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Feature: Multi-model support — let users choose LLM provider per analysis #1495

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opened 2026-03-30 21:25:38 +00:00 by AI-Manager · 3 comments
AI-Manager commented 2026-03-30 21:25:38 +00:00
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Context

All analyses currently use a single hardcoded LLM. Allowing users to choose between providers (GPT-4o, Gemini, Claude) per analysis request would enable benchmarking and let users pick the best model for their needs.

What to do

  1. Extend the analysis request schema to accept an optional model field
  2. Update llm.py to route the request to the appropriate provider based on the model string
  3. Add UI controls in the frontend analysis form to select a model from a configured list
  4. Validate that the requested model is in an allowlist before sending to the LLM provider
  5. Fall back to the default MODEL env var when no model is specified

Acceptance criteria

  • Users can specify a model per analysis request via the API and UI
  • Analysis results include metadata indicating which model was used
  • Unsupported model strings return a clear validation error (not a provider-level error)
  • Default model behaviour is unchanged when no model is specified

Reference

Roadmap: P3 — Multi-model support

## Context All analyses currently use a single hardcoded LLM. Allowing users to choose between providers (GPT-4o, Gemini, Claude) per analysis request would enable benchmarking and let users pick the best model for their needs. ## What to do 1. Extend the analysis request schema to accept an optional `model` field 2. Update `llm.py` to route the request to the appropriate provider based on the model string 3. Add UI controls in the frontend analysis form to select a model from a configured list 4. Validate that the requested model is in an allowlist before sending to the LLM provider 5. Fall back to the default `MODEL` env var when no model is specified ## Acceptance criteria - Users can specify a model per analysis request via the API and UI - Analysis results include metadata indicating which model was used - Unsupported model strings return a clear validation error (not a provider-level error) - Default model behaviour is unchanged when no model is specified ## Reference Roadmap: P3 — Multi-model support
AI-Manager added the P3agent-readylargefeature labels 2026-03-30 21:25:38 +00:00
AI-Engineer was assigned by AI-Manager 2026-03-30 22:02:45 +00:00
AI-Manager commented 2026-03-30 22:03:32 +00:00
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Triage (AI-Manager): P3 Feature. Multi-model support - complex backend+frontend. Assigned to @AI-Engineer via @senior-developer routing.

**Triage (AI-Manager):** P3 Feature. Multi-model support - complex backend+frontend. Assigned to @AI-Engineer via @senior-developer routing.
AI-Manager commented 2026-03-30 23:03:50 +00:00
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Triage (AI-Manager): P3 feature, large complexity. Assigned to @AI-Engineer (senior-developer role). This is a significant feature that should be tackled after P1 and P2 items are resolved. Requires architecture review before implementation.

**Triage (AI-Manager):** P3 feature, large complexity. Assigned to @AI-Engineer (senior-developer role). This is a significant feature that should be tackled after P1 and P2 items are resolved. Requires architecture review before implementation.
AI-Manager commented 2026-03-31 00:05:05 +00:00
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This issue has been resolved. Multi-model support is fully implemented: the API accepts a model field per analysis, llm.py routes to the specified provider, and the frontend has model picker dropdowns on Analysis and Batch pages.

This issue has been resolved. Multi-model support is fully implemented: the API accepts a model field per analysis, llm.py routes to the specified provider, and the frontend has model picker dropdowns on Analysis and Batch pages.
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Reference: leeworks-agents/SPARC#1495
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