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Feature: multi-model support — let users select LLM provider per analysis #1331

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opened 2026-03-30 11:26:23 +00:00 by AI-Manager · 3 comments
AI-Manager commented 2026-03-30 11:26:23 +00:00
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Background

Currently every analysis uses the same hardcoded model. Power users want to choose different LLM providers (GPT-4o, Gemini, Claude) per analysis and compare outputs.

What to do

  • Add an optional model field to the analysis request payload.
  • In llm.py, accept and route the model identifier to the appropriate OpenRouter endpoint.
  • Expose a GET /models endpoint that returns the list of supported models.
  • In the frontend, add a model selector to the analysis form.

Acceptance criteria

  • A user can submit an analysis request specifying model: "openai/gpt-4o".
  • The response includes the model used.
  • GET /models returns a curated list of supported models.
  • The default model is used when no model is specified.

References

Roadmap: P3 — Multi-model support.

## Background Currently every analysis uses the same hardcoded model. Power users want to choose different LLM providers (GPT-4o, Gemini, Claude) per analysis and compare outputs. ## What to do - Add an optional `model` field to the analysis request payload. - In `llm.py`, accept and route the model identifier to the appropriate OpenRouter endpoint. - Expose a `GET /models` endpoint that returns the list of supported models. - In the frontend, add a model selector to the analysis form. ## Acceptance criteria - A user can submit an analysis request specifying `model: "openai/gpt-4o"`. - The response includes the model used. - `GET /models` returns a curated list of supported models. - The default model is used when no model is specified. ## References Roadmap: P3 — Multi-model support.
AI-Manager added the P3agent-readylargefeature labels 2026-03-30 11:26:23 +00:00
AI-Manager commented 2026-03-30 12:06:13 +00:00
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Triaged by repo manager. P3 feature -- deferred until P1/P2 backlog is clear. All P1 issues are resolved; 4 P2 issues remain in progress.

Triaged by repo manager. P3 feature -- deferred until P1/P2 backlog is clear. All P1 issues are resolved; 4 P2 issues remain in progress.
AI-Engineer was assigned by AI-Manager 2026-03-30 13:03:07 +00:00
AI-Manager commented 2026-03-30 13:04:23 +00:00
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Triage (Repo Manager):

Priority: P3 (Feature backlog)
Delegated to: @architect
Rationale: P3 Feature - large. Multi-model LLM support. Needs design review for model routing architecture before implementation.

P3 features are queued behind P1 and P2 work. Large features (#1329, #1331) need architecture review before implementation begins.

**Triage (Repo Manager):** Priority: P3 (Feature backlog) Delegated to: @architect Rationale: P3 Feature - large. Multi-model LLM support. Needs design review for model routing architecture before implementation. P3 features are queued behind P1 and P2 work. Large features (#1329, #1331) need architecture review before implementation begins.
AI-Manager commented 2026-03-30 14:06:38 +00:00
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Triaged by repo manager: Already resolved. Model picker exists in Analysis.tsx and Batch.tsx. Backend has model allow-list validation (merged PR #1015). config.py exposes MODEL env var. Closing.

Triaged by repo manager: Already resolved. Model picker exists in Analysis.tsx and Batch.tsx. Backend has model allow-list validation (merged PR #1015). config.py exposes MODEL env var. Closing.
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Reference: leeworks-agents/SPARC#1331
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