ml-ref-sgr-agent-core/agents.yaml.example at main · haltingstate/ml-ref-sgr-agent-core · GitHub

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# Example Custom Agents Configuration
# =====================================
# This file demonstrates how to define custom agents for SGR Deep Research

# Notes:
# ------
# 1. Agent names must be unique or will be overridden
# 2. All tools must be registered in the tool registry
# 3. LLM, Search, Prompts, Execution, MCP settings are optional and inherit from global config
# 4. Agents override global settings by providing their own values

agents:
  # Example 1: Simple custom research agent with overrides
  custom_research_agent:
    base_class: "sgr_agent_core.agents.sgr_agent.ResearchSGRAgent"
    # 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: Override search settings
    search:
      max_results: 15
      max_pages: 8
      content_limit: 2000

    # Optional: Execution configuration
    execution:
      max_steps: 8
      max_iterations: 15
      max_clarifications: 5
      max_searches: 6
      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 this agent can use (must be registered in tool registry)
    tools:
      - "WebSearchTool"
      - "ExtractPageContentTool"
      - "CreateReportTool"
      - "ClarificationTool"
      - "GeneratePlanTool"
      - "AdaptPlanTool"
      - "FinalAnswerTool"

  # Example 2: Minimal agent with defaults
  simple_agent:
    base_class: "SGRToolCallingAgent"

    # Only override what's needed
    llm:
      model: "gpt-4o-mini"

    tools:
      - "WebSearchTool"
      - "FinalAnswerTool"

  # Example 3: Fast research agent optimized for speed
  fast_research_agent:
    base_class: "SGRToolCallingAgent"

    llm:
      model: "gpt-4o-mini"
      temperature: 0.1
      max_tokens: 4000

    execution:
      max_steps: 4
      max_iterations: 8
      max_clarifications: 2
      max_searches: 3

    tools:
      - "WebSearchTool"
      - "CreateReportTool"
      - "FinalAnswerTool"
      - "ReasoningTool"

  # Example 4: Specialized technical analyst with custom prompts
  technical_analyst:
    base_class: "SGRAgent"

    llm:
      model: "gpt-4o"
      temperature: 0.2

    prompts:
      system_prompt: "You are a highly specialized technical analyst."

    execution:
      max_steps: 10
      max_iterations: 20
      max_clarifications: 3
      max_searches: 8

    tools:
      - "WebSearchTool"
      - "ExtractPageContentTool"
      - "CreateReportTool"
      - "ClarificationTool"
      - "FinalAnswerTool"

  # Example 5: Agent using inline prompts instead of files
  inline_prompt_agent:
    base_class: "ResearchSGRAgent"

    prompts:
      system_prompt_str: |
        You are a helpful research assistant.
        Your goal is to provide accurate and concise information.
      initial_user_request_str: |
        User request: {user_request}
        Please analyze and respond.
      clarification_response_str: |
        I need clarification on: {clarification_needed}

    tools:
      - "WebSearchTool"
      - "FinalAnswerTool"