SuperLinux/config.example.yaml at master · userInner/SuperLinux · GitHub

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# Linux Agent Configuration Example
# Copy this file to config.yaml and fill in your settings

# =============================================================================
# AI Models Configuration
# =============================================================================

# Primary AI - the main model that handles tasks
primary_ai:
  name: DeepSeek
  provider: deepseek    # Options: openai, anthropic, deepseek, gemini
  model: deepseek-chat
  api_key: ${DEEPSEEK_API_KEY}  # Use env var or paste key directly

# Secondary AIs - consulted when primary fails repeatedly
secondary_ais:
  - name: Gemini-Consultant
    provider: gemini
    model: gemini-2.5-flash  # Options: gemini-2.5-flash, gemini-2.5-pro, gemini-3-flash-preview, gemini-3-pro-preview
    api_key: ${GEMINI_API_KEY}
    role: consultant

  # Uncomment to add more consultants
  # - name: GPT-4-Consultant
  #   provider: openai
  #   model: gpt-4o
  #   api_key: ${OPENAI_API_KEY}
  #   role: consultant

# =============================================================================
# Agent Behavior Settings
# =============================================================================

agent:
  max_retries: 3                    # Retry attempts before escalating
  search_attempts_before_consult: 2 # Web searches before consulting secondary AI
  max_iterations: 15                # Max tool call iterations per request

  # 🧬 自我进化模式设置
  auto_evolution:
    enabled: false                  # 是否启用自动进化（true/false）
    check_interval_hours: 24        # 检查间隔（小时）
    min_tasks_before_evolution: 10  # 触发进化前的最小任务数
    auto_apply_improvements: false  # 是否自动应用改进（不询问用户）

# =============================================================================
# Paths and Storage
# =============================================================================

workspace_path: ~/agent_workspace   # 代码文件操作的工作目录
sandbox_path: /tmp/agent_sandbox    # 临时文件目录
checkpoint_db: checkpoints.db       # SQLite database for state persistence

# =============================================================================
# MCP Server Configuration (for advanced usage)
# =============================================================================

mcp_servers:
  - name: system-monitor
    transport: stdio
    command: python3
    args: ["-m", "src.mcp_servers.system_monitor"]
    timeout: 30

  - name: file-manager
    transport: stdio
    command: python3
    args: ["-m", "src.mcp_servers.file_manager", "/tmp/agent_sandbox"]
    timeout: 30

  - name: network
    transport: stdio
    command: python3
    args: ["-m", "src.mcp_servers.network"]
    timeout: 60

  - name: service-manager
    transport: stdio
    command: python3
    args: ["-m", "src.mcp_servers.service_manager"]
    timeout: 30