Anthropic Python

Anthropic Python SDK

Installation

pip install anthropic>=0.25.0

Basic Usage

import anthropic

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

message = client.messages.create(
    model="claude-opus-4-6",
    max_tokens=1024,
    messages=[
        {"role": "user", "content": "Analyze this tag list and identify patterns."}
    ]
)
print(message.content[0].text)

Model Selection

System Prompts

message = client.messages.create(
    model="claude-sonnet-4-6",
    max_tokens=2048,
    system="You are an industrial automation expert specializing in DCS engineering.",
    messages=[
        {"role": "user", "content": "Review this motor tag list for ISA-5.1 compliance."}
    ]
)

Multi-Turn Conversations

def chat(client: anthropic.Anthropic, history: list, user_message: str) -> tuple[str, list]:
    history.append({"role": "user", "content": user_message})

    response = client.messages.create(
        model="claude-sonnet-4-6",
        max_tokens=1024,
        messages=history,
    )

    assistant_text = response.content[0].text
    history.append({"role": "assistant", "content": assistant_text})
    return assistant_text, history

Streaming

with client.messages.stream(
    model="claude-sonnet-4-6",
    max_tokens=1024,
    messages=[{"role": "user", "content": "Generate a motor PRT template."}],
) as stream:
    for text in stream.text_stream:
        print(text, end="", flush=True)

# Or get final message after stream
with client.messages.stream(...) as stream:
    message = stream.get_final_message()

Tool Use (Function Calling)

tools = [
    {
        "name": "validate_tag",
        "description": "Validate an ISA-5.1 tag name and return structured info",
        "input_schema": {
            "type": "object",
            "properties": {
                "tag": {"type": "string", "description": "The tag name to validate"},
                "area": {"type": "integer", "description": "Expected area code"},
            },
            "required": ["tag"],
        },
    }
]

response = client.messages.create(
    model="claude-sonnet-4-6",
    max_tokens=1024,
    tools=tools,
    messages=[{"role": "user", "content": "Validate tag 11301.FIC.056A for area 11301"}],
)

# Process tool calls
if response.stop_reason == "tool_use":
    for block in response.content:
        if block.type == "tool_use":
            tool_name = block.name
            tool_input = block.input
            result = handle_tool(tool_name, tool_input)

Vision (Image Input)

import base64
from pathlib import Path

def encode_image(path: str) -> str:
    return base64.standard_b64encode(Path(path).read_bytes()).decode("utf-8")

response = client.messages.create(
    model="claude-opus-4-6",
    max_tokens=1024,
    messages=[
        {
            "role": "user",
            "content": [
                {
                    "type": "image",
                    "source": {
                        "type": "base64",
                        "media_type": "image/png",
                        "data": encode_image("p&id_diagram.png"),
                    },
                },
                {"type": "text", "text": "Identify all motor symbols and extract their tag names."},
            ],
        }
    ],
)

Error Handling

from anthropic import APIError, APIConnectionError, RateLimitError, APIStatusError

def safe_claude_call(client: anthropic.Anthropic, prompt: str) -> str | None:
    try:
        message = client.messages.create(
            model="claude-sonnet-4-6",
            max_tokens=1024,
            messages=[{"role": "user", "content": prompt}],
        )
        return message.content[0].text

    except RateLimitError:
        # Exponential backoff
        import time
        time.sleep(60)
        return None

    except APIConnectionError as e:
        print(f"Connection error: {e}")
        return None

    except APIStatusError as e:
        print(f"API error {e.status_code}: {e.message}")
        return None

Async Client

import asyncio
import anthropic

async def analyze_batch(prompts: list[str]) -> list[str]:
    client = anthropic.AsyncAnthropic()

    async def call(prompt: str) -> str:
        msg = await client.messages.create(
            model="claude-haiku-4-5-20251001",
            max_tokens=512,
            messages=[{"role": "user", "content": prompt}],
        )
        return msg.content[0].text

    return await asyncio.gather(*[call(p) for p in prompts])

Usage Tracking

response = client.messages.create(...)

print(response.usage.input_tokens)   # tokens sent
print(response.usage.output_tokens)  # tokens received
# Total cost = input_tokens * price_in + output_tokens * price_out

Integration with Streamlit

import streamlit as st
import anthropic

@st.cache_resource
def get_anthropic_client() -> anthropic.Anthropic:
    return anthropic.Anthropic(api_key=st.secrets["anthropic"]["api_key"])

def stream_to_streamlit(prompt: str) -> str:
    client = get_anthropic_client()
    response_placeholder = st.empty()
    full_text = ""

    with client.messages.stream(
        model="claude-sonnet-4-6",
        max_tokens=2048,
        messages=[{"role": "user", "content": prompt}],
    ) as stream:
        for text in stream.text_stream:
            full_text += text
            response_placeholder.markdown(full_text + "▌")

    response_placeholder.markdown(full_text)
    return full_text

Best Practices