Migrate from OpenAI to OpenRouter with minimal code changes. Use when switching to OpenRouter or maintaining dual compatibility. Triggers: 'openrouter openai compatible', 'openrouter drop-in', 'openai to openrouter', 'openrouter migration'.
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Migrate from OpenAI to OpenRouter with minimal code changes. Use when switching to OpenRouter or maintaining dual compatibility. Triggers: 'openrouter openai compatible', 'openrouter drop-in', 'openai to openrouter', 'openrouter migration'.
Designed for Claude Code, also compatible with Codex and OpenClaw
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: base_url and api_key. 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 OPENROUTER_API_KEY — 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 OPENAI_API_KEY exported too, so the Dual-Provider Pattern can switch back to direct OpenAI
Instructions
Apply The Two-Line Migration: point base_url at https://openrouter.ai/api/v1 and swap api_key to OPENROUTER_API_KEY; optionally add the HTTP-Referer / X-Title headers for app attribution.
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.
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.
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.
Keep the migration reversible with the Dual-Provider Pattern — create_client() switches between direct OpenAI and OpenRouter off the LLM_PROVIDER 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"}],
)
import os
from openai import OpenAI
defcreate_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 create_client() — flipping LLM_PROVIDER 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
Issue
Cause
Fix
400 unsupported parameter
Model doesn't support a parameter
Conditionally set params based on model capabilities
Different response quality
Non-OpenAI model handles prompt differently
Adjust prompts per model family; test before switching
Missing organization
OpenRouter ignores org-level auth
Remove organization from client init
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)
