Prepare the backend registry implementation roadmap
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## Ideas
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### Extensible LLM backend registry
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Introduce a registry that separates backend-specific connection,
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authentication, and limited request defaults from model execution profiles.
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Initial support would cover OpenAI-compatible backends and include a small
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built-in catalog, potentially starting with OpenRouter. A profile could select
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a backend while optionally overriding its default endpoint, and each backend
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could name an optional environment variable for its API key without storing
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the credential itself. Downstream consumers could register additional,
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uniquely named backends, such as OpenAI or unauthenticated local-network
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services, but could not replace built-in IDs. Model selection and generation
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settings would remain profile concerns, and custom model clients would remain
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available for behavior outside the registry's supported protocol.
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### Backend-specific concurrency management
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Extend the proposed LLM backend registry with optional per-backend concurrency
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limits and bounded, buffered admission queues. Promptkit could then route
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simultaneous generation requests according to backend capacity while
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containing accidental runaway submission. Downstream consumers would continue
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invoking synchronous `Run` calls, including concurrently from multiple
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goroutines, and each admitted call would wait for and return its ordinary
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result.
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Extend the selected
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[LLM backend registry](backends.md) with optional per-backend concurrency limits
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and bounded, buffered admission queues. Promptkit could then route simultaneous
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generation requests according to backend capacity while containing accidental
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runaway submission. Downstream consumers would continue invoking synchronous
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`Run` calls, including concurrently from multiple goroutines, and each
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admitted call would wait for and return its ordinary result.
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- Scope limits to an engine instance rather than hidden process-global state.
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- Give different backend IDs independent capacity pools. A profile endpoint
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