Add max_model_len field support to router

[llm-net]

Description

This PR adds support for the max_model_len field in the router's /v1/models endpoint.

Problem

The vLLM backend returns max_model_len (context window size) in its /v1/models response, but the router was not forwarding this field. This made it impossible to know the context window size of models through the router API.

Solution

• Added max_model_len field to ModelInfo dataclass in service_discovery.py
• Added max_model_len field to ModelCard in protocols.py
• Updated main_router.py to pass max_model_len when creating ModelCard

Testing

Tested with a deployment running Qwen3-235B model which has a context window of 131072 tokens.

Before this change:

{
  "id": "Qwen/Qwen3-235B-A22B-Thinking-2507-FP8",
  "object": "model",
  "created": 1754905632,
  "owned_by": "vllm",
  "root": null,
  "parent": null
}

After this change (expected):

{
  "id": "Qwen/Qwen3-235B-A22B-Thinking-2507-FP8",
  "object": "model",
  "created": 1754905632,
  "owned_by": "vllm",
  "root": null,
  "parent": null,
  "max_model_len": 131072
}

This makes the router fully compatible with vLLM's extended OpenAI API format.

[gemini-code-assist]

Summary of Changes

Hello @llm-net, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

I've implemented support for the max_model_len field within the router's /v1/models endpoint. Previously, while the vLLM backend provided this crucial context window size information, the router was not forwarding it, making it impossible for clients to determine a model's context capabilities via the router's API. My changes ensure that this max_model_len value is now properly propagated and exposed, making the router fully compatible with vLLM's extended OpenAI API format and providing complete model information to consumers.

Highlights

• ModelInfo Update: I've added the max_model_len field to the ModelInfo dataclass, which is used internally for service discovery.
• ModelCard Enhancement: I've included the max_model_len field in the ModelCard dataclass, which defines the structure for model information exposed via the API.
• Router Logic Adjustment: I've updated the main router logic to ensure that the max_model_len value, retrieved from the backend, is correctly passed through and included when constructing the ModelCard for the /v1/models endpoint.

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[gemini-code-assist]

Code Review

This pull request correctly adds support for the max_model_len field, propagating it from the service discovery layer to the /v1/models API endpoint. The changes are well-implemented. My feedback includes suggestions to add a unit test to cover this new functionality and to extend support for this field to the StaticServiceDiscovery for feature consistency.

[gemini-code-assist]

This change correctly passes the max_model_len to the ModelCard. To ensure this functionality is robust and to prevent future regressions, it would be beneficial to add a unit test for the /v1/models endpoint. The test should verify that when a model's ModelInfo includes a max_model_len, this value is correctly included in the API response.

[gemini-code-assist]

While max_model_len is correctly added to ModelInfo and populated for Kubernetes-based service discovery, StaticServiceDiscovery currently has no mechanism to configure this value; it will always default to None. To ensure feature parity across discovery methods, consider enhancing StaticServiceDiscovery to allow static configuration of max_model_len for each model. This could be done by adding a max_model_lens list to its constructor, similar to how urls and models are handled.