anth-hello-world

Anthropic Hello World

Overview

Three minimal examples covering the Claude Messages API core surfaces: basic text completion, vision (image analysis), and streaming responses.

Prerequisites

Completed anth-install-auth setup
Valid ANTHROPIC_API_KEY in environment
Python 3.8+ with anthropic package or Node.js 18+ with @anthropic-ai/sdk

Instructions

Example 1: Basic Text Message (Python)

import anthropic

client = anthropic.Anthropic()

message = client.messages.create( model="claude-sonnet-4-20250514", max_tokens=1024, messages=[ {"role": "user", "content": "Explain quantum computing in 3 sentences."} ] )

Response structure

print(message.content[0].text) # The actual text response print(f"ID: {message.id}") # msg_01XFDUDYJgAACzvnptvVoYEL print(f"Model: {message.model}") # claude-sonnet-4-20250514 print(f"Stop: {message.stop_reason}")# end_turn print(f"Usage: {message.usage.input_tokens}in / {message.usage.output_tokens}out")


### Example 2: Vision — Analyze an Image (TypeScript)

```typescript
import Anthropic from '@anthropic-ai/sdk';
import * as fs from 'fs';

const client = new Anthropic();

// From file (base64)
const imageData = fs.readFileSync('chart.png').toString('base64');

const message = await client.messages.create({
  model: 'claude-sonnet-4-20250514',
  max_tokens: 1024,
  messages: [{
    role: 'user',
    content: [
      {
        type: 'image',
        source: {
          type: 'base64',
          media_type: 'image/png',
          data: imageData,
        },
      },
      { type: 'text', text: 'Describe what this chart shows.' },
    ],
  }],
});

console.log(message.content[0].type === 'text' ? message.content[0].text : '');

Example 3: Streaming Response (Python)

import anthropic

client = anthropic.Anthropic()

with client.messages.stream(
    model="claude-sonnet-4-20250514",
    max_tokens=1024,
    messages=[{"role": "user", "content": "Write a haiku about APIs."}]
) as stream:
    for text in stream.text_stream:
        print(text, end="", flush=True)

# Get final message with full metadata
final = stream.get_final_message()
print(f"\nTokens used: {final.usage.input_tokens}+{final.usage.output_tokens}")

Output

Working code file with Claude client initialization
Successful API response with text content
Console output showing model response and usage metadata

Examples

Use the text example first when verifying credentials: send a fixed, short prompt and confirm that message.content[0].text is present before integrating the client into application code. Use the vision example only after that check passes and replace chart.png with a non-sensitive local fixture. For an interactive command-line feature, use the streaming example so text is emitted incrementally, then read the final message to capture token usage for logs or cost controls.

Error Handling

Error	HTTP Code	Cause	Solution
`authentication_error`	401	Invalid API key	Check `ANTHROPIC_API_KEY`
`invalid_request_error`	400	Bad params (e.g., empty messages)	Validate request body
`rate_limit_error`	429	Too many requests	Implement backoff (see `anth-rate-limits`)
`overloaded_error`	529	API temporarily overloaded	Retry after 30-60s
`api_error`	500	Server error	Retry with exponential backoff

Key API Parameters

Parameter	Required	Description
`model`	Yes	Model ID: `claude-sonnet-4-20250514`, `claude-haiku-4-20250514`, `claude-opus-4-20250514`
`max_tokens`	Yes	Maximum output tokens (model-dependent max)
`messages`	Yes	Array of `{role, content}` objects
`system`	No	System prompt (string or content blocks)
`temperature`	No	0.0-1.0, default 1.0
`top_p`	No	Nucleus sampling (use temperature OR top_p)
`stop_sequences`	No	Array of strings that stop generation
`stream`	No	Enable SSE streaming

Resources

Next Steps

Proceed to anth-local-dev-loop for development workflow setup.