Originally published at claudeguide.io/claude-api-python-tutorial

Claude API Python Tutorial: Complete Guide with Code Examples

The Claude API Python SDK (2026) (anthropic) lets you call Claude models from any Python script or application with a single pip install. This guide covers every major feature — messages, system prompts, multi-turn conversations, streaming, tool use, prompt caching, and async — with working code you can paste and run today.

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Installation and environment setup

Install the SDK and python-dotenv for managing your API key securely:

pip install anthropic python-dotenv

Store your key in a .env file in your project root:

ANTHROPIC_API_KEY=sk-ant-...

Load it at the top of any script:

from dotenv import load_dotenv
import os

load_dotenv()
# ANTHROPIC_API_KEY is now available as an environment variable
# The Anthropic client picks it up automatically

The Anthropic() client reads ANTHROPIC_API_KEY from the environment by default, so you never need to pass it explicitly in your code.

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Basic messages.create() call

The messages.create() method is the core of the API. Every request sends a list of messages and receives a response object:

import anthropic

client = anthropic.Anthropic()

message = client.messages.create(
    model="claude-sonnet-4-5",
    max_tokens=1024,
    messages=[
        {"role": "user", "content": "Explain the difference between a list and a tuple in Python."}
    ]
)

print(message.content[0].text)

The response object contains:

• message.content — a list of content blocks (usually one TextBlock)
• message.usage.input_tokens and message.usage.output_tokens — token counts for cost tracking
• message.stop_reason — "end_turn", "max_tokens", or "tool_use"

---

System prompts

A system prompt sets the model's persona and constraints. Pass it as the system parameter (not as a message):

message = client.messages.create(
    model="claude-sonnet-4-5",
    max_tokens=1024,
    system="You are a senior Python engineer. Respond with concise, production-quality code. Include type hints. No explanatory prose unless asked.",
    messages=[
        {"role": "user", "content": "Write a function that retries a callable with exponential backoff."}
    ]
)

print(message.content[0].text)

A well-crafted system prompt reduces output tokens and improves consistency across requests. See How to Write System Prompts for Claude for patterns that work.

---

Multi-turn conversations

The API is stateless — you maintain conversation history yourself by appending each turn to the messages list:

python
import anthropic

client = anthropic.Anthropic()

conversation = []

def chat(user_message: str) -

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