Configure automatic model fallbacks for high availability on OpenRouter. Use when building resilient systems that need to survive provider outages. Triggers: 'openrouter fallback', 'model fallback', 'openrouter failover', 'openrouter backup model'.
Instrucciones de origen · Vista previa de solo lectura
name
openrouter-fallback-config
description
Configure automatic model fallbacks for high availability on OpenRouter. Use when building resilient systems that need to survive provider outages. Triggers: 'openrouter fallback', 'model fallback', 'openrouter failover', 'openrouter backup model'.
OpenRouter supports native model fallbacks: pass multiple model IDs and OpenRouter tries each in order until one succeeds. You can also use provider.order to control which provider serves a specific model. This skill covers native fallbacks, provider routing, client-side fallback chains, and timeout configuration.
Prerequisites
An OpenRouter API key (sk-or-v1-...) exported as OPENROUTER_API_KEY — see the openrouter-install-auth skill for setup
Python 3.8+ with the OpenAI SDK (pip install openai) for the fallback patterns; curl and jq for the Testing Fallbacks step
A ranked list of acceptable models for your workload, matched by capability (tool calling, vision, context length) so a fallback never silently drops a feature you depend on
Instructions
Start with Native Model Fallback (Server-Side): pass a models array plus route: "fallback" in extra_body and let OpenRouter try each model in order.
Log response.model after every call — it tells you which model actually served the request, which is how you detect that a fallback fired.
If you need the same model from specific vendors (e.g., Claude via Anthropic direct vs AWS Bedrock), use Provider Fallback with provider.order and allow_fallbacks.
For per-model timeouts and custom error handling, implement the Client-Side Fallback Chain: resilient_completion() walks FALLBACK_CHAIN (primary → secondary → budget-fallback → last-resort) and raises once every entry fails.
Pick chains per feature with Fallback with Capability Matching — CAPABILITY_CHAINS keeps tool-calling, vision, long-context, and budget workloads on models that actually support them.
Verify the behavior with Testing Fallbacks: send the curl request with an invalid primary model and confirm the response comes back from openai/gpt-4o-mini.
# Pass multiple models -- OpenRouter tries each in order
"anthropic/claude-3.5-sonnet"
# Primary (used for param validation)
"role"
"user"
"content"
"Explain recursion"
500
"models"
"anthropic/claude-3.5-sonnet"
"openai/gpt-4o"
"google/gemini-2.0-flash-001"
"route"
"fallback"
# Try in order until one succeeds
# Check which model actually served the request
print
f"Served by: {response.model}"
Provider Fallback (Same Model, Different Providers)
# Route to specific providers in priority order
response = client.chat.completions.create(
model="anthropic/claude-3.5-sonnet",
messages=[{"role": "user", "content": "Hello"}],
max_tokens=200,
extra_body={
"provider": {
"order": ["Anthropic", "AWS Bedrock", "GCP Vertex"],
"allow_fallbacks": True, # Fall to next provider if first fails
},
},
)
Client-Side Fallback Chain
import logging
from openai import OpenAI, APIError, APITimeoutError
log = logging.getLogger("openrouter.fallback")
FALLBACK_CHAIN = [
{"model": "anthropic/claude-3.5-sonnet", "timeout": 30.0, "label": "primary"},
{"model": "openai/gpt-4o", "timeout": 25.0, "label": "secondary"},
{"model": "openai/gpt-4o-mini", "timeout": 15.0, "label": "budget-fallback"},
{"model": "google/gemini-2.0-flash-001", "timeout": 15.0, "label": "last-resort"},
]
defresilient_completion(messages: list[dict], max_tokens: int = 1024, **kwargs):
"""Try each model in the fallback chain until one succeeds."""
last_error = Nonefor config in FALLBACK_CHAIN:
try:
client = OpenAI(
base_url="https://openrouter.ai/api/v1",
api_key=os.environ["OPENROUTER_API_KEY"],
timeout=config["timeout"],
default_headers={"HTTP-Referer": "https://my-app.com", "X-Title": "my-app"},
)
response = client.chat.completions.create(
model=config["model"],
messages=messages,
max_tokens=max_tokens,
**kwargs,
)
log.info(f"Served by {config['label']}: {response.model}")
return response
except (APIError, APITimeoutError) as e:
last_error = e
log.warning(f"{config['label']} failed ({config['model']}): {e}")
continueraise RuntimeError(f"All fallbacks exhausted. Last error: {last_error}")
# Test with an invalid model to trigger fallback
curl -s https://openrouter.ai/api/v1/chat/completions \
-H "Authorization: Bearer $OPENROUTER_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "invalid/model-name",
"messages": [{"role": "user", "content": "test"}],
"max_tokens": 10,
"models": ["invalid/model-name", "openai/gpt-4o-mini"],
"route": "fallback"
}' | jq '{model: .model, content: .choices[0].message.content}'# Should succeed with openai/gpt-4o-mini
Output
A configured fallback setup produces:
Chat completions whose response.model field reveals the model that actually served each request — the primary when healthy, a chain entry when a fallback fired
Log lines from resilient_completion(): Served by primary: anthropic/claude-3.5-sonnet on success, primary failed (anthropic/claude-3.5-sonnet): ... warnings per failed hop
A RuntimeError("All fallbacks exhausted. Last error: ...") when every model in FALLBACK_CHAIN fails — the signal to alert on
From the Testing Fallbacks curl: a {model, content} JSON showing the request survived an invalid primary model
Examples
Force a fallback by putting an invalid model first in the models array:
