Claude API

Build applications with the Anthropic Claude API and SDKs.

When to Activate

• Building applications that call the Claude API
• Code imports anthropic (Python) or @anthropic-ai/sdk (TypeScript)
• User asks about Claude API patterns, tool use, streaming, or vision
• Implementing agent workflows with Claude Agent SDK
• Optimizing API costs, token usage, or latency

Model Selection

Default to Sonnet 4 unless the task requires deep reasoning (Opus) or speed/cost optimization (Haiku). For production, prefer pinned snapshot IDs over aliases.

Python SDK

Installation

pip install anthropic

Basic Message

import anthropic

client = anthropic.Anthropic()  # reads ANTHROPIC_API_KEY from env

message = client.messages.create(
    model="claude-sonnet-4-0",
    max_tokens=1024,
    messages=[
        {"role": "user", "content": "Explain async/await in Python"}
    ]
)
print(message.content[0].text)

Streaming

with client.messages.stream(
    model="claude-sonnet-4-0",
    max_tokens=1024,
    messages=[{"role": "user", "content": "Write a haiku about coding"}]
) as stream:
    for text in stream.text_stream:
        print(text, end="", flush=True)

System Prompt

message = client.messages.create(
    model="claude-sonnet-4-0",
    max_tokens=1024,
    system="You are a senior Python developer. Be concise.",
    messages=[{"role": "user", "content": "Review this function"}]
)

TypeScript SDK

Installation

npm install @anthropic-ai/sdk

Basic Message

import Anthropic from "@anthropic-ai/sdk";

const client = new Anthropic(); // reads ANTHROPIC_API_KEY from env

const message = await client.messages.create({
  model: "claude-sonnet-4-0",
  max_tokens: 1024,
  messages: [
    { role: "user", content: "Explain async/await in TypeScript" }
  ],
});
console.log(message.content[0].text);

Streaming

const stream = client.messages.stream({
  model: "claude-sonnet-4-0",
  max_tokens: 1024,
  messages: [{ role: "user", content: "Write a haiku" }],
});

for await (const event of stream) {
  if (event.type === "content_block_delta" && event.delta.type === "text_delta") {
    process.stdout.write(event.delta.text);
  }
}

Tool Use

Define tools and let Claude call them:

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"]}
            },
            "required": ["location"]
        }
    }
]

message = client.messages.create(
    model="claude-sonnet-4-0",
    max_tokens=1024,
    tools=tools,
    messages=[{"role": "user", "content": "What's the weather in SF?"}]
)

# Handle tool use response
for block in message.content:
    if block.type == "tool_use":
        # Execute the tool with block.input
        result = get_weather(**block.input)
        # Send result back
        follow_up = client.messages.create(
            model="claude-sonnet-4-0",
            max_tokens=1024,
            tools=tools,
            messages=[
                {"role": "user", "content": "What's the weather in SF?"},
                {"role": "assistant", "content": message.content},
                {"role": "user", "content": [
                    {"type": "tool_result", "tool_use_id": block.id, "content": str(result)}
                ]}
            ]
        )

Vision

Send images for analysis:

import base64

with open("diagram.png", "rb") as f:
    image_data = base64.standard_b64encode(f.read()).decode("utf-8")

message = client.messages.create(
    model="claude-sonnet-4-0",
    max_tokens=1024,
    messages=[{
        "role": "user",
        "content": [
            {"type"