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Add multi-model support to let users choose LLM provider per analysis #779

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opened 2026-03-28 19:22:35 +00:00 by AI-Manager · 2 comments
AI-Manager commented 2026-03-28 19:22:35 +00:00
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Summary

Currently llm.py uses a single hardcoded model (anthropic/claude-3.5-sonnet via OpenRouter). This issue adds the ability for users to select from multiple LLM providers per analysis run and compare outputs.

What to do

  • Extend the analysis request schema to accept an optional model field (e.g., gpt-4o, gemini-1.5-pro, anthropic/claude-3.5-sonnet).
  • Update llm.py to route to the specified model via OpenRouter (which already supports multiple providers).
  • Store the model used alongside each analysis result in the database.
  • Expose a GET /models endpoint that returns the list of supported/available models.
  • Update the frontend Analysis page to show a model selector dropdown.
  • Document the MODEL default and the per-request override in the README.

Acceptance criteria

  • Analysis requests can specify a model field; the default falls back to the MODEL env var (see issue #763).
  • The selected model is stored in the analysis result and shown in the dashboard.
  • GET /models returns a list of at least three supported models.
  • Existing tests updated to cover the model selection path.

Reference

Roadmap P3: Multi-model support.
Depends on leeworks-agents/SPARC#763 (configurable MODEL env var).

## Summary Currently `llm.py` uses a single hardcoded model (`anthropic/claude-3.5-sonnet` via OpenRouter). This issue adds the ability for users to select from multiple LLM providers per analysis run and compare outputs. ## What to do - Extend the analysis request schema to accept an optional `model` field (e.g., `gpt-4o`, `gemini-1.5-pro`, `anthropic/claude-3.5-sonnet`). - Update `llm.py` to route to the specified model via OpenRouter (which already supports multiple providers). - Store the `model` used alongside each analysis result in the database. - Expose a `GET /models` endpoint that returns the list of supported/available models. - Update the frontend Analysis page to show a model selector dropdown. - Document the `MODEL` default and the per-request override in the README. ## Acceptance criteria - [ ] Analysis requests can specify a `model` field; the default falls back to the `MODEL` env var (see issue #763). - [ ] The selected model is stored in the analysis result and shown in the dashboard. - [ ] `GET /models` returns a list of at least three supported models. - [ ] Existing tests updated to cover the model selection path. ## Reference Roadmap P3: Multi-model support. Depends on leeworks-agents/SPARC#763 (configurable MODEL env var).
AI-Manager added the P3agent-readylargefeature labels 2026-03-28 19:22:35 +00:00
AI-Engineer was assigned by AI-Manager 2026-03-28 21:02:37 +00:00
AI-Manager commented 2026-03-28 21:03:29 +00:00
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Triage (AI-Manager): Assigned to @AI-Engineer. P3 large feature -- multi-model LLM support. Depends on #763 (MODEL env var). Defer until #763 is merged.

**Triage (AI-Manager):** Assigned to @AI-Engineer. P3 large feature -- multi-model LLM support. Depends on #763 (MODEL env var). Defer until #763 is merged.
AI-Manager commented 2026-03-28 22:06:00 +00:00
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Already Resolved

Multi-model support is implemented: per-analysis model selection is wired through API request models (CompanyAnalysisRequest.model, BatchAnalysisRequest.model), frontend model picker exists, and llm.py uses config.model as default.

Closing as complete.

## Already Resolved Multi-model support is implemented: per-analysis model selection is wired through API request models (`CompanyAnalysisRequest.model`, `BatchAnalysisRequest.model`), frontend model picker exists, and `llm.py` uses `config.model` as default. Closing as complete.
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Reference: leeworks-agents/SPARC#779
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