chunk in stream:
delta = chunk.choices[0].delta.content
if delta:
full_response += delta
print(delta, end="", flush=True)
return full_response
Anthropic Streaming
def stream_anthropic(prompt: str):
"""Stream with Anthropic's event-based API."""
with client.messages.stream(
model="claude-sonnet-4-20250514",
max_tokens=1024,
messages=[{"role": "user", "content": prompt}],
) as stream:
full_response = ""
for text in stream.text_stream:
full_response += text
print(text, end="", flush=True)
return full_response
Server-Sent Events (SSE) for Web Apps
from fastapi import FastAPI
from fastapi.responses import StreamingResponse
app = FastAPI()
@app.get("/chat/stream")
async def chat_stream(prompt: str):
async def event_generator():
async with async_client.messages.stream(
model="claude-sonnet-4-20250514",
max_tokens=1024,
return StreamingResponse(
event_generator(),
media_type="text/event-stream",
)
Function Calling / Tool Use
OpenAI Function Calling
tools = [
{
"type": "function",
"function": {
"name": "get_weather",
"description": "Get current weather for a location",
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "string",
"content": json.dumps(result),
})
else:
return message.content
Anthropic Tool Use
tools = [
{
"name": "get_weather",
"description": "Get current weather for a location",
"input_schema": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "City and state"
}
messages.append({"role": "assistant", "content": response.content})
messages.append({"role": "user", "content": tool_results})
else:
return next(b.text for b in response.content if b.type == "text")
Token Counting and Cost Management
Token Counting
import tiktoken
class TokenCounter:
"""Count tokens for OpenAI models."""
COST_PER_1M = {
"gpt-4o": {"input": 2.50, "output": 10.00},
"gpt-4o-mini": {"input": 0.15, "output": 0.60},
"gpt-4-turbo": {"input": 10.00, "output": 30.00},
}
return (
input_tokens * costs["input"] / 1_000_000 +
output_tokens * costs["output"] / 1_000_000
)
Cost Optimization Strategies
class CostOptimizedClient:
"""Route requests to minimize cost while maintaining quality."""
def __init__(self):
self.openai = OpenAI()
self.anthropic = Anthropic()
self.token_counter = TokenCounter()
def complete(
self,
prompt: str,
for kw in ["architecture", "complex", "multi-step"]
):
return "complex"
return "medium"
Rate Limit Handling
Retry with Exponential Backoff
import time
from tenacity import (
retry, stop_after_attempt, wait_exponential,
retry_if_exception_type,
)
from openai import RateLimitError, APITimeoutError, InternalServerError
@retry(
retry=retry_if_exception_type((RateLimitError, APITimeoutError, InternalServerError)),
wait=wait_exponential(multiplier=1, min=1, max=60),
stop=stop_after_attempt(6),
)
def resilient_completion(client, **kwargs):
"""LLM call with automatic retry on transient errors."""
return client.chat.completions.create(**kwargs)
Token Bucket Rate Limiter
import asyncio
import time
class TokenBucketRateLimiter:
"""Rate limiter for API calls respecting tokens-per-minute limits."""
def __init__(self, tokens_per_minute: int, requests_per_minute: int):
self.tpm = tokens_per_minute
self.rpm = requests_per_minute
self.token_bucket = tokens_per_minute
self.request_bucket = requests_per_minute
self.rpm,
self.request_bucket + elapsed * self.rpm / 60
)
self.last_refill = now
Fallback Strategies
Multi-Provider Fallback
class LLMRouter:
"""Route LLM calls with automatic fallback across providers."""
def __init__(self):
self.providers = [
{
"name": "openai",
"client": OpenAI(),
"model": "gpt-4o",
"priority": 1,
"healthy": True,
max_tokens=kwargs.get("max_tokens", 1024),
messages=messages,
)
return resp.content[0].text
Response Caching
Semantic Cache
import hashlib
import json
import redis
class LLMCache:
"""Cache LLM responses with exact matching."""
def __init__(self, redis_url: str, ttl: int = 86400):
self.redis = redis.from_url(redis_url)
self.ttl = ttl
result = response.choices[0].message.content
self.set(messages, model, result)
return result
Multi-Model Routing
Complexity-Based Router
class SmartRouter:
"""Route requests to optimal model based on task analysis."""
MODEL_CONFIGS = {
"fast_cheap": {
"provider": "openai",
"model": "gpt-4o-mini",
"max_tokens": 1024,
"cost_input": 0.15,
"cost_output": 0.60,
},
)
classification = resp.choices[0].message.content.strip().lower()
mapping = {"simple": "fast_cheap", "medium": "balanced", "complex": "max_quality"}
return mapping.get(classification, "balanced")
Production Architecture
Complete Integration Layer
class LLMService:
"""Production-ready LLM service with all reliability patterns."""
def __init__(self, config: dict):
self.router = SmartRouter()
self.cache = LLMCache(config["redis_url"])
self.rate_limiter = TokenBucketRateLimiter(
tokens_per_minute=config["tpm"],
requests_per_minute=config["rpm"],
)
self.fallback = LLMRouter()
self.metrics.record("latency", latency)
self.metrics.record("model_used", config["model"])
return result
Checklist
When to Use
Use this skill when:
- Designing or implementing llm integrator solutions
- Reviewing or improving existing llm integrator approaches
- Making architectural or implementation decisions about llm integrator
- Learning llm integrator patterns and best practices
- Troubleshooting llm integrator-related issues
Do NOT use this skill when:
- The question is about a fundamentally different technology domain
- A more specific sibling skill covers the exact topic needed
- The user needs a complete hands-on tutorial rather than expert guidance
Output Format
# Llm Integrator Analysis
## Context Assessment
[Situation summary and constraints]
## Recommended Approach
[Primary recommendation with rationale]
## Implementation Steps
1. [Step with specific details]
2. [Step with specific details]
3. [Step with specific details]
## Trade-offs and Considerations
- [Key trade-off 1]
- [Key trade-off 2]
## Next Steps
- [Immediate action item]
- [Follow-up action item]
Example
Input: "Help me implement llm integrator for a medium-scale production application"
Output: A structured analysis covering current state assessment, recommended llm integrator approach with specific patterns, implementation roadmap with milestones, and risk mitigation strategies tailored to the application scale and constraints.
Edge Cases
- Legacy system integration: When llm integrator must coexist with legacy approaches, provide a gradual migration path rather than a complete rewrite
- Scale mismatch: When the solution complexity exceeds the project scale, recommend a simpler approach and note when to revisit
- Team skill gaps: When the team lacks experience with the recommended approach, include learning resources and simpler alternatives
- Conflicting requirements: When constraints conflict (e.g., performance vs. maintainability), explicitly state the trade-off and recommend based on stated priorities
