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refactor: remove LLM_MODEL env var dependency — config.yaml is sole source of truth

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Model selection now comes exclusively from config.yaml (set via
'hermes model' or 'hermes setup'). The LLM_MODEL env var is no longer
read or written anywhere in production code.

Why: env vars are per-process/per-user and would conflict in
multi-agent or multi-tenant setups. Config.yaml is file-based and
can be scoped per-user or eventually per-session.

Changes:
- cli.py: Read model from CLI_CONFIG only, not LLM_MODEL/OPENAI_MODEL
- hermes_cli/auth.py: _save_model_choice() no longer writes LLM_MODEL
  to .env
- hermes_cli/setup.py: Remove 12 save_env_value('LLM_MODEL', ...)
  calls from all provider setup flows
- gateway/run.py: Remove LLM_MODEL fallback (HERMES_MODEL still works
  for gateway process runtime)
- cron/scheduler.py: Same
- agent/auxiliary_client.py: Remove LLM_MODEL from custom endpoint
  model detection
This commit is contained in:
teknium1 2026-03-11 22:04:42 -07:00
parent a29801286f
commit 9302690e1b
7 changed files with 36 additions and 32 deletions
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12
hermes_cli/setup.py
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@ -681,7 +681,6 @@ def setup_model_provider(config: dict):
save_env_value("OPENAI_API_KEY", api_key)
if model_name:
config['model'] = model_name
save_env_value("LLM_MODEL", model_name)
# Save provider and base_url to config.yaml so the gateway and CLI
# both resolve the correct provider without relying on env-var heuristics.
@ -913,7 +912,6 @@ def setup_model_provider(config: dict):
custom = prompt(f" Model name (Enter to keep '{current_model}')")
if custom:
config['model'] = custom
save_env_value("LLM_MODEL", custom)
elif selected_provider == "openai-codex":
from hermes_cli.codex_models import get_codex_model_ids
codex_models = get_codex_model_ids()
@ -927,12 +925,10 @@ def setup_model_provider(config: dict):
model_idx = prompt_choice("Select default model:", model_choices, default_codex)
if model_idx < len(codex_models):
config['model'] = codex_models[model_idx]
save_env_value("LLM_MODEL", codex_models[model_idx])
elif model_idx == len(codex_models):
custom = prompt("Enter model name")
if custom:
config['model'] = custom
save_env_value("LLM_MODEL", custom)
_update_config_for_provider("openai-codex", DEFAULT_CODEX_BASE_URL)
elif selected_provider == "zai":
# Coding Plan endpoints don't have GLM-5
@ -950,12 +946,10 @@ def setup_model_provider(config: dict):
if model_idx < len(zai_models):
config['model'] = zai_models[model_idx]
save_env_value("LLM_MODEL", zai_models[model_idx])
elif model_idx == len(zai_models):
custom = prompt("Enter model name")
if custom:
config['model'] = custom
save_env_value("LLM_MODEL", custom)
# else: keep current
elif selected_provider == "kimi-coding":
kimi_models = ["kimi-k2.5", "kimi-k2-thinking", "kimi-k2-turbo-preview"]
@ -968,12 +962,10 @@ def setup_model_provider(config: dict):
if model_idx < len(kimi_models):
config['model'] = kimi_models[model_idx]
save_env_value("LLM_MODEL", kimi_models[model_idx])
elif model_idx == len(kimi_models):
custom = prompt("Enter model name")
if custom:
config['model'] = custom
save_env_value("LLM_MODEL", custom)
# else: keep current
elif selected_provider in ("minimax", "minimax-cn"):
minimax_models = ["MiniMax-M2.5", "MiniMax-M2.5-highspeed", "MiniMax-M2.1"]
@ -986,12 +978,10 @@ def setup_model_provider(config: dict):
if model_idx < len(minimax_models):
config['model'] = minimax_models[model_idx]
save_env_value("LLM_MODEL", minimax_models[model_idx])
elif model_idx == len(minimax_models):
custom = prompt("Enter model name")
if custom:
config['model'] = custom
save_env_value("LLM_MODEL", custom)
# else: keep current
else:
# Static list for OpenRouter / fallback (from canonical list)
@ -1008,12 +998,10 @@ def setup_model_provider(config: dict):
if model_idx < len(ids):
config['model'] = ids[model_idx]
save_env_value("LLM_MODEL", ids[model_idx])
elif model_idx == len(ids): # Custom
custom = prompt("Enter model name (e.g., anthropic/claude-opus-4.6)")
if custom:
config['model'] = custom
save_env_value("LLM_MODEL", custom)
# else: Keep current
_final_model = config.get('model', '')