OpenRouter Hello World

Overview

This skill provides a minimal working example to verify your OpenRouter integration is functioning and introduces the basic request/response pattern.

Prerequisites

• OpenRouter API key configured
• Python 3.8+ or Node.js 18+
• OpenAI SDK installed

Instructions

Follow these steps to implement this skill:

1. Verify Prerequisites: Ensure all prerequisites listed above are met
2. Review the Implementation: Study the code examples and patterns below
3. Adapt to Your Environment: Modify configuration values for your setup
4. Test the Integration: Run the verification steps to confirm functionality
5. Monitor in Production: Set up appropriate logging and monitoring

Overview

This skill provides a minimal working example to verify your OpenRouter integration is functioning and introduces the basic request/response pattern.

Prerequisites

• OpenRouter API key configured
• Python 3.8+ or Node.js 18+
• OpenAI SDK installed

Your First Request

cURL

curl https://openrouter.ai/api/v1/chat/completions \
  -H "Authorization: Bearer $OPENROUTER_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "openai/gpt-3.5-turbo",
    "messages": [
      {"role": "user", "content": "Say hello!"}
    ]
  }'

Response

{
  "id": "gen-abc123",
  "model": "openai/gpt-3.5-turbo",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "Hello! How can I assist you today?"
      },
      "finish_reason": "stop"
    }
  ],
  "usage": {
    "prompt_tokens": 10,
    "completion_tokens": 9,
    "total_tokens": 19
  }
}

Python Example

Basic Request

import os
from openai import OpenAI

client = OpenAI(
    base_url="https://openrouter.ai/api/v1",
    api_key=os.environ.get("OPENROUTER_API_KEY"),
)

response = client.chat.completions.create(
    model="openai/gpt-3.5-turbo",
    messages=[
        {"role": "user", "content": "Say hello!"}
    ]
)

print(response.choices[0].message.content)

With System Message

response = client.chat.completions.create(
    model="anthropic/claude-3-sonnet",
    messages=[
        {"role": "system", "content": "You are a helpful coding assistant."},
        {"role": "user", "content": "Write a Python hello world"}
    ]
)

TypeScript Example

Basic Request

import OpenAI from 'openai';

const client = new OpenAI({
  baseURL: 'https://openrouter.ai/api/v1',
  apiKey: process.env.OPENROUTER_API_KEY,
});

async function main() {
  const response = await client.chat.completions.create({
    model: 'openai/gpt-3.5-turbo',
    messages: [
      { role: 'user', content: 'Say hello!' }
    ],
  });

  console.log(response.choices[0].message.content);
}

main();

With Streaming

const stream = await client.chat.completions.create({
  model: 'openai/gpt-4-turbo',
  messages: [{ role: 'user', content: 'Write a poem' }],
  stream: true,
});

for await (const chunk of stream) {
  process.stdout.write(chunk.choices[0]?.delta?.content || '');
}

Try Different Models

OpenAI Models

# GPT-4 Turbo
response = client.chat.completions.create(
    model="openai/gpt-4-turbo",
    messages=[{"role": "user", "content": "Hello!"}]
)

# GPT-4o
response = client.chat.completions.create(
    model="openai/gpt-4o",
    messages=[{"role": "user", "content": "Hello!"}]
)

Anthropic Models

# Claude 3.5 Sonnet
response = client.chat.completions.create(
    model="anthropic/claude-3.5-sonnet",
    messages=[{"role": "user", "content": "Hello!"}]
)

# Claude 3 Opus
response = client.chat.completions.create(
    model="anthropic/claude-3-opus",
    messages=[{"role": "user", "content": "Hello!"}]
)

Open Source Models

# Llama 3.1 70B
response = client.chat.completions.create(
    model="meta-llama/llama-3.1-70b-instruct",
    messages=[{"role": "user", "content": "Hello!"}]
)

# Mixtral
response = client.chat.completions.create(
    model="mistralai/mixtral-8x7b-instruct",
    messages=[{"role": "user", "content": "Hello!"}]
)

Request Parameters

Temperature

response = client.chat.completions.create(
    model="openai/gpt-4-turbo",
    messages=[{"role": "user", "content": "Write a story"}],
    temperature=0.7,  # 0-2, higher = more creative
)

Max Tokens

response = client.chat.completions.create(
    model="openai/gpt-4-turbo",
    messages=[{"role": "user", "content": "Summarize..."}],
    max_tokens=500,  # Limit response length
)

Top P

response = client.chat.completions.create(
    model="openai/gpt-4-turbo",
    messages=[{"role": "user", "content": "..."}],
    top_p=0.9,  # Nucleus sampling
)

Understanding the Response

Key Fields

response = client.chat.completions.create(...)

# The generated text
content = response.choices[0].message.content

# Why it stopped
finish_reason = response.choices[0].finish_reason
# "stop" = natural completion
# "length" = hit max_tokens
# "tool_calls" = function calling

# Token usage (for cost tracking)
usage = response.usage
print(f"Prompt: {usage.prompt_tokens}")
print(f"Completion: {usage.completion_tokens}")
print(f"Total: {usage.total_tokens}")

# Model used (may differ if fallback triggered)
model = response.model

Quick Comparison

Compare Models

models = [
    "openai/gpt-3.5-turbo",
    "anthropic/claude-3-haiku",
    "meta-llama/llama-3.1-8b-instruct",
]

prompt = "Explain quantum computing in one sentence."

for model in models:
    response = client.chat.completions.create(
        model=model,
        messages=[{"role": "user", "content": prompt}],
        max_tokens=100
    )
    print(f"{model}:")
    print(response.choices[0].message.content)
    print()

Next Steps

After hello world:

1. Explore model catalog
2. Set up fallbacks
3. Implement streaming
4. Add error handling
5. Configure for production

Output

Successful execution produces:

• Working OpenRouter integration
• Verified API connectivity
• Example responses demonstrating functionality

Error Handling

Common errors and solutions:

1. 401 Unauthorized: Check API key format (must start with sk-or-)
2. 429 Rate Limited: Implement exponential backoff
3. 500 Server Error: Retry with backoff, check OpenRouter status page
4. Model Not Found: Verify model ID includes provider prefix

Examples

See code examples in sections above for complete, runnable implementations.

Resources

• OpenRouter Documentation
• OpenRouter Models
• OpenRouter API Reference
• OpenRouter Status

Output

Successful execution produces:

• Working OpenRouter integration
• Verified API connectivity
• Example responses demonstrating functionality

Error Handling

Common errors and solutions:

1. 401 Unauthorized: Check API key format (must start with sk-or-)
2. 429 Rate Limited: Implement exponential backoff
3. 500 Server Error: Retry with backoff, check OpenRouter status page
4. Model Not Found: Verify model ID includes provider prefix

Examples

See code examples in sections above for complete, runnable implementations.

Resources

• OpenRouter Documentation
• OpenRouter Models
• OpenRouter API Reference
• OpenRouter Status