config.yaml

# SpecialistHub Configuration Example
# Version: 3.0
#
# ==============================================================================
# DEVELOPER-LEVEL CONFIGURATION
# This file is the system's blueprint, intended for developers to define the
# agent's architecture. It should NOT be exposed to or editable by end-users.
# ==============================================================================
#
# This file defines the structure of the agentic system. It is the single
# source of truth for wiring together providers, models, and specialists.
# Copy this file to `config.yaml` and customize it for your setup.

# ==============================================================================
# LLM PROVIDER CONFIGURATION
# ==============================================================================
# Define the available LLM providers. The keys here (e.g., 'lmstudio') are
# referenced by the specialists below.
# The application will read environment variables defined in .env for secrets.
#
# Supported providers: "lmstudio", "ollama", "gemini"
# ==============================================================================
llm_providers:
  # A configuration for a local model via LM Studio
  local_lmstudio:
    # Uses the LMSTUDIO_BASE_URL from your .env file
    # Example: LMSTUDIO_BASE_URL=http://localhost:1234/v1
    type: "lmstudio"
    api_identifier: "openai-oss-22b" # The exact model identifier for the server

  # A configuration for a local model via Ollama
  local_ollama:
    # Uses OLLAMA_BASE_URL and OLLAMA_MODEL from your .env file
    # Example: OLLAMA_BASE_URL=http://localhost:11434
    type: "ollama"
    api_identifier: "gemma3:27b" # The model name registered with Ollama

  # A configuration for a powerful, general-purpose Gemini model
  gemini_pro:
    # Uses GEMINI_API_KEY from your .env file
    type: "gemini"
    api_identifier: "gemini-2.5-pro"
    # Future parameters could go here, e.g., temperature: 0.5

  # A configuration for a fast, cheaper Gemini model
  gemini_flash:
    # Uses GEMINI_API_KEY from your .env file
    type: "gemini"
    api_identifier: "gemini-2.5-flash"

# ==============================================================================
# WORKFLOW CONFIGURATION
# ==============================================================================
# Define the entry point for the graph. Defaults to 'router_specialist'.
# ==============================================================================
workflow:
  entry_point: "prompt_triage_specialist"

# ==============================================================================
# SPECIALIST CONFIGURATION
# ==============================================================================
# Define each specialist agent. The key for each specialist (e.g., 'router_specialist')
# must match the specialist's Python module name (router_specialist.py) and the
# name passed to super().__init__() in its constructor.
# ==============================================================================
specialists:
  # --- Orchestration & Planning ---
  router_specialist:
    type: "llm"
    prompt_file: "router_prompt.md"
    description: "The master router and planner. It analyzes the user's request and routes it to the appropriate specialist. Its model should be capable of reliable tool/function calling."

  systems_architect:
    type: "llm"
    prompt_file: "systems_architect_prompt.md"
    description: "Analyzes a user's request and creates a high-level technical plan. Produces a 'system_plan' artifact. This is a good first step for complex tasks."

  # --- Core Capabilities ---
  web_builder:
    type: "llm"
    prompt_file: "web_builder_prompt.md"
    description: "Takes a 'system_plan' artifact and generates a self-contained HTML document based on it. Requires a 'system_plan' to be present in the state."
  code_writer_specialist:
    type: "llm"
    prompt_file: "code_writer_prompt.md"
    description: "A specialist that writes clean, efficient Python code based on a user's request."    
  file_specialist:
    type: "llm"
    prompt_file: "file_specialist_prompt.md"
    description: "Performs file system operations like read, write, and list directories using a Pydantic schema. Write operations are protected by a safety lock."
    root_dir: "./workspace" # Defaults to a 'workspace' folder in the project root.

  # --- Data & Analysis ---
  data_extractor_specialist:
    type: "llm"
    prompt_file: "data_extractor_prompt.md" # Use a prompt designed for Pydantic/JSON schema output.
    description: "Extracts structured data from unstructured text based on a Pydantic schema. Requires 'text_to_process' in the state."

  sentiment_classifier_specialist:
    type: "llm"
    prompt_file: "sentiment_classifier_prompt.md"
    description: "Classifies the sentiment of a given text as positive, negative, or neutral."

  data_processor_specialist:
    type: "procedural"
    description: "A specialist that performs deterministic data processing tasks, like formatting or cleaning, without calling an LLM."

  text_analysis_specialist:
    type: "llm"
    prompt_file: "text_analysis_prompt.md"
    description: "Analyzes, summarizes, or extracts information from a block of text. Use this after text has been retrieved by another specialist."

  # --- General & Fallback ---
  prompt_specialist:
    type: "llm"
    prompt_file: "prompt_specialist_prompt.md"
    description: "A general-purpose specialist for direct Q&A and instruction following. It can be bound to a simple or advanced model in user_settings.yaml."

  # --- Wrapped & External Specialists ---
  open_swe_specialist:
    type: "wrapped"
    source: "./external/open-swe/agent/run.py" # Path relative to project root
    class_name: "Agent" # The name of the class to instantiate from the source file.
    description: "A specialist that wraps the open-swe agent for software engineering tasks."

  archiver_specialist:
    type: "procedural"
    archive_path: "./archives" # The directory to save final run reports.
    # Pruning strategy for the archive directory to prevent it from growing indefinitely.
    # Options: "none", "count", "size".
    pruning_strategy: "count"
    pruning_max_count: 50      # Max number of reports to keep if strategy is "count".
    # pruning_max_size_mb: 100 # Max total size in MB if strategy is "size".
    description: "Summarizes the conversation and prepares the final report. This is the final step."

  prompt_triage_specialist:
    type: "llm"
    prompt_file: "prompt_triage_prompt.md"
    description: "A specialist that performs a pre-flight check on the user's initial prompt for sentiment, coherence, and actionability."