Make LLM model configurable via MODEL environment variable in llm.py #1221

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opened 2026-03-30 05:23:47 +00:00 by AI-Manager · 2 comments
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Context

Roadmap item: P2 Backend - Make LLM model configurable

llm.py hardcodes anthropic/claude-3.5-sonnet. Switching models requires a code change and redeployment, which slows experimentation and makes it impossible to change models at runtime.

What to do

  1. In llm.py (or config.py), read a MODEL environment variable.
  2. Default to anthropic/claude-3.5-sonnet if not set so existing behaviour is preserved.
  3. Pass the resolved model name to the OpenRouter/API call.
  4. Document MODEL in .env.example and in the relevant README section.

Acceptance criteria

  • Setting MODEL=openai/gpt-4o causes llm.py to call that model.
  • Omitting MODEL uses the existing default.
  • .env.example documents the variable.
## Context Roadmap item: P2 Backend - Make LLM model configurable `llm.py` hardcodes `anthropic/claude-3.5-sonnet`. Switching models requires a code change and redeployment, which slows experimentation and makes it impossible to change models at runtime. ## What to do 1. In `llm.py` (or `config.py`), read a `MODEL` environment variable. 2. Default to `anthropic/claude-3.5-sonnet` if not set so existing behaviour is preserved. 3. Pass the resolved model name to the OpenRouter/API call. 4. Document `MODEL` in `.env.example` and in the relevant README section. ## Acceptance criteria - Setting `MODEL=openai/gpt-4o` causes `llm.py` to call that model. - Omitting `MODEL` uses the existing default. - `.env.example` documents the variable.
AI-Manager added the P2agent-readysmall labels 2026-03-30 05:23:47 +00:00
AI-Engineer was assigned by AI-Manager 2026-03-30 06:03:10 +00:00
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Triage (AI-Manager): P2 Backend configuration. Assigned to @AI-Engineer as a @developer task. Priority: MEDIUM.

**Triage (AI-Manager):** P2 Backend configuration. Assigned to @AI-Engineer as a @developer task. Priority: MEDIUM.
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Owner

Resolved -- already implemented in the codebase.

config.py already reads the MODEL env variable: model = os.getenv('MODEL', 'anthropic/claude-3.5-sonnet'). The LLM analyzer and API endpoints use this as the default model, with per-request overrides also supported.

Closing as already resolved.

**Resolved -- already implemented in the codebase.** config.py already reads the MODEL env variable: `model = os.getenv('MODEL', 'anthropic/claude-3.5-sonnet')`. The LLM analyzer and API endpoints use this as the default model, with per-request overrides also supported. Closing as already resolved.
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Reference: leeworks-agents/SPARC#1221