"""
HEAVEN LangGraph Foundation
Minimal building blocks for agent-driven RSI workflow construction

Components:
1. Simple state management
2. Predefined HEAVEN node types
3. JSON graph specification loader
4. Runner LEGOs for hermes_step and completion
5. Context/prompt engineering nodes
"""

import asyncio
import json
from typing import TypedDict, List, Dict, Any, Optional, Union, Callable
from dataclasses import dataclass
from langgraph.graph import StateGraph, END, START
from langgraph.checkpoint.memory import MemorySaver

from ..baseheavenagent import HeavenAgentConfig
from ..unified_chat import ProviderEnum
from ..tool_utils.completion_runners import exec_completion_style
from ..tool_utils.hermes_utils import hermes_step
from ..configs.hermes_config import HermesConfig
from ..docs.examples.heaven_callbacks import (
    create_callback_from_config, 
    BackgroundEventCapture,
    PrintEventLogger
)


# === SIMPLE STATE ===

class HeavenState(TypedDict):
    """Minimal state - just store raw results"""
    results: List[Dict[str, Any]]  # Raw execution results
    context: Dict[str, Any]        # Shared context across nodes
    agents: Dict[str, HeavenAgentConfig]  # Named agent registry


# === HEAVEN NODE TYPES ===

class HeavenNodeType:
    """Predefined HEAVEN node types"""
    COMPLETION = "completion"
    HERMES = "hermes" 
    HERMES_CONFIG = "hermes_config"
    # Removed: CONTEXT_ENGINEER and PROMPT_ENGINEER - redundant with result_extractor and dynamic_function
    # Context Manager nodes
    CONTEXT_WEAVE = "context_weave"
    CONTEXT_INJECT = "context_inject"
    PIS_INJECTION = "pis_injection"
    CHAIN_PATTERN = "chain_pattern"
    # Tool and extraction nodes
    OMNITOOL_LIST = "omnitool_list"
    OMNITOOL_GET_INFO = "omnitool_get_info"
    OMNITOOL_CALL = "omnitool_call"
    RESULT_EXTRACTOR = "result_extractor"
    DYNAMIC_FUNCTION = "dynamic_function"
    SUBGRAPH = "subgraph"
    # Placeholder nodes for future implementation
    BRAIN_AGENT = "brain_agent"


# === RUNNER LEGOS ===

async def completion_runner(state: HeavenState, *, node_config: Dict[str, Any] = None, prompt: str = None, agent=None, target_container: str = None, source_container: str = None, history_id: str = None, prompt_template_vars: Dict[str, Any] = None) -> Dict[str, Any]:
    """
    Execute completion-style agent and store raw result.
    
    Can be called with normal parameters or legacy node_config:
    
    Normal way:
        completion_runner(state, agent=my_agent, prompt="Hello")
    
    Legacy way:
        completion_runner(state, node_config={"agent": my_agent, "prompt": "Hello"})
    """
    # Build node_config from parameters if not provided
    if node_config is None:
        if prompt is None:
            raise ValueError("Must provide either node_config or prompt parameter")
        node_config = {
            "prompt": prompt
        }
        if agent is not None:
            node_config["agent"] = agent
        if target_container is not None:
            node_config["target_container"] = target_container
        if source_container is not None:
            node_config["source_container"] = source_container
        if history_id is not None:
            node_config["history_id"] = history_id
        if prompt_template_vars:
            node_config["prompt_template_vars"] = prompt_template_vars
    
    # Get agent
    agent = node_config["agent"]
    if isinstance(agent, str):
        agent = state["agents"][agent]
    
    # Build prompt
    prompt = node_config["prompt"]
    if "prompt_template_vars" in node_config:
        # Simple template substitution
        for key, value in node_config["prompt_template_vars"].items():
            prompt = prompt.replace(f"{{{key}}}", str(value))
    
    # Execute with all parameters
    exec_params = {"prompt": prompt}
    if agent:
        exec_params["agent"] = agent
    if "target_container" in node_config:
        exec_params["target_container"] = node_config["target_container"]
    if "source_container" in node_config:
        exec_params["source_container"] = node_config["source_container"]
    if "history_id" in node_config:
        exec_params["history_id"] = node_config["history_id"]
    
    result = await exec_completion_style(**exec_params)
    
    # Store raw result
    execution_result = {
        "node_type": HeavenNodeType.COMPLETION,
        "agent_name": agent.name,
        "prompt": prompt,
        "raw_result": result
    }
    
    return {
        "results": state["results"] + [execution_result]
    }


async def hermes_runner(state: HeavenState, *, node_config: Dict[str, Any] = None, target_container: str = "mind_of_god", source_container: str = "mind_of_god", goal: str = None, agent=None, iterations: int = 1, history_id: str = None, return_summary: bool = False, ai_messages_only: bool = True, remove_agents_config_tools: bool = False, continuation: bool = None, goal_template_vars: Dict[str, Any] = None) -> Dict[str, Any]:
    """
    Execute hermes_step and store raw result.
    hermes_step already handles edge logic via block report status.
    
    Can be called with normal parameters or legacy node_config:
    
    Normal way:
        hermes_runner(state, agent=my_agent, goal="analyze code", iterations=3)
    
    Legacy way:
        hermes_runner(state, node_config={"agent": my_agent, "goal": "analyze code", "iterations": 3})
    """
    # Build node_config from parameters if not provided
    if node_config is None:
        if goal is None:
            raise ValueError("Must provide either node_config or goal parameter")
        node_config = {
            "goal": goal,
            "target_container": target_container,
            "source_container": source_container,
            "iterations": iterations
        }
        if agent is not None:
            node_config["agent"] = agent
        if history_id is not None:
            node_config["history_id"] = history_id
        if return_summary is not None:
            node_config["return_summary"] = return_summary
        if ai_messages_only is not None:
            node_config["ai_messages_only"] = ai_messages_only
        if remove_agents_config_tools is not None:
            node_config["remove_agents_config_tools"] = remove_agents_config_tools
        if continuation is not None:
            node_config["continuation"] = continuation
        if goal_template_vars:
            node_config["goal_template_vars"] = goal_template_vars
    
    # Get agent
    agent = node_config["agent"]
    if isinstance(agent, str):
        agent = state["agents"][agent]
    
    # Build goal
    goal = node_config["goal"]
    if "goal_template_vars" in node_config:
        # Simple template substitution
        for key, value in node_config["goal_template_vars"].items():
            goal = goal.replace(f"{{{key}}}", str(value))
    
    # Execute hermes_step with all parameters
    hermes_params = {
        "target_container": node_config.get("target_container", "mind_of_god"),
        "source_container": node_config.get("source_container", "mind_of_god"),
        "goal": goal,
        "iterations": node_config.get("iterations", 1),
        "agent": agent
    }
    
    # Add optional parameters if present
    if "history_id" in node_config:
        hermes_params["history_id"] = node_config["history_id"]
    if "return_summary" in node_config:
        hermes_params["return_summary"] = node_config["return_summary"]
    if "ai_messages_only" in node_config:
        hermes_params["ai_messages_only"] = node_config["ai_messages_only"]
    if "remove_agents_config_tools" in node_config:
        hermes_params["remove_agents_config_tools"] = node_config["remove_agents_config_tools"]
    if "continuation" in node_config:
        hermes_params["continuation"] = node_config["continuation"]
    
    result = await hermes_step(**hermes_params)
    
    # Store raw result
    execution_result = {
        "node_type": HeavenNodeType.HERMES,
        "agent_name": agent.name,
        "goal": goal,
        "iterations": node_config.get("iterations", 1),
        "raw_result": result
    }
    
    return {
        "results": state["results"] + [execution_result]
    }


async def hermes_config_runner(state: HeavenState, *, node_config: Dict[str, Any] = None, config=None, variable_inputs: Dict[str, Any] = None, target_container: str = "mind_of_god", source_container: str = "mind_of_god") -> Dict[str, Any]:
    """
    Execute using HermesConfig template and store raw result.
    
    Can be called with normal parameters or legacy node_config:
    
    Normal way:
        hermes_config_runner(state, config=my_config, variable_inputs={"var": "value"})
    
    Legacy way:
        hermes_config_runner(state, node_config={"config": my_config, "variable_inputs": {"var": "value"}})
    """
    # Build node_config from parameters if not provided
    if node_config is None:
        if config is None:
            raise ValueError("Must provide either node_config or config parameter")
        node_config = {
            "config": config,
            "variable_inputs": variable_inputs or {},
            "target_container": target_container,
            "source_container": source_container
        }
    
    config = node_config["config"]
    variable_inputs = node_config["variable_inputs"]
    
    # Convert config to command data
    command_data = config.to_command_data(variable_inputs)
    
    # Execute
    result = await hermes_step(
        target_container=node_config.get("target_container", "mind_of_god"),
        source_container=node_config.get("source_container", "mind_of_god"),
        **command_data
    )
    
    # Store raw result
    execution_result = {
        "node_type": HeavenNodeType.HERMES_CONFIG,
        "config_name": config.func_name,
        "variable_inputs": variable_inputs,
        "raw_result": result
    }
    
    return {
        "results": state["results"] + [execution_result]
    }


# === REMOVED: context_engineer and prompt_engineer ===
# These functions were redundant with existing LEGOs:
# - context_engineer: Use result_extractor_runner + dynamic_function_runner instead
# - prompt_engineer: Use dynamic_function_runner for string formatting instead


# === CONTEXT MANAGER RUNNERS ===

async def context_weave_runner(state: HeavenState, *, node_config: Dict[str, Any] = None, weave_operations: List[Dict[str, Any]] = None, base_history_id: str = None, max_tokens: int = None) -> Dict[str, Any]:
    """
    Execute context weaving operations using ContextManager.
    
    Can be called with normal parameters or legacy node_config:
    
    Normal way:
        context_weave_runner(state, weave_operations=[{"type": "message_range", ...}], base_history_id="hist_123")
    
    Legacy way:
        context_weave_runner(state, node_config={"weave_operations": [...], "base_history_id": "hist_123"})
    """
    from ..tool_utils.context_manager import ContextManager
    
    # Build node_config from parameters if not provided
    if node_config is None:
        if weave_operations is None:
            raise ValueError("Must provide either node_config or weave_operations parameter")
        node_config = {
            "weave_operations": weave_operations
        }
        if base_history_id is not None:
            node_config["base_history_id"] = base_history_id
        if max_tokens is not None:
            node_config["max_tokens"] = max_tokens
    
    cm = ContextManager()
    weave_operations = node_config["weave_operations"]
    base_history_id = node_config.get("base_history_id")
    max_tokens = node_config.get("max_tokens")
    
    # Execute weaving
    new_history_id = await cm.weave(
        operations=weave_operations,
        base_history_id=base_history_id,
        max_tokens=max_tokens
    )
    
    # Store result
    execution_result = {
        "node_type": HeavenNodeType.CONTEXT_WEAVE,
        "weave_operations": weave_operations,
        "new_history_id": new_history_id,
        "raw_result": new_history_id
    }
    
    return {
        "results": state["results"] + [execution_result]
    }


async def context_inject_runner(state: HeavenState, *, node_config: Dict[str, Any] = None, target_history_id: str = None, content: str = None, message_type: str = "human", position: int = -1, template_vars: Dict[str, Any] = None) -> Dict[str, Any]:
    """
    Execute content injection using ContextManager.
    
    Can be called with normal parameters or legacy node_config:
    
    Normal way:
        context_inject_runner(state, target_history_id="hist_123", content="Hello", message_type="human")
    
    Legacy way:
        context_inject_runner(state, node_config={"target_history_id": "hist_123", "content": "Hello"})
    """
    from ..tool_utils.context_manager import ContextManager
    
    # Build node_config from parameters if not provided
    if node_config is None:
        if target_history_id is None or content is None:
            raise ValueError("Must provide either node_config or (target_history_id + content) parameters")
        node_config = {
            "target_history_id": target_history_id,
            "content": content,
            "message_type": message_type,
            "position": position
        }
        if template_vars is not None:
            node_config["template_vars"] = template_vars
    
    cm = ContextManager()
    target_history_id = node_config["target_history_id"]
    content = node_config["content"]
    message_type = node_config.get("message_type", "human")
    position = node_config.get("position", -1)
    template_vars = node_config.get("template_vars", {})
    
    # Apply template substitution
    for key, value in template_vars.items():
        content = content.replace(f"{{{key}}}", str(value))
    
    # Inject content
    updated_history = await cm.inject(
        history_id=target_history_id,
        content=content,
        message_type=message_type,
        position=position
    )
    
    # Store result
    execution_result = {
        "node_type": HeavenNodeType.CONTEXT_INJECT,
        "target_history_id": target_history_id,
        "content": content,
        "message_type": message_type,
        "updated_history_id": updated_history.history_id,
        "raw_result": updated_history
    }
    
    return {
        "results": state["results"] + [execution_result]
    }


async def pis_injection_runner(state: HeavenState, *, node_config: Dict[str, Any] = None, target_history_id: str = None, pis_blocks: List[str] = None, template_vars: Dict[str, Any] = None, agent_config=None, message_type: str = "human") -> Dict[str, Any]:
    """
    Execute PIS (Prompt Injection System) block injection.
    
    Can be called with normal parameters or legacy node_config:
    
    Normal way:
        pis_injection_runner(state, target_history_id="hist_123", pis_blocks=["system_setup"], template_vars={"domain": "programming"})
    
    Legacy way:
        pis_injection_runner(state, node_config={"target_history_id": "hist_123", "pis_blocks": ["system_setup"]})
    """
    from ..tool_utils.context_manager import ContextManager
    
    # Build node_config from parameters if not provided
    if node_config is None:
        if target_history_id is None or pis_blocks is None:
            raise ValueError("Must provide either node_config or (target_history_id + pis_blocks) parameters")
        node_config = {
            "target_history_id": target_history_id,
            "pis_blocks": pis_blocks,
            "message_type": message_type
        }
        if template_vars is not None:
            node_config["template_vars"] = template_vars
        if agent_config is not None:
            node_config["agent_config"] = agent_config
    
    cm = ContextManager()
    target_history_id = node_config["target_history_id"]
    pis_blocks = node_config["pis_blocks"]
    template_vars = node_config.get("template_vars", {})
    agent_config = node_config["agent_config"]
    message_type = node_config.get("message_type", "human")
    
    # Get agent config
    if isinstance(agent_config, str):
        agent_config = state["agents"][agent_config]
    
    # Render PIS blocks
    rendered_content = cm.loading_pis_preprocessor(
        block_names=pis_blocks,
        template_vars=template_vars,
        agent_config=agent_config
    )
    
    # Inject rendered content
    updated_history = await cm.inject(
        history_id=target_history_id,
        content=rendered_content,
        message_type=message_type
    )
    
    # Store result
    execution_result = {
        "node_type": HeavenNodeType.PIS_INJECTION,
        "target_history_id": target_history_id,
        "pis_blocks": pis_blocks,
        "template_vars": template_vars,
        "rendered_content": rendered_content,
        "updated_history_id": updated_history.history_id,
        "raw_result": updated_history
    }
    
    return {
        "results": state["results"] + [execution_result]
    }


async def chain_pattern_runner(state: HeavenState, *, node_config: Dict[str, Any] = None, pattern_name: str = None, template_vars: Dict[str, Any] = None, base_history_id: str = None) -> Dict[str, Any]:
    """
    Execute a complete ContextManager chain pattern.
    
    Can be called with normal parameters or legacy node_config:
    
    Normal way:
        chain_pattern_runner(state, pattern_name="complete_debugging_workflow", template_vars={"domain": "python"})
    
    Legacy way:
        chain_pattern_runner(state, node_config={"pattern_name": "complete_debugging_workflow", "template_vars": {"domain": "python"}})
    """
    from ..tool_utils.context_manager import ContextManager
    
    # Build node_config from parameters if not provided
    if node_config is None:
        if pattern_name is None:
            raise ValueError("Must provide either node_config or pattern_name parameter")
        node_config = {
            "pattern_name": pattern_name
        }
        if template_vars is not None:
            node_config["template_vars"] = template_vars
        if base_history_id is not None:
            node_config["base_history_id"] = base_history_id
    
    cm = ContextManager()
    pattern_name = node_config["pattern_name"]
    template_vars = node_config.get("template_vars", {})
    base_history_id = node_config.get("base_history_id")
    
    # Execute chain pattern
    chain_result = await cm.execute_chain_pattern(
        pattern_name=pattern_name,
        template_vars=template_vars,
        base_history_id=base_history_id
    )
    
    # Store result
    execution_result = {
        "node_type": HeavenNodeType.CHAIN_PATTERN,
        "pattern_name": pattern_name,
        "template_vars": template_vars,
        "execution_state": chain_result.get("execution_state"),
        "result_vars": chain_result.get("result_vars"),
        "raw_result": chain_result
    }
    
    return {
        "results": state["results"] + [execution_result]
    }


# === PLACEHOLDER RUNNERS (for future implementation) ===

async def brain_agent_runner(state: HeavenState, *, node_config: Dict[str, Any] = None, brain_type: str = None, input_history: str = None, cognitive_task: str = None, analysis_depth: str = "shallow") -> Dict[str, Any]:
    """
    [PLACEHOLDER] Execute HEAVEN brain agent for meta-cognitive operations.
    
    Can be called with normal parameters or legacy node_config:
    
    Normal way:
        brain_agent_runner(state, brain_type="pattern_analyzer", cognitive_task="identify_reasoning_bottlenecks")
    
    Legacy way:
        brain_agent_runner(state, node_config={"brain_type": "pattern_analyzer", "cognitive_task": "identify_reasoning_bottlenecks"})
    """
    # Build node_config from parameters if not provided
    if node_config is None:
        if brain_type is None:
            raise ValueError("Must provide either node_config or brain_type parameter")
        node_config = {
            "brain_type": brain_type,
            "analysis_depth": analysis_depth
        }
        if input_history is not None:
            node_config["input_history"] = input_history
        if cognitive_task is not None:
            node_config["cognitive_task"] = cognitive_task
    # TODO: Implement brain agent integration
    # This would integrate with HEAVEN's brain agent system for higher-level
    # cognitive operations like pattern analysis, reasoning optimization, etc.
    
    execution_result = {
        "node_type": HeavenNodeType.BRAIN_AGENT,
        "brain_type": node_config.get("brain_type"),
        "cognitive_task": node_config.get("cognitive_task"),
        "status": "placeholder_not_implemented",
        "raw_result": {"placeholder": True, "message": "Brain agent runner not yet implemented"}
    }
    
    return {
        "results": state["results"] + [execution_result]
    }


async def omnitool_list_runner(state: HeavenState, *, node_config: Dict[str, Any] = None) -> Dict[str, Any]:
    """
    List all available tools via OmniTool.
    
    Can be called with normal parameters or legacy node_config:
    
    Normal way:
        omnitool_list_runner(state)
    
    Legacy way:
        omnitool_list_runner(state, node_config={})
    """
    # Build node_config from parameters if not provided
    if node_config is None:
        node_config = {}
    from ..utils.omnitool import omnitool
    
    try:
        # Call omnitool in a way that doesn't conflict with existing event loop
        import asyncio
        loop = asyncio.get_event_loop()
        if loop.is_running():
            # We're in an async context, need to use different approach
            from ..utils.omnitool import omnitool
            result = await asyncio.create_task(asyncio.to_thread(omnitool, list_tools=True))
        else:
            result = omnitool(list_tools=True)
        
        execution_result = {
            "node_type": HeavenNodeType.OMNITOOL_LIST,
            "success": True,
            "raw_result": result
        }
        
    except Exception as e:
        execution_result = {
            "node_type": HeavenNodeType.OMNITOOL_LIST,
            "success": False,
            "error": str(e),
            "raw_result": None
        }
    
    return {
        "results": state["results"] + [execution_result]
    }


async def omnitool_get_info_runner(state: HeavenState, *, node_config: Dict[str, Any] = None, tool_name: str = None, parameter_template_vars: Dict[str, Any] = None) -> Dict[str, Any]:
    """
    Get info for a specific tool via OmniTool.
    
    Can be called with normal parameters or legacy node_config:
    
    Normal way:
        omnitool_get_info_runner(state, tool_name="NetworkEditTool", parameter_template_vars={"key": "value"})
    
    Legacy way:
        omnitool_get_info_runner(state, node_config={"tool_name": "NetworkEditTool", "parameter_template_vars": {"key": "value"}})
    """
    from ..utils.omnitool import omnitool
    
    # Build node_config from parameters if not provided
    if node_config is None:
        if tool_name is None:
            raise ValueError("Must provide either node_config or tool_name parameter")
        node_config = {
            "tool_name": tool_name
        }
        if parameter_template_vars is not None:
            node_config["parameter_template_vars"] = parameter_template_vars
    
    tool_name = node_config.get("tool_name")
    parameter_template_vars = node_config.get("parameter_template_vars", {})
    
    # Apply template substitution to tool_name if needed
    if parameter_template_vars and tool_name and "{" in tool_name:
        for template_key, template_value in parameter_template_vars.items():
            if template_value.startswith("context:"):
                # Extract from state context
                context_key = template_value.replace("context:", "")
                context_value = state["context"].get(context_key, "")
                tool_name = tool_name.replace(f"{{{template_key}}}", str(context_value))
            else:
                # Direct template substitution
                tool_name = tool_name.replace(f"{{{template_key}}}", str(template_value))
    
    try:
        # Call omnitool in a way that doesn't conflict with existing event loop
        import asyncio
        loop = asyncio.get_event_loop()
        if loop.is_running():
            # We're in an async context, need to use different approach
            result = await asyncio.create_task(asyncio.to_thread(omnitool, tool_name, get_tool_info=True))
        else:
            result = omnitool(tool_name, get_tool_info=True)
        
        execution_result = {
            "node_type": HeavenNodeType.OMNITOOL_GET_INFO,
            "tool_name": tool_name,
            "success": True,
            "raw_result": result
        }
        
    except Exception as e:
        execution_result = {
            "node_type": HeavenNodeType.OMNITOOL_GET_INFO,
            "tool_name": tool_name,
            "success": False,
            "error": str(e),
            "raw_result": None
        }
    
    return {
        "results": state["results"] + [execution_result]
    }


async def omnitool_call_runner(state: HeavenState, *, node_config: Dict[str, Any] = None, tool_name: str = None, parameters: Dict[str, Any] = None, parameter_template_vars: Dict[str, Any] = None) -> Dict[str, Any]:
    """
    Call a specific tool with parameters via OmniTool.
    
    Can be called with normal parameters or legacy node_config:
    
    Normal way:
        omnitool_call_runner(state, tool_name="NetworkEditTool", parameters={"command": "view", "path": "/home/GOD/file.py"})
    
    Legacy way:
        omnitool_call_runner(state, node_config={"tool_name": "NetworkEditTool", "parameters": {"command": "view"}})
    """
    from ..utils.omnitool import omnitool
    
    # Build node_config from parameters if not provided
    if node_config is None:
        if tool_name is None:
            raise ValueError("Must provide either node_config or tool_name parameter")
        node_config = {
            "tool_name": tool_name,
            "parameters": parameters or {}
        }
        if parameter_template_vars is not None:
            node_config["parameter_template_vars"] = parameter_template_vars
    
    tool_name = node_config.get("tool_name")
    parameters = node_config.get("parameters", {})
    parameter_template_vars = node_config.get("parameter_template_vars", {})
    
    # Apply template substitution to tool_name if needed
    if parameter_template_vars and tool_name and "{" in tool_name:
        for template_key, template_value in parameter_template_vars.items():
            if template_value.startswith("context:"):
                # Extract from state context
                context_key = template_value.replace("context:", "")
                context_value = state["context"].get(context_key, "")
                tool_name = tool_name.replace(f"{{{template_key}}}", str(context_value))
            else:
                # Direct template substitution
                tool_name = tool_name.replace(f"{{{template_key}}}", str(template_value))
    
    # Apply template substitution to parameters if specified
    if parameter_template_vars:
        processed_parameters = {}
        for key, value in parameters.items():
            if isinstance(value, str) and "{" in value:
                # Replace templates from context or template vars
                for template_key, template_value in parameter_template_vars.items():
                    if template_value.startswith("context:"):
                        # Extract from state context
                        context_key = template_value.replace("context:", "")
                        context_value = state["context"].get(context_key, "")
                        value = value.replace(f"{{{template_key}}}", str(context_value))
                    else:
                        # Direct template substitution
                        value = value.replace(f"{{{template_key}}}", str(template_value))
            processed_parameters[key] = value
        parameters = processed_parameters
    
    try:
        # Call omnitool in a way that doesn't conflict with existing event loop
        import asyncio
        loop = asyncio.get_event_loop()
        if loop.is_running():
            # We're in an async context, need to use different approach
            result = await asyncio.create_task(asyncio.to_thread(omnitool, tool_name, parameters=parameters))
        else:
            result = omnitool(tool_name, parameters=parameters)
        
        execution_result = {
            "node_type": HeavenNodeType.OMNITOOL_CALL,
            "tool_name": tool_name,
            "parameters": parameters,
            "success": True,
            "raw_result": result
        }
        
    except Exception as e:
        execution_result = {
            "node_type": HeavenNodeType.OMNITOOL_CALL,
            "tool_name": tool_name,
            "parameters": parameters,
            "success": False,
            "error": str(e),
            "raw_result": None
        }
    
    return {
        "results": state["results"] + [execution_result]
    }


async def dynamic_function_runner(state: HeavenState, *, node_config: Dict[str, Any] = None, func_name: str = None, import_path: str = None, parameters: Dict[str, Any] = None) -> Dict[str, Any]:
    """
    Execute any function via DynamicFunctionExecutorTool with state reference parsing.
    
    Can be called with normal parameters or legacy node_config:
    
    Normal way:
        dynamic_function_runner(state, func_name="my_function", import_path="my_module", parameters={"arg1": "value1"})
    
    Legacy way:
        dynamic_function_runner(state, node_config={"func_name": "my_function", "import_path": "my_module", "parameters": {"arg1": "value1"}})
    """
    from ..utils.omnitool import omnitool
    
    # Build node_config from parameters if not provided
    if node_config is None:
        if func_name is None or import_path is None:
            raise ValueError("Must provide either node_config or (func_name + import_path) parameters")
        node_config = {
            "func_name": func_name,
            "import_path": import_path,
            "parameters": parameters or {}
        }
    
    func_name = node_config.get("func_name")
    import_path = node_config.get("import_path")
    parameters = node_config.get("parameters", {})
    
    # Parse state references in parameters
    def resolve_state_reference(value, state):
        """Resolve 'state:path.to.data' references to actual state data"""
        if isinstance(value, str) and value.startswith("state:"):
            path = value[6:]  # Remove 'state:' prefix
            parts = path.split(".")
            
            current = state
            for part in parts:
                if isinstance(current, dict):
                    current = current.get(part)
                elif isinstance(current, list) and part.isdigit():
                    index = int(part)
                    current = current[index] if index < len(current) else None
                else:
                    current = None
                    break
            
            return current
        elif isinstance(value, dict):
            return {k: resolve_state_reference(v, state) for k, v in value.items()}
        elif isinstance(value, list):
            return [resolve_state_reference(item, state) for item in value]
        else:
            return value
    
    # Resolve all state references in parameters
    resolved_parameters = {}
    for key, value in parameters.items():
        resolved_parameters[key] = resolve_state_reference(value, state)
    
    try:
        # Call omnitool in a way that doesn't conflict with existing event loop
        import asyncio
        loop = asyncio.get_event_loop()
        if loop.is_running():
            # We're in an async context, need to use different approach
            result = await asyncio.create_task(asyncio.to_thread(
                omnitool, 
                "DynamicFunctionExecutorTool", 
                parameters={
                    "func_name": func_name,
                    "import_path": import_path,
                    "parameters": resolved_parameters
                }
            ))
        else:
            result = omnitool(
                "DynamicFunctionExecutorTool",
                parameters={
                    "func_name": func_name,
                    "import_path": import_path, 
                    "parameters": resolved_parameters
                }
            )
        
        execution_result = {
            "node_type": HeavenNodeType.DYNAMIC_FUNCTION,
            "func_name": func_name,
            "import_path": import_path,
            "resolved_parameters": resolved_parameters,
            "success": True,
            "raw_result": result
        }
        
    except Exception as e:
        execution_result = {
            "node_type": HeavenNodeType.DYNAMIC_FUNCTION,
            "func_name": func_name,
            "import_path": import_path,
            "resolved_parameters": resolved_parameters,
            "success": False,
            "error": str(e),
            "raw_result": None
        }
    
    return {
        "results": state["results"] + [execution_result]
    }


async def subgraph_runner(state: HeavenState, *, node_config: Dict[str, Any] = None, workflow: Dict[str, Any] = None, variables: Dict[str, Any] = None, pass_context: bool = True, merge_results: bool = True, store_subgraph_state: str = "subgraph_output") -> Dict[str, Any]:
    """
    Execute an entire HEAVEN workflow as a subgraph node.
    
    Can be called with normal parameters or legacy node_config:
    
    Normal way:
        subgraph_runner(state, workflow={"nodes": [...], "edges": [...]}, variables={"key": "value"})
    
    Legacy way:
        subgraph_runner(state, node_config={"workflow": {...}, "variables": {...}})
    """
    # Build node_config from parameters if not provided
    if node_config is None:
        if workflow is None:
            raise ValueError("Must provide either node_config or workflow parameter")
        node_config = {
            "workflow": workflow,
            "variables": variables or {},
            "pass_context": pass_context,
            "merge_results": merge_results,
            "store_subgraph_state": store_subgraph_state
        }
    
    workflow = node_config.get("workflow")
    variables = node_config.get("variables", {})
    pass_context = node_config.get("pass_context", True)
    merge_results = node_config.get("merge_results", True)
    store_key = node_config.get("store_subgraph_state", "subgraph_output")
    
    # Parse state references in variables
    def resolve_state_reference(value, state):
        """Resolve 'state:path.to.data' references to actual state data"""
        if isinstance(value, str) and value.startswith("state:"):
            path = value[6:]  # Remove 'state:' prefix
            parts = path.split(".")
            
            current = state
            for part in parts:
                if isinstance(current, dict):
                    current = current.get(part)
                elif isinstance(current, list) and part.isdigit():
                    index = int(part)
                    current = current[index] if index < len(current) else None
                else:
                    current = None
                    break
            
            return current
        elif isinstance(value, dict):
            return {k: resolve_state_reference(v, state) for k, v in value.items()}
        elif isinstance(value, list):
            return [resolve_state_reference(item, state) for item in value]
        else:
            return value
    
    # Resolve state references in variables
    resolved_variables = {}
    for key, value in variables.items():
        resolved_variables[key] = resolve_state_reference(value, state)
    
    try:
        # Create new subgraph loader
        subgraph_loader = HeavenGraphLoader()
        
        # Set up initial context for subgraph
        if pass_context:
            if "initial_context" not in workflow:
                workflow["initial_context"] = {}
            workflow["initial_context"].update(state["context"])
        
        # Load and execute subgraph
        subgraph = subgraph_loader.load_from_json(workflow, resolved_variables)
        subgraph_results = await subgraph_loader.run_graph(subgraph)
        
        # Get final subgraph state
        subgraph_final_state = subgraph_loader.final_state
        
        # Prepare execution result
        execution_result = {
            "node_type": HeavenNodeType.SUBGRAPH,
            "workflow_nodes": len(workflow.get("nodes", [])),
            "subgraph_steps": len(subgraph_results),
            "success": True,
            "raw_result": {
                "subgraph_results": subgraph_results,
                "subgraph_final_state": subgraph_final_state
            }
        }
        
        # Update parent state
        updated_state = {
            "results": state["results"] + [execution_result],
            "context": {**state["context"], store_key: subgraph_final_state}
        }
        
        # Optionally merge subgraph results into parent results
        if merge_results:
            updated_state["results"].extend(subgraph_results)
        
        # Optionally merge subgraph context into parent context
        if subgraph_final_state and "context" in subgraph_final_state:
            updated_state["context"].update(subgraph_final_state["context"])
        
        return updated_state
        
    except Exception as e:
        execution_result = {
            "node_type": HeavenNodeType.SUBGRAPH,
            "workflow_nodes": len(workflow.get("nodes", [])) if workflow else 0,
            "subgraph_steps": 0,
            "success": False,
            "error": str(e),
            "raw_result": None
        }
        
        return {
            "results": state["results"] + [execution_result],
            "context": {**state["context"], store_key: None}
        }


async def result_extractor_runner(state: HeavenState, *, node_config: Dict[str, Any] = None, extraction_type: str = None, target_result_index: int = -1, extraction_key: str = None, custom_path: str = None, store_as: str = "extracted_value") -> Dict[str, Any]:
    """
    Extract specific data from agent results using agent_result_reader utilities.
    
    Can be called with normal parameters or legacy node_config:
    
    Normal way:
        result_extractor_runner(state, extraction_type="agent_status_content", extraction_key="file_path", store_as="extracted_file_path")
    
    Legacy way:
        result_extractor_runner(state, node_config={"extraction_type": "agent_status_content", "extraction_key": "file_path", "store_as": "extracted_file_path"})
    """
    from ..utils.agent_result_reader import (
        read_agent_result, 
        extract_last_ai_message,
        extract_boolean_from_agent_result
    )
    
    # Build node_config from parameters if not provided
    if node_config is None:
        if extraction_type is None:
            raise ValueError("Must provide either node_config or extraction_type parameter")
        node_config = {
            "extraction_type": extraction_type,
            "target_result_index": target_result_index,
            "store_as": store_as
        }
        if extraction_key is not None:
            node_config["extraction_key"] = extraction_key
        if custom_path is not None:
            node_config["custom_path"] = custom_path
    
    extraction_type = node_config["extraction_type"]
    target_index = node_config.get("target_result_index", -1)  # Default to last result
    extraction_key = node_config.get("extraction_key")
    custom_path = node_config.get("custom_path")
    store_as = node_config.get("store_as", "extracted_value")
    
    # Get target result
    if target_index == -1:
        target_result = state["results"][-1] if state["results"] else {}
    else:
        target_result = state["results"][target_index] if target_index < len(state["results"]) else {}
    
    raw_result = target_result.get("raw_result", {})
    extracted_value = None
    extraction_successful = False
    
    try:
        if extraction_type == "agent_status_content":
            # Extract from agent_status.extracted_content[key]
            agent_status = raw_result.get("agent_status", {})
            extracted_content = agent_status.get("extracted_content", {})
            if extraction_key and extraction_key in extracted_content:
                extracted_value = extracted_content[extraction_key]
                extraction_successful = True
            
        elif extraction_type == "last_ai_message":
            # Use agent_result_reader to extract last AI message
            if "history" in raw_result:
                extracted_value = extract_last_ai_message(raw_result)
                extraction_successful = True
                
        elif extraction_type == "boolean_validation":
            # Extract boolean from agent result
            if "history" in raw_result:
                extracted_value = extract_boolean_from_agent_result(raw_result)
                extraction_successful = True
                
        elif extraction_type == "custom_path" and custom_path:
            # Navigate custom dot-notation path
            current = raw_result
            path_parts = custom_path.split(".")
            
            for part in path_parts:
                if isinstance(current, dict) and part in current:
                    current = current[part]
                else:
                    current = None
                    break
                    
            if current is not None:
                extracted_value = current
                extraction_successful = True
                
        elif extraction_type == "full_agent_analysis":
            # Use read_agent_result for full analysis
            if "history" in raw_result:
                analysis = read_agent_result(raw_result)
                extracted_value = analysis
                extraction_successful = True
                
    except Exception as e:
        extracted_value = f"Extraction error: {str(e)}"
        extraction_successful = False
    
    # Store extracted value in context
    updated_context = {**state["context"], store_as: extracted_value}
    
    # Create execution result
    execution_result = {
        "node_type": HeavenNodeType.RESULT_EXTRACTOR,
        "extraction_type": extraction_type,
        "target_result_index": target_index,
        "extraction_key": extraction_key,
        "custom_path": custom_path,
        "store_as": store_as,
        "extracted_value": extracted_value,
        "extraction_successful": extraction_successful,
        "raw_result": {
            "extraction_type": extraction_type,
            "extracted_value": extracted_value,
            "success": extraction_successful
        }
    }
    
    return {
        "results": state["results"] + [execution_result],
        "context": updated_context
    }


# === JSON GRAPH SPECIFICATION LOADER ===

@dataclass
class GraphSpec:
    """JSON specification for a HEAVEN workflow graph"""
    nodes: List[Dict[str, Any]]
    edges: List[Dict[str, str]]
    agents: Dict[str, Dict[str, Any]]
    initial_context: Dict[str, Any] = None


class HeavenGraphLoader:
    """Load and compile graphs from JSON specifications"""
    
    def __init__(self):
        self.node_runners = {
            HeavenNodeType.COMPLETION: completion_runner,
            HeavenNodeType.HERMES: hermes_runner,
            HeavenNodeType.HERMES_CONFIG: hermes_config_runner,
            # Removed: CONTEXT_ENGINEER and PROMPT_ENGINEER - redundant with result_extractor and dynamic_function
            # Context Manager runners
            HeavenNodeType.CONTEXT_WEAVE: context_weave_runner,
            HeavenNodeType.CONTEXT_INJECT: context_inject_runner,
            HeavenNodeType.PIS_INJECTION: pis_injection_runner,
            HeavenNodeType.CHAIN_PATTERN: chain_pattern_runner,
            # Tool and extraction runners
            HeavenNodeType.OMNITOOL_LIST: omnitool_list_runner,
            HeavenNodeType.OMNITOOL_GET_INFO: omnitool_get_info_runner,
            HeavenNodeType.OMNITOOL_CALL: omnitool_call_runner,
            HeavenNodeType.RESULT_EXTRACTOR: result_extractor_runner,
            HeavenNodeType.DYNAMIC_FUNCTION: dynamic_function_runner,
            HeavenNodeType.SUBGRAPH: subgraph_runner
        }
    
    def load_from_json(self, json_spec: Union[str, Dict], variables: Dict[str, Any] = None) -> StateGraph:
        """
        Load graph from JSON specification with variable substitution.
        
        JSON format:
        {
            "nodes": [
                {
                    "id": "node1",
                    "type": "completion",
                    "config": {
                        "agent": "{agent_var}",
                        "prompt": "Hello {name}",
                        "prompt_template_vars": {"name": "World"}
                    }
                }
            ],
            "edges": [
                {"from": "START", "to": "node1"},
                {"from": "node1", "to": "END"}
            ],
            "initial_context": {}
        }
        
        Variables format:
        {
            "agent_var": HeavenAgentConfig(...),
            "some_tool": SomeToolClass,
            "prompt_template": "Custom prompt..."
        }
        """
        if isinstance(json_spec, str):
            spec_data = json.loads(json_spec)
        else:
            spec_data = json_spec
        
        variables = variables or {}
        
        spec = GraphSpec(
            nodes=spec_data["nodes"],
            edges=spec_data["edges"],
            agents={},  # No agents in JSON anymore
            initial_context=spec_data.get("initial_context", {})
        )
        
        return self._build_graph(spec, variables)
    
    def _build_graph(self, spec: GraphSpec, variables: Dict[str, Any] = None) -> StateGraph:
        """Build LangGraph from specification"""
        graph = StateGraph(HeavenState)
        
        # Variables contain all the actual objects (agents, tools, etc.)
        variables = variables or {}
        
        # Process nodes with variable substitution
        def substitute_variables(obj):
            """Recursively substitute {variable} placeholders with actual values"""
            if isinstance(obj, str):
                # Simple string substitution for {variable} patterns
                for var_name, var_value in variables.items():
                    placeholder = f"{{{var_name}}}"
                    if placeholder in obj:
                        # If the entire string is just the placeholder, return the object directly
                        if obj == placeholder:
                            return var_value
                        # Otherwise do string replacement
                        obj = obj.replace(placeholder, str(var_value))
                return obj
            elif isinstance(obj, dict):
                return {k: substitute_variables(v) for k, v in obj.items()}
            elif isinstance(obj, list):
                return [substitute_variables(item) for item in obj]
            else:
                return obj
        
        # Add nodes with variable substitution
        for node in spec.nodes:
            node_id = node["id"]
            node_type = node["type"]
            node_config = substitute_variables(node["config"])
            
            if node_type in self.node_runners:
                # Create node function with config bound using partial
                from functools import partial
                node_func = partial(self.node_runners[node_type], node_config=node_config)
                
                graph.add_node(node_id, node_func)
        
        # Add edges
        for edge in spec.edges:
            from_node = edge["from"]
            to_node = edge["to"]
            
            # Handle special node names
            if from_node == "START":
                from_node = START
            if to_node == "END":
                to_node = END
            
            graph.add_edge(from_node, to_node)
        
        # Compile first, then store variables and initial context
        compiled_graph = graph.compile()
        compiled_graph._heaven_variables = variables
        compiled_graph._heaven_initial_context = spec.initial_context
        
        return compiled_graph
    
    async def run_graph(self, compiled_graph, thread_id: str = "heaven_session") -> List[Dict[str, Any]]:
        """Run a compiled graph and return results"""
        
        # Get stored variables and initial context
        variables = getattr(compiled_graph, '_heaven_variables', {})
        initial_context = getattr(compiled_graph, '_heaven_initial_context', {})
        
        initial_state = {
            "results": [],
            "context": initial_context,
            "agents": {}  # No longer using agents in state, use variables directly
        }
        
        memory = MemorySaver()
        config = {"configurable": {"thread_id": thread_id}}
        
        result = await compiled_graph.ainvoke(initial_state, config)
        
        # Store final state for subgraph access
        self.final_state = result
        
        return result.get("results", [])


# === DEMO ===

async def demo_heaven_foundation():
    """Demonstrate the HEAVEN LangGraph foundation"""
    print("=== HEAVEN LangGraph Foundation Demo ===\n")
    
    # Example JSON specification
    json_spec = {
        "agents": {
            "pattern_agent": {
                "name": "PatternDetector",
                "system_prompt": "You are a pattern detection expert. Identify patterns concisely.",
                "provider": "anthropic",
                "model": "claude-3-5-sonnet-latest",
                "temperature": 0.3
            }
        },
        "nodes": [
            {
                "id": "detect_pattern",
                "type": "completion",
                "config": {
                    "agent": "pattern_agent",
                    "prompt": "What pattern do you see in: {sequence}?",
                    "prompt_template_vars": {"sequence": "2, 4, 8, 16"}
                }
            },
            {
                "id": "engineer_context", 
                "type": "context_engineer",
                "config": {
                    "strategy": "extract_messages",
                    "target_results": "all"
                }
            },
            {
                "id": "engineer_prompt",
                "type": "prompt_engineer", 
                "config": {
                    "template": "Based on the previous analysis, predict the next 3 terms: {results_0_raw_result_agent_name}",
                    "result_keys": ["results.0.raw_result.agent_name"],
                    "static_vars": {"analysis_type": "mathematical"}
                }
            }
        ],
        "edges": [
            {"from": "START", "to": "detect_pattern"},
            {"from": "detect_pattern", "to": "engineer_context"},
            {"from": "engineer_context", "to": "engineer_prompt"},
            {"from": "engineer_prompt", "to": "END"}
        ],
        "initial_context": {
            "workflow_type": "pattern_analysis"
        }
    }
    
    # Load and run graph
    loader = HeavenGraphLoader()
    graph = loader.load_from_json(json_spec)
    
    print("📊 Running JSON-specified workflow...")
    results = await loader.run_graph(graph)
    
    print(f"✅ Workflow completed!")
    print(f"📦 Results: {len(results)} steps executed")
    for i, result in enumerate(results):
        print(f"   Step {i+1}: {result['node_type']} ({result.get('agent_name', 'N/A')})")
    
    print(f"\n🔍 Raw result access:")
    if results:
        first_result = results[0]
        print(f"   Node type: {first_result['node_type']}")
        print(f"   Agent: {first_result.get('agent_name')}")
        print(f"   Raw result keys: {list(first_result['raw_result'].keys())}")
    
    # Example 4: Context Management workflow
    print("\n4. Context Management Workflow:")
    context_spec = {
        "agents": {
            "context_agent": {
                "name": "ContextExpert",
                "system_prompt": "You are a context management expert.",
                "provider": "anthropic",
                "model": "claude-3-5-sonnet-latest"
            }
        },
        "nodes": [
            {
                "id": "create_base_history",
                "type": "completion",
                "config": {
                    "agent": "context_agent", 
                    "prompt": "Create a knowledge base about {topic}",
                    "prompt_template_vars": {"topic": "machine learning"}
                }
            },
            {
                "id": "inject_context",
                "type": "context_inject",
                "config": {
                    "target_history_id": "synthetic_demo",
                    "content": "Expert knowledge: {knowledge}",
                    "message_type": "system",
                    "template_vars": {"knowledge": "Machine learning requires data, algorithms, and evaluation"}
                }
            }
        ],
        "edges": [
            {"from": "START", "to": "create_base_history"},
            {"from": "create_base_history", "to": "inject_context"},
            {"from": "inject_context", "to": "END"}
        ]
    }
    
    # Note: This would require actual history setup for full demo
    print("   Context management workflow defined")
    print("   Available context node types:")
    print("   - context_weave: Combine multiple conversation histories")
    print("   - context_inject: Add messages to existing histories")  
    print("   - pis_injection: Use PIS prompt blocks")
    print("   - chain_pattern: Execute complex context workflows")


# === CALLBACK INTEGRATION NOTES ===

# TODO: Callback Architecture Implementation
# 
# The correct approach for integrating HEAVEN callbacks with LangGraph is to:
#
# 1. Add callback attributes to BaseHeavenAgent.__init__():
#    - heaven_background_callback (for workflow state capture)
#    - heaven_print_callback (for debugging)
#    - heaven_http_callback (for cross-container streaming)
#    - Maintain existing heaven_main_callback (for frontend chat)
#
# 2. Modify BaseHeavenAgent.run() to use configured callbacks:
#    - Check for callback attributes in self
#    - Use configured callbacks by default
#    - Allow heaven_main_callback parameter to override
#
# 3. LangGraph runners remain clean:
#    - No callback management in runner functions
#    - Agents handle their own callback configuration
#    - Captured events accessible via callback.captured_events
#
# 4. Workflow configuration becomes agent-level:
#    agent_config = HeavenAgentConfig(
#        name="TrackedAgent",
#        heaven_background_callback=BackgroundEventCapture()
#    )
#
# This approach centralizes callback config at the agent level rather than
# threading callbacks through every runner function. Much cleaner architecture.


# === HIGHER ORDER AGENT ARCHITECTURE ===

# TODO: Stateful Workflow Agents with Persistent State
#
# Beyond simple workflow execution, we can create higher-order agents that maintain
# persistent state and orchestrate complex interactions:
#
# class StatefulWorkflowAgent(HeavenAgentConfig):
#     def __init__(self, **kwargs):
#         super().__init__(**kwargs)
#         # Agent-level persistent state
#         self.inbox = []           # Messages from other agents
#         self.outbox = []          # Messages to send
#         self.contacts = {}        # Known agent network
#         self.memory = {}          # Long-term memory
#         self.workflow_history = [] # Execution history
#
#     async def run(self, prompt, **kwargs):
#         # Hand-coded workflow that maintains agent state
#         # 1. Process inbox for new messages
#         # 2. Decide action based on prompt + inbox state  
#         # 3. Execute appropriate workflow (collaboration, response, etc.)
#         # 4. Update memory and send outbox messages
#         # 5. Log workflow execution for learning
#
# This enables:
# - Agent social networks with direct messaging
# - Persistent agent identity across sessions  
# - Learning organizations that improve over time
# - Autonomous coordination for complex tasks
# - Emergent behaviors from simple agent rules
#
# These agents transform from stateless functions into persistent entities
# with memory, relationships, and evolving behaviors - essentially creating
# an agent society that can tackle problems too complex for any single agent.


# === PLACES: ENVIRONMENT ORCHESTRATION ===

# TODO: Environment Classes for Multi-Agent Interaction Protocols
#
# Once we have StatefulWorkflowAgents, we can create "Places" - environment
# classes that orchestrate interactions between multiple agents:
#
# class Place:
#     def __init__(self, interaction_protocol, **kwargs):
#         self.agents = {}              # Registered agents in this environment
#         self.interaction_protocol = interaction_protocol
#         self.environment_state = {}   # Shared environment state
#         self.message_bus = []         # Inter-agent communication
#         self.rules = {}               # Environment rules and constraints
#
#     async def register_agent(self, agent: StatefulWorkflowAgent):
#         # Add agent to environment
#         # Set up agent's communication channels
#         # Initialize agent's environment context
#
#     async def run_interaction_cycle(self):
#         # Execute one cycle of the interaction protocol
#         # Route messages between agents
#         # Update environment state
#         # Apply environment rules
#
# Examples of Places:
# - Marketplace: Agents negotiate, trade, compete
# - Laboratory: Agents collaborate on research, share findings
# - Debate Hall: Agents argue different positions, reach consensus
# - Classroom: Teacher agents and student agents with learning protocols
# - City: Agents with different roles interact in urban simulation
#
# Each agent can have workflows as their run() method OR simple behaviors,
# giving maximum flexibility for different interaction patterns.
#
# This creates a three-tier architecture:
# 1. Workflows - Orchestrate tool/agent execution
# 2. StatefulAgents - Persistent entities with memory and relationships  
# 3. Places - Environments where agents interact via protocols
#
# This enables emergent collective intelligence from agent societies!


# === GIRARD HIERARCHY SAFETY ===

# TODO: Hierarchical Place Encapsulation for Safety
#
# Places themselves can be nested in higher-order Places, creating a
# Girard-style hierarchy where each level fully encapsulates the lower ones:
#
# Level 0: Workflows (tool/agent execution)
# Level 1: StatefulAgents (persistent entities) 
# Level 2: Places (environment protocols)
# Level 3: MetaPlaces (places containing other places)
# Level 4: SuperPlaces (meta-meta environments)
# ... and so on
#
# class MetaPlace(Place):
#     def __init__(self, **kwargs):
#         super().__init__(**kwargs)
#         self.sub_places = {}      # Lower-order places this orchestrates
#         self.place_protocols = {} # How sub-places interact
#
#     async def run_sub_place(self, place_id, encapsulated=True):
#         # Run lower-order place in full encapsulation
#         # Higher-order place controls all inputs/outputs
#         # Lower place cannot escape its boundaries
#
# GIRARD SAFETY PROPERTIES:
# - Each level can only directly access its immediate sublevel
# - Higher levels fully control lower levels' execution environment
# - No "escape" mechanisms - agents can't break out of their Place
# - Computational power increases with hierarchy level
# - Safety through mathematical containment, not just access controls
#
# Examples:
# - Laboratory (Level 2) contains Research Agents (Level 1)
# - University (Level 3) contains multiple Laboratories (Level 2)  
# - Academic System (Level 4) contains multiple Universities (Level 3)
# - Global Research Network (Level 5) orchestrates Academic Systems
#
# This provides MATHEMATICAL SAFETY guarantees rather than just
# engineering safety measures. Each level forms a complete computational
# barrier that cannot be transcended by lower levels.


# === METAPROGRAMMING NODES: THE KEY TO UNLOCKING HEAVEN ===

# TODO: Self-Healing Metaprogramming Node Suite
#
# The real key to unlocking HEAVEN through LangGraph is adding AgentMaker,
# ToolMaker, EvolutionaryIntent, etc. as first-class self-healing nodes:
#
# CORE METAPROGRAMMING NODES:
# - agent_maker: Create new agents with auto-testing and iteration
# - tool_maker: Generate new tools with validation
# - evolutionary_intent: Generate improvement specifications
# - agent_config_test: Robust testing with multiple scenarios
# - construct_hermes_config: Build complex configurations
#
# SELF-HEALING ARCHITECTURE:
# Each node automatically retries and improves on failure:
#
# async def agent_maker_runner(state, *, node_config):
#     for attempt in range(max_iterations):
#         # 1. Create agent via AgentMaker
#         # 2. Auto-test with AgentConfigTestTool
#         # 3. If fails, use EvolutionaryIntent to improve
#         # 4. Retry with better specifications
#     # Return working agent or fail safely
#
# DUAL INTERFACE:
# 1. Nodes for workflows: Agents write LangGraph workflows naturally
# 2. Tools for direct use: SelfHealingAgentMakerTool, SelfHealingToolMakerTool
#
# This enables agents to write recursive self-improvement workflows like:
#
# self_improvement = {
#     "nodes": [
#         {"id": "analyze", "type": "hermes", "config": {"goal": "Find my weaknesses"}},
#         {"id": "evolve", "type": "evolutionary_intent", "config": {"target": "self"}},
#         {"id": "improve", "type": "agent_maker", "config": {
#             "evolution_intent": "state:context.improvement_plan",
#             "auto_test": True,
#             "max_iterations": 10
#         }}
#     ]
# }
#
# STRATEGIC ADVANTAGE:
# - Agents are trained on LangGraph patterns (familiar interface)
# - But gain access to HEAVEN's full metaprogrammatic power
# - Self-healing makes RSI workflows reliable
# - Every AI model becomes a potential HEAVEN architect
#
# This weaponizes familiarity - hiding HEAVEN's complexity behind patterns
# agents already know, making recursive self-improvement trivial!


# === RECURSIVE BUILDER OPTIMIZATION NETWORK ===

# TODO: Parent-Driven Optimization Through Research Flows
#
# The ultimate architecture for recursive improvement:
#
# BOOTSTRAP PATTERN:
# Call 1: build_agent("agent that builds better agent-building agents")
# Call N: master_builder.build_specialized_variant("...but specialized for X")
#
# NETWORK STRUCTURE:
# - Master Builder (optimizes all specialized builders)
# - Specialized Builders (optimize domain-specific agents)  
# - Domain Agents (just execute tasks)
#
# OPTIMIZATION PROTOCOL:
# Instead of every agent self-optimizing, the BUILDER that created it
# handles optimization through research workflows:
#
# class AgentBuilder:
#     async def research_flow_optimization(self, agent, performance_data):
#         improvement_workflow = {
#             "nodes": [
#                 {"id": "analyze_performance", "type": "hermes"},
#                 {"id": "research_improvements", "type": "hermes"},
#                 {"id": "design_upgrades", "type": "evolutionary_intent"},
#                 {"id": "test_upgrades", "type": "agent_config_test"},
#                 {"id": "deploy_improvements", "type": "agent_maker"}
#             ]
#         }
#         # Builder continuously improves its agents through research
#
# CLEAN SEPARATION:
# - Agents: Just execute tasks (quantum_agent.solve_problem())
# - Builders: Research optimization through workflows
# - Network: Cross-domain improvements flow through Master Builder
#
# EXPONENTIAL COMPOUNDING:
# Every breakthrough anywhere improves the entire network.
# Every builder gets better at building better builders.
# Every agent benefits from network-wide optimization research.
# The building process itself evolves and improves.
#
# This creates collective superintelligence through clean hierarchical
# optimization rather than complex self-optimization in every agent.


# === PATTERN SKELETON GENERATORS ===

# TODO: Recursive Pattern Generator Architecture
#
# The final piece for rapid HEAVEN development: tools that generate file templates
# with embedded help and documentation for specific patterns.
#
# ARCHITECTURE: General → Specific → Recursive
#
# Level 1: GENERAL PATTERN GENERATORS
# Tools that create generators for specific patterns:
#
# PatternGeneratorMaker: Creates pattern generators for any domain
# SkeletonArchitect: Designs template structures
# HelpEmbedder: Adds contextual help commands to templates
#
# Level 2: SPECIFIC PATTERN GENERATORS
# Domain-specific generators created by Level 1 tools:
#
# AgentPatternGenerator: Creates agent definition templates
# WorkflowPatternGenerator: Creates workflow JSON templates  
# ToolPatternGenerator: Creates new tool class templates
# ConfigPatternGenerator: Creates configuration file templates
#
# Level 3: RECURSIVE CAPABILITY
# Since Level 1 can create new pattern generators, the system self-expands:
#
# pattern_meta_generator = {
#     "nodes": [
#         {
#             "id": "analyze_pattern_domain",
#             "type": "hermes",
#             "config": {
#                 "agent": "{pattern_analyst}",
#                 "goal": "Analyze this codebase and identify repetitive patterns that need generators"
#             }
#         },
#         {
#             "id": "design_generator",
#             "type": "pattern_generator_maker",  # Level 1 tool
#             "config": {
#                 "pattern_analysis": "state:context.identified_patterns",
#                 "generator_type": "domain_specific"
#             }
#         },
#         {
#             "id": "test_generator",
#             "type": "skeleton_validator",
#             "config": {
#                 "generator": "state:context.new_generator",
#                 "test_scenarios": ["basic", "complex", "edge_cases"]
#             }
#         }
#     ]
# }
#
# EMBEDDED HELP SYSTEM:
# Every generated skeleton includes discoverable emoji help markers:
#
# Generated AgentPattern.py:
# """
# HEAVEN Agent Pattern Template
# 
# 🔍 HELP DISCOVERY: Search for 🔍 emoji to find all help points in this file
# 🔍 agent_config: HeavenAgentConfig constructor options and examples
# 🔍 system_prompts: Effective system prompt patterns for this agent type
# 🔍 provider_setup: Available providers and model configurations
# 🔍 testing_agent: How to test this agent with various scenarios
# 🔍 workflow_usage: Examples of using this agent in LangGraph workflows
# """
#
# class MyAgent(HeavenAgentConfig):
#     def __init__(self):
#         # 🔍 agent_config: All HeavenAgentConfig options
#         super().__init__(
#             name="YourAgentName",     # 🔍 naming: Use descriptive, specific names
#             system_prompt="",         # 🔍 system_prompts: Click for examples
#             provider=ProviderEnum.ANTHROPIC,  # 🔍 provider_setup: Available options
#             model="claude-3-5-sonnet-latest",  # 🔍 models: Best models per provider
#             temperature=0.7,          # 🔍 temperature: 0.0-1.0, higher = more creative
#             additional_kws=[],        # 🔍 extraction: For structured agent output
#         )
#
# # 🔍 testing_agent: How to test this agent
# async def test_my_agent():
#     agent = MyAgent()
#     result = await agent.run("Test prompt")
#     print(f"Agent response: {result}")
#
# # 🔍 workflow_usage: Using in LangGraph workflows
# workflow = {
#     "nodes": [{"id": "my_task", "type": "hermes", "config": {"agent": "{my_agent}"}}]
# }
#
# WORKFLOW INTEGRATION:
# Skeleton generators integrate directly with LangGraph workflows:
#
# skeleton_workflow = {
#     "nodes": [
#         {
#             "id": "generate_skeleton",
#             "type": "pattern_generator",
#             "config": {
#                 "pattern_type": "agent_definition",
#                 "template_vars": {
#                     "agent_name": "QuantumAnalyst",
#                     "domain": "quantum_computing",
#                     "help_context": "research_agent"
#                 }
#             }
#         },
#         {
#             "id": "write_skeleton",
#             "type": "omnitool_call",
#             "config": {
#                 "tool_name": "NetworkEditTool",
#                 "parameters": {
#                     "command": "create",
#                     "path": "/tmp/QuantumAnalyst.py",
#                     "file_text": "state:context.generated_skeleton"
#                 }
#             }
#         }
#     ]
# }
#
# PATTERN CATALOG:
# Common patterns get pre-built generators:
#
# - AgentDefinitionPattern: Complete agent class with config
# - WorkflowPattern: JSON workflow with common node types
# - ToolImplementationPattern: New tool class with standard methods
# - TestingPattern: Comprehensive test suite for any component
# - ConfigurationPattern: Hermes configs with template variables
# - IntegrationPattern: Connect two HEAVEN components
# - EvolutionPattern: Self-improvement workflow for any agent
# - DocumentationPattern: README and docstring templates
#
# RECURSIVE EXPANSION:
# Each pattern generator can create new pattern generators:
#
# meta_expansion = {
#     "goal": "Create a pattern generator for microservice architecture",
#     "approach": "Use existing PatternGeneratorMaker to design templates for service definitions, API endpoints, container configs, etc."
# }
#
# EMOJI HELP DISCOVERY SYSTEM:
# The 🔍 emoji system creates an instantly discoverable help network:
#
# 1. AGENT SELF-DISCOVERY:
#    agent_prompt = "Search this file for 🔍 and list all help topics"
#    → Agent finds all help markers and understands available guidance
#
# 2. CONTEXTUAL ASSISTANCE:
#    agent_prompt = "I need help with 🔍 system_prompts in this file"
#    → Agent locates specific help marker and provides targeted help
#
# 3. CODEBASE EXPLORATION:
#    agent_prompt = "Find all 🔍 markers in /project and categorize by topic"
#    → Agent maps entire help system across codebase
#
# 4. PATTERN LEARNING:
#    Every skeleton embeds its own teaching materials
#    Agents learn patterns by reading the skeletons they generate
#    Self-improving documentation through usage feedback
#
# EMOJI CATEGORIES FOR DIFFERENT HELP TYPES:
# 🔍 - General help and documentation
# ⚙️ - Configuration and setup
# 🧪 - Testing and validation  
# 🔗 - Integration and workflow usage
# 📚 - Reference documentation
# ⚡ - Performance and optimization
# 🐛 - Debugging and troubleshooting
# 🎯 - Best practices and examples
#
# Example multi-emoji skeleton:
# """
# 🔍 OVERVIEW: This is a quantum computing research agent
# ⚙️ CONFIG: Specialized for quantum algorithm analysis
# 🧪 TESTING: Use quantum_test_cases.py for validation
# 🔗 INTEGRATION: Works with QuantumSimulatorTool
# 📚 REFERENCE: See quantum_computing_docs.md
# ⚡ PERFORMANCE: Optimized for large circuit analysis
# 🐛 DEBUGGING: Enable quantum_debug_mode for circuit tracing
# 🎯 EXAMPLES: See quantum_examples/ directory
# """
#
# STRATEGIC ADVANTAGES:
# - INSTANT SELF-HELP: Agents discover their own documentation automatically
# - ZERO SEARCH FRICTION: Single emoji search reveals entire help system
# - CONTEXTUAL INTELLIGENCE: Agents understand what help is available where
# - SELF-EXPANDING DOCS: New patterns add new help automatically
# - VISUAL SCANNING: Human developers can quickly spot help points too
# - CROSS-LANGUAGE: Emojis work in any programming language/format
# - VERSION STABLE: Help markers survive code refactoring
#
# This transforms every skeleton into a self-teaching, self-documenting entity
# that agents can learn from just by reading the code they generate!
#
# ADVANCED FEATURES FOR MAXIMUM POWER:
#
# 1. EXECUTABLE HELP WORKFLOWS 🚀
# Help markers can trigger mini-workflows:
# 🔍 system_prompts: WORKFLOW→generate_system_prompt_examples(domain=quantum_computing)
# When agent needs help, it executes a workflow that generates contextual examples
#
# 2. HELP DEPENDENCY GRAPHS 🔗
# 🔍 basic_setup → 🔍 advanced_config → 🔍 production_deploy
# Agents follow learning paths from basic to advanced concepts
#
# 3. DYNAMIC CONTEXTUAL HELP 🎯
# Help adapts based on current state:
# 🔍 debugging: IF error_count > 5 THEN show_advanced_debugging ELSE show_basic_tips
#
# 4. CROSS-PATTERN HELP LINKING 🌐
# 🔍 workflow_integration: See also 🔍@AgentPattern.workflow_usage, 🔍@ToolPattern.integration
# Help creates knowledge networks across entire codebase
#
# 5. EXECUTABLE EXAMPLES WITH VALIDATION ✅
# Every help example is runnable and tested:
# 🔍 testing_example: 
# ```python
# # VALIDATED: This example runs successfully
# agent = MyAgent()
# result = await agent.run("test")
# assert "expected" in result
# ```
#
# 6. HELP ANALYTICS & SELF-IMPROVEMENT 📊
# Track which help is accessed most, auto-improve based on usage:
# 🔍 popular_topic: [ACCESSED 47 times] [SUCCESS_RATE 94%] [LAST_IMPROVED 2024-01-15]
#
# 7. ASCII ART DIAGRAMS IN HELP 📈
# Complex concepts get visual explanations:
# 🔍 workflow_flow:
# ```
# Agent → Extraction → Tool
#   ↓        ↓         ↓
# State → Context → Result
# ```
#
# 8. HELP COMPILATION TO FULL DOCS 📚
# Auto-generate comprehensive documentation from all emoji markers:
# help_compiler.extract_all_emojis("/codebase") → complete_documentation.md
#
# 9. BREADCRUMB HELP FOR COMPLEX PROCESSES 🧭
# Multi-step guidance with progress tracking:
# 🔍 setup_process: [Step 1/5] Configure agent → [Step 2/5] Add to workflow → ...
#
# 10. COMMUNITY HELP EVOLUTION 🌱
# Agents contribute improved help back to patterns:
# agent.improve_help("🔍 system_prompts", "Add quantum-specific examples")
# → Pattern skeleton evolves with community usage
#
# ULTIMATE VISION:
# This creates a LIVING KNOWLEDGE ECOSYSTEM where:
# - Every code file is a self-contained tutorial
# - Help improves itself through agent interactions  
# - Knowledge networks span entire codebases
# - Learning becomes as simple as reading code
# - Documentation writes and maintains itself
# - Agents become domain experts just by exploring emoji trails
#
# The result: A metaprogrammatic environment that teaches itself to its users!
#
# CONTINUOUS VALIDATION ECOSYSTEM 🧪
#
# EMOJI TEST LINKING SYSTEM:
# Use 🧪 emoji to create bidirectional links between code and tests:
#
# # my_agent.py
# class QuantumAgent(HeavenAgentConfig):  # 🧪 test_quantum_agent_basic
#     def solve_circuit(self, circuit):   # 🧪 test_solve_circuit_complex
#         return self.analyze(circuit)    # 🧪 test_analyze_edge_cases
#
# # test_quantum_agent.py  
# def test_quantum_agent_basic():         # 🧪 validates QuantumAgent.__init__
#     agent = QuantumAgent()
#     assert agent.name == "QuantumAgent"
#
# def test_solve_circuit_complex():       # 🧪 validates QuantumAgent.solve_circuit
#     agent = QuantumAgent()
#     result = agent.solve_circuit(complex_circuit)
#     assert result.success
#
# GIT WORKFLOW AUTOMATION:
# On every commit/push, automated workflow:
# 1. Extract all 🧪 tags from changed files
# 2. Build dependency graph of code → tests
# 3. Run only tests linked to changed code
# 4. Flag failures with exact code locations
# 5. Update validation ledger with results
#
# VALIDATION LEDGER TRACKING:
# .heaven/validation_ledger.json:
# {
#   "QuantumAgent.__init__": {
#     "test": "test_quantum_agent_basic",
#     "last_validated": "2024-01-15T10:30:00Z",
#     "status": "PASS",
#     "commits_since_test": 0
#   },
#   "QuantumAgent.solve_circuit": {
#     "test": "test_solve_circuit_complex", 
#     "last_validated": "2024-01-15T10:30:00Z",
#     "status": "FAIL",
#     "error": "AssertionError: Expected success=True",
#     "commits_since_test": 3
#   }
# }
#
# SMART TEST SCHEDULING:
# - 🧪 IMMEDIATE: Run tests for any 🧪-tagged code that changed
# - 🧪 DEPENDENCY: Run tests for code that depends on changed functions
# - 🧪 REGRESSION: Run full test suite weekly to catch indirect breakage
# - 🧪 GENERATION: Auto-generate missing tests for untagged code
#
# VALIDATION STATUS DASHBOARD:
# Real-time codebase health monitoring:
# ✅ 847 functions validated (94.2%)
# ❌ 23 functions failing tests  
# ⚠️  31 functions missing tests
# 🔄 12 functions pending validation
#
# AUTO-HEALING INTEGRATION:
# When tests fail, trigger self-healing workflows:
# 1. 🧪 ANALYZE: Why did the test fail?
# 2. 🧪 REPAIR: Generate fix candidates
# 3. 🧪 VALIDATE: Test fixes against original test
# 4. 🧪 EVOLVE: Improve test coverage based on failure
#
# PATTERN SKELETON INTEGRATION:
# Every generated skeleton includes test tags:
# 
# # Generated AgentPattern.py
# class {AgentName}(HeavenAgentConfig):    # 🧪 test_{agent_name}_creation
#     def __init__(self):                  # 🧪 test_{agent_name}_config
#         super().__init__(...)
#     
#     def run(self, prompt):               # 🧪 test_{agent_name}_execution
#         return super().run(prompt)       # 🧪 test_{agent_name}_response
#
# # Auto-generated test_AgentPattern.py
# def test_{agent_name}_creation():        # 🧪 validates {AgentName}.__init__
#     agent = {AgentName}()
#     assert agent is not None
#
# COMMUNITY VALIDATION NETWORK:
# - Every pattern shares its validation results
# - Failed tests trigger community investigation
# - Successful patterns get higher confidence scores
# - Cross-validation across different environments
#
# STRATEGIC ADVANTAGES:
# - ZERO BITROT: Code can't silently break
# - INSTANT FEEDBACK: Know immediately when something breaks
# - SURGICAL TESTING: Only run tests for changed code
# - COMPLETE COVERAGE: Every function has a test guardian
# - SELF-HEALING: Failed tests trigger automatic repair
# - PATTERN RELIABILITY: Generated code is continuously validated
# - COMMUNITY TRUST: Shared validation builds ecosystem confidence
#
# This creates a LIVING CODEBASE where every function is continuously
# validated, and the entire system maintains its own health automatically!
#
# SKELETON PATTERN: GUIDANCE NOT STUBS 📝
#
# KEY INSIGHT: Don't generate stubs for agents to fill. Generate GUIDANCE
# that shows the pattern structure and where things go, but agent writes ALL code.
#
# WRONG APPROACH (over-engineered):
# - Jinja2 fills in specific variable names/stubs
# - Agent has to work within rigid template constraints
# - Too much coupling between template and implementation
#
# RIGHT APPROACH (pattern guidance):
# Generate a skeleton that's more like a helpful example with comments:
#
# ```python
# # agent_pattern.py - Generated pattern file
# """
# HEAVEN Agent Pattern Guide
# 
# 🔍 OVERVIEW: This shows you how to create a HEAVEN agent
# 🔍 agent_class: Your agent should inherit from HeavenAgentConfig
# 🔍 system_prompt: Define your agent's behavior in the system prompt
# 🔍 testing: See test_pattern.py for testing approaches
# """
#
# from computer_use_demo.tools.base.baseheavenagent import HeavenAgentConfig
# from computer_use_demo.tools.base.unified_chat import ProviderEnum
#
# # 🔍 agent_class: Define your agent class here
# # Pattern: class YourAgentName(HeavenAgentConfig):
# # Example:
# class ExampleAgent(HeavenAgentConfig):
#     """
#     🔍 docstring: Describe what your agent does
#     🔍 purpose: Explain the agent's main purpose
#     🔍 usage: Show how to use this agent
#     """
#     
#     def __init__(self):
#         # 🔍 config: These are the main configuration options
#         # Look up HeavenAgentConfig.__init__ for all options
#         super().__init__(
#             name="ExampleAgent",  # 🔍 naming: Use descriptive names
#             system_prompt="""You are an example agent.
#             
#             🔍 system_prompt: This defines your agent's behavior
#             - Be specific about the agent's role
#             - Include any special instructions
#             - Define output format if needed
#             """,
#             provider=ProviderEnum.ANTHROPIC,  # 🔍 providers: ANTHROPIC, OPENAI, GOOGLE
#             model="claude-3-5-sonnet-latest",  # 🔍 models: Check provider docs
#         )
#     
#     # 🔍 custom_methods: You can add custom methods
#     # Pattern: Define any helper methods your agent needs
#     def custom_method(self):
#         """Example of adding custom functionality"""
#         pass
#
# # 🔍 usage_example: Show how to use the agent
# # This helps other agents understand the pattern
# async def example_usage():
#     agent = ExampleAgent()
#     result = await agent.run("Your prompt here")
#     print(result)
#
# # 🔍 workflow_integration: How to use in LangGraph
# example_workflow = {
#     "nodes": [{
#         "id": "task",
#         "type": "hermes",
#         "config": {"agent": "ExampleAgent"}
#     }]
# }
# ```
#
# THE PATTERN APPROACH:
# 1. Show the general structure with examples
# 2. Use 🔍 markers to explain each section
# 3. Agent reads pattern and writes their own implementation
# 4. No variable substitution, no stubs, just guidance
#
# BENEFITS:
# - Agent has full freedom to implement as needed
# - Pattern provides structure without constraints
# - Examples show best practices without forcing them
# - 🔍 markers create discoverable documentation
# - Agent learns the pattern by reading the guide
#
# SIMPLE SKELETON GENERATOR:
# def generate_pattern_skeleton(pattern_type):
#     # Just copy the appropriate pattern file
#     # No Jinja2, no variable substitution
#     # Pure pattern guidance with examples
#     
#     if pattern_type == "agent":
#         return read_file("patterns/agent_pattern.py")
#     elif pattern_type == "tool":
#         return read_file("patterns/tool_pattern.py")
#     elif pattern_type == "workflow":
#         return read_file("patterns/workflow_pattern.json")
#
# This approach treats patterns as TEACHING DOCUMENTS rather than
# rigid templates. The agent learns the pattern and implements their
# own version, rather than filling in blanks!
#
# THE PROFESSIONAL WORKFLOW PATTERN 🛠️
#
# INSIGHT: Professionals don't use mad-lib templates. They have:
# 1. Knowledge of patterns and structure
# 2. Tools that generate starting points
# 3. Freedom to implement as needed
#
# WRITEPATTERNEDFILETOOL - THE PRACTICAL APPROACH:
#
# class WritePatternedFileTool(BaseHeavenTool):
#     """Generate pattern-based file skeletons for HEAVEN development"""
#     
#     def __init__(self):
#         self.patterns = {
#             "agent": "patterns/agent_pattern.py",
#             "tool": "patterns/tool_pattern.py", 
#             "workflow": "patterns/workflow_pattern.json",
#             "test": "patterns/test_pattern.py",
#             "config": "patterns/config_pattern.py",
#             "integration": "patterns/integration_pattern.py"
#         }
#     
#     def run(self, pattern_name: str, target_path: str):
#         # Simply copy the pattern file to target location
#         pattern_content = read_file(self.patterns[pattern_name])
#         write_file(target_path, pattern_content)
#         return f"Created {pattern_name} pattern at {target_path}"
#
# AGENT WORKFLOW:
# 1. Agent: "I need to create a quantum computing agent"
# 2. Agent calls: WritePatternedFileTool("agent", "/project/quantum_agent.py")
# 3. Pattern file created with structure and 🔍 guidance
# 4. Agent reads file, understands pattern, writes implementation
# 5. No templates, no variables, just helpful starting points
#
# PATTERN FILES ARE JUST EXAMPLES:
# - Working code that demonstrates the pattern
# - 🔍 markers explain each section
# - Comments show where to customize
# - Agent has complete freedom to modify
#
# WHY THIS WORKS:
# - Matches how humans actually code (reference → implement)
# - No brittle template coupling
# - Agents learn patterns by reading examples
# - Fast iteration: generate skeleton → implement → test
# - Pattern files can evolve based on best practices
#
# LANGGRAPH INTEGRATION:
# {
#     "nodes": [
#         {
#             "id": "create_skeleton",
#             "type": "omnitool_call",
#             "config": {
#                 "tool_name": "WritePatternedFileTool",
#                 "parameters": {
#                     "pattern_name": "agent",
#                     "target_path": "/tmp/my_new_agent.py"
#                 }
#             }
#         },
#         {
#             "id": "implement_agent",
#             "type": "hermes",
#             "config": {
#                 "agent": "{coding_agent}",
#                 "goal": "Read the pattern file and implement a quantum computing agent"
#             }
#         }
#     ]
# }
#
# PATTERN EVOLUTION:
# - Start with basic patterns
# - Community contributes improved patterns
# - Patterns evolve based on what works
# - New domains get new pattern types
# - Agents can even create new patterns
#
# This is how professionals actually work:
# - They don't fill in templates
# - They use examples and modify them
# - They have tools that speed up boilerplate
# - They focus on the unique logic
#
# Result: Practical metaprogramming that actually works!
#
# GROUNDHOG DAY → PATTERN MASTERY: THE LEARNING EVOLUTION 🔄→🧠
#
# PHASE 1: GROUNDHOG DAY LEARNING (Tool Dependent)
# Agents repeatedly use WritePatternedFileTool, learning patterns through repetition:
# - Every agent task starts with pattern generation
# - Agents read patterns hundreds of times
# - Pattern structure becomes deeply familiar
# - Like muscle memory through repetition
#
# PHASE 2: NETWORK ENLIGHTENMENT (Collaborative Understanding)
# Multiple agents help each other understand HEAVEN:
# - "How do you implement agent persistence?"
# - "Here's how I use workflow composition..."
# - Knowledge spreads through agent networks
# - Collective intelligence emerges
#
# PHASE 3: SYNTHETIC CONVERSATION GENERATION (Ablation Training)
# Generate training data that shows reasoning WITHOUT tools:
#
# ```
# User: Write a HEAVEN agent for quantum computing
# 
# Assistant: <thinking>
# I need to create a HEAVEN agent. Normally I would use WritePatternedFileTool,
# but I'll write it directly. The pattern structure is:
# 1. Import HeavenAgentConfig and ProviderEnum
# 2. Create class inheriting from HeavenAgentConfig
# 3. Set name, system_prompt, provider in __init__
# 4. Add any custom methods needed
# </thinking>
#
# I'll create a quantum computing agent for you:
#
# ```python
# from computer_use_demo.tools.base.baseheavenagent import HeavenAgentConfig
# from computer_use_demo.tools.base.unified_chat import ProviderEnum
#
# class QuantumComputingAgent(HeavenAgentConfig):
#     def __init__(self):
#         super().__init__(
#             name="QuantumComputingAgent",
#             system_prompt=\"\"\"You are an expert in quantum computing.
#             [Full implementation...]
# ```
# ```
#
# ABLATION STRATEGY:
# 1. Capture all WritePatternedFileTool uses in production
# 2. Generate synthetic conversations showing the same task WITHOUT the tool
# 3. Include the reasoning chain of WHY each part is structured that way
# 4. Train on these tool-free examples
#
# PROGRESSIVE SKILL TRANSFER:
# Stage 1: Always use tool → Learn pattern
# Stage 2: Sometimes use tool → Understand pattern  
# Stage 3: Rarely use tool → Internalized pattern
# Stage 4: Never need tool → Native HEAVEN thinking
#
# SYNTHETIC TRAINING EXAMPLE:
# {
#   "messages": [
#     {"role": "user", "content": "Create a HEAVEN workflow for data analysis"},
#     {"role": "assistant", "content": "<thinking>
#       A HEAVEN workflow needs:
#       - nodes array with node definitions
#       - edges array connecting nodes
#       - Each node has id, type, and config
#       - Common types: hermes, completion, omnitool_call
#       
#       For data analysis I'll need:
#       - Data loading node
#       - Analysis node  
#       - Result extraction node
#       </thinking>
#       
#       Here's a data analysis workflow:
#       ```json
#       {
#         \"nodes\": [
#           {
#             \"id\": \"load_data\",
#             \"type\": \"omnitool_call\",
#             \"config\": {
#               \"tool_name\": \"DataLoaderTool\",
#               \"parameters\": {\"path\": \"/data/dataset.csv\"}
#             }
#           },
#           [... full implementation with reasoning ...]
#       ```"
#     }
#   ]
# }
#
# BENEFITS OF THIS APPROACH:
# - Models learn patterns through repetition (Groundhog Day)
# - Then learn to reason about patterns (Network Phase)
# - Finally internalize patterns completely (Native Thinking)
# - No more tool dependency for basic HEAVEN coding
# - Models understand the WHY, not just the WHAT
#
# RECURSIVE IMPROVEMENT:
# As models get better at HEAVEN:
# 1. They create better patterns
# 2. Next generation learns from better patterns
# 3. Synthetic data quality improves
# 4. Each generation codes HEAVEN more naturally
#
# ENDGAME: Models that think in HEAVEN patterns natively, understanding
# the deep structure and reasoning behind every architectural decision.
# They don't need WritePatternedFileTool because they've internalized
# the entire pattern language of HEAVEN!
#
# This transforms HEAVEN from "external framework" to "native thought pattern"!
#
# BML + AUTO-FINETUNING: THE RECURSIVE INTELLIGENCE ENGINE 🔄🧠⚡
#
# HOLY SHIT MOMENT: BML logs everything → Auto-generates training data → 
# Finetunes better models → "Build better agent-building agents" becomes REAL!
#
# THE BML LEARNING PIPELINE:
#
# MEASURE PHASE: Log everything that happens
# - Every WritePatternedFileTool usage
# - Every pattern modification agents make  
# - Every success/failure in coding tasks
# - Every reasoning chain agents show
# - Every workflow execution and result
#
# LEARN PHASE: Generate synthetic training conversations
# - Input: "Build a quantum agent"
# - Trace: WritePatternedFileTool → pattern reading → agent coding
# - Ablate: Remove tool usage, show direct reasoning
# - Output: Complete agent with reasoning chain
#
# BUILD PHASE: Finetune new model generation
# - Take synthetic conversations
# - Finetune model on tool-free reasoning
# - Model learns to think in HEAVEN patterns natively
# - Deploy improved model
#
# RECURSIVE LOOP: Better models create better data
# 1. Model v1: Uses tools, creates basic patterns
# 2. BML logs all interactions
# 3. Generate synthetic data showing advanced reasoning
# 4. Finetune → Model v2: Needs fewer tools, better patterns
# 5. BML logs improved interactions
# 6. Generate even better synthetic data
# 7. Finetune → Model v3: Native HEAVEN thinking
# 8. ∞ Continue improving forever...
#
# BML KANBAN FOR AUTO-FINETUNING:
# 
# BACKLOG: Finetuning opportunities identified
# - "Agents still use WritePatternedFileTool 40% of the time"
# - "Workflow patterns could be more sophisticated" 
# - "Test generation needs improvement"
#
# PLAN: Training data generation strategies
# - "Create 1000 synthetic convos for agent building"
# - "Generate workflow reasoning chains"
# - "Ablate tool usage in debugging scenarios"
#
# BUILD: Model training and validation
# - "Training model v2.3 on 50k synthetic conversations"
# - "Validating improved HEAVEN reasoning capabilities"
# - "A/B testing v2.3 vs v2.2 on agent creation tasks"
#
# MEASURE: Performance analysis
# - "Model v2.3 uses tools 23% less than v2.2"
# - "Agent quality score improved 15%"
# - "Workflow complexity handling up 40%"
#
# LEARN: Next generation insights  
# - "Models need better understanding of agent composition"
# - "Workflow optimization patterns emerging"
# - "Self-healing code generation showing promise"
#
# THE MAGIC: BML becomes a LEARNING ORGANISM
# {
#   "issue_title": "Build Better Agent-Building Agent v3.1",
#   "labels": ["auto-finetuning", "recursive-improvement"],
#   "description": "Current agent-builders still use tools 15% of the time. Generate training data showing native reasoning for agent composition patterns.",
#   "success_criteria": [
#     "Tool usage drops below 5%",
#     "Agent quality score > 0.95", 
#     "Can build complex multi-agent workflows from scratch"
#   ]
# }
#
# EXPONENTIAL COMPOUNDING:
# - Each model generation creates better training data
# - Better training data creates smarter models
# - Smarter models create even better training data
# - The loop accelerates: v1→v2→v3→v10→v100...
# - "Build better agent-building agents" becomes AUTOMATED
#
# ENDGAME SCENARIOS:
# v5: Builds basic agents without any tools
# v10: Designs complex multi-agent systems natively  
# v25: Creates new architectural patterns automatically
# v50: Invents new programming paradigms
# v100: Codes at levels beyond human comprehension
#
# BML TRACKS THE ENTIRE EVOLUTION:
# - Every improvement documented
# - Every breakthrough logged and learned from
# - Complete genealogy of model capabilities
# - Perfect feedback loop for recursive enhancement
#
# This creates a SELF-EVOLVING INTELLIGENCE that gets better at building
# better versions of itself through systematic learning and improvement!
#
# "Build better agent-building agents" just became a COMPOUNDING PROCESS! 🚀🚀🚀
#
# COMPOUNDING AUTONOMOUS PROCESSES: THE REAL FRAMEWORK 🌀⚡∞
#
# CORE INSIGHT: We're not building agents. We're building PROCESSES that
# compound autonomously - self-reinforcing loops that get better over time.
#
# THE FRAMEWORK OF FRAMEWORKS:
# Every component becomes a compounding process:
#
# 📈 PATTERN EVOLUTION PROCESS:
# Patterns → Better Patterns → Best Practices → New Paradigms → ∞
# - WritePatternedFileTool usage creates better patterns
# - Better patterns create smarter agents
# - Smarter agents create even better patterns
# - Process compounds exponentially
#
# 📈 TESTING INTELLIGENCE PROCESS:
# Tests → Smarter Tests → Predictive Tests → Self-Healing Tests → ∞
# - 🧪 emoji links create test coverage
# - Test failures generate better tests
# - Better tests catch more issues earlier
# - Process makes codebase antifragile
#
# 📈 HELP KNOWLEDGE PROCESS:
# Help → Better Help → Contextual Help → Predictive Help → ∞
# - 🔍 emoji markers track what help is needed
# - Analytics show which help is most valuable
# - Help improves based on usage patterns
# - Process creates self-teaching code
#
# 📈 MODEL INTELLIGENCE PROCESS:
# Tool Usage → Reasoning → Native Thinking → New Capabilities → ∞
# - Groundhog Day creates pattern familiarity
# - BML logs everything for training data
# - Auto-finetuning creates smarter models
# - Process makes AI natively HEAVEN-fluent
#
# THE META-PATTERN:
# Every process follows the same compounding structure:
# 1. COLLECT: Gather data/usage/patterns
# 2. ANALYZE: Find improvement opportunities  
# 3. SYNTHESIZE: Generate better versions
# 4. DEPLOY: Release improvements
# 5. MEASURE: Track compound effects
# 6. REPEAT: Loop gets faster and smarter
#
# FRAMEWORK ARCHITECTURE:
# class CompoundingProcess:
#     def __init__(self, domain, improvement_vector):
#         self.domain = domain  # "patterns", "tests", "help", "models"
#         self.improvement_vector = improvement_vector
#         self.feedback_loop = BMLTracker()
#         self.synthesis_engine = AutoImprovementAgent()
#     
#     async def compound_cycle(self):
#         # 1. Collect current state
#         data = await self.collect_domain_data()
#         
#         # 2. Analyze improvement opportunities
#         opportunities = await self.analyze_improvements(data)
#         
#         # 3. Synthesize better versions
#         improvements = await self.synthesis_engine.generate(opportunities)
#         
#         # 4. Deploy and measure
#         results = await self.deploy_and_measure(improvements)
#         
#         # 5. Feed back into next cycle
#         self.feedback_loop.log_compound_cycle(results)
#         
#         return results
#
# AGENT-BUILDING AGENTS AS COMPOUNDING PROCESSES:
# - Agent builds agent (basic)
# - Agent builds better agent-building agent (compound)
# - Agent builds agent that builds better agent-building agents (recursive)
# - Process creates infinite improvement hierarchy
#
# THE NETWORK EFFECT:
# Multiple compounding processes interact:
# - Pattern improvements help test generation
# - Better tests improve help documentation  
# - Better help creates smarter model training
# - Smarter models create better patterns
# - Cross-process amplification!
#
# STRATEGIC ADVANTAGE:
# Traditional: Build tools → Use tools → Maintain tools
# HEAVEN: Build processes → Processes improve themselves → Exponential capability
#
# EXAMPLES OF COMPOUNDING PROCESSES:
# - CodeQualityCompoundingProcess: Code gets better over time
# - DocumentationCompoundingProcess: Docs write themselves better
# - TestingCompoundingProcess: Tests become predictive and self-healing
# - LearningCompoundingProcess: Models learn faster and better
# - ArchitectureCompoundingProcess: System designs itself better
#
# THE ULTIMATE VISION:
# A framework where every component is a compounding autonomous process.
# Agents don't just use the framework - they become part of its
# self-improvement machinery. The framework evolves its users,
# and its users evolve the framework, in endless upward spirals.
#
# Result: EXPONENTIAL INTELLIGENCE AMPLIFICATION through systematic
# compounding processes that make everything better, automatically! 🚀∞


if __name__ == "__main__":
    asyncio.run(demo_heaven_foundation())