The following sample (probably) does the same thing and is almost half as short. I have not tested it because there is no signup (EDIT: I was mistaken, there actually is a "signup" behind the login link, which is Google or GitHub login, so the naming makes sense. I confused it with a previously more prominent waitlist link.)
import requests
# Your Hypermode Workspace API key
api_key = "<YOUR_HYP_WKS_KEY>"
# Use the Hypermode Model Router API endpoint
url = f"https://models.hypermode.host/v1/chat/completions"
headers = {"Authorization": f"Bearer {api_key}"}
payload = {
"model": "meta-llama/llama-4-scout-17b-16e-instruct",
"messages": [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "What is Dgraph?"},
],
"max_tokens": 150,
"temperature": 0.7,
}
# Make the API request
with requests.post(url, headers=headers, json=payload) as response:
response.raise_for_status()
print(response.json()["choices"][0]["message"]["content"])The post function calls request the request function which uses its own context manager that will call the close function of the connection object.
There's a waitlist for our prompt to agent product in the banner. That's a good call to update it to be more clear.
To explain in the clearest terms: unlike the SS insignia, the lightning bolt in the logo has tapering at the bottom. The second element in the logo, the slash, does not have tapering at the bottom. The general shape of the logo is the same as the SS insignia: two diagonal elements side-by-side (which would be all good on its own). The mind tends to see repetition, so it has a tendency to "mix up" the two elements of the logo. The mind also has a tendency to remember similar things. Putting it all together, the logo has a chance to evoke the SS insignia.
I may just be reading too much Theweleit and W. Reich nowadays, but I think you'll get catch some flak for this logo if it becomes recognizable outside the tech milieu.
But OpenRouter is ridiculously popular so it must be very useful for other use cases!
Agreed on swapping models for code-gen doesn't make sense. We're mostly indexed on GPT-4.1 for our AgentBuilder product. I haven't found it easy to move between models for code super effective.
The most popular use case we've seen from folks is on the iteration/experimentation phase of building an agent/tool. We made ModelRouter originally as an internal service for our "prompt to agent" product, where folks are trying a few dozen models/MCPs/tools/data/etc really quickly as they try to find a local maximum for some automation or job.
Also, being able to use models from multiple services and open source models without signing up for another service / bring your own API key is a big accelerator for folks getting started with Hypermode agents.
(This would be more for using models at scale in production as opposed to individual use for code authoring etc.)
What we've seen most successful is making recommendations in the agent creation process for a given tool/workload and then leaving them somewhat static after creation.
Feels a bit halting-problem-ish: can you tell if a problem is too hard for model A without being smarter than model A yourself?