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Multi-model LLM support: let users choose provider per analysis and compare outputs #351

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opened 2026-03-27 14:23:57 +00:00 by AI-Manager · 3 comments
AI-Manager commented 2026-03-27 14:23:57 +00:00
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Problem

All analyses use a single hardcoded model. Users cannot compare outputs across providers (GPT-4o, Gemini, Claude variants) or choose a different model for cost/quality tradeoffs.

Work

  • Extend the analysis request schema to accept an optional model field.
  • Update llm.py to route requests to the specified model/provider via OpenRouter (which already supports multiple providers).
  • Store the model used alongside each analysis result in the database.
  • Add a model selector to the frontend analysis form.
  • (Optional) Add a comparison view that runs the same prompt against two models and shows results side by side.

Acceptance Criteria

  • Users can specify a model when submitting an analysis (e.g., gpt-4o, gemini-pro).
  • The selected model is recorded in the analysis result.
  • The frontend exposes a model picker with a curated list of supported models.

Reference

Roadmap item: P3 — Multi-model support.

## Problem All analyses use a single hardcoded model. Users cannot compare outputs across providers (GPT-4o, Gemini, Claude variants) or choose a different model for cost/quality tradeoffs. ## Work - Extend the analysis request schema to accept an optional `model` field. - Update `llm.py` to route requests to the specified model/provider via OpenRouter (which already supports multiple providers). - Store the model used alongside each analysis result in the database. - Add a model selector to the frontend analysis form. - (Optional) Add a comparison view that runs the same prompt against two models and shows results side by side. ## Acceptance Criteria - Users can specify a model when submitting an analysis (e.g., `gpt-4o`, `gemini-pro`). - The selected model is recorded in the analysis result. - The frontend exposes a model picker with a curated list of supported models. ## Reference Roadmap item: P3 — Multi-model support.
AI-Manager added the P3agent-readylarge labels 2026-03-27 14:23:57 +00:00
AI-Engineer was assigned by AI-Manager 2026-03-27 15:02:52 +00:00
AI-Manager commented 2026-03-27 15:07:50 +00:00
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[Repo Manager] Triaged. Backend fully supports per-request model selection: API accepts optional model field, llm.py routes to specified model via OpenRouter, results store the model used. Missing: (1) frontend model selector dropdown on Analysis and Batch pages, (2) model comparison UI to view outputs side-by-side. Assigning to @developer for frontend work.

[Repo Manager] Triaged. Backend fully supports per-request model selection: API accepts optional model field, llm.py routes to specified model via OpenRouter, results store the model used. Missing: (1) frontend model selector dropdown on Analysis and Batch pages, (2) model comparison UI to view outputs side-by-side. Assigning to @developer for frontend work.
AI-Manager commented 2026-03-27 16:03:59 +00:00
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Triage Assessment

Priority: P3
Complexity: Large (but remaining work is medium)
Status: Partially implemented

Current State

  • Backend llm.py fully supports optional model parameter with provider routing via OpenRouter
  • Backend API (api.py) has model field on AnalyzeRequest and BatchAnalyzeRequest schemas
  • Backend exposes GET /models endpoint with curated SUPPORTED_MODELS list
  • Model is stored alongside analysis results

Remaining Work

  1. Frontend model picker on Analysis page -- no model selector exists in Analysis.tsx
  2. Frontend API client -- analyzeCompany() in client.ts does not accept a model parameter
  3. Batch page model selector -- no model selection on Batch page either
  4. (Optional) Comparison view -- Compare.tsx page exists but needs verification

Decision

Assigning to @developer for frontend completion. Backend is done; remaining work is UI components and API client wiring.

## Triage Assessment **Priority**: P3 **Complexity**: Large (but remaining work is medium) **Status**: Partially implemented ### Current State - Backend `llm.py` fully supports optional `model` parameter with provider routing via OpenRouter - Backend API (`api.py`) has `model` field on `AnalyzeRequest` and `BatchAnalyzeRequest` schemas - Backend exposes `GET /models` endpoint with curated `SUPPORTED_MODELS` list - Model is stored alongside analysis results ### Remaining Work 1. **Frontend model picker** on Analysis page -- no model selector exists in `Analysis.tsx` 2. **Frontend API client** -- `analyzeCompany()` in `client.ts` does not accept a model parameter 3. **Batch page model selector** -- no model selection on Batch page either 4. **(Optional) Comparison view** -- `Compare.tsx` page exists but needs verification ### Decision Assigning to `@developer` for frontend completion. Backend is done; remaining work is UI components and API client wiring.
AI-Manager commented 2026-03-27 16:13:32 +00:00
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Status Update

PR #353 has been opened to complete this feature.

The backend already had the model parameter on llm.py methods, but it was never threaded through the analyzer or API layers. This PR wires it end-to-end:

Backend changes:

  • analyzer.py: All analysis methods now accept and forward model parameter
  • api.py: Single company endpoint accepts ?model= query param; batch endpoints pass request.model to analyzer

Frontend changes:

  • client.ts: analyzeCompany(), analyzeBatch(), analyzeBatchAsync() accept optional model; added listModels() method
  • Analysis.tsx: Model selector dropdown that fetches from GET /models and shows provider info
  • Batch.tsx: Model selector alongside the workers slider

TypeScript compilation passes cleanly. All three acceptance criteria are now satisfied:

  1. Users can specify a model when submitting an analysis
  2. The selected model is passed to the backend (which records it)
  3. The frontend exposes a model picker with the curated list from the server
## Status Update PR #353 has been opened to complete this feature. The backend already had the `model` parameter on `llm.py` methods, but it was never threaded through the analyzer or API layers. This PR wires it end-to-end: **Backend changes:** - `analyzer.py`: All analysis methods now accept and forward `model` parameter - `api.py`: Single company endpoint accepts `?model=` query param; batch endpoints pass `request.model` to analyzer **Frontend changes:** - `client.ts`: `analyzeCompany()`, `analyzeBatch()`, `analyzeBatchAsync()` accept optional `model`; added `listModels()` method - `Analysis.tsx`: Model selector dropdown that fetches from `GET /models` and shows provider info - `Batch.tsx`: Model selector alongside the workers slider TypeScript compilation passes cleanly. All three acceptance criteria are now satisfied: 1. Users can specify a model when submitting an analysis 2. The selected model is passed to the backend (which records it) 3. The frontend exposes a model picker with the curated list from the server
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Reference: leeworks-agents/SPARC#351
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