config.yaml.example - main - githubmirror/sgr-agent-core

config.yaml.example
144 строки6 KB
Pavel Rykov
refactor(skills): address review — BaseSkill, drop MCP prompts, default .agent/skills
14 июл 2026, 19:48
14 июл 2026, 19:488a0a7e7
# SGR Agent Core - Configuration Template
# Copy this file to config.yaml and fill in your data

# LLM Configuration
llm:
  api_key: "your-openai-api-key-here"  # Your OpenAI API key
  base_url: "https://api.openai.com/v1"  # API base URL
  model: "gpt-4o-mini"  # Model name
  max_tokens: 8000  # Max output tokens
  temperature: 0.4  # Temperature (0.0-1.0)
  # proxy: "socks5://127.0.0.1:1081"  # Optional proxy (socks5:// or http://)

# Observability (Langfuse)
# When enabled, AgentFactory will create Langfuse AsyncOpenAI client instead of standard AsyncOpenAI.
# Credentials can be set here or via LANGFUSE_PUBLIC_KEY / LANGFUSE_SECRET_KEY / LANGFUSE_HOST env vars.
langfuse:
  enabled: false
  # public_key: "pk-lf-xxx"
  # secret_key: "sk-lf-xxx"
  # host: "http://localhost:3000"

# Execution Settings
execution:
  max_clarifications: 3  # Max clarification requests
  max_iterations: 10  # Max agent iterations
  mcp_context_limit: 15000  # Max context length from MCP server response
  logs_dir: "logs"  # Directory for saving agent execution logs
  reports_dir: "reports"  # Directory for saving agent reports

# Optional: Agent Client Protocol (stdio) via `sgracp --config config.yaml`
# acp:
#   agent: "sgr_agent"       # Default agent; must match a name under agents:
#   models:                  # Extra models offered in the ACP "model" selector,
#     - "gpt-4o"             # in addition to each agent's own configured model.
#     - "o3-mini"            # Clients let the user switch agent and model at runtime.

# Prompts Configuration
# prompts:
#   # Option 1: Use file paths (absolute or relative to project root)
#   system_prompt_file: "path/to/your/system_prompt.txt"
#   initial_user_request_file: "path/to/your/initial_user_request.txt"
#   clarification_response_file: "path/to/your/clarification_response.txt"

#  # Option 2: Provide prompts directly as strings
#   system_prompt_str: "Your custom system prompt here..."
#   initial_user_request_str: "Your custom initial request template..."
#   clarification_response_str: "Your custom clarification template..."

  # Note: If both file and string are provided, string takes precedence

# MCP (Model Context Protocol) Configuration
mcp:
  mcpServers:
    deepwiki:
      url: "https://mcp.deepwiki.com/mcp"

    # Add more MCP servers here:
    # your_server:
    #   url: "https://your-mcp-server.com/mcp"
    #   headers:
    #     Authorization: "Bearer your-token"


# Note: The 'agents' field is optional and can be loaded from either:
# - This config.yaml file
# - Any separate file by GlobalConfig.definitions_from_yaml method
# See examples in agents.yaml.example for agent configuration options

tools:
  # Global tool config: base_class (optional) and any params passed to the tool at runtime
  # Agents that use the tool by name get these params as kwargs; per-agent inline config overrides.
  my_custom_tool:
    base_class: path.to.my.tools.CustomTool
  my_other_tool:
    base_class: "name_of_tool_class_in_registry"
  # Search tools: configure search provider and API keys per tool
  # (can be overridden per-agent in tools list)
  web_search_tool:
    engine: "tavily"  # Search engine: "tavily" (default), "brave", or "perplexity"
    api_key: "your-search-api-key-here"  # API key for the selected engine
    # api_base_url: "https://custom-url"  # Optional, uses engine default
    max_results: 12
    max_searches: 6
  extract_page_content_tool:
    tavily_api_key: "your-tavily-api-key-here"  # Tavily API key (Tavily-only feature)
    tavily_api_base_url: "https://api.tavily.com"
    content_limit: 2000

agents:
  custom_research_agent:
    base_class: "sgr_agent_core.agents.sgr_agent.SGRAgent"
    # Optional: Override LLM settings for this agent
    llm:
      model: "gpt-4o"
      temperature: 0.3
      max_tokens: 16000
      # api_key: "your-custom-api-key"  # Optional: use different API key
      # base_url: "https://api.openai.com/v1"  # Optional: use different endpoint
      # proxy: "http://127.0.0.1:8080"  # Optional: use proxy

    # Optional: Execution configuration
    execution:
      max_iterations: 15
      max_clarifications: 5
      mcp_context_limit: 20000
      logs_dir: "logs/custom_agent"
      reports_dir: "reports/custom_agent"

    # Optional: MCP configuration
    mcp:
      mcpServers:
        deepwiki:
          url: "https://mcp.deepwiki.com/mcp"

    # Tools: names, or per-tool overrides with optional kwargs
    # Example with per-tool overrides:
    # - web_search_tool:
    #     max_results: 15
    #     max_searches: 6
    tools:
      - "web_search_tool"
      - "extract_page_content_tool"
      - "create_report_tool"
      - "clarification_tool"
      - "generate_plan_tool"
      - "adapt_plan_tool"
      - "final_answer_tool"
      - "my_custom_tool"
      - "my_other_tool"

    # Optional: Skills (Anthropic Agent Skills model)
    # Each skill is a directory with a SKILL.md (frontmatter: name, description).
    # Skill name + description are auto-registered into the system prompt so the
    # agent can invoke a skill on its own via the `use_skill` tool; skills are
    # also exposed as commands over ACP (available_commands).
    # By default skills are read from ./.agent/skills (CWD) and ~/.agent/skills;
    # missing/empty folders are fine. Use the block below to customize.
    # skills:
    #   enabled: true
    #   paths:                   # overrides the default roots (relative to CWD)
    #     - "./.agent/skills"
    #   include: null            # optional allowlist of skill names to activate
    #   exclude: null            # optional denylist of skill names
    # The prompt listing budget is global: execution.max_skill_desc_chars (500).