OpenRouter OpenAI Compatibility

Overview

OpenRouter implements the OpenAI Chat Completions API specification (/v1/chat/completions). Existing OpenAI SDK code works with OpenRouter by changing two values: baseurl and apikey. This gives you access to 400+ models from all providers through the same SDK interface.

Prerequisites

• An existing OpenAI SDK integration to migrate — Python or TypeScript code calling chat.completions.create
• An OpenRouter API key exported as OPENROUTERAPIKEY — see the openrouter-install-auth skill for setup
• Python 3.8+ with the openai package, or Node.js 18+ with the openai npm package — the same SDK you already use, no new dependency
• Optionally keep OPENAIAPIKEY exported too, so the Dual-Provider Pattern can switch back to direct OpenAI

Instructions

1. Apply The Two-Line Migration: point baseurl at https://openrouter.ai/api/v1 and swap apikey to OPENROUTERAPIKEY; optionally add the HTTP-Referer / X-Title headers for app attribution.
2. Prefix every model string per Model ID Mapping — gpt-4o becomes openai/gpt-4o, o1 becomes openai/o1 — and try a non-OpenAI model (anthropic/claude-3.5-sonnet) through the same client.
3. Confirm your feature usage against What Works Identically (streaming, tools, JSON mode, stop, n) and adjust per What Differs — remove the organization param, plan around limited embeddings, and check logprobs support per model via /api/v1/models.
4. Layer in OpenRouter-Only Features through extra_body: ordered fallback model lists with "route": "fallback", provider preferences with sort: "price", or the plugins: [{"id": "web"}] web-search plugin.
5. Keep the migration reversible with the Dual-Provider Pattern — createclient() switches between direct OpenAI and OpenRouter off the LLMPROVIDER environment variable.

The Two-Line Migration

Python (Before)

from openai import OpenAI

client = OpenAI(api_key=os.environ["OPENAI_API_KEY"])  # OpenAI direct
response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Hello"}],
)

Python (After)

from openai import OpenAI

client = OpenAI(
    base_url="https://openrouter.ai/api/v1",              # Changed
    api_key=os.environ["OPENROUTER_API_KEY"],              # Changed
    default_headers={
        "HTTP-Referer": "https://your-app.com",            # Added (optional)
        "X-Title": "Your App",                             # Added (optional)
    },
)
response = client.chat.completions.create(
    model="openai/gpt-4o",  # Prefix with provider namespace
    messages=[{"role": "user", "content": "Hello"}],
)

TypeScript (After)

import OpenAI from "openai";

const client = new OpenAI({
  baseURL: "https://openrouter.ai/api/v1",
  apiKey: process.env.OPENROUTER_API_KEY,
  defaultHeaders: { "HTTP-Referer": "https://your-app.com", "X-Title": "Your App" },
});

const res = await client.chat.completions.create({
  model: "openai/gpt-4o",
  messages: [{ role: "user", content: "Hello" }],
});

Model ID Mapping

You also gain access to non-OpenAI models through the same SDK:

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

response = client.chat.completions.create(
    model="google/gemini-2.0-flash",  # Google
    messages=[{"role": "user", "content": "Hello"}],
)

What Works Identically

What Differs

OpenRouter-Only Features

These are available through the same SDK but are unique to OpenRouter:

# Model fallbacks (try models in order)
response = client.chat.completions.create(
    model="anthropic/claude-3.5-sonnet",
    messages=[{"role": "user", "content": "Hello"}],
    extra_body={
        "models": [
            "anthropic/claude-3.5-sonnet",
            "openai/gpt-4o",
            "google/gemini-2.0-flash",
        ],
        "route": "fallback",
    },
)

# Provider preferences
response = client.chat.completions.create(
    model="anthropic/claude-3.5-sonnet",
    messages=[{"role": "user", "content": "Hello"}],
    extra_body={
        "provider": {
            "order": ["anthropic"],             # Prefer Anthropic direct
            "allow_fallbacks": True,
            "sort": "price",                    # Cheapest first
        },
    },
)

# Plugins (web search, response healing)
response = client.chat.completions.create(
    model="openai/gpt-4o",
    messages=[{"role": "user", "content": "What happened today?"}],
    extra_body={
        "plugins": [{"id": "web"}],  # Enable real-time web search
    },
)

Dual-Provider Pattern

import os
from openai import OpenAI

def create_client(provider: str = "openrouter") -> OpenAI:
    if provider == "openai":
        return OpenAI(api_key=os.environ["OPENAI_API_KEY"])
    return OpenAI(
        base_url="https://openrouter.ai/api/v1",
        api_key=os.environ["OPENROUTER_API_KEY"],
        default_headers={"HTTP-Referer": "https://your-app.com"},
    )

# Switch providers without changing application code
client = create_client(os.environ.get("LLM_PROVIDER", "openrouter"))

Output

• Standard OpenAI-SDK ChatCompletion objects — choices[0].message.content, usage token counts, and model reporting the provider-prefixed ID that actually served the request
• The identical code path returning completions from non-OpenAI models (Claude, Gemini) with only the model string changed
• A provider-switchable client from createclient() — flipping LLMPROVIDER moves traffic between direct OpenAI and OpenRouter with zero application-code changes

Examples

After the two-line change, the untouched OpenAI SDK call round-trips through OpenRouter:

client = OpenAI(base_url="https://openrouter.ai/api/v1",
                api_key=os.environ["OPENROUTER_API_KEY"])
response = client.chat.completions.create(
    model="openai/gpt-3.5-turbo",
    messages=[{"role": "user", "content": "What is the capital of France?"}],
    max_tokens=100,
)
print(response.choices[0].message.content)  # The capital of France is Paris.
print(response.model)                        # openai/gpt-3.5-turbo

Swap the model string to anthropic/claude-3.5-sonnet and the same code returns Claude's answer — that swap is the entire multi-provider story. More worked examples: references/examples.md.

Error Handling

Enterprise Considerations

• Use environment variables to switch between direct OpenAI and OpenRouter without code changes
• Test your full prompt suite across providers before migrating production traffic
• Monitor response quality and latency after migration; some prompts may need tuning
• OpenRouter normalizes the API across providers, but subtle behavioral differences exist between model families
• Use extra_body for OpenRouter-specific features (provider preferences, plugins, fallbacks)

References

• Examples | Errors
• OpenRouter Quickstart | API Parameters