.env.example

# Template for .env. Copy to `.env` and fill in real values.
# .env is gitignored; .env.example is committed.

# ============================================================
# Hugging Face dataset cache
# ============================================================
# data_loader.py caches the published dataset here on first call.
# Relative paths resolve to the project root.
# Reset with `rm -rf .hf_cache`.
HF_HOME=.hf_cache

# ============================================================
# Audit-trail SQLite database
# ============================================================
# AuditStore writes to this file (created on first call to .initialize()).
# Relative paths resolve to the project root. The parent directory is
# auto-created if missing.
#
# For Docker: mount a host volume to a known path inside the container
# and override here, e.g. AUDIT_DB_PATH=/data/audit_db/audit.db.
#
# Wipe with `rm -rf .audit_db` between runs.
AUDIT_DB_PATH=.audit_db/audit.db

# ============================================================
# LLM judge (eval-set Mid + Rich layers)
# ============================================================
# ANTHROPIC_API_KEY is required for judge calls. When the key is not
# set, Mid and Rich layers return "skipped" markers and only Shape +
# Correctness run.
#
# Anthropic is the only supported provider. A previous OpenAI path
# is disabled in code (see src/agents/llm_client.py and
# src/evaluator/judge_client.py) — any other provider value raises
# at startup. See docs/decisions.md for rationale.

# ANTHROPIC_API_KEY=sk-ant-...

LLM_JUDGE_PROVIDER=anthropic
LLM_JUDGE_MODEL=claude-haiku-4-5-20251001

# ============================================================
# Agent LLMs (Step 11b — tier specialists + Cross-Tier Evaluator)
# ============================================================
# Two tiers, each independently configurable. ANTHROPIC_API_KEY above
# must be set. Anthropic is the only currently supported provider.
SPECIALIST_PROVIDER=anthropic
SPECIALIST_MODEL=claude-opus-4-6
EVALUATOR_PROVIDER=anthropic
EVALUATOR_MODEL=claude-opus-4-6

# ============================================================
# LangSmith tracing (optional — only needed for `langgraph dev`
# Studio panel and remote trace inspection)
# ============================================================
# When enabled, every LLM call and graph step is sent to LangSmith.
# `langgraph dev` works without these, but the per-node detail panel
# (token counts, retries, full message history) only populates when
# tracing is on.
#
# Free tier covers ~5K traces/month, which is plenty for development.
# Sign up: https://smith.langchain.com/
#
# Leave commented to run without tracing.
# LANGCHAIN_TRACING_V2=true
# LANGSMITH_API_KEY=ls-...
# LANGSMITH_PROJECT=cloud-governance-agent
# LANGSMITH_ENDPOINT=https://api.smith.langchain.com