claude-api

name	claude-api
description	Use when building or debugging apps that call the Claude API — implementing tool use, streaming, vision, prompt caching, batch processing, extended thinking, or an agentic loop with the Anthropic SDK.

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

Model	ID	Best For
Opus 4.1	claude-opus-4-1	Complex reasoning, architecture, research
Sonnet 4	claude-sonnet-4-0	Balanced coding, most development tasks
Haiku 3.5	claude-3-5-haiku-latest	Fast responses, high-volume, cost-sensitive

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", : message.content},
                {: , : [
                    {: , : block., : (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": "image", "source": {"type": "base64", "media_type": "image/png", "data": image_data}},
            {"type": "text", "text": "Describe this diagram"}
        ]
    }]
)

Extended Thinking

For complex reasoning tasks:

message = client.messages.create(
    model="claude-sonnet-4-0",
    max_tokens=16000,
    thinking={
        "type": "enabled",
        "budget_tokens": 10000
    },
    messages=[{"role": "user", "content": "Solve this math problem step by step..."}]
)

for block in message.content:
    if block.type == "thinking":
        print(f"Thinking: {block.thinking}")
    elif block.type == "text":
        print(f"Answer: {block.text}")

Prompt Caching

Cache large system prompts or context to reduce costs:

message = client.messages.create(
    model="claude-sonnet-4-0",
    max_tokens=1024,
    system=[
        {"type": "text", "text": large_system_prompt, "cache_control": {"type": "ephemeral"}}
    ],
    messages=[{"role": "user", "content": "Question about the cached context"}]
)
# Check cache usage
print(f"Cache read: {message.usage.cache_read_input_tokens}")
print(f"Cache creation: {message.usage.cache_creation_input_tokens}")

Batches API

Process large volumes asynchronously at 50% cost reduction:

import time

batch = client.messages.batches.create(
    requests=[
        {
            "custom_id": f"request-{i}",
            "params": {
                "model": "claude-sonnet-4-0",
                "max_tokens": 1024,
                "messages": [{"role": "user", "content": prompt}]
            }
        }
        for i, prompt in enumerate(prompts)
    ]
)

# Poll for completion
while True:
    status = client.messages.batches.retrieve(batch.id)
    if status.processing_status == "ended":
        break
    time.sleep(30)

# Get results
for result in client.messages.batches.results(batch.id):
    print(result.result.message.content[0].text)

Claude Agent SDK

Build multi-step agents:

# Note: Agent SDK API surface may change — check official docs
import anthropic

# Define tools as functions
tools = [{
    "name": "search_codebase",
    "description": "Search the codebase for relevant code",
    "input_schema": {
        "type": "object",
        "properties": {"query": {"type": "string"}},
        "required": ["query"]
    }
}]

# Run an agentic loop with tool use
client = anthropic.Anthropic()
messages = [{"role": "user", "content": "Review the auth module for security issues"}]

while True:
    response = client.messages.create(
        model="claude-sonnet-4-0",
        max_tokens=4096,
        tools=tools,
        messages=messages,
    )
    if response.stop_reason == "end_turn":
        break
    # Handle tool calls and continue the loop
    messages.append({"role": "assistant", "content": response.content})
    # ... execute tools and append tool_result messages

Cost Optimization

Strategy	Savings	When to Use
Prompt caching	Up to 90% on cached tokens	Repeated system prompts or context
Batches API	50%	Non-time-sensitive bulk processing
Haiku instead of Sonnet	~75%	Simple tasks, classification, extraction
Shorter max_tokens	Variable	When you know output will be short
Streaming	None (same cost)	Better UX, same price

Error Handling

import time

from anthropic import APIError, RateLimitError, APIConnectionError

try:
    message = client.messages.create(...)
except RateLimitError:
    # Back off and retry
    time.sleep(60)
except APIConnectionError:
    # Network issue, retry with backoff
    pass
except APIError as e:
    print(f"API error {e.status_code}: {e.message}")

Environment Setup

# Required
export ANTHROPIC_API_KEY="your-api-key-here"

# Optional: set default model
export ANTHROPIC_MODEL="claude-sonnet-4-0"

Never hardcode API keys. Always use environment variables.

Red Flags

• Not setting max_tokens explicitly — omitting it uses the SDK default which may be far too low for your use case, causing truncated responses with no error raised
• Using client.messages.create() in a tight loop without backoff — hitting rate limits with immediate retries amplifies the problem; use the SDK's built-in retry config or tenacity with exponential backoff
• Passing raw user input directly as the user message — prompt injection can redirect the model's behavior; sanitize or structure user input within a constrained template
• Enabling extended thinking (budget_tokens) without raising max_tokens — thinking tokens count against max_tokens; a small max_tokens causes the request to error before the model produces output
• Polling the Batches API in a tight while True loop with time.sleep(1) — batches can take minutes to hours; use time.sleep(30) at minimum, or a webhook/callback if available
• Caching the Anthropic client instance across forked processes — the underlying httpx session is not fork-safe; instantiate a new client per process in multiprocessing scenarios
• Using an alias model ID (e.g., claude-sonnet-4-0) in production — aliases can be remapped to a new model version without warning, changing behavior; pin to a dated snapshot ID for reproducible production behavior
• Logging the full request/response payload — responses may echo back sensitive user data; log only metadata (model, token counts, stop reason), never message content

Checklist

• API key loaded from environment variable, not hardcoded
• Model pinned to a specific version (not latest)
• max_tokens set explicitly for every request
• Rate limit and API errors caught and retried with backoff
• Streaming used for long responses to avoid timeout
• Tool schemas validated; required fields listed explicitly
• Prompt caching headers set for repeated system prompts
• Costs estimated before running batch or high-volume jobs
• Response content checked for stop_reason before use
• No sensitive data logged from request/response payloads