MicroFish/.env.example

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# LLM API configuration (supports any OpenAI-compatible API)
# Recommended: Alibaba Qwen via Bailian platform: https://bailian.console.aliyun.com/
# Note: high token consumption — start with simulations under 40 rounds
LLM_API_KEY=your_api_key_here
LLM_BASE_URL=https://dashscope.aliyuncs.com/compatible-mode/v1
LLM_MODEL_NAME=qwen-plus
# ===== Graph backend selection =====
# Options: "zep" (default, cloud-managed) or "graphiti" (self-hosted Neo4j)
GRAPH_BACKEND=zep
# ===== Zep Cloud configuration (required when GRAPH_BACKEND=zep) =====
# Free monthly quota is sufficient for basic use: https://app.getzep.com/
ZEP_API_KEY=your_zep_api_key_here
# ===== Graphiti + Neo4j configuration (required when GRAPH_BACKEND=graphiti) =====
# NEO4J_URI=bolt://localhost:7687
# NEO4J_USER=neo4j
# NEO4J_PASSWORD=your_neo4j_password_here
# NEO4J_DATABASE=neo4j
# ===== Boost LLM configuration (optional) =====
# If not using a boost LLM, remove or leave these variables empty
LLM_BOOST_API_KEY=
LLM_BOOST_BASE_URL=
LLM_BOOST_MODEL_NAME=
# ===== Authentication =====
# Password for the "demo" user — REQUIRED in production
# Empty default = login disabled until set
DEMO_PASSWORD=
# Flask secret key — used to sign JWT tokens
# Generate with: python -c "import secrets; print(secrets.token_hex(32))"
SECRET_KEY=your-secret-key-here