openrouter-openai-compat
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'.
Best use case
openrouter-openai-compat is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
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'.
Teams using openrouter-openai-compat should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/openrouter-openai-compat/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How openrouter-openai-compat Compares
| Feature / Agent | openrouter-openai-compat | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
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'.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
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SKILL.md Source
# 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.
## The Two-Line Migration
### Python (Before)
```python
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)
```python
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)
```typescript
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
| OpenAI Direct | OpenRouter ID |
|---------------|---------------|
| `gpt-4o` | `openai/gpt-4o` |
| `gpt-4o-mini` | `openai/gpt-4o-mini` |
| `gpt-4-turbo` | `openai/gpt-4-turbo` |
| `o1` | `openai/o1` |
| `o1-mini` | `openai/o1-mini` |
You also gain access to non-OpenAI models through the same SDK:
```python
# 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
| Feature | Status | Notes |
|---------|--------|-------|
| `chat.completions.create` | Fully supported | Main endpoint, all parameters |
| `stream: true` | Fully supported | SSE format identical to OpenAI |
| `tools` / `tool_choice` | Supported | OpenRouter transforms for non-OpenAI providers |
| `response_format: { type: "json_object" }` | Supported | Basic JSON mode |
| `response_format: { type: "json_schema" }` | Supported | Strict schema mode |
| `temperature`, `top_p`, `max_tokens` | Supported | Standard parameters |
| `stop` sequences | Supported | Array of stop strings |
| `n` (multiple completions) | Supported | Multiple choices |
## What Differs
| Feature | Difference | Workaround |
|---------|-----------|------------|
| Model IDs | Prefixed with `provider/` | Update model strings |
| `organization` param | Not used | Remove from client init |
| Embeddings | Limited support | Use direct provider or dedicated embedding service |
| Fine-tuned models | Not directly accessible | Use provider's fine-tuned model ID if hosted |
| `logprobs` | Model-dependent | Check model capabilities via `/api/v1/models` |
| Responses API | Beta support | Use `/api/v1/responses` endpoint |
## OpenRouter-Only Features
These are available through the same SDK but are unique to OpenRouter:
```python
# 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
```python
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"))
```
## 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)
## References
- [Examples](${CLAUDE_SKILL_DIR}/references/examples.md) | [Errors](${CLAUDE_SKILL_DIR}/references/errors.md)
- [OpenRouter Quickstart](https://openrouter.ai/docs/quickstart) | [API Parameters](https://openrouter.ai/docs/api/reference/parameters)Related Skills
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