Claude API Integration Guide 2025: Complete Developer Tutorial with Code Examples

The Claude API from Anthropic has become one of the most powerful and reliable AI APIs available to developers in 2025. With Claude Sonnet 4 and Claude Opus 4 now available, integrating Claude’s advanced conversational AI capabilities into your applications has never been more accessible. This comprehensive guide covers everything from basic setup to advanced implementation patterns, complete with real-world code examples and best practices.

If you’re also interested in running AI models locally, check out our comprehensive guide on Best Ollama Models 2025: Complete Performance Guide for self-hosted alternatives. For containerized AI deployments, our Getting Started with Ollama on Kubernetes tutorial provides excellent insights into production-scale AI infrastructure.

What is the Claude API?

The Claude API is Anthropic’s REST API that provides programmatic access to Claude’s family of large language models. Unlike other AI APIs, Claude is specifically designed for helpful, harmless, and honest interactions, making it ideal for production applications where safety and reliability are paramount.

For developers interested in comparing different AI solutions, our AI Models Comparison 2025: Claude, Grok, GPT & More provides an in-depth analysis of leading models. If you’re considering local deployment options, explore our Complete Ollama Guide: Installation, Usage & Code Examples for self-hosted AI solutions.

Key Features of Claude API in 2025:

• Constitutional AI: Built-in safety mechanisms reduce harmful outputs
• Long Context Windows: Up to 200,000 tokens for complex documents
• Tool Use: Native function calling capabilities similar to OpenAI’s function calling
• Vision Capabilities: Image analysis and understanding
• Streaming Responses: Real-time response generation
• Multiple Model Options: From efficient Haiku to powerful Opus

Getting Started: API Keys and Authentication

Step 1: Create an Anthropic Account

1. Visit console.anthropic.com
2. Sign up for a developer account
3. Verify your email address
4. Add billing information (required for API access)

For enterprise users, Anthropic offers dedicated support and custom pricing plans. If you’re working with containerized applications, consider reading our Docker Best Practices for Python Developers in 2025 for optimal deployment strategies.

Step 2: Generate API Key

# Navigate to API Keys section in the console
# Click "Create Key"
# Copy your API key (starts with 'sk-ant-api03-')

Important: Store your API key securely using tools like HashiCorp Vault or AWS Secrets Manager, and never commit it to version control. For local development, consider using python-dotenv for environment variable management.

Step 3: Set Up Environment Variables

# .env file
ANTHROPIC_API_KEY=sk-ant-api03-your-key-here

Claude API Models Comparison 2025

For a comprehensive comparison with other AI models, check out our detailed analysis in The Top 10 AI Models Every Developer Should Know in 2025. If you’re interested in cost optimization strategies, our Kubernetes Cost Optimization guide offers insights that apply to AI infrastructure as well.

For current pricing information, visit Anthropic’s pricing page and compare with OpenAI’s pricing structure to make informed decisions for your specific use case.

Basic Integration Examples

Python Integration

First, install the official Anthropic Python SDK:

pip install anthropic

For production environments, consider using Poetry or pipenv for dependency management. If you’re working with containerized Python applications, our Docker Best Practices for Python Developers guide provides essential optimization techniques.

import anthropic
import os
from typing import Dict, List

class ClaudeAPIClient:
    def __init__(self):
        self.client = anthropic.Anthropic(
            api_key=os.getenv("ANTHROPIC_API_KEY")
        )
    
    def simple_chat(self, message: str, model: str = "claude-sonnet-4-20250514") -> str:
        """Basic chat completion with Claude"""
        try:
            response = self.client.messages.create(
                model=model,
                max_tokens=1000,
                temperature=0.7,
                messages=[
                    {"role": "user", "content": message}
                ]
            )
            return response.content[0].text
        except Exception as e:
            return f"Error: {str(e)}"

    def chat_with_context(self, messages: List[Dict], model: str = "claude-sonnet-4-20250514") -> str:
        """Chat with conversation history"""
        try:
            response = self.client.messages.create(
                model=model,
                max_tokens=1000,
                temperature=0.7,
                messages=messages
            )
            return response.content[0].text
        except Exception as e:
            return f"Error: {str(e)}"

# Usage example
claude = ClaudeAPIClient()
response = claude.simple_chat("Explain quantum computing in simple terms")
print(response)

Node.js Integration

Install the official Anthropic Node.js SDK:

npm install @anthropic-ai/sdk

For TypeScript projects, the SDK includes built-in type definitions. If you’re building scalable Node.js applications, consider our MCP Server Tutorial: Build with TypeScript from Scratch for advanced patterns. For containerized Node.js deployments, Docker’s official Node.js guide provides excellent best practices.

const Anthropic = require('@anthropic-ai/sdk');

class ClaudeAPIClient {
  constructor() {
    this.anthropic = new Anthropic({
      apiKey: process.env.ANTHROPIC_API_KEY,
    });
  }

  async simpleChat(message, model = 'claude-sonnet-4-20250514') {
    try {
      const response = await this.anthropic.messages.create({
        model: model,
        max_tokens: 1000,
        temperature: 0.7,
        messages: [
          { role: 'user', content: message }
        ]
      });
      return response.content[0].text;
    } catch (error) {
      console.error('Claude API Error:', error);
      return `Error: ${error.message}`;
    }
  }

  async streamingChat(message, model = 'claude-sonnet-4-20250514') {
    try {
      const stream = await this.anthropic.messages.create({
        model: model,
        max_tokens: 1000,
        temperature: 0.7,
        messages: [
          { role: 'user', content: message }
        ],
        stream: true
      });

      let fullResponse = '';
      for await (const chunk of stream) {
        if (chunk.type === 'content_block_delta') {
          fullResponse += chunk.delta.text;
          process.stdout.write(chunk.delta.text);
        }
      }
      return fullResponse;
    } catch (error) {
      console.error('Streaming Error:', error);
      return `Error: ${error.message}`;
    }
  }
}

// Usage
const claude = new ClaudeAPIClient();
claude.simpleChat("Write a Python function to calculate fibonacci numbers")
  .then(response => console.log(response));

cURL Examples

# Basic API call
curl https://api.anthropic.com/v1/messages \
  -H "Content-Type: application/json" \
  -H "x-api-key: $ANTHROPIC_API_KEY" \
  -H "anthropic-version: 2023-06-01" \
  -d '{
    "model": "claude-sonnet-4-20250514",
    "max_tokens": 1000,
    "messages": [
      {"role": "user", "content": "Hello, Claude!"}
    ]
  }'

# With system prompt
curl https://api.anthropic.com/v1/messages \
  -H "Content-Type: application/json" \
  -H "x-api-key: $ANTHROPIC_API_KEY" \
  -H "anthropic-version: 2023-06-01" \
  -d '{
    "model": "claude-sonnet-4-20250514",
    "max_tokens": 1000,
    "system": "You are a helpful coding assistant.",
    "messages": [
      {"role": "user", "content": "Write a Python class for a binary tree"}
    ]
  }'

Advanced Claude API Patterns

1. Function Calling with Tools

Claude’s tool use capabilities enable sophisticated AI agents that can interact with external systems. This is particularly powerful when combined with containerized microservices – check out our Building Secure Remote MCP Servers guide for production-ready implementations.

For more insights on AI agent development, explore our Claude Code Best Practices guide, which covers advanced command-line AI development patterns.

import json

def get_weather(location: str) -> str:
    """Mock weather function"""
    return f"The weather in {location} is sunny, 75°F"

def calculate_sum(a: int, b: int) -> int:
    """Calculate sum of two numbers"""
    return a + b

class AdvancedClaudeClient:
    def __init__(self):
        self.client = anthropic.Anthropic(
            api_key=os.getenv("ANTHROPIC_API_KEY")
        )
        
        self.tools = [
            {
                "name": "get_weather",
                "description": "Get current weather for a location",
                "input_schema": {
                    "type": "object",
                    "properties": {
                        "location": {
                            "type": "string",
                            "description": "City name"
                        }
                    },
                    "required": ["location"]
                }
            },
            {
                "name": "calculate_sum",
                "description": "Add two numbers together",
                "input_schema": {
                    "type": "object",
                    "properties": {
                        "a": {"type": "integer"},
                        "b": {"type": "integer"}
                    },
                    "required": ["a", "b"]
                }
            }
        ]
    
    def chat_with_tools(self, message: str):
        response = self.client.messages.create(
            model="claude-sonnet-4-20250514",
            max_tokens=1000,
            tools=self.tools,
            messages=[{"role": "user", "content": message}]
        )
        
        if response.stop_reason == "tool_use":
            tool_use = response.content[-1]
            if tool_use.name == "get_weather":
                result = get_weather(tool_use.input["location"])
            elif tool_use.name == "calculate_sum":
                result = calculate_sum(tool_use.input["a"], tool_use.input["b"])
            
            # Send result back to Claude
            follow_up = self.client.messages.create(
                model="claude-sonnet-4-20250514",
                max_tokens=1000,
                tools=self.tools,
                messages=[
                    {"role": "user", "content": message},
                    {"role": "assistant", "content": response.content},
                    {
                        "role": "user",
                        "content": [
                            {
                                "type": "tool_result",
                                "tool_use_id": tool_use.id,
                                "content": str(result)
                            }
                        ]
                    }
                ]
            )
            return follow_up.content[0].text
        
        return response.content[0].text

# Usage
advanced_claude = AdvancedClaudeClient()
response = advanced_claude.chat_with_tools("What's the weather in New York?")
print(response)

2. Image Analysis Integration

Claude’s vision capabilities enable sophisticated image analysis for applications like document processing, diagram interpretation, and visual content moderation. This feature is particularly useful for technical documentation – learn more about optimizing visual content in our Kubernetes Pod Optimization guide.

For handling large image processing workloads, consider implementing Redis caching to optimize performance and reduce API costs.

import base64

class VisionClaudeClient:
    def __init__(self):
        self.client = anthropic.Anthropic(
            api_key=os.getenv("ANTHROPIC_API_KEY")
        )
    
    def analyze_image(self, image_path: str, prompt: str = "Describe this image"):
        # Read and encode image
        with open(image_path, "rb") as image_file:
            image_data = base64.b64encode(image_file.read()).decode()
        
        # Determine media type
        if image_path.lower().endswith('.png'):
            media_type = "image/png"
        elif image_path.lower().endswith('.jpg') or image_path.lower().endswith('.jpeg'):
            media_type = "image/jpeg"
        else:
            raise ValueError("Unsupported image format")
        
        response = self.client.messages.create(
            model="claude-sonnet-4-20250514",
            max_tokens=1000,
            messages=[
                {
                    "role": "user",
                    "content": [
                        {
                            "type": "image",
                            "source": {
                                "type": "base64",
                                "media_type": media_type,
                                "data": image_data
                            }
                        },
                        {
                            "type": "text",
                            "text": prompt
                        }
                    ]
                }
            ]
        )
        
        return response.content[0].text

# Usage
vision_claude = VisionClaudeClient()
analysis = vision_claude.analyze_image("diagram.png", "Explain this system architecture diagram")
print(analysis)

3. Long Document Processing

Claude’s 200,000 token context window makes it excellent for processing large documents, technical specifications, and comprehensive code reviews. For document-heavy applications, consider implementing our Production-Ready LLM Infrastructure patterns for optimal scalability.

When working with extremely large documents, Apache Kafka can help manage document processing queues, while MinIO provides efficient object storage for document archives.

class DocumentProcessor:
    def __init__(self):
        self.client = anthropic.Anthropic(
            api_key=os.getenv("ANTHROPIC_API_KEY")
        )
    
    def process_long_document(self, document_text: str, task: str):
        """Process documents up to 200k tokens"""
        response = self.client.messages.create(
            model="claude-sonnet-4-20250514",
            max_tokens=4000,
            messages=[
                {
                    "role": "user",
                    "content": f"""
                    Please {task} for the following document:
                    
                    <document>
                    {document_text}
                    </document>
                    """
                }
            ]
        )
        
        return response.content[0].text
    
    def chunk_and_summarize(self, large_document: str, chunk_size: int = 50000):
        """Process very large documents in chunks"""
        chunks = [large_document[i:i+chunk_size] 
                 for i in range(0, len(large_document), chunk_size)]
        
        summaries = []
        for i, chunk in enumerate(chunks):
            print(f"Processing chunk {i+1}/{len(chunks)}")
            summary = self.process_long_document(
                chunk, 
                "provide a detailed summary"
            )
            summaries.append(summary)
        
        # Combine summaries
        combined_summary = self.process_long_document(
            "\n\n".join(summaries),
            "create a comprehensive summary from these individual summaries"
        )
        
        return combined_summary

# Usage
processor = DocumentProcessor()
with open("large_document.txt", "r") as f:
    document = f.read()

summary = processor.process_long_document(document, "summarize the key points")
print(summary)

Error Handling and Best Practices

Comprehensive Error Handling

Robust error handling is crucial for production AI applications. For comprehensive monitoring and observability, consider implementing Prometheus metrics and Grafana dashboards as outlined in our MCP Security Best Practices 2025 guide.

For distributed systems, circuit breaker patterns can prevent cascade failures when the Claude API experiences issues. Consider using libraries like py-breaker for Python or opossum for Node.js.

import time
from typing import Optional
import anthropic

class RobustClaudeClient:
    def __init__(self, max_retries: int = 3, base_delay: float = 1.0):
        self.client = anthropic.Anthropic(
            api_key=os.getenv("ANTHROPIC_API_KEY")
        )
        self.max_retries = max_retries
        self.base_delay = base_delay
    
    def robust_request(self, message: str, model: str = "claude-sonnet-4-20250514") -> Optional[str]:
        """Make API request with retry logic and comprehensive error handling"""
        
        for attempt in range(self.max_retries):
            try:
                response = self.client.messages.create(
                    model=model,
                    max_tokens=1000,
                    temperature=0.7,
                    messages=[{"role": "user", "content": message}]
                )
                return response.content[0].text
                
            except anthropic.APIConnectionError as e:
                print(f"Network error (attempt {attempt + 1}): {e}")
                if attempt < self.max_retries - 1:
                    time.sleep(self.base_delay * (2 ** attempt))
                    continue
                
            except anthropic.RateLimitError as e:
                print(f"Rate limit hit (attempt {attempt + 1}): {e}")
                if attempt < self.max_retries - 1:
                    # Exponential backoff for rate limits
                    time.sleep(self.base_delay * (2 ** (attempt + 2)))
                    continue
                    
            except anthropic.APIStatusError as e:
                print(f"API error {e.status_code}: {e.message}")
                if e.status_code >= 500 and attempt < self.max_retries - 1:
                    # Retry on server errors
                    time.sleep(self.base_delay * (2 ** attempt))
                    continue
                else:
                    # Don't retry on client errors (4xx)
                    break
                    
            except Exception as e:
                print(f"Unexpected error (attempt {attempt + 1}): {e}")
                if attempt < self.max_retries - 1:
                    time.sleep(self.base_delay)
                    continue
        
        return None

# Usage with error handling
robust_claude = RobustClaudeClient()
response = robust_claude.robust_request("Explain machine learning")
if response:
    print(response)
else:
    print("Failed to get response after all retries")

Input Validation and Sanitization

import re
from typing import List, Dict

class SafeClaudeClient:
    def __init__(self):
        self.client = anthropic.Anthropic(
            api_key=os.getenv("ANTHROPIC_API_KEY")
        )
    
    def validate_input(self, message: str) -> bool:
        """Validate input before sending to API"""
        if not message or not message.strip():
            return False
        
        # Check length (approximate token limit)
        if len(message) > 800000:  # ~200k tokens
            return False
        
        # Basic content filtering
        prohibited_patterns = [
            r'(?i)hack|exploit|malware',
            r'(?i)generate\s+fake\s+id',
            r'(?i)illegal\s+activities'
        ]
        
        for pattern in prohibited_patterns:
            if re.search(pattern, message):
                return False
        
        return True
    
    def safe_chat(self, message: str) -> str:
        """Chat with input validation"""
        if not self.validate_input(message):
            return "Invalid input. Please check your message and try again."
        
        try:
            response = self.client.messages.create(
                model="claude-sonnet-4-20250514",
                max_tokens=1000,
                temperature=0.7,
                messages=[{"role": "user", "content": message}]
            )
            return response.content[0].text
        except Exception as e:
            return f"Error processing request: {str(e)}"

# Usage
safe_claude = SafeClaudeClient()
response = safe_claude.safe_chat("How do I implement OAuth2?")
print(response)

Claude API Rate Limits and Optimization

Understanding Rate Limits (2025)

For high-volume applications, implement Redis-based rate limiting or use managed solutions like AWS API Gateway. Our Kubernetes Cost Optimization strategies include patterns applicable to AI API cost management.

Consider implementing exponential backoff algorithms for optimal retry behavior, especially when working with batch processing scenarios outlined in our Docker and Wasm Containers guide.

Rate Limiting Implementation

import asyncio
import aiohttp
from asyncio import Semaphore
from datetime import datetime, timedelta

class RateLimitedClaudeClient:
    def __init__(self, requests_per_minute: int = 50):
        self.client = anthropic.Anthropic(
            api_key=os.getenv("ANTHROPIC_API_KEY")
        )
        self.semaphore = Semaphore(requests_per_minute)
        self.request_times = []
    
    async def rate_limited_request(self, message: str) -> str:
        """Make rate-limited API request"""
        async with self.semaphore:
            # Clean old request times
            now = datetime.now()
            self.request_times = [
                t for t in self.request_times 
                if now - t < timedelta(minutes=1)
            ]
            
            # Wait if we're at the limit
            if len(self.request_times) >= 50:
                sleep_time = 60 - (now - self.request_times[0]).seconds
                await asyncio.sleep(sleep_time)
            
            # Make request
            self.request_times.append(now)
            
            response = self.client.messages.create(
                model="claude-sonnet-4-20250514",
                max_tokens=1000,
                messages=[{"role": "user", "content": message}]
            )
            
            return response.content[0].text

# Usage for batch processing
async def process_multiple_requests(requests: List[str]):
    client = RateLimitedClaudeClient()
    tasks = [client.rate_limited_request(req) for req in requests]
    responses = await asyncio.gather(*tasks)
    return responses

# Example
requests = [
    "Explain Python decorators",
    "What is async programming?",
    "How do I use Docker with Python?"
]

responses = asyncio.run(process_multiple_requests(requests))
for response in responses:
    print(response)
    print("---")

Production Deployment Strategies

1. Environment Configuration

For production deployments, follow 12-factor app principles for configuration management. Our Docker Best Practices for R Developers includes patterns applicable to any containerized application.

Consider using Kubernetes ConfigMaps and Secrets for managing environment-specific configurations, as detailed in our Kubernetes Pod Optimization guide.

# config.py
import os
from dataclasses import dataclass
from typing import Optional

@dataclass
class ClaudeConfig:
    api_key: str
    model: str = "claude-sonnet-4-20250514"
    max_tokens: int = 1000
    temperature: float = 0.7
    timeout: int = 30
    max_retries: int = 3
    
    @classmethod
    def from_env(cls) -> 'ClaudeConfig':
        return cls(
            api_key=os.getenv("ANTHROPIC_API_KEY"),
            model=os.getenv("CLAUDE_MODEL", "claude-sonnet-4-20250514"),
            max_tokens=int(os.getenv("CLAUDE_MAX_TOKENS", "1000")),
            temperature=float(os.getenv("CLAUDE_TEMPERATURE", "0.7")),
            timeout=int(os.getenv("CLAUDE_TIMEOUT", "30")),
            max_retries=int(os.getenv("CLAUDE_MAX_RETRIES", "3"))
        )

# app.py
from config import ClaudeConfig

config = ClaudeConfig.from_env()

2. Logging and Monitoring

import logging
import time
from functools import wraps

# Configure logging
logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
)
logger = logging.getLogger(__name__)

def log_api_calls(func):
    """Decorator to log API calls"""
    @wraps(func)
    def wrapper(*args, **kwargs):
        start_time = time.time()
        try:
            result = func(*args, **kwargs)
            duration = time.time() - start_time
            logger.info(f"API call successful - Duration: {duration:.2f}s")
            return result
        except Exception as e:
            duration = time.time() - start_time
            logger.error(f"API call failed - Duration: {duration:.2f}s - Error: {str(e)}")
            raise
    return wrapper

class ProductionClaudeClient:
    def __init__(self, config: ClaudeConfig):
        self.client = anthropic.Anthropic(api_key=config.api_key)
        self.config = config
        self.logger = logging.getLogger(self.__class__.__name__)
    
    @log_api_calls
    def chat(self, message: str) -> str:
        """Production-ready chat method"""
        self.logger.info(f"Processing chat request - Length: {len(message)} chars")
        
        response = self.client.messages.create(
            model=self.config.model,
            max_tokens=self.config.max_tokens,
            temperature=self.config.temperature,
            messages=[{"role": "user", "content": message}]
        )
        
        response_text = response.content[0].text
        self.logger.info(f"Response generated - Length: {len(response_text)} chars")
        
        return response_text

# Usage
config = ClaudeConfig.from_env()
claude = ProductionClaudeClient(config)
response = claude.chat("Explain containerization")

3. Caching Implementation

import redis
import json
import hashlib
from typing import Optional

class CachedClaudeClient:
    def __init__(self, config: ClaudeConfig, redis_url: str = "redis://localhost:6379"):
        self.client = anthropic.Anthropic(api_key=config.api_key)
        self.config = config
        self.redis_client = redis.from_url(redis_url)
        self.cache_ttl = 3600  # 1 hour
    
    def _get_cache_key(self, message: str, model: str) -> str:
        """Generate cache key for request"""
        content = f"{model}:{message}:{self.config.temperature}"
        return f"claude:{hashlib.md5(content.encode()).hexdigest()}"
    
    def chat_with_cache(self, message: str) -> str:
        """Chat with Redis caching"""
        cache_key = self._get_cache_key(message, self.config.model)
        
        # Try to get from cache
        cached_response = self.redis_client.get(cache_key)
        if cached_response:
            logger.info("Cache hit - returning cached response")
            return json.loads(cached_response)
        
        # Make API call
        logger.info("Cache miss - making API call")
        response = self.client.messages.create(
            model=self.config.model,
            max_tokens=self.config.max_tokens,
            temperature=self.config.temperature,
            messages=[{"role": "user", "content": message}]
        )
        
        response_text = response.content[0].text
        
        # Cache the response
        self.redis_client.setex(
            cache_key, 
            self.cache_ttl, 
            json.dumps(response_text)
        )
        
        return response_text

# Usage
cached_claude = CachedClaudeClient(config)
response = cached_claude.chat_with_cache("What is Docker?")

Troubleshooting Common Issues

1. Authentication Errors

def diagnose_auth_issues():
    """Diagnose common authentication problems"""
    api_key = os.getenv("ANTHROPIC_API_KEY")
    
    if not api_key:
        return "❌ ANTHROPIC_API_KEY environment variable not set"
    
    if not api_key.startswith("sk-ant-api03-"):
        return "❌ Invalid API key format. Should start with 'sk-ant-api03-'"
    
    try:
        client = anthropic.Anthropic(api_key=api_key)
        response = client.messages.create(
            model="claude-sonnet-4-20250514",
            max_tokens=10,
            messages=[{"role": "user", "content": "Hi"}]
        )
        return "✅ Authentication successful"
    except anthropic.AuthenticationError:
        return "❌ Invalid API key"
    except Exception as e:
        return f"❌ Unexpected error: {str(e)}"

print(diagnose_auth_issues())

2. Model Selection Issues

def check_model_availability():
    """Check which models are available"""
    client = anthropic.Anthropic(api_key=os.getenv("ANTHROPIC_API_KEY"))
    
    models_to_test = [
        "claude-sonnet-4-20250514",
        "claude-opus-4-20250514", 
        "claude-haiku-20240307"
    ]
    
    available_models = []
    
    for model in models_to_test:
        try:
            response = client.messages.create(
                model=model,
                max_tokens=10,
                messages=[{"role": "user", "content": "Test"}]
            )
            available_models.append(f"✅ {model}")
        except anthropic.NotFoundError:
            available_models.append(f"❌ {model} - Not available")
        except Exception as e:
            available_models.append(f"⚠️ {model} - Error: {str(e)}")
    
    return "\n".join(available_models)

print(check_model_availability())

3. Token Limit Management

import tiktoken

def estimate_tokens(text: str, model: str = "claude-sonnet-4-20250514") -> int:
    """Estimate token count for Claude models"""
    # Use GPT-4 tokenizer as approximation
    encoding = tiktoken.encoding_for_model("gpt-4")
    return len(encoding.encode(text))

def safe_api_call(message: str, max_response_tokens: int = 1000) -> str:
    """Make API call with token limit checking"""
    input_tokens = estimate_tokens(message)
    total_tokens = input_tokens + max_response_tokens
    
    if total_tokens > 200000:  # Claude's context limit
        return f"Error: Request too large ({total_tokens} tokens). Max is 200,000."
    
    client = anthropic.Anthropic(api_key=os.getenv("ANTHROPIC_API_KEY"))
    
    response = client.messages.create(
        model="claude-sonnet-4-20250514",
        max_tokens=min(max_response_tokens, 4000),  # Claude's max output
        messages=[{"role": "user", "content": message}]
    )
    
    return response.content[0].text

# Usage
large_document = "..." * 1000  # Large text
response = safe_api_call(large_document)
print(response)

Claude API vs OpenAI: 2025 Comparison

For a detailed comparison including newer models like DeepSeek R1, check our comprehensive AI Models Comparison guide.

If you’re considering local AI deployment, explore our Ollama AI Models guide for self-hosted alternatives that complement cloud-based APIs.

Migration from OpenAI to Claude

class OpenAIToClaudeMigrator:
    def __init__(self, anthropic_key: str):
        self.claude_client = anthropic.Anthropic(api_key=anthropic_key)
    
    def convert_openai_request(self, openai_messages: List[Dict]) -> str:
        """Convert OpenAI format to Claude format"""
        claude_messages = []
        system_prompt = None
        
        for message in openai_messages:
            if message["role"] == "system":
                system_prompt = message["content"]
            else:
                claude_messages.append({
                    "role": message["role"],
                    "content": message["content"]
                })
        
        kwargs = {
            "model": "claude-sonnet-4-20250514",
            "max_tokens": 1000,
            "messages": claude_messages
        }
        
        if system_prompt:
            kwargs["system"] = system_prompt
        
        response = self.claude_client.messages.create(**kwargs)
        return response.content[0].text

# Example migration
openai_format = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "Explain REST APIs"},
    {"role": "assistant", "content": "REST APIs are..."},
    {"role": "user", "content": "Give me an example"}
]

migrator = OpenAIToClaudeMigrator(os.getenv("ANTHROPIC_API_KEY"))
response = migrator.convert_openai_request(openai_format)
print(response)

Conclusion

The Claude API offers powerful capabilities for developers building AI-powered applications in 2025. With its focus on safety, long context windows, and sophisticated reasoning abilities, Claude is particularly well-suited for production applications where reliability and accuracy are paramount.

Key takeaways from this guide:

1. Start Simple: Begin with basic chat completions and gradually add advanced features
2. Handle Errors Gracefully: Implement comprehensive error handling and retry logic
3. Optimize for Production: Use caching, rate limiting, and monitoring in production deployments
4. Leverage Unique Features: Take advantage of Claude’s long context window and tool use capabilities
5. Stay Updated: Monitor Anthropic’s blog for new models and features

Next Steps

• Experiment with the code examples in your development environment
• Build a simple chatbot using the patterns shown
• Implement caching and monitoring for production use
• Explore Claude’s vision capabilities for multimodal applications
• Join the Anthropic Discord community for updates and best practices

For additional learning resources, explore our related guides:

• What is Claude Code and What Problem Does It Solve
• MCP Inspector: The Ultimate Developer Tool
• Perplexity AI Review 2025: Complete Guide

Resources

• Anthropic API Documentation
• Claude API Reference
• Anthropic Console
• Rate Limits and Pricing
• Anthropic Python SDK
• Anthropic TypeScript SDK
• Constitutional AI Research
• Anthropic Safety Research

Related Posts:

• Best Ollama Models 2025: Complete Performance Guide
• The Top 10 AI Models Every Developer Should Know in 2025
• Claude Code Best Practices: Advanced Command Line AI Development