hermes-agent/model_tools.py

#!/usr/bin/env python3
"""
Model Tools Module

Thin orchestration layer over the tool registry. Each tool file in tools/
self-registers its schema, handler, and metadata via tools.registry.register().
This module triggers discovery (by importing all tool modules), then provides
the public API that run_agent.py, cli.py, batch_runner.py, and the RL
environments consume.

Public API (signatures preserved from the original 2,400-line version):
    get_tool_definitions(enabled_toolsets, disabled_toolsets, quiet_mode) -> list
    handle_function_call(function_name, function_args, task_id, user_task) -> str
    TOOL_TO_TOOLSET_MAP: dict          (for batch_runner.py)
    TOOLSET_REQUIREMENTS: dict         (for cli.py, doctor.py)
    get_all_tool_names() -> list
    get_toolset_for_tool(name) -> str
    get_available_toolsets() -> dict
    check_toolset_requirements() -> dict
    check_tool_availability(quiet) -> tuple
"""

import json
import asyncio
import logging
import threading
import concurrent.futures
from functools import lru_cache
from typing import Dict, Any, List, Optional, Tuple

from tools.registry import registry
from toolsets import resolve_toolset, validate_toolset

logger = logging.getLogger(__name__)


# =============================================================================
# Async Bridging  (single source of truth -- used by registry.dispatch too)
# =============================================================================

_tool_loop = None          # persistent loop for the main (CLI) thread
_tool_loop_lock = threading.Lock()
_worker_thread_local = threading.local()  # per-worker-thread persistent loops

# Singleton ThreadPoolExecutor for async bridging - reused across all calls
# to avoid the performance overhead of creating/destroying thread pools per call
_async_bridge_executor = None
_async_bridge_executor_lock = threading.Lock()


def _get_async_bridge_executor() -> concurrent.futures.ThreadPoolExecutor:
    """Return a singleton ThreadPoolExecutor for async bridging.
    
    Using a persistent executor avoids the overhead of creating/destroying
    thread pools for every async call when running inside an async context.
    The executor is lazily initialized on first use.
    """
    global _async_bridge_executor
    if _async_bridge_executor is None:
        with _async_bridge_executor_lock:
            if _async_bridge_executor is None:
                _async_bridge_executor = concurrent.futures.ThreadPoolExecutor(
                    max_workers=4,  # Allow some parallelism for concurrent async calls
                    thread_name_prefix="async_bridge"
                )
    return _async_bridge_executor


def _get_tool_loop():
    """Return a long-lived event loop for running async tool handlers.

    Using a persistent loop (instead of asyncio.run() which creates and
    *closes* a fresh loop every time) prevents "Event loop is closed"
    errors that occur when cached httpx/AsyncOpenAI clients attempt to
    close their transport on a dead loop during garbage collection.
    """
    global _tool_loop
    with _tool_loop_lock:
        if _tool_loop is None or _tool_loop.is_closed():
            _tool_loop = asyncio.new_event_loop()
        return _tool_loop


def _get_worker_loop():
    """Return a persistent event loop for the current worker thread.

    Each worker thread (e.g., delegate_task's ThreadPoolExecutor threads)
    gets its own long-lived loop stored in thread-local storage.  This
    prevents the "Event loop is closed" errors that occurred when
    asyncio.run() was used per-call: asyncio.run() creates a loop, runs
    the coroutine, then *closes* the loop — but cached httpx/AsyncOpenAI
    clients remain bound to that now-dead loop and raise RuntimeError
    during garbage collection or subsequent use.

    By keeping the loop alive for the thread's lifetime, cached clients
    stay valid and their cleanup runs on a live loop.
    """
    loop = getattr(_worker_thread_local, 'loop', None)
    if loop is None or loop.is_closed():
        loop = asyncio.new_event_loop()
        asyncio.set_event_loop(loop)
        _worker_thread_local.loop = loop
    return loop


def _run_async(coro):
    """Run an async coroutine from a sync context.

    If the current thread already has a running event loop (e.g., inside
    the gateway's async stack or Atropos's event loop), we use the singleton
    thread pool so asyncio.run() can create its own loop without conflicting.

    For the common CLI path (no running loop), we use a persistent event
    loop so that cached async clients (httpx / AsyncOpenAI) remain bound
    to a live loop and don't trigger "Event loop is closed" on GC.

    When called from a worker thread (parallel tool execution), we use a
    per-thread persistent loop to avoid both contention with the main
    thread's shared loop AND the "Event loop is closed" errors caused by
    asyncio.run()'s create-and-destroy lifecycle.

    This is the single source of truth for sync->async bridging in tool
    handlers. The RL paths (agent_loop.py, tool_context.py) also provide
    outer thread-pool wrapping as defense-in-depth, but each handler is
    self-protecting via this function.
    """
    try:
        loop = asyncio.get_running_loop()
    except RuntimeError:
        loop = None

    if loop and loop.is_running():
        # Inside an async context (gateway, RL env) — run in the singleton thread pool.
        # Using a persistent executor avoids creating/destroying thread pools per call.
        executor = _get_async_bridge_executor()
        future = executor.submit(asyncio.run, coro)
        return future.result(timeout=300)

    # If we're on a worker thread (e.g., parallel tool execution in
    # delegate_task), use a per-thread persistent loop.  This avoids
    # contention with the main thread's shared loop while keeping cached
    # httpx/AsyncOpenAI clients bound to a live loop for the thread's
    # lifetime — preventing "Event loop is closed" on GC cleanup.
    if threading.current_thread() is not threading.main_thread():
        worker_loop = _get_worker_loop()
        return worker_loop.run_until_complete(coro)

    tool_loop = _get_tool_loop()
    return tool_loop.run_until_complete(coro)


# =============================================================================
# Tool Discovery  (importing each module triggers its registry.register calls)
# =============================================================================

# Module-level flag to track if tools have been discovered
_tools_discovered = False
_tools_discovery_lock = threading.Lock()


def _discover_tools():
    """Import all tool modules to trigger their registry.register() calls.

    Wrapped in a function so import errors in optional tools (e.g., fal_client
    not installed) don't prevent the rest from loading.
    """
    global _tools_discovered
    
    if _tools_discovered:
        return
    
    with _tools_discovery_lock:
        if _tools_discovered:
            return
        
        _modules = [
            "tools.web_tools",
            "tools.terminal_tool",
            "tools.file_tools",
            "tools.vision_tools",
            "tools.mixture_of_agents_tool",
            "tools.image_generation_tool",
            "tools.skills_tool",
            "tools.skill_manager_tool",
            "tools.browser_tool",
            "tools.cronjob_tools",
            "tools.rl_training_tool",
            "tools.tts_tool",
            "tools.todo_tool",
            "tools.memory_tool",
            "tools.session_search_tool",
            "tools.clarify_tool",
            "tools.code_execution_tool",
            "tools.delegate_tool",
            "tools.process_registry",
            "tools.send_message_tool",
            "tools.honcho_tools",
            "tools.homeassistant_tool",
        ]
        import importlib
        for mod_name in _modules:
            try:
                importlib.import_module(mod_name)
            except Exception as e:
                logger.warning("Could not import tool module %s: %s", mod_name, e)
        
        # MCP tool discovery (external MCP servers from config)
        try:
            from tools.mcp_tool import discover_mcp_tools
            discover_mcp_tools()
        except Exception as e:
            logger.debug("MCP tool discovery failed: %s", e)
        
        # Plugin tool discovery (user/project/pip plugins)
        try:
            from hermes_cli.plugins import discover_plugins
            discover_plugins()
        except Exception as e:
            logger.debug("Plugin discovery failed: %s", e)
        
        _tools_discovered = True


@lru_cache(maxsize=1)
def _get_discovered_tools():
    """Lazy-load tools and return registry data.
    
    Uses LRU cache to ensure tools are only discovered once.
    Returns tuple of (tool_to_toolset_map, toolset_requirements).
    """
    _discover_tools()
    return (
        registry.get_tool_to_toolset_map(),
        registry.get_toolset_requirements()
    )


def _ensure_tools_discovered():
    """Ensure tools are discovered (lazy loading). Call before accessing registry."""
    _discover_tools()


# =============================================================================
# Backward-compat constants  (lazily evaluated)
# =============================================================================

class _LazyToolsetMap:
    """Lazy proxy for TOOL_TO_TOOLSET_MAP - loads tools on first access."""
    _data = None
    
    def _load(self):
        if self._data is None:
            _discover_tools()
            self._data = registry.get_tool_to_toolset_map()
        return self._data
    
    def __getitem__(self, key):
        return self._load()[key]
    
    def __setitem__(self, key, value):
        self._load()[key] = value
    
    def __delitem__(self, key):
        del self._load()[key]
    
    def __contains__(self, key):
        return key in self._load()
    
    def __iter__(self):
        return iter(self._load())
    
    def __len__(self):
        return len(self._load())
    
    def keys(self):
        return self._load().keys()
    
    def values(self):
        return self._load().values()
    
    def items(self):
        return self._load().items()
    
    def get(self, key, default=None):
        return self._load().get(key, default)
    
    def update(self, other):
        self._load().update(other)


class _LazyToolsetRequirements:
    """Lazy proxy for TOOLSET_REQUIREMENTS - loads tools on first access."""
    _data = None
    
    def _load(self):
        if self._data is None:
            _discover_tools()
            self._data = registry.get_toolset_requirements()
        return self._data
    
    def __getitem__(self, key):
        return self._load()[key]
    
    def __setitem__(self, key, value):
        self._load()[key] = value
    
    def __delitem__(self, key):
        del self._load()[key]
    
    def __contains__(self, key):
        return key in self._load()
    
    def __iter__(self):
        return iter(self._load())
    
    def __len__(self):
        return len(self._load())
    
    def keys(self):
        return self._load().keys()
    
    def values(self):
        return self._load().values()
    
    def items(self):
        return self._load().items()
    
    def get(self, key, default=None):
        return self._load().get(key, default)
    
    def update(self, other):
        self._load().update(other)


# Create lazy proxy objects for backward compatibility
TOOL_TO_TOOLSET_MAP = _LazyToolsetMap()

TOOLSET_REQUIREMENTS = _LazyToolsetRequirements()

# Resolved tool names from the last get_tool_definitions() call.
# Used by code_execution_tool to know which tools are available in this session.
_last_resolved_tool_names: List[str] = []


# =============================================================================
# Legacy toolset name mapping  (old _tools-suffixed names -> tool name lists)
# =============================================================================

_LEGACY_TOOLSET_MAP = {
    "web_tools": ["web_search", "web_extract"],
    "terminal_tools": ["terminal"],
    "vision_tools": ["vision_analyze"],
    "moa_tools": ["mixture_of_agents"],
    "image_tools": ["image_generate"],
    "skills_tools": ["skills_list", "skill_view", "skill_manage"],
    "browser_tools": [
        "browser_navigate", "browser_snapshot", "browser_click",
        "browser_type", "browser_scroll", "browser_back",
        "browser_press", "browser_close", "browser_get_images",
        "browser_vision", "browser_console"
    ],
    "cronjob_tools": ["cronjob"],
    "rl_tools": [
        "rl_list_environments", "rl_select_environment",
        "rl_get_current_config", "rl_edit_config",
        "rl_start_training", "rl_check_status",
        "rl_stop_training", "rl_get_results",
        "rl_list_runs", "rl_test_inference"
    ],
    "file_tools": ["read_file", "write_file", "patch", "search_files"],
    "tts_tools": ["text_to_speech"],
}


# =============================================================================
# get_tool_definitions  (the main schema provider)
# =============================================================================

def get_tool_definitions_lazy(
    enabled_toolsets: List[str] = None,
    disabled_toolsets: List[str] = None,
    quiet_mode: bool = False,
) -> List[Dict[str, Any]]:
    """Get tool definitions with lazy loading - tools are only imported when needed.
    
    This is the lazy version that delays tool discovery until the first call,
    improving startup performance for CLI commands that don't need tools.
    
    Args:
        enabled_toolsets: Only include tools from these toolsets.
        disabled_toolsets: Exclude tools from these toolsets (if enabled_toolsets is None).
        quiet_mode: Suppress status prints.
    
    Returns:
        Filtered list of OpenAI-format tool definitions.
    """
    # Ensure tools are discovered (lazy loading - only happens on first call)
    _ensure_tools_discovered()
    
    # Delegate to the main implementation
    return _get_tool_definitions_impl(enabled_toolsets, disabled_toolsets, quiet_mode)


def _get_tool_definitions_impl(
    enabled_toolsets: List[str] = None,
    disabled_toolsets: List[str] = None,
    quiet_mode: bool = False,
) -> List[Dict[str, Any]]:
    """
    Get tool definitions for model API calls with toolset-based filtering.

    All tools must be part of a toolset to be accessible.

    Args:
        enabled_toolsets: Only include tools from these toolsets.
        disabled_toolsets: Exclude tools from these toolsets (if enabled_toolsets is None).
        quiet_mode: Suppress status prints.

    Returns:
        Filtered list of OpenAI-format tool definitions.
    """
    # Determine which tool names the caller wants
    tools_to_include: set = set()

    if enabled_toolsets:
        for toolset_name in enabled_toolsets:
            if validate_toolset(toolset_name):
                resolved = resolve_toolset(toolset_name)
                tools_to_include.update(resolved)
                if not quiet_mode:
                    print(f"✅ Enabled toolset '{toolset_name}': {', '.join(resolved) if resolved else 'no tools'}")
            elif toolset_name in _LEGACY_TOOLSET_MAP:
                legacy_tools = _LEGACY_TOOLSET_MAP[toolset_name]
                tools_to_include.update(legacy_tools)
                if not quiet_mode:
                    print(f"✅ Enabled legacy toolset '{toolset_name}': {', '.join(legacy_tools)}")
            else:
                if not quiet_mode:
                    print(f"⚠️  Unknown toolset: {toolset_name}")

    elif disabled_toolsets:
        from toolsets import get_all_toolsets
        for ts_name in get_all_toolsets():
            tools_to_include.update(resolve_toolset(ts_name))

        for toolset_name in disabled_toolsets:
            if validate_toolset(toolset_name):
                resolved = resolve_toolset(toolset_name)
                tools_to_include.difference_update(resolved)
                if not quiet_mode:
                    print(f"🚫 Disabled toolset '{toolset_name}': {', '.join(resolved) if resolved else 'no tools'}")
            elif toolset_name in _LEGACY_TOOLSET_MAP:
                legacy_tools = _LEGACY_TOOLSET_MAP[toolset_name]
                tools_to_include.difference_update(legacy_tools)
                if not quiet_mode:
                    print(f"🚫 Disabled legacy toolset '{toolset_name}': {', '.join(legacy_tools)}")
            else:
                if not quiet_mode:
                    print(f"⚠️  Unknown toolset: {toolset_name}")
    else:
        from toolsets import get_all_toolsets
        for ts_name in get_all_toolsets():
            tools_to_include.update(resolve_toolset(ts_name))

    # Plugin-registered tools are now resolved through the normal toolset
    # path — validate_toolset() / resolve_toolset() / get_all_toolsets()
    # all check the tool registry for plugin-provided toolsets.  No bypass
    # needed; plugins respect enabled_toolsets / disabled_toolsets like any
    # other toolset.

    # Ask the registry for schemas (only returns tools whose check_fn passes)
    filtered_tools = registry.get_definitions(tools_to_include, quiet=quiet_mode)

    # The set of tool names that actually passed check_fn filtering.
    # Use this (not tools_to_include) for any downstream schema that references
    # other tools by name — otherwise the model sees tools mentioned in
    # descriptions that don't actually exist, and hallucinates calls to them.
    available_tool_names = {t["function"]["name"] for t in filtered_tools}

    # Rebuild execute_code schema to only list sandbox tools that are actually
    # available.  Without this, the model sees "web_search is available in
    # execute_code" even when the API key isn't configured or the toolset is
    # disabled (#560-discord).
    if "execute_code" in available_tool_names:
        from tools.code_execution_tool import SANDBOX_ALLOWED_TOOLS, build_execute_code_schema
        sandbox_enabled = SANDBOX_ALLOWED_TOOLS & available_tool_names
        dynamic_schema = build_execute_code_schema(sandbox_enabled)
        for i, td in enumerate(filtered_tools):
            if td.get("function", {}).get("name") == "execute_code":
                filtered_tools[i] = {"type": "function", "function": dynamic_schema}
                break

    # Strip web tool cross-references from browser_navigate description when
    # web_search / web_extract are not available.  The static schema says
    # "prefer web_search or web_extract" which causes the model to hallucinate
    # those tools when they're missing.
    if "browser_navigate" in available_tool_names:
        web_tools_available = {"web_search", "web_extract"} & available_tool_names
        if not web_tools_available:
            for i, td in enumerate(filtered_tools):
                if td.get("function", {}).get("name") == "browser_navigate":
                    desc = td["function"].get("description", "")
                    desc = desc.replace(
                        " For simple information retrieval, prefer web_search or web_extract (faster, cheaper).",
                        "",
                    )
                    filtered_tools[i] = {
                        "type": "function",
                        "function": {**td["function"], "description": desc},
                    }
                    break

    if not quiet_mode:
        if filtered_tools:
            tool_names = [t["function"]["name"] for t in filtered_tools]
            print(f"🛠️  Final tool selection ({len(filtered_tools)} tools): {', '.join(tool_names)}")
        else:
            print("🛠️  No tools selected (all filtered out or unavailable)")

    global _last_resolved_tool_names
    _last_resolved_tool_names = [t["function"]["name"] for t in filtered_tools]

    return filtered_tools


def get_tool_definitions(
    enabled_toolsets: List[str] = None,
    disabled_toolsets: List[str] = None,
    quiet_mode: bool = False,
) -> List[Dict[str, Any]]:
    """
    Get tool definitions for model API calls with toolset-based filtering.
    
    All tools must be part of a toolset to be accessible.
    This is the eager-loading version for backward compatibility.
    New code should use get_tool_definitions_lazy() for better startup performance.

    Args:
        enabled_toolsets: Only include tools from these toolsets.
        disabled_toolsets: Exclude tools from these toolsets (if enabled_toolsets is None).
        quiet_mode: Suppress status prints.

    Returns:
        Filtered list of OpenAI-format tool definitions.
    """
    # Eager discovery for backward compatibility
    _ensure_tools_discovered()
    return _get_tool_definitions_impl(enabled_toolsets, disabled_toolsets, quiet_mode)


# =============================================================================
# handle_function_call  (the main dispatcher)
# =============================================================================

# Tools whose execution is intercepted by the agent loop (run_agent.py)
# because they need agent-level state (TodoStore, MemoryStore, etc.).
# The registry still holds their schemas; dispatch just returns a stub error
# so if something slips through, the LLM sees a sensible message.
_AGENT_LOOP_TOOLS = {"todo", "memory", "session_search", "delegate_task"}
_READ_SEARCH_TOOLS = {"read_file", "search_files"}


def handle_function_call(
    function_name: str,
    function_args: Dict[str, Any],
    task_id: Optional[str] = None,
    user_task: Optional[str] = None,
    enabled_tools: Optional[List[str]] = None,
    honcho_manager: Optional[Any] = None,
    honcho_session_key: Optional[str] = None,
) -> str:
    """
    Main function call dispatcher that routes calls to the tool registry.

    Args:
        function_name: Name of the function to call.
        function_args: Arguments for the function.
        task_id: Unique identifier for terminal/browser session isolation.
        user_task: The user's original task (for browser_snapshot context).
        enabled_tools: Tool names enabled for this session.  When provided,
                       execute_code uses this list to determine which sandbox
                       tools to generate.  Falls back to the process-global
                       ``_last_resolved_tool_names`` for backward compat.

    Returns:
        Function result as a JSON string.
    """
    # Ensure tools are discovered before dispatching
    _ensure_tools_discovered()
    
    # Notify the read-loop tracker when a non-read/search tool runs,
    # so the *consecutive* counter resets (reads after other work are fine).
    if function_name not in _READ_SEARCH_TOOLS:
        try:
            from tools.file_tools import notify_other_tool_call
            notify_other_tool_call(task_id or "default")
        except Exception:
            pass  # file_tools may not be loaded yet

    try:
        if function_name in _AGENT_LOOP_TOOLS:
            return json.dumps({"error": f"{function_name} must be handled by the agent loop"})

        try:
            from hermes_cli.plugins import invoke_hook
            invoke_hook("pre_tool_call", tool_name=function_name, args=function_args, task_id=task_id or "")
        except Exception:
            pass

        if function_name == "execute_code":
            # Prefer the caller-provided list so subagents can't overwrite
            # the parent's tool set via the process-global.
            sandbox_enabled = enabled_tools if enabled_tools is not None else _last_resolved_tool_names
            result = registry.dispatch(
                function_name, function_args,
                task_id=task_id,
                enabled_tools=sandbox_enabled,
                honcho_manager=honcho_manager,
                honcho_session_key=honcho_session_key,
            )
        else:
            result = registry.dispatch(
                function_name, function_args,
                task_id=task_id,
                user_task=user_task,
                honcho_manager=honcho_manager,
                honcho_session_key=honcho_session_key,
            )

        try:
            from hermes_cli.plugins import invoke_hook
            invoke_hook("post_tool_call", tool_name=function_name, args=function_args, result=result, task_id=task_id or "")
        except Exception:
            pass

        return result

    except Exception as e:
        error_msg = f"Error executing {function_name}: {str(e)}"
        logger.error(error_msg)
        return json.dumps({"error": error_msg}, ensure_ascii=False)


# =============================================================================
# Backward-compat wrapper functions
# =============================================================================

def get_all_tool_names() -> List[str]:
    """Return all registered tool names."""
    _ensure_tools_discovered()
    return registry.get_all_tool_names()


def get_toolset_for_tool(tool_name: str) -> Optional[str]:
    """Return the toolset a tool belongs to."""
    _ensure_tools_discovered()
    return registry.get_toolset_for_tool(tool_name)


def get_available_toolsets() -> Dict[str, dict]:
    """Return toolset availability info for UI display."""
    _ensure_tools_discovered()
    return registry.get_available_toolsets()


def check_toolset_requirements() -> Dict[str, bool]:
    """Return {toolset: available_bool} for every registered toolset."""
    _ensure_tools_discovered()
    return registry.check_toolset_requirements()


def check_tool_availability(quiet: bool = False) -> Tuple[List[str], List[dict]]:
    """Return (available_toolsets, unavailable_info)."""
    _ensure_tools_discovered()
    return registry.check_tool_availability(quiet=quiet)