config.yaml

app:
  # backend API response language: en | zh
  language: "en"
  # user avatar upload directory (relative to config.yaml)
  avatars_dir: "avatars"
  # 登录会话设置
  session:
    # 用户超过该秒数不活跃后，要求重新登录（3600 = 1小时）
    idle_timeout_seconds: 3600
    # 活跃时间写库节流间隔（秒）
    touch_interval_seconds: 60
  # 每个用户默认对话数量上限
  # -1: 不限制；0: 禁止创建新对话；>0: 允许创建的最大对话数
  default_user_conversation_limit: 4
  debug:
    llm_message: true
  prompts:
    # 是否启用外部提示词目录
    enabled: true
    # 提示词目录（相对于 config.yaml 所在目录）
    dir: "prompts"
    # system prompt 文件名
    system_file: "system.md"
    # 行为规则文件名（追加到 system prompt 之后）
    behavior_file: "behavior.md"
    # system.md 的使用模式:
    # - append: deepagents 默认 system prompt + system.md + behavior.md
    # - override: 仅使用 system.md（再追加 behavior.md）
    system_mode: "append"
    version: "v0.6.2"

# SMTP settings (password reset emails)
smtp:
  enabled: true
  host: "smtp.example.com"
  port: 465
  username: "user@example.com"
  password: "xxxxxx"
  from_email: "user@example.com"
  from_name: "user@example.com"
  use_tls: false
  use_ssl: true
  timeout_seconds: 15
  reset_subject: "TeamClaw Password Reset"
  reset_url_template: "http://cookeem.com:8080/forgot-password?code={code}&email={email}"

database:
  host: "127.0.0.1"
  port: 5432
  username: "teamclaw"
  password: "teamclaw_dev_password"
  dbname: "teamclaw"
  echo: false

# LLM model settings
# Notes:
# 1) provider keys must be supported by langchain init_chat_model (openai/anthropic/google_genai/...).
# 2) For each provider:
#    - models: candidate model list
#    - params: shared params (temperature/max_tokens), can be overridden by model name.
# 3) Backend currently uses the first provider and its models[0] as the runtime model.
#    Put your target provider first, and the target model as the first item in models.
models:
  providers:
    # -----------------------------
    # Enabled example: OpenAI-compatible APIs
    # -----------------------------
    openai:
      # # Official OpenAI:
      # base_url: "https://api.openai.com/v1"
      # api_key: "sk-xxxx"
      # # ###############################
      # # Ali Dashscope coding (paid):
      # # ###############################
      base_url: "http://example.com/v1"
      api_key: "xxxx"
      models:
        - "qwen3.5-plus"
      params:
        temperature: 0
        max_tokens: 4096
        qwen3.5-plus:
          temperature: 0.7


    # -----------------------------
    # Other provider templates (disabled by default)
    # -----------------------------
    # anthropic:
    #   # Optional; usually no base_url required (official endpoint)
    #   # base_url: "https://api.anthropic.com"
    #   api_key: "sk-ant-xxxx"
    #   models:
    #     - "claude-sonnet-4-5"
    #     - "claude-opus-4-1"
    #   params:
    #     temperature: 0
    #     max_tokens: 4096

    # google_genai:
    #   # Gemini API
    #   api_key: "AIzaSyxxxx"
    #   models:
    #     - "gemini-2.5-pro"
    #     - "gemini-2.5-flash"
    #   params:
    #     temperature: 0
    #     max_tokens: 4096

    # google_vertexai:
    #   # Vertex AI common parameters
    #   project: "your-gcp-project-id"
    #   location: "us-central1"
    #   # credentials_path: "/path/to/service-account.json"
    #   models:
    #     - "gemini-2.5-pro"
    #   params:
    #     temperature: 0
    #     max_tokens: 4096

    # azure_openai:
    #   # Azure OpenAI
    #   base_url: "https://<resource-name>.openai.azure.com/"
    #   api_key: "<azure-api-key>"
    #   api_version: "2024-10-21"
    #   models:
    #     # Use deployment names here (not raw model IDs)
    #     - "gpt-4o-deployment"
    #   params:
    #     temperature: 0
    #     max_tokens: 4096

    # xai:
    #   api_key: "xai-xxxx"
    #   models:
    #     - "grok-3"
    #     - "grok-3-mini"
    #   params:
    #     temperature: 0

    # groq:
    #   api_key: "gsk_xxxx"
    #   models:
    #     - "llama-3.3-70b-versatile"
    #     - "mixtral-8x7b-32768"
    #   params:
    #     temperature: 0

    # together:
    #   api_key: "xxxx"
    #   base_url: "https://api.together.xyz/v1"
    #   models:
    #     - "meta-llama/Llama-3.3-70B-Instruct-Turbo"
    #   params:
    #     temperature: 0

    # mistralai:
    #   api_key: "xxxx"
    #   models:
    #     - "mistral-large-latest"
    #     - "ministral-8b-latest"
    #   params:
    #     temperature: 0

    # cohere:
    #   api_key: "xxxx"
    #   models:
    #     - "command-r-plus"
    #     - "command-r"
    #   params:
    #     temperature: 0

    # ollama:
    #   # Local/private deployment
    #   base_url: "http://127.0.0.1:11434"
    #   models:
    #     - "qwen2.5:1.5b-16k"
    #   params:
    #     temperature: 0
    #     max_tokens: 24096
    #     qwen2.5:1.5b-16k:
    #       temperature: 0.7

    # bedrock:
    #   # AWS Bedrock
    #   region_name: "us-east-1"
    #   # access_key_id: "AKIA..."
    #   # secret_access_key: "..."
    #   models:
    #     - "anthropic.claude-3-5-sonnet-20241022-v2:0"
    #   params:
    #     temperature: 0

# Docker sandbox settings
docker:
  # dockersandbox使用的容器镜像
  image: "cookeem/teamclaw-sandbox:v0.6.0"
  # dockersandbox使用的容器名称的前缀
  container_name_prefix: "teamclaw-sandbox"
  # dockersandbox挂装哪个目录作为租户们的工作目录，这个目录下边会为每个对话创建一个子目录，例如workspaces/<对话id>
  # dockersandbox挂装对话子目录（例如workspaces/<对话id>）到容器中的workdir，例如/workspace
  # 不启用Remote Docker daemon (dind)情况下生效
  workspace_root: "workspaces"
  # dockersandbox的对话工作空间目录
  workdir: "/workspace"
  # dockersandbox挂装内置skill目录到容器中的<workdir>/<skills_builtin_dir>，例如/workspace/skills-builtin
  # 不启用Remote Docker daemon (dind)情况下生效
  skills_builtin_dir: "skills-builtin"
  # dockersandbox挂装用户skill目录到容器中的<workdir>/skills，例如/workspace/skills
  # 不启用Remote Docker daemon (dind)情况下生效
  skills_user_dir: "skills/user"
  # readonly_workdir: false
  # auto_remove: false
  timeout: 300
  client_timeout: 10
  # sandbox CPU配额，单位核（支持小数），例如 0.5 / 1 / 2
  cpu_limit: 0.5
  # sandbox内存配额，支持字节或带单位（k/m/g/t, ki/mi/gi/ti），例如 512m / 2g
  memory_limit: "256m"
  # sandbox容器内的主机名解析（/etc/hosts）
  hostnames:
    - "api.internal:10.0.0.10"
    - "db.internal:10.0.0.11"
  environment:
    - "PYTHONUNBUFFERED=1"
    - "TZ=Asia/Shanghai"
  # Remote Docker daemon (dind) settings
  daemon_hosts:
    - name: "dind-0"
      host: "tcp://127.0.0.1:12376"
    - name: "dind-1"
      host: "tcp://127.0.0.1:12377"
  daemon_workspace_root: "/workspaces"
  daemon_skills_builtin_dir: "/skills-builtin"
  daemon_skills_user_dir: "/skills/user"
  daemon_tls:
    enabled: true
    certs_dir: "certs"
    ca_cert: "certs/ca.crt"
    client_cert: "certs/tls.crt"
    client_key: "certs/tls.key"
    verify: true

scheduled_tasks:
  enabled: true
  poll_interval_seconds: 10
  batch_size: 3
  llm_wait_timeout_seconds: 1800
  max_script_output_chars: 20000
  max_summary_input_chars: 12000