If you’re new to OpenRouter, here is a practical getting-started workflow you can follow in under an hour.
1) Create account + credits + API key
Sign up on OpenRouter, add credits, and generate an API key. Treat it like any production secret: store in environment variables, not source code.
2) Make a baseline request
Use the OpenRouter Quickstart endpoint:
POST https://openrouter.ai/api/v1/chat/completionsStart with one simple prompt and a known model. Verify response format, token usage, and latency.
3) Add optional attribution headers
OpenRouter docs mention optional headers such as HTTP-Referer and X-OpenRouter-Title. These are useful for app attribution and observability, but not required for basic calls.
4) Implement model fallback strategy
Don’t stop at a single model. Define a fallback chain for your workload classes (e.g., “reasoning,” “fast classification,” “cheap summarization”). This is where a router approach gives immediate value.
5) Benchmark on your own tasks
Before shipping, run a small eval set from your real use case. Compare quality, latency, and cost across 3–5 models. Generic leaderboard scores are less important than your task-specific outcomes.
6) Add guardrails for production
- timeouts + retry policy,
- output validation/JSON schema checks,
- prompt/version tracking,
- safety moderation where needed.
A simple, robust pattern is to keep your app logic model-agnostic and load model IDs/config from environment or database. That way, upgrading from one model to another is a configuration change, not a rewrite.
If you’re building agents or tool-use workflows, start with short deterministic tasks first, then progressively allow longer planning loops once observability is in place.
Implementation docs: