Advanced Claude API Cheat Sheet

Basic API Setup

import anthropic

client = anthropic.Anthropic(
    api_key="your_api_key_here"
)

message = client.messages.create(
    model="claude-sonnet-4-20250514",
    max_tokens=1024,
    messages=[
        {"role": "user", "content": "Hello, Claude"}
    ]
)
print(message.content)

Available Models

Function Calling (Tool Use)

tools = [
    {
        "name": "get_weather",
        "description": "Get current weather for a location",
        "input_schema": {
            "type": "object",
            "properties": {
                "location": {
                    "type": "string",
                    "description": "City name"
                },
                "unit": {
                    "type": "string",
                    "enum": ["celsius", "fahrenheit"],
                    "description": "Temperature unit"
                }
            },
            "required": ["location"]
        }
    }
]

message = client.messages.create(
    model="claude-sonnet-4-20250514",
    max_tokens=1024,
    tools=tools,
    messages=[
        {"role": "user", "content": "What's the weather in San Francisco?"}
    ]
)

# Handle tool use response
if message.stop_reason == "tool_use":
    for content in message.content:
        if content.type == "tool_use":
            tool_name = content.name
            tool_input = content.input
            # Execute your function here
            result = get_weather(**tool_input)

            # Send result back to Claude
            response = client.messages.create(
                model="claude-sonnet-4-20250514",
                max_tokens=1024,
                tools=tools,
                messages=[
                    {"role": "user", "content": "What's the weather?"},
                    {"role": "assistant", "content": message.content},
                    {
                        "role": "user",
                        "content": [
                            {
                                "type": "tool_result",
                                "tool_use_id": content.id,
                                "content": str(result)
                            }
                        ]
                    }
                ]
            )

Streaming Responses

with client.messages.stream(
    model="claude-sonnet-4-20250514",
    max_tokens=1024,
    messages=[
        {"role": "user", "content": "Write a story"}
    ]
) as stream:
    for text in stream.text_stream:
        print(text, end="", flush=True)

# With event handling
with client.messages.stream(
    model="claude-sonnet-4-20250514",
    max_tokens=1024,
    messages=[{"role": "user", "content": "Hello"}]
) as stream:
    for event in stream:
        if event.type == "content_block_start":
            print("Content started")
        elif event.type == "content_block_delta":
            print(event.delta.text, end="")
        elif event.type == "message_stop":
            print("\nDone")

Prompt Caching

message = client.messages.create(
    model="claude-sonnet-4-20250514",
    max_tokens=1024,
    system=[
        {
            "type": "text",
            "text": "You are an AI assistant with expertise in Python.",
            "cache_control": {"type": "ephemeral"}
        }
    ],
    messages=[
        {
            "role": "user",
            "content": [
                {
                    "type": "text",
                    "text": large_code_base,
                    "cache_control": {"type": "ephemeral"}
                },
                {
                    "type": "text",
                    "text": "Explain this code"
                }
            ]
        }
    ]
)

# Cache hits reduce cost by 90% and latency significantly

Vision Capabilities

import base64

with open("image.jpg", "rb") as image_file:
    image_data = base64.standard_b64encode(image_file.read()).decode("utf-8")

message = client.messages.create(
    model="claude-sonnet-4-20250514",
    max_tokens=1024,
    messages=[
        {
            "role": "user",
            "content": [
                {
                    "type": "image",
                    "source": {
                        "type": "base64",
                        "media_type": "image/jpeg",
                        "data": image_data
                    }
                },
                {
                    "type": "text",
                    "text": "Describe this image"
                }
            ]
        }
    ]
)

# Supported formats: JPEG, PNG, GIF, WebP
# Max size: 5MB per image

System Prompts & Context

message = client.messages.create(
    model="claude-sonnet-4-20250514",
    max_tokens=1024,
    system=[
        {
            "type": "text",
            "text": "You are a helpful AI coding assistant.",
        },
        {
            "type": "text",
            "text": "Always provide code examples with explanations.",
            "cache_control": {"type": "ephemeral"}
        }
    ],
    messages=[
        {"role": "user", "content": "Help me with Python"}
    ]
)

# System prompts strongly influence Claude's behavior

Message History & Multi-turn Conversations

conversation = [
    {"role": "user", "content": "What's 2+2?"},
    {"role": "assistant", "content": "2+2 equals 4."},
    {"role": "user", "content": "What about 3+3?"}
]

message = client.messages.create(
    model="claude-sonnet-4-20250514",
    max_tokens=1024,
    messages=conversation
)

# Add response to conversation
conversation.append({
    "role": "assistant",
    "content": message.content[0].text
})

Temperature & Sampling Parameters

message = client.messages.create(
    model="claude-sonnet-4-20250514",
    max_tokens=1024,
    temperature=0.7,
    top_p=0.9,
    messages=[{"role": "user", "content": "Be creative"}]
)

Error Handling

from anthropic import (
    APIError,
    RateLimitError,
    APIConnectionError,
    AuthenticationError
)

try:
    message = client.messages.create(
        model="claude-sonnet-4-20250514",
        max_tokens=1024,
        messages=[{"role": "user", "content": "Hello"}]
    )
except RateLimitError:
    print("Rate limit exceeded, wait before retrying")
except AuthenticationError:
    print("Invalid API key")
except APIConnectionError:
    print("Network connection failed")
except APIError as e:
    print(f"API error: {e}")

Best Practices

Pricing Optimization