The Anthropic Python SDK makes it simple to integrate Claude into your applications. In this guide you'll go from zero to a working chatbot in under 10 minutes — covering installation, your first API call, streaming, multi-turn conversations, and error handling. Python 3.8+ An Anthropic API key (console.anthropic.com) Basic Python knowledge pip install anthropic That's the only dependency you need. The SDK includes everything: the client, streaming support, and type hints. Store your key as an environment variable — never hardcode it in your source files: export ANTHROPIC_API_KEY="sk-ant-..." Or create a .env file in your project root: ANTHROPIC_API_KEY=sk-ant-... Load it with python-dotenv: pip install python-dotenv Create a file main.py and add: import anthropic client = anthropic.Anthropic() # reads ANTHROPIC_API_KEY from env message = client.messages.create( model="claude-sonnet-4-6", max_tokens=1024, messages=[ {"role": "user", "content": "Explain what an API is in 2 sentences."} ] ) print(message.content[0].text) Run it: python main.py You'll get a clean, concise response from Claude. The message.content[0].text contains the text output. A system prompt sets the context and personality for Claude — it's the first thing Claude reads before any user message: client = anthropic.Anthropic() message = client.messages.create( model="claude-sonnet-4-6", max_tokens=1024, system="You are a senior Python developer. Answer concisely with code examples.", messages=[ {"role": "user", "content": "How do I read a JSON file in Python?"} ] ) print(message.content[0].text) For a better user experience — especially with long outputs — use streaming so text appears word by word: import anthropic client = anthropic.Anthropic() with client.messages.stream( model="claude-sonnet-4-6", max_tokens=1024, messages=[{"role": "user", "content": "Write a Python function to parse CSV files"}] ) as stream: for text in stream.text_stream: print(text, end="", flush=True) print() # newline at the end Build a simple chatbot by keeping track of the message history: import anthropic client = anthropic.Anthropic() conversation_history = [] def chat(user_message: str) -> str: conversation_history.append({ "role": "user", "content": user_message }) response = client.messages.create( model="claude-sonnet-4-6", max_tokens=1024, system="You are a helpful AI assistant.", messages=conversation_history ) assistant_message = response.content[0].text conversation_history.append({ "role": "assistant", "content": assistant_message }) return assistant_message # Example conversation print(chat("What is Python?")) print(chat("What are its main use cases?")) print(chat("Which one is best for AI development?")) Each call passes the full history so Claude remembers what was said earlier in the conversation. Always wrap API calls in try/except for production code: import anthropic client = anthropic.Anthropic() try: message = client.messages.create( model="claude-sonnet-4-6", max_tokens=1024, messages=[{"role": "user", "content": "Hello!"}] ) print(message.content[0].text) except anthropic.APIConnectionError as e: print(f"Connection error: {e}") except anthropic.RateLimitError as e: print(f"Rate limit hit — slow down: {e}") except anthropic.APIStatusError as e: print(f"API error {e.status_code}: {e.message}") Choose the right Claude model for your use case: claude-opus-4-7 — most capable, best for complex reasoning and analysis claude-sonnet-4-6 — best balance of speed and intelligence (recommended for most apps) claude-haiku-4-5-20251001 — fastest and most affordable, great for simple tasks The most important parameters in messages.create(): message = client.messages.create( model="claude-sonnet-4-6", # which Claude model to use max_tokens=1024, # maximum tokens in the response temperature=0.7, # 0 = deterministic, 1 = creative system="...", # system prompt (optional) messages=[...] # conversation history ) 💡 Tip: Use temperature=0 for code generation and factual tasks. Use higher values (0.7–1.0) for creative writing. import anthropic def main(): client = anthropic.Anthropic() history = [] print("Claude Chatbot — type 'quit' to exit\n") while True: user_input = input("You: ").strip() if user_input.lower() in ("quit", "exit"): break if not user_input: continue history.append({"role": "user", "content": user_input}) with client.messages.stream( model="claude-sonnet-4-6", max_tokens=2048, system="You are a helpful assistant.", messages=history ) as stream: print("Claude: ", end="", flush=True) response_text = "" for text in stream.text_stream: print(text, end="", flush=True) response_text += text print() history.append({"role": "assistant", "content": response_text}) if __name__ == "__main__": main() Now that you have the basics working, here's what to explore next: Tool use — let Claude call functions and APIs in your code Vision — send images to Claude for analysis Prompt caching — reduce costs on repeated context (up to 90% savings) Batch API — process thousands of requests asynchronously at 50% discount 💡 Resources: Anthropic Docs | Python SDK on GitHub Originally published at kalyna.pro