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 anthropicBasic 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/sdkBasic 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": "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 messagesCost 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.