# Required for inference.py (baseline agent)
# OPENROUTER_API_KEY is passed as api_key to the OpenAI client — use the token for
# whatever provider matches API_BASE_URL (Hugging Face, OpenRouter, etc.).

# Option A — Hugging Face router (defaults in code if env unset)
# API_BASE_URL=https://router.huggingface.co/v1
# MODEL_NAME=meta-llama/Llama-3.3-70B-Instruct
# OPENROUTER_API_KEY=your-huggingface-token-here

# Option B — OpenRouter (e.g. Meta Llama 3.2 3B Instruct free)
# API_BASE_URL=https://openrouter.ai/api/v1
# MODEL_NAME=meta-llama/llama-3.2-3b-instruct:free
# OPENROUTER_API_KEY=your-openrouter-key-here

# Optional: point inference at the local env
ENV_URL=http://localhost:8000

# Optional: append every raw LLM response to a file (debug / audit)
# INFERENCE_LOG_LLM=outputs/llm_raw.log
# Optional: append API errors (e.g. 429) when the client falls back to list_tools
# INFERENCE_LOG_API=outputs/api_errors.log
# Optional: wait for Enter between steps to avoid rate limits (or use: python inference.py -i)
# INFERENCE_INTERACTIVE=1
# INFERENCE_PAUSE=step   # step = after each env step; scenario = only between easy/medium/hard
# INFERENCE_SUMMARY_FILE=outputs/reward_grader_summary.txt
# Optional: sampling temperature for the LLM (default 0.5; raise for more exploration on small models)
# INFERENCE_TEMPERATURE=0.5