#!/usr/bin/env python3 """ AI Agent Runner with Tool Calling This module provides a clean, standalone agent that can execute AI models with tool calling capabilities. It handles the conversation loop, tool execution, and response management. Features: - Automatic tool calling loop until completion - Configurable model parameters - Error handling and recovery - Message history management - Support for multiple model providers Usage: from run_agent import AIAgent agent = AIAgent(base_url="http://localhost:30000/v1", model="claude-opus-4-20250514") response = agent.run_conversation("Tell me about the latest Python updates") """ import atexit import asyncio import base64 import concurrent.futures import copy import hashlib import json import logging logger = logging.getLogger(__name__) import os import random import re import sys import tempfile import time import threading import weakref from types import SimpleNamespace import uuid from typing import List, Dict, Any, Optional from openai import OpenAI import fire from datetime import datetime from pathlib import Path from hermes_constants import get_hermes_home # Load .env from ~/.hermes/.env first, then project root as dev fallback. # User-managed env files should override stale shell exports on restart. from hermes_cli.env_loader import load_hermes_dotenv _hermes_home = get_hermes_home() _project_env = Path(__file__).parent / '.env' _loaded_env_paths = load_hermes_dotenv(hermes_home=_hermes_home, project_env=_project_env) if _loaded_env_paths: for _env_path in _loaded_env_paths: logger.info("Loaded environment variables from %s", _env_path) else: logger.info("No .env file found. Using system environment variables.") # Import our tool system from model_tools import get_tool_definitions, handle_function_call, check_toolset_requirements from tools.terminal_tool import cleanup_vm from tools.interrupt import set_interrupt as _set_interrupt from tools.browser_tool import cleanup_browser from hermes_constants import OPENROUTER_BASE_URL # Agent internals extracted to agent/ package for modularity from agent.prompt_builder import ( DEFAULT_AGENT_IDENTITY, PLATFORM_HINTS, MEMORY_GUIDANCE, SESSION_SEARCH_GUIDANCE, SKILLS_GUIDANCE, ) from agent.model_metadata import ( fetch_model_metadata, estimate_tokens_rough, estimate_messages_tokens_rough, get_next_probe_tier, parse_context_limit_from_error, save_context_length, ) from agent.context_compressor import ContextCompressor from agent.prompt_caching import apply_anthropic_cache_control from agent.prompt_builder import build_skills_system_prompt, build_context_files_prompt, load_soul_md from agent.usage_pricing import estimate_usage_cost, normalize_usage from agent.display import ( KawaiiSpinner, build_tool_preview as _build_tool_preview, get_cute_tool_message as _get_cute_tool_message_impl, _detect_tool_failure, get_tool_emoji as _get_tool_emoji, ) from agent.trajectory import ( convert_scratchpad_to_think, has_incomplete_scratchpad, save_trajectory as _save_trajectory_to_file, ) from utils import atomic_json_write HONCHO_TOOL_NAMES = { "honcho_context", "honcho_profile", "honcho_search", "honcho_conclude", } class _SafeWriter: """Transparent stdio wrapper that catches OSError/ValueError from broken pipes. When hermes-agent runs as a systemd service, Docker container, or headless daemon, the stdout/stderr pipe can become unavailable (idle timeout, buffer exhaustion, socket reset). Any print() call then raises ``OSError: [Errno 5] Input/output error``, which can crash agent setup or run_conversation() — especially via double-fault when an except handler also tries to print. Additionally, when subagents run in ThreadPoolExecutor threads, the shared stdout handle can close between thread teardown and cleanup, raising ``ValueError: I/O operation on closed file`` instead of OSError. This wrapper delegates all writes to the underlying stream and silently catches both OSError and ValueError. It is transparent when the wrapped stream is healthy. """ __slots__ = ("_inner",) def __init__(self, inner): object.__setattr__(self, "_inner", inner) def write(self, data): try: return self._inner.write(data) except (OSError, ValueError): return len(data) if isinstance(data, str) else 0 def flush(self): try: self._inner.flush() except (OSError, ValueError): pass def fileno(self): return self._inner.fileno() def isatty(self): try: return self._inner.isatty() except (OSError, ValueError): return False def __getattr__(self, name): return getattr(self._inner, name) def _install_safe_stdio() -> None: """Wrap stdout/stderr so best-effort console output cannot crash the agent.""" for stream_name in ("stdout", "stderr"): stream = getattr(sys, stream_name, None) if stream is not None and not isinstance(stream, _SafeWriter): setattr(sys, stream_name, _SafeWriter(stream)) class IterationBudget: """Thread-safe iteration counter for an agent. Each agent (parent or subagent) gets its own ``IterationBudget``. The parent's budget is capped at ``max_iterations`` (default 90). Each subagent gets an independent budget capped at ``delegation.max_iterations`` (default 50) — this means total iterations across parent + subagents can exceed the parent's cap. Users control the per-subagent limit via ``delegation.max_iterations`` in config.yaml. ``execute_code`` (programmatic tool calling) iterations are refunded via :meth:`refund` so they don't eat into the budget. """ def __init__(self, max_total: int): self.max_total = max_total self._used = 0 self._lock = threading.Lock() def consume(self) -> bool: """Try to consume one iteration. Returns True if allowed.""" with self._lock: if self._used >= self.max_total: return False self._used += 1 return True def refund(self) -> None: """Give back one iteration (e.g. for execute_code turns).""" with self._lock: if self._used > 0: self._used -= 1 @property def used(self) -> int: return self._used @property def remaining(self) -> int: with self._lock: return max(0, self.max_total - self._used) # Tools that must never run concurrently (interactive / user-facing). # When any of these appear in a batch, we fall back to sequential execution. _NEVER_PARALLEL_TOOLS = frozenset({"clarify"}) # Read-only tools with no shared mutable session state. _PARALLEL_SAFE_TOOLS = frozenset({ "ha_get_state", "ha_list_entities", "ha_list_services", "honcho_context", "honcho_profile", "honcho_search", "read_file", "search_files", "session_search", "skill_view", "skills_list", "vision_analyze", "web_extract", "web_search", }) # File tools can run concurrently when they target independent paths. _PATH_SCOPED_TOOLS = frozenset({"read_file", "write_file", "patch"}) # Maximum number of concurrent worker threads for parallel tool execution. _MAX_TOOL_WORKERS = 8 # Patterns that indicate a terminal command may modify/delete files. _DESTRUCTIVE_PATTERNS = re.compile( r"""(?:^|\s|&&|\|\||;|`)(?: rm\s|rmdir\s| mv\s| sed\s+-i| truncate\s| dd\s| shred\s| git\s+(?:reset|clean|checkout)\s )""", re.VERBOSE, ) # Output redirects that overwrite files (> but not >>) _REDIRECT_OVERWRITE = re.compile(r'[^>]>[^>]|^>[^>]') def _is_destructive_command(cmd: str) -> bool: """Heuristic: does this terminal command look like it modifies/deletes files?""" if not cmd: return False if _DESTRUCTIVE_PATTERNS.search(cmd): return True if _REDIRECT_OVERWRITE.search(cmd): return True return False def _should_parallelize_tool_batch(tool_calls) -> bool: """Return True when a tool-call batch is safe to run concurrently.""" if len(tool_calls) <= 1: return False tool_names = [tc.function.name for tc in tool_calls] if any(name in _NEVER_PARALLEL_TOOLS for name in tool_names): return False reserved_paths: list[Path] = [] for tool_call in tool_calls: tool_name = tool_call.function.name try: function_args = json.loads(tool_call.function.arguments) except Exception: logging.debug( "Could not parse args for %s — defaulting to sequential; raw=%s", tool_name, tool_call.function.arguments[:200], ) return False if not isinstance(function_args, dict): logging.debug( "Non-dict args for %s (%s) — defaulting to sequential", tool_name, type(function_args).__name__, ) return False if tool_name in _PATH_SCOPED_TOOLS: scoped_path = _extract_parallel_scope_path(tool_name, function_args) if scoped_path is None: return False if any(_paths_overlap(scoped_path, existing) for existing in reserved_paths): return False reserved_paths.append(scoped_path) continue if tool_name not in _PARALLEL_SAFE_TOOLS: return False return True def _extract_parallel_scope_path(tool_name: str, function_args: dict) -> Path | None: """Return the normalized file target for path-scoped tools.""" if tool_name not in _PATH_SCOPED_TOOLS: return None raw_path = function_args.get("path") if not isinstance(raw_path, str) or not raw_path.strip(): return None # Avoid resolve(); the file may not exist yet. return Path(raw_path).expanduser() def _paths_overlap(left: Path, right: Path) -> bool: """Return True when two paths may refer to the same subtree.""" left_parts = left.parts right_parts = right.parts if not left_parts or not right_parts: # Empty paths shouldn't reach here (guarded upstream), but be safe. return bool(left_parts) == bool(right_parts) and bool(left_parts) common_len = min(len(left_parts), len(right_parts)) return left_parts[:common_len] == right_parts[:common_len] def _inject_honcho_turn_context(content, turn_context: str): """Append Honcho recall to the current-turn user message without mutating history. The returned content is sent to the API for this turn only. Keeping Honcho recall out of the system prompt preserves the stable cache prefix while still giving the model continuity context. """ if not turn_context: return content note = ( "[System note: The following Honcho memory was retrieved from prior " "sessions. It is continuity context for this turn only, not new user " "input.]\n\n" f"{turn_context}" ) if isinstance(content, list): return list(content) + [{"type": "text", "text": note}] text = "" if content is None else str(content) if not text.strip(): return note return f"{text}\n\n{note}" class AIAgent: """ AI Agent with tool calling capabilities. This class manages the conversation flow, tool execution, and response handling for AI models that support function calling. """ @property def base_url(self) -> str: return self._base_url @base_url.setter def base_url(self, value: str) -> None: self._base_url = value self._base_url_lower = value.lower() if value else "" def __init__( self, base_url: str = None, api_key: str = None, provider: str = None, api_mode: str = None, acp_command: str = None, acp_args: list[str] | None = None, command: str = None, args: list[str] | None = None, model: str = "anthropic/claude-opus-4.6", # OpenRouter format max_iterations: int = 90, # Default tool-calling iterations (shared with subagents) tool_delay: float = 1.0, enabled_toolsets: List[str] = None, disabled_toolsets: List[str] = None, save_trajectories: bool = False, verbose_logging: bool = False, quiet_mode: bool = False, ephemeral_system_prompt: str = None, log_prefix_chars: int = 100, log_prefix: str = "", providers_allowed: List[str] = None, providers_ignored: List[str] = None, providers_order: List[str] = None, provider_sort: str = None, provider_require_parameters: bool = False, provider_data_collection: str = None, session_id: str = None, tool_progress_callback: callable = None, thinking_callback: callable = None, reasoning_callback: callable = None, clarify_callback: callable = None, step_callback: callable = None, stream_delta_callback: callable = None, tool_gen_callback: callable = None, status_callback: callable = None, max_tokens: int = None, reasoning_config: Dict[str, Any] = None, prefill_messages: List[Dict[str, Any]] = None, platform: str = None, skip_context_files: bool = False, skip_memory: bool = False, session_db=None, honcho_session_key: str = None, honcho_manager=None, honcho_config=None, iteration_budget: "IterationBudget" = None, fallback_model: Dict[str, Any] = None, checkpoints_enabled: bool = False, checkpoint_max_snapshots: int = 50, pass_session_id: bool = False, ): """ Initialize the AI Agent. Args: base_url (str): Base URL for the model API (optional) api_key (str): API key for authentication (optional, uses env var if not provided) provider (str): Provider identifier (optional; used for telemetry/routing hints) api_mode (str): API mode override: "chat_completions" or "codex_responses" model (str): Model name to use (default: "anthropic/claude-opus-4.6") max_iterations (int): Maximum number of tool calling iterations (default: 90) tool_delay (float): Delay between tool calls in seconds (default: 1.0) enabled_toolsets (List[str]): Only enable tools from these toolsets (optional) disabled_toolsets (List[str]): Disable tools from these toolsets (optional) save_trajectories (bool): Whether to save conversation trajectories to JSONL files (default: False) verbose_logging (bool): Enable verbose logging for debugging (default: False) quiet_mode (bool): Suppress progress output for clean CLI experience (default: False) ephemeral_system_prompt (str): System prompt used during agent execution but NOT saved to trajectories (optional) log_prefix_chars (int): Number of characters to show in log previews for tool calls/responses (default: 100) log_prefix (str): Prefix to add to all log messages for identification in parallel processing (default: "") providers_allowed (List[str]): OpenRouter providers to allow (optional) providers_ignored (List[str]): OpenRouter providers to ignore (optional) providers_order (List[str]): OpenRouter providers to try in order (optional) provider_sort (str): Sort providers by price/throughput/latency (optional) session_id (str): Pre-generated session ID for logging (optional, auto-generated if not provided) tool_progress_callback (callable): Callback function(tool_name, args_preview) for progress notifications clarify_callback (callable): Callback function(question, choices) -> str for interactive user questions. Provided by the platform layer (CLI or gateway). If None, the clarify tool returns an error. max_tokens (int): Maximum tokens for model responses (optional, uses model default if not set) reasoning_config (Dict): OpenRouter reasoning configuration override (e.g. {"effort": "none"} to disable thinking). If None, defaults to {"enabled": True, "effort": "medium"} for OpenRouter. Set to disable/customize reasoning. prefill_messages (List[Dict]): Messages to prepend to conversation history as prefilled context. Useful for injecting a few-shot example or priming the model's response style. Example: [{"role": "user", "content": "Hi!"}, {"role": "assistant", "content": "Hello!"}] platform (str): The interface platform the user is on (e.g. "cli", "telegram", "discord", "whatsapp"). Used to inject platform-specific formatting hints into the system prompt. skip_context_files (bool): If True, skip auto-injection of SOUL.md, AGENTS.md, and .cursorrules into the system prompt. Use this for batch processing and data generation to avoid polluting trajectories with user-specific persona or project instructions. honcho_session_key (str): Session key for Honcho integration (e.g., "telegram:123456" or CLI session_id). When provided and Honcho is enabled in config, enables persistent cross-session user modeling. honcho_manager: Optional shared HonchoSessionManager owned by the caller. honcho_config: Optional HonchoClientConfig corresponding to honcho_manager. """ _install_safe_stdio() self.model = model self.max_iterations = max_iterations # Shared iteration budget — parent creates, children inherit. # Consumed by every LLM turn across parent + all subagents. self.iteration_budget = iteration_budget or IterationBudget(max_iterations) self.tool_delay = tool_delay self.save_trajectories = save_trajectories self.verbose_logging = verbose_logging self.quiet_mode = quiet_mode self.ephemeral_system_prompt = ephemeral_system_prompt self.platform = platform # "cli", "telegram", "discord", "whatsapp", etc. # Pluggable print function — CLI replaces this with _cprint so that # raw ANSI status lines are routed through prompt_toolkit's renderer # instead of going directly to stdout where patch_stdout's StdoutProxy # would mangle the escape sequences. None = use builtins.print. self._print_fn = None self.skip_context_files = skip_context_files self.pass_session_id = pass_session_id self.log_prefix_chars = log_prefix_chars self.log_prefix = f"{log_prefix} " if log_prefix else "" # Store effective base URL for feature detection (prompt caching, reasoning, etc.) # When no base_url is provided, the client defaults to OpenRouter, so reflect that here. self.base_url = base_url or OPENROUTER_BASE_URL provider_name = provider.strip().lower() if isinstance(provider, str) and provider.strip() else None self.provider = provider_name or "openrouter" self.acp_command = acp_command or command self.acp_args = list(acp_args or args or []) if api_mode in {"chat_completions", "codex_responses", "anthropic_messages"}: self.api_mode = api_mode elif self.provider == "openai-codex": self.api_mode = "codex_responses" elif (provider_name is None) and "chatgpt.com/backend-api/codex" in self._base_url_lower: self.api_mode = "codex_responses" self.provider = "openai-codex" elif self.provider == "anthropic" or (provider_name is None and "api.anthropic.com" in self._base_url_lower): self.api_mode = "anthropic_messages" self.provider = "anthropic" elif self._base_url_lower.rstrip("/").endswith("/anthropic"): # Third-party Anthropic-compatible endpoints (e.g. MiniMax, DashScope) # use a URL convention ending in /anthropic. Auto-detect these so the # Anthropic Messages API adapter is used instead of chat completions. self.api_mode = "anthropic_messages" else: self.api_mode = "chat_completions" # Direct OpenAI sessions use the Responses API path. GPT-5.x tool # calls with reasoning are rejected on /v1/chat/completions, and # Hermes is a tool-using client by default. if self.api_mode == "chat_completions" and self._is_direct_openai_url(): self.api_mode = "codex_responses" # Pre-warm OpenRouter model metadata cache in a background thread. # fetch_model_metadata() is cached for 1 hour; this avoids a blocking # HTTP request on the first API response when pricing is estimated. if self.provider == "openrouter" or "openrouter" in self._base_url_lower: threading.Thread( target=lambda: fetch_model_metadata(), daemon=True, ).start() self.tool_progress_callback = tool_progress_callback self.thinking_callback = thinking_callback self.reasoning_callback = reasoning_callback self.clarify_callback = clarify_callback self.step_callback = step_callback self.stream_delta_callback = stream_delta_callback self.status_callback = status_callback self.tool_gen_callback = tool_gen_callback self._last_reported_tool = None # Track for "new tool" mode # Tool execution state — allows _vprint during tool execution # even when stream consumers are registered (no tokens streaming then) self._executing_tools = False # Interrupt mechanism for breaking out of tool loops self._interrupt_requested = False self._interrupt_message = None # Optional message that triggered interrupt self._client_lock = threading.RLock() # Subagent delegation state self._delegate_depth = 0 # 0 = top-level agent, incremented for children self._active_children = [] # Running child AIAgents (for interrupt propagation) self._active_children_lock = threading.Lock() # Store OpenRouter provider preferences self.providers_allowed = providers_allowed self.providers_ignored = providers_ignored self.providers_order = providers_order self.provider_sort = provider_sort self.provider_require_parameters = provider_require_parameters self.provider_data_collection = provider_data_collection # Store toolset filtering options self.enabled_toolsets = enabled_toolsets self.disabled_toolsets = disabled_toolsets # Model response configuration self.max_tokens = max_tokens # None = use model default self.reasoning_config = reasoning_config # None = use default (medium for OpenRouter) self.prefill_messages = prefill_messages or [] # Prefilled conversation turns # Anthropic prompt caching: auto-enabled for Claude models via OpenRouter. # Reduces input costs by ~75% on multi-turn conversations by caching the # conversation prefix. Uses system_and_3 strategy (4 breakpoints). is_openrouter = "openrouter" in self._base_url_lower is_claude = "claude" in self.model.lower() is_native_anthropic = self.api_mode == "anthropic_messages" self._use_prompt_caching = (is_openrouter and is_claude) or is_native_anthropic self._cache_ttl = "5m" # Default 5-minute TTL (1.25x write cost) # Iteration budget pressure: warn the LLM as it approaches max_iterations. # Warnings are injected into the last tool result JSON (not as separate # messages) so they don't break message structure or invalidate caching. self._budget_caution_threshold = 0.7 # 70% — nudge to start wrapping up self._budget_warning_threshold = 0.9 # 90% — urgent, respond now self._budget_pressure_enabled = True # Context pressure warnings: notify the USER (not the LLM) as context # fills up. Purely informational — displayed in CLI output and sent via # status_callback for gateway platforms. Does NOT inject into messages. self._context_pressure_warned = False # Persistent error log -- always writes WARNING+ to ~/.hermes/logs/errors.log # so tool failures, API errors, etc. are inspectable after the fact. # In gateway mode, each incoming message creates a new AIAgent instance, # while the root logger is process-global. Re-adding the same errors.log # handler would cause each warning/error line to be written multiple times. from logging.handlers import RotatingFileHandler root_logger = logging.getLogger() error_log_dir = _hermes_home / "logs" error_log_path = error_log_dir / "errors.log" resolved_error_log_path = error_log_path.resolve() has_errors_log_handler = any( isinstance(handler, RotatingFileHandler) and Path(getattr(handler, "baseFilename", "")).resolve() == resolved_error_log_path for handler in root_logger.handlers ) from agent.redact import RedactingFormatter if not has_errors_log_handler: error_log_dir.mkdir(parents=True, exist_ok=True) error_file_handler = RotatingFileHandler( error_log_path, maxBytes=2 * 1024 * 1024, backupCount=2, ) error_file_handler.setLevel(logging.WARNING) error_file_handler.setFormatter(RedactingFormatter( '%(asctime)s %(levelname)s %(name)s: %(message)s', )) root_logger.addHandler(error_file_handler) if self.verbose_logging: logging.basicConfig( level=logging.DEBUG, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s', datefmt='%H:%M:%S' ) for handler in logging.getLogger().handlers: handler.setFormatter(RedactingFormatter( '%(asctime)s - %(name)s - %(levelname)s - %(message)s', datefmt='%H:%M:%S', )) # Keep third-party libraries at WARNING level to reduce noise # We have our own retry and error logging that's more informative logging.getLogger('openai').setLevel(logging.WARNING) logging.getLogger('openai._base_client').setLevel(logging.WARNING) logging.getLogger('httpx').setLevel(logging.WARNING) logging.getLogger('httpcore').setLevel(logging.WARNING) logging.getLogger('asyncio').setLevel(logging.WARNING) # Suppress Modal/gRPC related debug spam logging.getLogger('hpack').setLevel(logging.WARNING) logging.getLogger('hpack.hpack').setLevel(logging.WARNING) logging.getLogger('grpc').setLevel(logging.WARNING) logging.getLogger('modal').setLevel(logging.WARNING) logging.getLogger('rex-deploy').setLevel(logging.INFO) # Keep INFO for sandbox status logger.info("Verbose logging enabled (third-party library logs suppressed)") else: # Set logging to INFO level for important messages only logging.basicConfig( level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s', datefmt='%H:%M:%S' ) # Suppress noisy library logging logging.getLogger('openai').setLevel(logging.ERROR) logging.getLogger('openai._base_client').setLevel(logging.ERROR) logging.getLogger('httpx').setLevel(logging.ERROR) logging.getLogger('httpcore').setLevel(logging.ERROR) if self.quiet_mode: # In quiet mode (CLI default), suppress all tool/infra log # noise. The TUI has its own rich display for status; logger # INFO/WARNING messages just clutter it. for quiet_logger in [ 'tools', # all tools.* (terminal, browser, web, file, etc.) 'run_agent', # agent runner internals 'trajectory_compressor', 'cron', # scheduler (only relevant in daemon mode) 'hermes_cli', # CLI helpers ]: logging.getLogger(quiet_logger).setLevel(logging.ERROR) # Internal stream callback (set during streaming TTS). # Initialized here so _vprint can reference it before run_conversation. self._stream_callback = None # Deferred paragraph break flag — set after tool iterations so a # single "\n\n" is prepended to the next real text delta. self._stream_needs_break = False # Optional current-turn user-message override used when the API-facing # user message intentionally differs from the persisted transcript # (e.g. CLI voice mode adds a temporary prefix for the live call only). self._persist_user_message_idx = None self._persist_user_message_override = None # Cache anthropic image-to-text fallbacks per image payload/URL so a # single tool loop does not repeatedly re-run auxiliary vision on the # same image history. self._anthropic_image_fallback_cache: Dict[str, str] = {} # Initialize LLM client via centralized provider router. # The router handles auth resolution, base URL, headers, and # Codex/Anthropic wrapping for all known providers. # raw_codex=True because the main agent needs direct responses.stream() # access for Codex Responses API streaming. self._anthropic_client = None if self.api_mode == "anthropic_messages": from agent.anthropic_adapter import build_anthropic_client, resolve_anthropic_token # Only fall back to ANTHROPIC_TOKEN when the provider is actually Anthropic. # Other anthropic_messages providers (MiniMax, Alibaba, etc.) must use their own API key. # Falling back would send Anthropic credentials to third-party endpoints (Fixes #1739, #minimax-401). _is_native_anthropic = self.provider == "anthropic" effective_key = (api_key or resolve_anthropic_token() or "") if _is_native_anthropic else (api_key or "") self.api_key = effective_key self._anthropic_api_key = effective_key self._anthropic_base_url = base_url from agent.anthropic_adapter import _is_oauth_token as _is_oat self._is_anthropic_oauth = _is_oat(effective_key) self._anthropic_client = build_anthropic_client(effective_key, base_url) # No OpenAI client needed for Anthropic mode self.client = None self._client_kwargs = {} if not self.quiet_mode: print(f"🤖 AI Agent initialized with model: {self.model} (Anthropic native)") if effective_key and len(effective_key) > 12: print(f"🔑 Using token: {effective_key[:8]}...{effective_key[-4:]}") else: if api_key and base_url: # Explicit credentials from CLI/gateway — construct directly. # The runtime provider resolver already handled auth for us. client_kwargs = {"api_key": api_key, "base_url": base_url} if self.provider == "copilot-acp": client_kwargs["command"] = self.acp_command client_kwargs["args"] = self.acp_args effective_base = base_url if "openrouter" in effective_base.lower(): client_kwargs["default_headers"] = { "HTTP-Referer": "https://hermes-agent.nousresearch.com", "X-OpenRouter-Title": "Hermes Agent", "X-OpenRouter-Categories": "productivity,cli-agent", } elif "api.githubcopilot.com" in effective_base.lower(): from hermes_cli.models import copilot_default_headers client_kwargs["default_headers"] = copilot_default_headers() elif "api.kimi.com" in effective_base.lower(): client_kwargs["default_headers"] = { "User-Agent": "KimiCLI/1.3", } else: # No explicit creds — use the centralized provider router from agent.auxiliary_client import resolve_provider_client _routed_client, _ = resolve_provider_client( self.provider or "auto", model=self.model, raw_codex=True) if _routed_client is not None: client_kwargs = { "api_key": _routed_client.api_key, "base_url": str(_routed_client.base_url), } # Preserve any default_headers the router set if hasattr(_routed_client, '_default_headers') and _routed_client._default_headers: client_kwargs["default_headers"] = dict(_routed_client._default_headers) else: # When the user explicitly chose a non-OpenRouter provider # but no credentials were found, fail fast with a clear # message instead of silently routing through OpenRouter. _explicit = (self.provider or "").strip().lower() if _explicit and _explicit not in ("auto", "openrouter", "custom"): raise RuntimeError( f"Provider '{_explicit}' is set in config.yaml but no API key " f"was found. Set the {_explicit.upper()}_API_KEY environment " f"variable, or switch to a different provider with `hermes model`." ) # Final fallback: try raw OpenRouter key client_kwargs = { "api_key": os.getenv("OPENROUTER_API_KEY", ""), "base_url": OPENROUTER_BASE_URL, "default_headers": { "HTTP-Referer": "https://hermes-agent.nousresearch.com", "X-OpenRouter-Title": "Hermes Agent", "X-OpenRouter-Categories": "productivity,cli-agent", }, } self._client_kwargs = client_kwargs # stored for rebuilding after interrupt self.api_key = client_kwargs.get("api_key", "") try: self.client = self._create_openai_client(client_kwargs, reason="agent_init", shared=True) if not self.quiet_mode: print(f"🤖 AI Agent initialized with model: {self.model}") if base_url: print(f"🔗 Using custom base URL: {base_url}") # Always show API key info (masked) for debugging auth issues key_used = client_kwargs.get("api_key", "none") if key_used and key_used != "dummy-key" and len(key_used) > 12: print(f"🔑 Using API key: {key_used[:8]}...{key_used[-4:]}") else: print(f"⚠️ Warning: API key appears invalid or missing (got: '{key_used[:20] if key_used else 'none'}...')") except Exception as e: raise RuntimeError(f"Failed to initialize OpenAI client: {e}") # Provider fallback — a single backup model/provider tried when the # primary is exhausted (rate-limit, overload, connection failure). # Config shape: {"provider": "openrouter", "model": "anthropic/claude-sonnet-4"} self._fallback_model = fallback_model if isinstance(fallback_model, dict) else None self._fallback_activated = False if self._fallback_model: fb_p = self._fallback_model.get("provider", "") fb_m = self._fallback_model.get("model", "") if fb_p and fb_m and not self.quiet_mode: print(f"🔄 Fallback model: {fb_m} ({fb_p})") # Get available tools with filtering self.tools = get_tool_definitions( enabled_toolsets=enabled_toolsets, disabled_toolsets=disabled_toolsets, quiet_mode=self.quiet_mode, ) # Show tool configuration and store valid tool names for validation self.valid_tool_names = set() if self.tools: self.valid_tool_names = {tool["function"]["name"] for tool in self.tools} tool_names = sorted(self.valid_tool_names) if not self.quiet_mode: print(f"🛠️ Loaded {len(self.tools)} tools: {', '.join(tool_names)}") # Show filtering info if applied if enabled_toolsets: print(f" ✅ Enabled toolsets: {', '.join(enabled_toolsets)}") if disabled_toolsets: print(f" ❌ Disabled toolsets: {', '.join(disabled_toolsets)}") elif not self.quiet_mode: print("🛠️ No tools loaded (all tools filtered out or unavailable)") # Check tool requirements if self.tools and not self.quiet_mode: requirements = check_toolset_requirements() missing_reqs = [name for name, available in requirements.items() if not available] if missing_reqs: print(f"⚠️ Some tools may not work due to missing requirements: {missing_reqs}") # Show trajectory saving status if self.save_trajectories and not self.quiet_mode: print("📝 Trajectory saving enabled") # Show ephemeral system prompt status if self.ephemeral_system_prompt and not self.quiet_mode: prompt_preview = self.ephemeral_system_prompt[:60] + "..." if len(self.ephemeral_system_prompt) > 60 else self.ephemeral_system_prompt print(f"🔒 Ephemeral system prompt: '{prompt_preview}' (not saved to trajectories)") # Show prompt caching status if self._use_prompt_caching and not self.quiet_mode: source = "native Anthropic" if is_native_anthropic else "Claude via OpenRouter" print(f"💾 Prompt caching: ENABLED ({source}, {self._cache_ttl} TTL)") # Session logging setup - auto-save conversation trajectories for debugging self.session_start = datetime.now() if session_id: # Use provided session ID (e.g., from CLI) self.session_id = session_id else: # Generate a new session ID timestamp_str = self.session_start.strftime("%Y%m%d_%H%M%S") short_uuid = uuid.uuid4().hex[:6] self.session_id = f"{timestamp_str}_{short_uuid}" # Session logs go into ~/.hermes/sessions/ alongside gateway sessions hermes_home = get_hermes_home() self.logs_dir = hermes_home / "sessions" self.logs_dir.mkdir(parents=True, exist_ok=True) self.session_log_file = self.logs_dir / f"session_{self.session_id}.json" # Track conversation messages for session logging self._session_messages: List[Dict[str, Any]] = [] # Cached system prompt -- built once per session, only rebuilt on compression self._cached_system_prompt: Optional[str] = None # Filesystem checkpoint manager (transparent — not a tool) from tools.checkpoint_manager import CheckpointManager self._checkpoint_mgr = CheckpointManager( enabled=checkpoints_enabled, max_snapshots=checkpoint_max_snapshots, ) # SQLite session store (optional -- provided by CLI or gateway) self._session_db = session_db self._last_flushed_db_idx = 0 # tracks DB-write cursor to prevent duplicate writes if self._session_db: try: self._session_db.create_session( session_id=self.session_id, source=self.platform or "cli", model=self.model, model_config={ "max_iterations": self.max_iterations, "reasoning_config": reasoning_config, "max_tokens": max_tokens, }, user_id=None, ) except Exception as e: logger.warning("Session DB create_session failed — messages will NOT be indexed: %s", e) self._session_db = None # prevent silent data loss on every subsequent flush # In-memory todo list for task planning (one per agent/session) from tools.todo_tool import TodoStore self._todo_store = TodoStore() # Load config once for memory, skills, and compression sections try: from hermes_cli.config import load_config as _load_agent_config _agent_cfg = _load_agent_config() except Exception: _agent_cfg = {} # Persistent memory (MEMORY.md + USER.md) -- loaded from disk self._memory_store = None self._memory_enabled = False self._user_profile_enabled = False self._memory_nudge_interval = 10 self._memory_flush_min_turns = 6 self._turns_since_memory = 0 self._iters_since_skill = 0 if not skip_memory: try: mem_config = _agent_cfg.get("memory", {}) self._memory_enabled = mem_config.get("memory_enabled", False) self._user_profile_enabled = mem_config.get("user_profile_enabled", False) self._memory_nudge_interval = int(mem_config.get("nudge_interval", 10)) self._memory_flush_min_turns = int(mem_config.get("flush_min_turns", 6)) if self._memory_enabled or self._user_profile_enabled: from tools.memory_tool import MemoryStore self._memory_store = MemoryStore( memory_char_limit=mem_config.get("memory_char_limit", 2200), user_char_limit=mem_config.get("user_char_limit", 1375), ) self._memory_store.load_from_disk() except Exception: pass # Memory is optional -- don't break agent init # Honcho AI-native memory (cross-session user modeling) # Reads $HERMES_HOME/honcho.json (instance) or ~/.honcho/config.json (global). self._honcho = None # HonchoSessionManager | None self._honcho_session_key = honcho_session_key self._honcho_config = None # HonchoClientConfig | None self._honcho_exit_hook_registered = False if not skip_memory: try: if honcho_manager is not None: hcfg = honcho_config or getattr(honcho_manager, "_config", None) self._honcho_config = hcfg if hcfg and self._honcho_should_activate(hcfg): self._honcho = honcho_manager self._activate_honcho( hcfg, enabled_toolsets=enabled_toolsets, disabled_toolsets=disabled_toolsets, session_db=session_db, ) else: from honcho_integration.client import HonchoClientConfig, get_honcho_client hcfg = HonchoClientConfig.from_global_config() self._honcho_config = hcfg if self._honcho_should_activate(hcfg): from honcho_integration.session import HonchoSessionManager client = get_honcho_client(hcfg) self._honcho = HonchoSessionManager( honcho=client, config=hcfg, context_tokens=hcfg.context_tokens, ) self._activate_honcho( hcfg, enabled_toolsets=enabled_toolsets, disabled_toolsets=disabled_toolsets, session_db=session_db, ) else: if not hcfg.enabled: logger.debug("Honcho disabled in global config") elif not hcfg.api_key: logger.debug("Honcho enabled but no API key configured") else: logger.debug("Honcho enabled but missing API key or disabled in config") except Exception as e: logger.warning("Honcho init failed — memory disabled: %s", e) print(f" Honcho init failed: {e}") print(" Run 'hermes honcho setup' to reconfigure.") self._honcho = None # Tools are initially discovered before Honcho activation. If Honcho # stays inactive, remove any stale honcho_* tools from prior process state. if not self._honcho: self._strip_honcho_tools_from_surface() # Gate local memory writes based on per-peer memory modes. # AI peer governs MEMORY.md; user peer governs USER.md. # "honcho" = Honcho only, disable local writes. if self._honcho_config and self._honcho: _hcfg = self._honcho_config _agent_mode = _hcfg.peer_memory_mode(_hcfg.ai_peer) _user_mode = _hcfg.peer_memory_mode(_hcfg.peer_name or "user") if _agent_mode == "honcho": self._memory_flush_min_turns = 0 self._memory_enabled = False logger.debug("peer %s memory_mode=honcho: local MEMORY.md writes disabled", _hcfg.ai_peer) if _user_mode == "honcho": self._user_profile_enabled = False logger.debug("peer %s memory_mode=honcho: local USER.md writes disabled", _hcfg.peer_name or "user") # Skills config: nudge interval for skill creation reminders self._skill_nudge_interval = 10 try: skills_config = _agent_cfg.get("skills", {}) self._skill_nudge_interval = int(skills_config.get("creation_nudge_interval", 10)) except Exception: pass # Initialize context compressor for automatic context management # Compresses conversation when approaching model's context limit # Configuration via config.yaml (compression section) _compression_cfg = _agent_cfg.get("compression", {}) if not isinstance(_compression_cfg, dict): _compression_cfg = {} compression_threshold = float(_compression_cfg.get("threshold", 0.50)) compression_enabled = str(_compression_cfg.get("enabled", True)).lower() in ("true", "1", "yes") compression_summary_model = _compression_cfg.get("summary_model") or None compression_target_ratio = float(_compression_cfg.get("target_ratio", 0.20)) compression_protect_last = int(_compression_cfg.get("protect_last_n", 20)) # Read explicit context_length override from model config _model_cfg = _agent_cfg.get("model", {}) if isinstance(_model_cfg, dict): _config_context_length = _model_cfg.get("context_length") else: _config_context_length = None if _config_context_length is not None: try: _config_context_length = int(_config_context_length) except (TypeError, ValueError): _config_context_length = None # Check custom_providers per-model context_length if _config_context_length is None: _custom_providers = _agent_cfg.get("custom_providers") if isinstance(_custom_providers, list): for _cp_entry in _custom_providers: if not isinstance(_cp_entry, dict): continue _cp_url = (_cp_entry.get("base_url") or "").rstrip("/") if _cp_url and _cp_url == self.base_url.rstrip("/"): _cp_models = _cp_entry.get("models", {}) if isinstance(_cp_models, dict): _cp_model_cfg = _cp_models.get(self.model, {}) if isinstance(_cp_model_cfg, dict): _cp_ctx = _cp_model_cfg.get("context_length") if _cp_ctx is not None: try: _config_context_length = int(_cp_ctx) except (TypeError, ValueError): pass break self.context_compressor = ContextCompressor( model=self.model, threshold_percent=compression_threshold, protect_first_n=3, protect_last_n=compression_protect_last, summary_target_ratio=compression_target_ratio, summary_model_override=compression_summary_model, quiet_mode=self.quiet_mode, base_url=self.base_url, api_key=getattr(self, "api_key", ""), config_context_length=_config_context_length, provider=self.provider, ) self.compression_enabled = compression_enabled self._user_turn_count = 0 # Cumulative token usage for the session self.session_prompt_tokens = 0 self.session_completion_tokens = 0 self.session_total_tokens = 0 self.session_api_calls = 0 self.session_input_tokens = 0 self.session_output_tokens = 0 self.session_cache_read_tokens = 0 self.session_cache_write_tokens = 0 self.session_reasoning_tokens = 0 self.session_estimated_cost_usd = 0.0 self.session_cost_status = "unknown" self.session_cost_source = "none" if not self.quiet_mode: if compression_enabled: print(f"📊 Context limit: {self.context_compressor.context_length:,} tokens (compress at {int(compression_threshold*100)}% = {self.context_compressor.threshold_tokens:,})") else: print(f"📊 Context limit: {self.context_compressor.context_length:,} tokens (auto-compression disabled)") def reset_session_state(self): """Reset all session-scoped token counters to 0 for a fresh session. This method encapsulates the reset logic for all session-level metrics including: - Token usage counters (input, output, total, prompt, completion) - Cache read/write tokens - API call count - Reasoning tokens - Estimated cost tracking - Context compressor internal counters The method safely handles optional attributes (e.g., context compressor) using ``hasattr`` checks. This keeps the counter reset logic DRY and maintainable in one place rather than scattering it across multiple methods. """ # Token usage counters self.session_total_tokens = 0 self.session_input_tokens = 0 self.session_output_tokens = 0 self.session_prompt_tokens = 0 self.session_completion_tokens = 0 self.session_cache_read_tokens = 0 self.session_cache_write_tokens = 0 self.session_reasoning_tokens = 0 self.session_api_calls = 0 self.session_estimated_cost_usd = 0.0 self.session_cost_status = "unknown" self.session_cost_source = "none" # Turn counter (added after reset_session_state was first written — #2635) self._user_turn_count = 0 # Context compressor internal counters (if present) if hasattr(self, "context_compressor") and self.context_compressor: self.context_compressor.last_prompt_tokens = 0 self.context_compressor.last_completion_tokens = 0 self.context_compressor.last_total_tokens = 0 self.context_compressor.compression_count = 0 self.context_compressor._context_probed = False # Iterative summary from previous session must not bleed into new one (#2635) self.context_compressor._previous_summary = None def _safe_print(self, *args, **kwargs): """Print that silently handles broken pipes / closed stdout. In headless environments (systemd, Docker, nohup) stdout may become unavailable mid-session. A raw ``print()`` raises ``OSError`` which can crash cron jobs and lose completed work. Internally routes through ``self._print_fn`` (default: builtin ``print``) so callers such as the CLI can inject a renderer that handles ANSI escape sequences properly (e.g. prompt_toolkit's ``print_formatted_text(ANSI(...))``) without touching this method. """ try: fn = self._print_fn or print fn(*args, **kwargs) except OSError: pass def _vprint(self, *args, force: bool = False, **kwargs): """Verbose print — suppressed when actively streaming tokens. Pass ``force=True`` for error/warning messages that should always be shown even during streaming playback (TTS or display). During tool execution (``_executing_tools`` is True), printing is allowed even with stream consumers registered because no tokens are being streamed at that point. After the main response has been delivered and the remaining tool calls are post-response housekeeping (``_mute_post_response``), all non-forced output is suppressed. """ if not force and getattr(self, "_mute_post_response", False): return if not force and self._has_stream_consumers() and not self._executing_tools: return self._safe_print(*args, **kwargs) def _is_direct_openai_url(self, base_url: str = None) -> bool: """Return True when a base URL targets OpenAI's native API.""" url = (base_url or self._base_url_lower).lower() return "api.openai.com" in url and "openrouter" not in url def _max_tokens_param(self, value: int) -> dict: """Return the correct max tokens kwarg for the current provider. OpenAI's newer models (gpt-4o, o-series, gpt-5+) require 'max_completion_tokens'. OpenRouter, local models, and older OpenAI models use 'max_tokens'. """ if self._is_direct_openai_url(): return {"max_completion_tokens": value} return {"max_tokens": value} def _has_content_after_think_block(self, content: str) -> bool: """ Check if content has actual text after any reasoning/thinking blocks. This detects cases where the model only outputs reasoning but no actual response, which indicates an incomplete generation that should be retried. Must stay in sync with _strip_think_blocks() tag variants. Args: content: The assistant message content to check Returns: True if there's meaningful content after think blocks, False otherwise """ if not content: return False # Remove all reasoning tag variants (must match _strip_think_blocks) cleaned = self._strip_think_blocks(content) # Check if there's any non-whitespace content remaining return bool(cleaned.strip()) def _strip_think_blocks(self, content: str) -> str: """Remove reasoning/thinking blocks from content, returning only visible text.""" if not content: return "" # Strip all reasoning tag variants: , , , # , content = re.sub(r'.*?', '', content, flags=re.DOTALL) content = re.sub(r'.*?', '', content, flags=re.DOTALL | re.IGNORECASE) content = re.sub(r'.*?', '', content, flags=re.DOTALL) content = re.sub(r'.*?', '', content, flags=re.DOTALL) return content def _looks_like_codex_intermediate_ack( self, user_message: str, assistant_content: str, messages: List[Dict[str, Any]], ) -> bool: """Detect a planning/ack message that should continue instead of ending the turn.""" if any(isinstance(msg, dict) and msg.get("role") == "tool" for msg in messages): return False assistant_text = self._strip_think_blocks(assistant_content or "").strip().lower() if not assistant_text: return False if len(assistant_text) > 1200: return False has_future_ack = bool( re.search(r"\b(i['’]ll|i will|let me|i can do that|i can help with that)\b", assistant_text) ) if not has_future_ack: return False action_markers = ( "look into", "look at", "inspect", "scan", "check", "analyz", "review", "explore", "read", "open", "run", "test", "fix", "debug", "search", "find", "walkthrough", "report back", "summarize", ) workspace_markers = ( "directory", "current directory", "current dir", "cwd", "repo", "repository", "codebase", "project", "folder", "filesystem", "file tree", "files", "path", ) user_text = (user_message or "").strip().lower() user_targets_workspace = ( any(marker in user_text for marker in workspace_markers) or "~/" in user_text or "/" in user_text ) assistant_mentions_action = any(marker in assistant_text for marker in action_markers) assistant_targets_workspace = any( marker in assistant_text for marker in workspace_markers ) return (user_targets_workspace or assistant_targets_workspace) and assistant_mentions_action def _extract_reasoning(self, assistant_message) -> Optional[str]: """ Extract reasoning/thinking content from an assistant message. OpenRouter and various providers can return reasoning in multiple formats: 1. message.reasoning - Direct reasoning field (DeepSeek, Qwen, etc.) 2. message.reasoning_content - Alternative field (Moonshot AI, Novita, etc.) 3. message.reasoning_details - Array of {type, summary, ...} objects (OpenRouter unified) Args: assistant_message: The assistant message object from the API response Returns: Combined reasoning text, or None if no reasoning found """ reasoning_parts = [] # Check direct reasoning field if hasattr(assistant_message, 'reasoning') and assistant_message.reasoning: reasoning_parts.append(assistant_message.reasoning) # Check reasoning_content field (alternative name used by some providers) if hasattr(assistant_message, 'reasoning_content') and assistant_message.reasoning_content: # Don't duplicate if same as reasoning if assistant_message.reasoning_content not in reasoning_parts: reasoning_parts.append(assistant_message.reasoning_content) # Check reasoning_details array (OpenRouter unified format) # Format: [{"type": "reasoning.summary", "summary": "...", ...}, ...] if hasattr(assistant_message, 'reasoning_details') and assistant_message.reasoning_details: for detail in assistant_message.reasoning_details: if isinstance(detail, dict): # Extract summary from reasoning detail object summary = detail.get('summary') or detail.get('content') or detail.get('text') if summary and summary not in reasoning_parts: reasoning_parts.append(summary) # Some providers embed reasoning directly inside assistant content # instead of returning structured reasoning fields. Only fall back # to inline extraction when no structured reasoning was found. content = getattr(assistant_message, "content", None) if not reasoning_parts and isinstance(content, str) and content: inline_patterns = ( r"(.*?)", r"(.*?)", r"(.*?)", r"(.*?)", ) for pattern in inline_patterns: flags = re.DOTALL | re.IGNORECASE for block in re.findall(pattern, content, flags=flags): cleaned = block.strip() if cleaned and cleaned not in reasoning_parts: reasoning_parts.append(cleaned) # Combine all reasoning parts if reasoning_parts: return "\n\n".join(reasoning_parts) return None def _cleanup_task_resources(self, task_id: str) -> None: """Clean up VM and browser resources for a given task.""" try: cleanup_vm(task_id) except Exception as e: if self.verbose_logging: logging.warning(f"Failed to cleanup VM for task {task_id}: {e}") try: cleanup_browser(task_id) except Exception as e: if self.verbose_logging: logging.warning(f"Failed to cleanup browser for task {task_id}: {e}") # ------------------------------------------------------------------ # Background memory/skill review # ------------------------------------------------------------------ _MEMORY_REVIEW_PROMPT = ( "Review the conversation above and consider saving to memory if appropriate.\n\n" "Focus on:\n" "1. Has the user revealed things about themselves — their persona, desires, " "preferences, or personal details worth remembering?\n" "2. Has the user expressed expectations about how you should behave, their work " "style, or ways they want you to operate?\n\n" "If something stands out, save it using the memory tool. " "If nothing is worth saving, just say 'Nothing to save.' and stop." ) _SKILL_REVIEW_PROMPT = ( "Review the conversation above and consider saving or updating a skill if appropriate.\n\n" "Focus on: was a non-trivial approach used to complete a task that required trial " "and error, or changing course due to experiential findings along the way, or did " "the user expect or desire a different method or outcome?\n\n" "If a relevant skill already exists, update it with what you learned. " "Otherwise, create a new skill if the approach is reusable.\n" "If nothing is worth saving, just say 'Nothing to save.' and stop." ) _COMBINED_REVIEW_PROMPT = ( "Review the conversation above and consider two things:\n\n" "**Memory**: Has the user revealed things about themselves — their persona, " "desires, preferences, or personal details? Has the user expressed expectations " "about how you should behave, their work style, or ways they want you to operate? " "If so, save using the memory tool.\n\n" "**Skills**: Was a non-trivial approach used to complete a task that required trial " "and error, or changing course due to experiential findings along the way, or did " "the user expect or desire a different method or outcome? If a relevant skill " "already exists, update it. Otherwise, create a new one if the approach is reusable.\n\n" "Only act if there's something genuinely worth saving. " "If nothing stands out, just say 'Nothing to save.' and stop." ) def _spawn_background_review( self, messages_snapshot: List[Dict], review_memory: bool = False, review_skills: bool = False, ) -> None: """Spawn a background thread to review the conversation for memory/skill saves. Creates a full AIAgent fork with the same model, tools, and context as the main session. The review prompt is appended as the next user turn in the forked conversation. Writes directly to the shared memory/skill stores. Never modifies the main conversation history or produces user-visible output. """ import threading # Pick the right prompt based on which triggers fired if review_memory and review_skills: prompt = self._COMBINED_REVIEW_PROMPT elif review_memory: prompt = self._MEMORY_REVIEW_PROMPT else: prompt = self._SKILL_REVIEW_PROMPT def _run_review(): import contextlib, os as _os review_agent = None try: with open(_os.devnull, "w") as _devnull, \ contextlib.redirect_stdout(_devnull), \ contextlib.redirect_stderr(_devnull): review_agent = AIAgent( model=self.model, max_iterations=8, quiet_mode=True, platform=self.platform, provider=self.provider, ) review_agent._memory_store = self._memory_store review_agent._memory_enabled = self._memory_enabled review_agent._user_profile_enabled = self._user_profile_enabled review_agent._memory_nudge_interval = 0 review_agent._skill_nudge_interval = 0 review_agent.run_conversation( user_message=prompt, conversation_history=messages_snapshot, ) # Scan the review agent's messages for successful tool actions # and surface a compact summary to the user. actions = [] for msg in getattr(review_agent, "_session_messages", []): if not isinstance(msg, dict) or msg.get("role") != "tool": continue try: data = json.loads(msg.get("content", "{}")) except (json.JSONDecodeError, TypeError): continue if not data.get("success"): continue message = data.get("message", "") target = data.get("target", "") if "created" in message.lower(): actions.append(message) elif "updated" in message.lower(): actions.append(message) elif "added" in message.lower() or (target and "add" in message.lower()): label = "Memory" if target == "memory" else "User profile" if target == "user" else target actions.append(f"{label} updated") elif "Entry added" in message: label = "Memory" if target == "memory" else "User profile" if target == "user" else target actions.append(f"{label} updated") elif "removed" in message.lower() or "replaced" in message.lower(): label = "Memory" if target == "memory" else "User profile" if target == "user" else target actions.append(f"{label} updated") if actions: summary = " · ".join(dict.fromkeys(actions)) self._safe_print(f" 💾 {summary}") except Exception as e: logger.debug("Background memory/skill review failed: %s", e) finally: # Explicitly close the OpenAI/httpx client so GC doesn't # try to clean it up on a dead asyncio event loop (which # produces "Event loop is closed" errors in the terminal). if review_agent is not None: client = getattr(review_agent, "client", None) if client is not None: try: review_agent._close_openai_client( client, reason="bg_review_done", shared=True ) review_agent.client = None except Exception: pass t = threading.Thread(target=_run_review, daemon=True, name="bg-review") t.start() def _apply_persist_user_message_override(self, messages: List[Dict]) -> None: """Rewrite the current-turn user message before persistence/return. Some call paths need an API-only user-message variant without letting that synthetic text leak into persisted transcripts or resumed session history. When an override is configured for the active turn, mutate the in-memory messages list in place so both persistence and returned history stay clean. """ idx = getattr(self, "_persist_user_message_idx", None) override = getattr(self, "_persist_user_message_override", None) if override is None or idx is None: return if 0 <= idx < len(messages): msg = messages[idx] if isinstance(msg, dict) and msg.get("role") == "user": msg["content"] = override def _persist_session(self, messages: List[Dict], conversation_history: List[Dict] = None): """Save session state to both JSON log and SQLite on any exit path. Ensures conversations are never lost, even on errors or early returns. """ self._apply_persist_user_message_override(messages) self._session_messages = messages self._save_session_log(messages) self._flush_messages_to_session_db(messages, conversation_history) def _flush_messages_to_session_db(self, messages: List[Dict], conversation_history: List[Dict] = None): """Persist any un-flushed messages to the SQLite session store. Uses _last_flushed_db_idx to track which messages have already been written, so repeated calls (from multiple exit paths) only write truly new messages — preventing the duplicate-write bug (#860). """ if not self._session_db: return self._apply_persist_user_message_override(messages) try: start_idx = len(conversation_history) if conversation_history else 0 flush_from = max(start_idx, self._last_flushed_db_idx) for msg in messages[flush_from:]: role = msg.get("role", "unknown") content = msg.get("content") tool_calls_data = None if hasattr(msg, "tool_calls") and msg.tool_calls: tool_calls_data = [ {"name": tc.function.name, "arguments": tc.function.arguments} for tc in msg.tool_calls ] elif isinstance(msg.get("tool_calls"), list): tool_calls_data = msg["tool_calls"] self._session_db.append_message( session_id=self.session_id, role=role, content=content, tool_name=msg.get("tool_name"), tool_calls=tool_calls_data, tool_call_id=msg.get("tool_call_id"), finish_reason=msg.get("finish_reason"), reasoning=msg.get("reasoning") if role == "assistant" else None, reasoning_details=msg.get("reasoning_details") if role == "assistant" else None, codex_reasoning_items=msg.get("codex_reasoning_items") if role == "assistant" else None, ) self._last_flushed_db_idx = len(messages) except Exception as e: logger.warning("Session DB append_message failed: %s", e) def _get_messages_up_to_last_assistant(self, messages: List[Dict]) -> List[Dict]: """ Get messages up to (but not including) the last assistant turn. This is used when we need to "roll back" to the last successful point in the conversation, typically when the final assistant message is incomplete or malformed. Args: messages: Full message list Returns: Messages up to the last complete assistant turn (ending with user/tool message) """ if not messages: return [] # Find the index of the last assistant message last_assistant_idx = None for i in range(len(messages) - 1, -1, -1): if messages[i].get("role") == "assistant": last_assistant_idx = i break if last_assistant_idx is None: # No assistant message found, return all messages return messages.copy() # Return everything up to (not including) the last assistant message return messages[:last_assistant_idx] def _format_tools_for_system_message(self) -> str: """ Format tool definitions for the system message in the trajectory format. Returns: str: JSON string representation of tool definitions """ if not self.tools: return "[]" # Convert tool definitions to the format expected in trajectories formatted_tools = [] for tool in self.tools: func = tool["function"] formatted_tool = { "name": func["name"], "description": func.get("description", ""), "parameters": func.get("parameters", {}), "required": None # Match the format in the example } formatted_tools.append(formatted_tool) return json.dumps(formatted_tools, ensure_ascii=False) def _convert_to_trajectory_format(self, messages: List[Dict[str, Any]], user_query: str, completed: bool) -> List[Dict[str, Any]]: """ Convert internal message format to trajectory format for saving. Args: messages (List[Dict]): Internal message history user_query (str): Original user query completed (bool): Whether the conversation completed successfully Returns: List[Dict]: Messages in trajectory format """ trajectory = [] # Add system message with tool definitions system_msg = ( "You are a function calling AI model. You are provided with function signatures within XML tags. " "You may call one or more functions to assist with the user query. If available tools are not relevant in assisting " "with user query, just respond in natural conversational language. Don't make assumptions about what values to plug " "into functions. After calling & executing the functions, you will be provided with function results within " " XML tags. Here are the available tools:\n" f"\n{self._format_tools_for_system_message()}\n\n" "For each function call return a JSON object, with the following pydantic model json schema for each:\n" "{'title': 'FunctionCall', 'type': 'object', 'properties': {'name': {'title': 'Name', 'type': 'string'}, " "'arguments': {'title': 'Arguments', 'type': 'object'}}, 'required': ['name', 'arguments']}\n" "Each function call should be enclosed within XML tags.\n" "Example:\n\n{'name': ,'arguments': }\n" ) trajectory.append({ "from": "system", "value": system_msg }) # Add the actual user prompt (from the dataset) as the first human message trajectory.append({ "from": "human", "value": user_query }) # Skip the first message (the user query) since we already added it above. # Prefill messages are injected at API-call time only (not in the messages # list), so no offset adjustment is needed here. i = 1 while i < len(messages): msg = messages[i] if msg["role"] == "assistant": # Check if this message has tool calls if "tool_calls" in msg and msg["tool_calls"]: # Format assistant message with tool calls # Add tags around reasoning for trajectory storage content = "" # Prepend reasoning in tags if available (native thinking tokens) if msg.get("reasoning") and msg["reasoning"].strip(): content = f"\n{msg['reasoning']}\n\n" if msg.get("content") and msg["content"].strip(): # Convert any tags to tags # (used when native thinking is disabled and model reasons via XML) content += convert_scratchpad_to_think(msg["content"]) + "\n" # Add tool calls wrapped in XML tags for tool_call in msg["tool_calls"]: if not tool_call or not isinstance(tool_call, dict): continue # Parse arguments - should always succeed since we validate during conversation # but keep try-except as safety net try: arguments = json.loads(tool_call["function"]["arguments"]) if isinstance(tool_call["function"]["arguments"], str) else tool_call["function"]["arguments"] except json.JSONDecodeError: # This shouldn't happen since we validate and retry during conversation, # but if it does, log warning and use empty dict logging.warning(f"Unexpected invalid JSON in trajectory conversion: {tool_call['function']['arguments'][:100]}") arguments = {} tool_call_json = { "name": tool_call["function"]["name"], "arguments": arguments } content += f"\n{json.dumps(tool_call_json, ensure_ascii=False)}\n\n" # Ensure every gpt turn has a block (empty if no reasoning) # so the format is consistent for training data if "" not in content: content = "\n\n" + content trajectory.append({ "from": "gpt", "value": content.rstrip() }) # Collect all subsequent tool responses tool_responses = [] j = i + 1 while j < len(messages) and messages[j]["role"] == "tool": tool_msg = messages[j] # Format tool response with XML tags tool_response = f"\n" # Try to parse tool content as JSON if it looks like JSON tool_content = tool_msg["content"] try: if tool_content.strip().startswith(("{", "[")): tool_content = json.loads(tool_content) except (json.JSONDecodeError, AttributeError): pass # Keep as string if not valid JSON tool_index = len(tool_responses) tool_name = ( msg["tool_calls"][tool_index]["function"]["name"] if tool_index < len(msg["tool_calls"]) else "unknown" ) tool_response += json.dumps({ "tool_call_id": tool_msg.get("tool_call_id", ""), "name": tool_name, "content": tool_content }, ensure_ascii=False) tool_response += "\n" tool_responses.append(tool_response) j += 1 # Add all tool responses as a single message if tool_responses: trajectory.append({ "from": "tool", "value": "\n".join(tool_responses) }) i = j - 1 # Skip the tool messages we just processed else: # Regular assistant message without tool calls # Add tags around reasoning for trajectory storage content = "" # Prepend reasoning in tags if available (native thinking tokens) if msg.get("reasoning") and msg["reasoning"].strip(): content = f"\n{msg['reasoning']}\n\n" # Convert any tags to tags # (used when native thinking is disabled and model reasons via XML) raw_content = msg["content"] or "" content += convert_scratchpad_to_think(raw_content) # Ensure every gpt turn has a block (empty if no reasoning) if "" not in content: content = "\n\n" + content trajectory.append({ "from": "gpt", "value": content.strip() }) elif msg["role"] == "user": trajectory.append({ "from": "human", "value": msg["content"] }) i += 1 return trajectory def _save_trajectory(self, messages: List[Dict[str, Any]], user_query: str, completed: bool): """ Save conversation trajectory to JSONL file. Args: messages (List[Dict]): Complete message history user_query (str): Original user query completed (bool): Whether the conversation completed successfully """ if not self.save_trajectories: return trajectory = self._convert_to_trajectory_format(messages, user_query, completed) _save_trajectory_to_file(trajectory, self.model, completed) def _mask_api_key_for_logs(self, key: Optional[str]) -> Optional[str]: if not key: return None if len(key) <= 12: return "***" return f"{key[:8]}...{key[-4:]}" def _clean_error_message(self, error_msg: str) -> str: """ Clean up error messages for user display, removing HTML content and truncating. Args: error_msg: Raw error message from API or exception Returns: Clean, user-friendly error message """ if not error_msg: return "Unknown error" # Remove HTML content (common with CloudFlare and gateway error pages) if error_msg.strip().startswith(' 150: cleaned = cleaned[:150] + "..." return cleaned def _dump_api_request_debug( self, api_kwargs: Dict[str, Any], *, reason: str, error: Optional[Exception] = None, ) -> Optional[Path]: """ Dump a debug-friendly HTTP request record for the active inference API. Captures the request body from api_kwargs (excluding transport-only keys like timeout). Intended for debugging provider-side 4xx failures where retries are not useful. """ try: body = copy.deepcopy(api_kwargs) body.pop("timeout", None) body = {k: v for k, v in body.items() if v is not None} api_key = None try: api_key = getattr(self.client, "api_key", None) except Exception as e: logger.debug("Could not extract API key for debug dump: %s", e) dump_payload: Dict[str, Any] = { "timestamp": datetime.now().isoformat(), "session_id": self.session_id, "reason": reason, "request": { "method": "POST", "url": f"{self.base_url.rstrip('/')}{'/responses' if self.api_mode == 'codex_responses' else '/chat/completions'}", "headers": { "Authorization": f"Bearer {self._mask_api_key_for_logs(api_key)}", "Content-Type": "application/json", }, "body": body, }, } if error is not None: error_info: Dict[str, Any] = { "type": type(error).__name__, "message": str(error), } for attr_name in ("status_code", "request_id", "code", "param", "type"): attr_value = getattr(error, attr_name, None) if attr_value is not None: error_info[attr_name] = attr_value body_attr = getattr(error, "body", None) if body_attr is not None: error_info["body"] = body_attr response_obj = getattr(error, "response", None) if response_obj is not None: try: error_info["response_status"] = getattr(response_obj, "status_code", None) error_info["response_text"] = response_obj.text except Exception as e: logger.debug("Could not extract error response details: %s", e) dump_payload["error"] = error_info timestamp = datetime.now().strftime("%Y%m%d_%H%M%S_%f") dump_file = self.logs_dir / f"request_dump_{self.session_id}_{timestamp}.json" dump_file.write_text( json.dumps(dump_payload, ensure_ascii=False, indent=2, default=str), encoding="utf-8", ) self._vprint(f"{self.log_prefix}🧾 Request debug dump written to: {dump_file}") if os.getenv("HERMES_DUMP_REQUEST_STDOUT", "").strip().lower() in {"1", "true", "yes", "on"}: print(json.dumps(dump_payload, ensure_ascii=False, indent=2, default=str)) return dump_file except Exception as dump_error: if self.verbose_logging: logging.warning(f"Failed to dump API request debug payload: {dump_error}") return None @staticmethod def _clean_session_content(content: str) -> str: """Convert REASONING_SCRATCHPAD to think tags and clean up whitespace.""" if not content: return content content = convert_scratchpad_to_think(content) content = re.sub(r'\n+()', r'\n\1', content) content = re.sub(r'()\n+', r'\1\n', content) return content.strip() def _save_session_log(self, messages: List[Dict[str, Any]] = None): """ Save the full raw session to a JSON file. Stores every message exactly as the agent sees it: user messages, assistant messages (with reasoning, finish_reason, tool_calls), tool responses (with tool_call_id, tool_name), and injected system messages (compression summaries, todo snapshots, etc.). REASONING_SCRATCHPAD tags are converted to blocks for consistency. Overwritten after each turn so it always reflects the latest state. """ messages = messages or self._session_messages if not messages: return try: # Clean assistant content for session logs cleaned = [] for msg in messages: if msg.get("role") == "assistant" and msg.get("content"): msg = dict(msg) msg["content"] = self._clean_session_content(msg["content"]) cleaned.append(msg) entry = { "session_id": self.session_id, "model": self.model, "base_url": self.base_url, "platform": self.platform, "session_start": self.session_start.isoformat(), "last_updated": datetime.now().isoformat(), "system_prompt": self._cached_system_prompt or "", "tools": self.tools or [], "message_count": len(cleaned), "messages": cleaned, } atomic_json_write( self.session_log_file, entry, indent=2, default=str, ) except Exception as e: if self.verbose_logging: logging.warning(f"Failed to save session log: {e}") def interrupt(self, message: str = None) -> None: """ Request the agent to interrupt its current tool-calling loop. Call this from another thread (e.g., input handler, message receiver) to gracefully stop the agent and process a new message. Also signals long-running tool executions (e.g. terminal commands) to terminate early, so the agent can respond immediately. Args: message: Optional new message that triggered the interrupt. If provided, the agent will include this in its response context. Example (CLI): # In a separate input thread: if user_typed_something: agent.interrupt(user_input) Example (Messaging): # When new message arrives for active session: if session_has_running_agent: running_agent.interrupt(new_message.text) """ self._interrupt_requested = True self._interrupt_message = message # Signal all tools to abort any in-flight operations immediately _set_interrupt(True) # Propagate interrupt to any running child agents (subagent delegation) with self._active_children_lock: children_copy = list(self._active_children) for child in children_copy: try: child.interrupt(message) except Exception as e: logger.debug("Failed to propagate interrupt to child agent: %s", e) if not self.quiet_mode: print(f"\n⚡ Interrupt requested" + (f": '{message[:40]}...'" if message and len(message) > 40 else f": '{message}'" if message else "")) def clear_interrupt(self) -> None: """Clear any pending interrupt request and the global tool interrupt signal.""" self._interrupt_requested = False self._interrupt_message = None _set_interrupt(False) def _hydrate_todo_store(self, history: List[Dict[str, Any]]) -> None: """ Recover todo state from conversation history. The gateway creates a fresh AIAgent per message, so the in-memory TodoStore is empty. We scan the history for the most recent todo tool response and replay it to reconstruct the state. """ # Walk history backwards to find the most recent todo tool response last_todo_response = None for msg in reversed(history): if msg.get("role") != "tool": continue content = msg.get("content", "") # Quick check: todo responses contain "todos" key if '"todos"' not in content: continue try: data = json.loads(content) if "todos" in data and isinstance(data["todos"], list): last_todo_response = data["todos"] break except (json.JSONDecodeError, TypeError): continue if last_todo_response: # Replay the items into the store (replace mode) self._todo_store.write(last_todo_response, merge=False) if not self.quiet_mode: self._vprint(f"{self.log_prefix}📋 Restored {len(last_todo_response)} todo item(s) from history") _set_interrupt(False) @property def is_interrupted(self) -> bool: """Check if an interrupt has been requested.""" return self._interrupt_requested # ── Honcho integration helpers ── def _honcho_should_activate(self, hcfg) -> bool: """Return True when remote Honcho should be active.""" if not hcfg or not hcfg.enabled or not hcfg.api_key: return False return True def _strip_honcho_tools_from_surface(self) -> None: """Remove Honcho tools from the active tool surface.""" if not self.tools: self.valid_tool_names = set() return self.tools = [ tool for tool in self.tools if tool.get("function", {}).get("name") not in HONCHO_TOOL_NAMES ] self.valid_tool_names = { tool["function"]["name"] for tool in self.tools } if self.tools else set() def _activate_honcho( self, hcfg, *, enabled_toolsets: Optional[List[str]], disabled_toolsets: Optional[List[str]], session_db, ) -> None: """Finish Honcho setup once a session manager is available.""" if not self._honcho: return if not self._honcho_session_key: session_title = None if session_db is not None: try: session_title = session_db.get_session_title(self.session_id or "") except Exception: pass self._honcho_session_key = ( hcfg.resolve_session_name( session_title=session_title, session_id=self.session_id, ) or "hermes-default" ) honcho_sess = self._honcho.get_or_create(self._honcho_session_key) if not honcho_sess.messages: try: from hermes_cli.config import get_hermes_home mem_dir = str(get_hermes_home() / "memories") self._honcho.migrate_memory_files( self._honcho_session_key, mem_dir, ) except Exception as exc: logger.debug("Memory files migration failed (non-fatal): %s", exc) from tools.honcho_tools import set_session_context set_session_context(self._honcho, self._honcho_session_key) # Rebuild tool surface after Honcho context injection. Tool availability # is check_fn-gated and may change once session context is attached. self.tools = get_tool_definitions( enabled_toolsets=enabled_toolsets, disabled_toolsets=disabled_toolsets, quiet_mode=True, ) self.valid_tool_names = { tool["function"]["name"] for tool in self.tools } if self.tools else set() if hcfg.recall_mode == "context": self._strip_honcho_tools_from_surface() if not self.quiet_mode: print(" Honcho active — recall_mode: context (Honcho tools hidden)") else: if not self.quiet_mode: print(f" Honcho active — recall_mode: {hcfg.recall_mode}") logger.info( "Honcho active (session: %s, user: %s, workspace: %s, " "write_frequency: %s, memory_mode: %s)", self._honcho_session_key, hcfg.peer_name, hcfg.workspace_id, hcfg.write_frequency, hcfg.memory_mode, ) recall_mode = hcfg.recall_mode if recall_mode != "tools": try: ctx = self._honcho.get_prefetch_context(self._honcho_session_key) if ctx: self._honcho.set_context_result(self._honcho_session_key, ctx) logger.debug("Honcho context pre-warmed for first turn") except Exception as exc: logger.debug("Honcho context prefetch failed (non-fatal): %s", exc) self._register_honcho_exit_hook() def _register_honcho_exit_hook(self) -> None: """Register a process-exit flush hook without clobbering signal handlers.""" if self._honcho_exit_hook_registered or not self._honcho: return honcho_ref = weakref.ref(self._honcho) def _flush_honcho_on_exit(): manager = honcho_ref() if manager is None: return try: manager.flush_all() except Exception as exc: logger.debug("Honcho flush on exit failed (non-fatal): %s", exc) atexit.register(_flush_honcho_on_exit) self._honcho_exit_hook_registered = True def _queue_honcho_prefetch(self, user_message: str) -> None: """Queue turn-end Honcho prefetch so the next turn can consume cached results.""" if not self._honcho or not self._honcho_session_key: return recall_mode = (self._honcho_config.recall_mode if self._honcho_config else "hybrid") if recall_mode == "tools": return try: self._honcho.prefetch_context(self._honcho_session_key, user_message) self._honcho.prefetch_dialectic(self._honcho_session_key, user_message or "What were we working on?") except Exception as exc: logger.debug("Honcho background prefetch failed (non-fatal): %s", exc) def _honcho_prefetch(self, user_message: str) -> str: """Assemble the first-turn Honcho context from the pre-warmed cache.""" if not self._honcho or not self._honcho_session_key: return "" try: parts = [] ctx = self._honcho.pop_context_result(self._honcho_session_key) if ctx: rep = ctx.get("representation", "") card = ctx.get("card", "") if rep: parts.append(f"## User representation\n{rep}") if card: parts.append(card) ai_rep = ctx.get("ai_representation", "") ai_card = ctx.get("ai_card", "") if ai_rep: parts.append(f"## AI peer representation\n{ai_rep}") if ai_card: parts.append(ai_card) dialectic = self._honcho.pop_dialectic_result(self._honcho_session_key) if dialectic: parts.append(f"## Continuity synthesis\n{dialectic}") if not parts: return "" header = ( "# Honcho Memory (persistent cross-session context)\n" "Use this to answer questions about the user, prior sessions, " "and what you were working on together. Do not call tools to " "look up information that is already present here.\n" ) return header + "\n\n".join(parts) except Exception as e: logger.debug("Honcho prefetch failed (non-fatal): %s", e) return "" def _honcho_save_user_observation(self, content: str) -> str: """Route a memory tool target=user add to Honcho. Sends the content as a user peer message so Honcho's reasoning model can incorporate it into the user representation. """ if not content or not content.strip(): return json.dumps({"success": False, "error": "Content cannot be empty."}) try: session = self._honcho.get_or_create(self._honcho_session_key) session.add_message("user", f"[observation] {content.strip()}") self._honcho.save(session) return json.dumps({ "success": True, "target": "user", "message": "Saved to Honcho user model.", }) except Exception as e: logger.debug("Honcho user observation failed: %s", e) return json.dumps({"success": False, "error": f"Honcho save failed: {e}"}) def _honcho_sync(self, user_content: str, assistant_content: str) -> None: """Sync the user/assistant message pair to Honcho.""" if not self._honcho or not self._honcho_session_key: return try: session = self._honcho.get_or_create(self._honcho_session_key) session.add_message("user", user_content) session.add_message("assistant", assistant_content) self._honcho.save(session) logger.info("Honcho sync queued for session %s (%d messages)", self._honcho_session_key, len(session.messages)) except Exception as e: logger.warning("Honcho sync failed: %s", e) if not self.quiet_mode: print(f" Honcho write failed: {e}") def _build_system_prompt(self, system_message: str = None) -> str: """ Assemble the full system prompt from all layers. Called once per session (cached on self._cached_system_prompt) and only rebuilt after context compression events. This ensures the system prompt is stable across all turns in a session, maximizing prefix cache hits. """ # Layers (in order): # 1. Agent identity — SOUL.md when available, else DEFAULT_AGENT_IDENTITY # 2. User / gateway system prompt (if provided) # 3. Persistent memory (frozen snapshot) # 4. Skills guidance (if skills tools are loaded) # 5. Context files (AGENTS.md, .cursorrules — SOUL.md excluded here when used as identity) # 6. Current date & time (frozen at build time) # 7. Platform-specific formatting hint # Try SOUL.md as primary identity (unless context files are skipped) _soul_loaded = False if not self.skip_context_files: _soul_content = load_soul_md() if _soul_content: prompt_parts = [_soul_content] _soul_loaded = True if not _soul_loaded: # Fallback to hardcoded identity _ai_peer_name = ( self._honcho_config.ai_peer if self._honcho_config and self._honcho_config.ai_peer != "hermes" else None ) if _ai_peer_name: _identity = DEFAULT_AGENT_IDENTITY.replace( "You are Hermes Agent", f"You are {_ai_peer_name}", 1, ) else: _identity = DEFAULT_AGENT_IDENTITY prompt_parts = [_identity] # Tool-aware behavioral guidance: only inject when the tools are loaded tool_guidance = [] if "memory" in self.valid_tool_names: tool_guidance.append(MEMORY_GUIDANCE) if "session_search" in self.valid_tool_names: tool_guidance.append(SESSION_SEARCH_GUIDANCE) if "skill_manage" in self.valid_tool_names: tool_guidance.append(SKILLS_GUIDANCE) if tool_guidance: prompt_parts.append(" ".join(tool_guidance)) # Honcho CLI awareness: tell Hermes about its own management commands # so it can refer the user to them rather than reinventing answers. if self._honcho and self._honcho_session_key: hcfg = self._honcho_config mode = hcfg.memory_mode if hcfg else "hybrid" freq = hcfg.write_frequency if hcfg else "async" recall_mode = hcfg.recall_mode if hcfg else "hybrid" honcho_block = ( "# Honcho memory integration\n" f"Active. Session: {self._honcho_session_key}. " f"Mode: {mode}. Write frequency: {freq}. Recall: {recall_mode}.\n" ) if recall_mode == "context": honcho_block += ( "Honcho context is injected into this system prompt below. " "All memory retrieval comes from this context — no Honcho tools " "are available. Answer questions about the user, prior sessions, " "and recent work directly from the Honcho Memory section.\n" ) elif recall_mode == "tools": honcho_block += ( "Honcho tools:\n" " honcho_context — ask Honcho a question, LLM-synthesized answer\n" " honcho_search — semantic search, raw excerpts, no LLM\n" " honcho_profile — user's peer card, key facts, no LLM\n" " honcho_conclude — write a fact about the user to memory\n" ) else: # hybrid honcho_block += ( "Honcho context (user representation, peer card, and recent session summary) " "is injected into this system prompt below. Use it to answer continuity " "questions ('where were we?', 'what were we working on?') WITHOUT calling " "any tools. Only call Honcho tools when you need information beyond what is " "already present in the Honcho Memory section.\n" "Honcho tools:\n" " honcho_context — ask Honcho a question, LLM-synthesized answer\n" " honcho_search — semantic search, raw excerpts, no LLM\n" " honcho_profile — user's peer card, key facts, no LLM\n" " honcho_conclude — write a fact about the user to memory\n" ) honcho_block += ( "Management commands (refer users here instead of explaining manually):\n" " hermes honcho status — show full config + connection\n" " hermes honcho mode [hybrid|honcho] — show or set memory mode\n" " hermes honcho tokens [--context N] [--dialectic N] — show or set token budgets\n" " hermes honcho peer [--user NAME] [--ai NAME] [--reasoning LEVEL]\n" " hermes honcho sessions — list directory→session mappings\n" " hermes honcho map — map cwd to a session name\n" " hermes honcho identity [] [--show] — seed or show AI peer identity\n" " hermes honcho migrate — migration guide from openclaw-honcho\n" " hermes honcho setup — full interactive wizard" ) prompt_parts.append(honcho_block) # Note: ephemeral_system_prompt is NOT included here. It's injected at # API-call time only so it stays out of the cached/stored system prompt. if system_message is not None: prompt_parts.append(system_message) if self._memory_store: if self._memory_enabled: mem_block = self._memory_store.format_for_system_prompt("memory") if mem_block: prompt_parts.append(mem_block) # USER.md is always included when enabled -- Honcho prefetch is additive. if self._user_profile_enabled: user_block = self._memory_store.format_for_system_prompt("user") if user_block: prompt_parts.append(user_block) has_skills_tools = any(name in self.valid_tool_names for name in ['skills_list', 'skill_view', 'skill_manage']) if has_skills_tools: avail_toolsets = {ts for ts, avail in check_toolset_requirements().items() if avail} skills_prompt = build_skills_system_prompt( available_tools=self.valid_tool_names, available_toolsets=avail_toolsets, ) else: skills_prompt = "" if skills_prompt: prompt_parts.append(skills_prompt) if not self.skip_context_files: # Use TERMINAL_CWD for context file discovery when set (gateway # mode). The gateway process runs from the hermes-agent install # dir, so os.getcwd() would pick up the repo's AGENTS.md and # other dev files — inflating token usage by ~10k for no benefit. _context_cwd = os.getenv("TERMINAL_CWD") or None context_files_prompt = build_context_files_prompt( cwd=_context_cwd, skip_soul=_soul_loaded) if context_files_prompt: prompt_parts.append(context_files_prompt) from hermes_time import now as _hermes_now now = _hermes_now() timestamp_line = f"Conversation started: {now.strftime('%A, %B %d, %Y %I:%M %p')}" if self.pass_session_id and self.session_id: timestamp_line += f"\nSession ID: {self.session_id}" if self.model: timestamp_line += f"\nModel: {self.model}" if self.provider: timestamp_line += f"\nProvider: {self.provider}" prompt_parts.append(timestamp_line) # Alibaba Coding Plan API always returns "glm-4.7" as model name regardless # of the requested model. Inject explicit model identity into the system prompt # so the agent can correctly report which model it is (workaround for API bug). if self.provider == "alibaba": _model_short = self.model.split("/")[-1] if "/" in self.model else self.model prompt_parts.append( f"You are powered by the model named {_model_short}. " f"The exact model ID is {self.model}. " f"When asked what model you are, always answer based on this information, " f"not on any model name returned by the API." ) platform_key = (self.platform or "").lower().strip() if platform_key in PLATFORM_HINTS: prompt_parts.append(PLATFORM_HINTS[platform_key]) return "\n\n".join(prompt_parts) # ========================================================================= # Pre/post-call guardrails (inspired by PR #1321 — @alireza78a) # ========================================================================= @staticmethod def _get_tool_call_id_static(tc) -> str: """Extract call ID from a tool_call entry (dict or object).""" if isinstance(tc, dict): return tc.get("id", "") or "" return getattr(tc, "id", "") or "" @staticmethod def _sanitize_api_messages(messages: List[Dict[str, Any]]) -> List[Dict[str, Any]]: """Fix orphaned tool_call / tool_result pairs before every LLM call. Runs unconditionally — not gated on whether the context compressor is present — so orphans from session loading or manual message manipulation are always caught. """ surviving_call_ids: set = set() for msg in messages: if msg.get("role") == "assistant": for tc in msg.get("tool_calls") or []: cid = AIAgent._get_tool_call_id_static(tc) if cid: surviving_call_ids.add(cid) result_call_ids: set = set() for msg in messages: if msg.get("role") == "tool": cid = msg.get("tool_call_id") if cid: result_call_ids.add(cid) # 1. Drop tool results with no matching assistant call orphaned_results = result_call_ids - surviving_call_ids if orphaned_results: messages = [ m for m in messages if not (m.get("role") == "tool" and m.get("tool_call_id") in orphaned_results) ] logger.debug( "Pre-call sanitizer: removed %d orphaned tool result(s)", len(orphaned_results), ) # 2. Inject stub results for calls whose result was dropped missing_results = surviving_call_ids - result_call_ids if missing_results: patched: List[Dict[str, Any]] = [] for msg in messages: patched.append(msg) if msg.get("role") == "assistant": for tc in msg.get("tool_calls") or []: cid = AIAgent._get_tool_call_id_static(tc) if cid in missing_results: patched.append({ "role": "tool", "content": "[Result unavailable — see context summary above]", "tool_call_id": cid, }) messages = patched logger.debug( "Pre-call sanitizer: added %d stub tool result(s)", len(missing_results), ) return messages @staticmethod def _cap_delegate_task_calls(tool_calls: list) -> list: """Truncate excess delegate_task calls to MAX_CONCURRENT_CHILDREN. The delegate_tool caps the task list inside a single call, but the model can emit multiple separate delegate_task tool_calls in one turn. This truncates the excess, preserving all non-delegate calls. Returns the original list if no truncation was needed. """ from tools.delegate_tool import MAX_CONCURRENT_CHILDREN delegate_count = sum(1 for tc in tool_calls if tc.function.name == "delegate_task") if delegate_count <= MAX_CONCURRENT_CHILDREN: return tool_calls kept_delegates = 0 truncated = [] for tc in tool_calls: if tc.function.name == "delegate_task": if kept_delegates < MAX_CONCURRENT_CHILDREN: truncated.append(tc) kept_delegates += 1 else: truncated.append(tc) logger.warning( "Truncated %d excess delegate_task call(s) to enforce " "MAX_CONCURRENT_CHILDREN=%d limit", delegate_count - MAX_CONCURRENT_CHILDREN, MAX_CONCURRENT_CHILDREN, ) return truncated @staticmethod def _deduplicate_tool_calls(tool_calls: list) -> list: """Remove duplicate (tool_name, arguments) pairs within a single turn. Only the first occurrence of each unique pair is kept. Returns the original list if no duplicates were found. """ seen: set = set() unique: list = [] for tc in tool_calls: key = (tc.function.name, tc.function.arguments) if key not in seen: seen.add(key) unique.append(tc) else: logger.warning("Removed duplicate tool call: %s", tc.function.name) return unique if len(unique) < len(tool_calls) else tool_calls def _repair_tool_call(self, tool_name: str) -> str | None: """Attempt to repair a mismatched tool name before aborting. 1. Try lowercase 2. Try normalized (lowercase + hyphens/spaces -> underscores) 3. Try fuzzy match (difflib, cutoff=0.7) Returns the repaired name if found in valid_tool_names, else None. """ from difflib import get_close_matches # 1. Lowercase lowered = tool_name.lower() if lowered in self.valid_tool_names: return lowered # 2. Normalize normalized = lowered.replace("-", "_").replace(" ", "_") if normalized in self.valid_tool_names: return normalized # 3. Fuzzy match matches = get_close_matches(lowered, self.valid_tool_names, n=1, cutoff=0.7) if matches: return matches[0] return None def _invalidate_system_prompt(self): """ Invalidate the cached system prompt, forcing a rebuild on the next turn. Called after context compression events. Also reloads memory from disk so the rebuilt prompt captures any writes from this session. """ self._cached_system_prompt = None if self._memory_store: self._memory_store.load_from_disk() def _responses_tools(self, tools: Optional[List[Dict[str, Any]]] = None) -> Optional[List[Dict[str, Any]]]: """Convert chat-completions tool schemas to Responses function-tool schemas.""" source_tools = tools if tools is not None else self.tools if not source_tools: return None converted: List[Dict[str, Any]] = [] for item in source_tools: fn = item.get("function", {}) if isinstance(item, dict) else {} name = fn.get("name") if not isinstance(name, str) or not name.strip(): continue converted.append({ "type": "function", "name": name, "description": fn.get("description", ""), "strict": False, "parameters": fn.get("parameters", {"type": "object", "properties": {}}), }) return converted or None @staticmethod def _split_responses_tool_id(raw_id: Any) -> tuple[Optional[str], Optional[str]]: """Split a stored tool id into (call_id, response_item_id).""" if not isinstance(raw_id, str): return None, None value = raw_id.strip() if not value: return None, None if "|" in value: call_id, response_item_id = value.split("|", 1) call_id = call_id.strip() or None response_item_id = response_item_id.strip() or None return call_id, response_item_id if value.startswith("fc_"): return None, value return value, None def _derive_responses_function_call_id( self, call_id: str, response_item_id: Optional[str] = None, ) -> str: """Build a valid Responses `function_call.id` (must start with `fc_`).""" if isinstance(response_item_id, str): candidate = response_item_id.strip() if candidate.startswith("fc_"): return candidate source = (call_id or "").strip() if source.startswith("fc_"): return source if source.startswith("call_") and len(source) > len("call_"): return f"fc_{source[len('call_'):]}" sanitized = re.sub(r"[^A-Za-z0-9_-]", "", source) if sanitized.startswith("fc_"): return sanitized if sanitized.startswith("call_") and len(sanitized) > len("call_"): return f"fc_{sanitized[len('call_'):]}" if sanitized: return f"fc_{sanitized[:48]}" seed = source or str(response_item_id or "") or uuid.uuid4().hex digest = hashlib.sha1(seed.encode("utf-8")).hexdigest()[:24] return f"fc_{digest}" def _chat_messages_to_responses_input(self, messages: List[Dict[str, Any]]) -> List[Dict[str, Any]]: """Convert internal chat-style messages to Responses input items.""" items: List[Dict[str, Any]] = [] for msg in messages: if not isinstance(msg, dict): continue role = msg.get("role") if role == "system": continue if role in {"user", "assistant"}: content = msg.get("content", "") content_text = str(content) if content is not None else "" if role == "assistant": # Replay encrypted reasoning items from previous turns # so the API can maintain coherent reasoning chains. codex_reasoning = msg.get("codex_reasoning_items") has_codex_reasoning = False if isinstance(codex_reasoning, list): for ri in codex_reasoning: if isinstance(ri, dict) and ri.get("encrypted_content"): items.append(ri) has_codex_reasoning = True if content_text.strip(): items.append({"role": "assistant", "content": content_text}) elif has_codex_reasoning: # The Responses API requires a following item after each # reasoning item (otherwise: missing_following_item error). # When the assistant produced only reasoning with no visible # content, emit an empty assistant message as the required # following item. items.append({"role": "assistant", "content": ""}) tool_calls = msg.get("tool_calls") if isinstance(tool_calls, list): for tc in tool_calls: if not isinstance(tc, dict): continue fn = tc.get("function", {}) fn_name = fn.get("name") if not isinstance(fn_name, str) or not fn_name.strip(): continue embedded_call_id, embedded_response_item_id = self._split_responses_tool_id( tc.get("id") ) call_id = tc.get("call_id") if not isinstance(call_id, str) or not call_id.strip(): call_id = embedded_call_id if not isinstance(call_id, str) or not call_id.strip(): if ( isinstance(embedded_response_item_id, str) and embedded_response_item_id.startswith("fc_") and len(embedded_response_item_id) > len("fc_") ): call_id = f"call_{embedded_response_item_id[len('fc_'):]}" else: call_id = f"call_{uuid.uuid4().hex[:12]}" call_id = call_id.strip() arguments = fn.get("arguments", "{}") if isinstance(arguments, dict): arguments = json.dumps(arguments, ensure_ascii=False) elif not isinstance(arguments, str): arguments = str(arguments) arguments = arguments.strip() or "{}" items.append({ "type": "function_call", "call_id": call_id, "name": fn_name, "arguments": arguments, }) continue items.append({"role": role, "content": content_text}) continue if role == "tool": raw_tool_call_id = msg.get("tool_call_id") call_id, _ = self._split_responses_tool_id(raw_tool_call_id) if not isinstance(call_id, str) or not call_id.strip(): if isinstance(raw_tool_call_id, str) and raw_tool_call_id.strip(): call_id = raw_tool_call_id.strip() if not isinstance(call_id, str) or not call_id.strip(): continue items.append({ "type": "function_call_output", "call_id": call_id, "output": str(msg.get("content", "") or ""), }) return items def _preflight_codex_input_items(self, raw_items: Any) -> List[Dict[str, Any]]: if not isinstance(raw_items, list): raise ValueError("Codex Responses input must be a list of input items.") normalized: List[Dict[str, Any]] = [] for idx, item in enumerate(raw_items): if not isinstance(item, dict): raise ValueError(f"Codex Responses input[{idx}] must be an object.") item_type = item.get("type") if item_type == "function_call": call_id = item.get("call_id") name = item.get("name") if not isinstance(call_id, str) or not call_id.strip(): raise ValueError(f"Codex Responses input[{idx}] function_call is missing call_id.") if not isinstance(name, str) or not name.strip(): raise ValueError(f"Codex Responses input[{idx}] function_call is missing name.") arguments = item.get("arguments", "{}") if isinstance(arguments, dict): arguments = json.dumps(arguments, ensure_ascii=False) elif not isinstance(arguments, str): arguments = str(arguments) arguments = arguments.strip() or "{}" normalized.append( { "type": "function_call", "call_id": call_id.strip(), "name": name.strip(), "arguments": arguments, } ) continue if item_type == "function_call_output": call_id = item.get("call_id") if not isinstance(call_id, str) or not call_id.strip(): raise ValueError(f"Codex Responses input[{idx}] function_call_output is missing call_id.") output = item.get("output", "") if output is None: output = "" if not isinstance(output, str): output = str(output) normalized.append( { "type": "function_call_output", "call_id": call_id.strip(), "output": output, } ) continue if item_type == "reasoning": encrypted = item.get("encrypted_content") if isinstance(encrypted, str) and encrypted: reasoning_item = {"type": "reasoning", "encrypted_content": encrypted} item_id = item.get("id") if isinstance(item_id, str) and item_id: reasoning_item["id"] = item_id summary = item.get("summary") if isinstance(summary, list): reasoning_item["summary"] = summary else: reasoning_item["summary"] = [] normalized.append(reasoning_item) continue role = item.get("role") if role in {"user", "assistant"}: content = item.get("content", "") if content is None: content = "" if not isinstance(content, str): content = str(content) normalized.append({"role": role, "content": content}) continue raise ValueError( f"Codex Responses input[{idx}] has unsupported item shape (type={item_type!r}, role={role!r})." ) return normalized def _preflight_codex_api_kwargs( self, api_kwargs: Any, *, allow_stream: bool = False, ) -> Dict[str, Any]: if not isinstance(api_kwargs, dict): raise ValueError("Codex Responses request must be a dict.") required = {"model", "instructions", "input"} missing = [key for key in required if key not in api_kwargs] if missing: raise ValueError(f"Codex Responses request missing required field(s): {', '.join(sorted(missing))}.") model = api_kwargs.get("model") if not isinstance(model, str) or not model.strip(): raise ValueError("Codex Responses request 'model' must be a non-empty string.") model = model.strip() instructions = api_kwargs.get("instructions") if instructions is None: instructions = "" if not isinstance(instructions, str): instructions = str(instructions) instructions = instructions.strip() or DEFAULT_AGENT_IDENTITY normalized_input = self._preflight_codex_input_items(api_kwargs.get("input")) tools = api_kwargs.get("tools") normalized_tools = None if tools is not None: if not isinstance(tools, list): raise ValueError("Codex Responses request 'tools' must be a list when provided.") normalized_tools = [] for idx, tool in enumerate(tools): if not isinstance(tool, dict): raise ValueError(f"Codex Responses tools[{idx}] must be an object.") if tool.get("type") != "function": raise ValueError(f"Codex Responses tools[{idx}] has unsupported type {tool.get('type')!r}.") name = tool.get("name") parameters = tool.get("parameters") if not isinstance(name, str) or not name.strip(): raise ValueError(f"Codex Responses tools[{idx}] is missing a valid name.") if not isinstance(parameters, dict): raise ValueError(f"Codex Responses tools[{idx}] is missing valid parameters.") description = tool.get("description", "") if description is None: description = "" if not isinstance(description, str): description = str(description) strict = tool.get("strict", False) if not isinstance(strict, bool): strict = bool(strict) normalized_tools.append( { "type": "function", "name": name.strip(), "description": description, "strict": strict, "parameters": parameters, } ) store = api_kwargs.get("store", False) if store is not False: raise ValueError("Codex Responses contract requires 'store' to be false.") allowed_keys = { "model", "instructions", "input", "tools", "store", "reasoning", "include", "max_output_tokens", "temperature", "tool_choice", "parallel_tool_calls", "prompt_cache_key", } normalized: Dict[str, Any] = { "model": model, "instructions": instructions, "input": normalized_input, "tools": normalized_tools, "store": False, } # Pass through reasoning config reasoning = api_kwargs.get("reasoning") if isinstance(reasoning, dict): normalized["reasoning"] = reasoning include = api_kwargs.get("include") if isinstance(include, list): normalized["include"] = include # Pass through max_output_tokens and temperature max_output_tokens = api_kwargs.get("max_output_tokens") if isinstance(max_output_tokens, (int, float)) and max_output_tokens > 0: normalized["max_output_tokens"] = int(max_output_tokens) temperature = api_kwargs.get("temperature") if isinstance(temperature, (int, float)): normalized["temperature"] = float(temperature) # Pass through tool_choice, parallel_tool_calls, prompt_cache_key for passthrough_key in ("tool_choice", "parallel_tool_calls", "prompt_cache_key"): val = api_kwargs.get(passthrough_key) if val is not None: normalized[passthrough_key] = val if allow_stream: stream = api_kwargs.get("stream") if stream is not None and stream is not True: raise ValueError("Codex Responses 'stream' must be true when set.") if stream is True: normalized["stream"] = True allowed_keys.add("stream") elif "stream" in api_kwargs: raise ValueError("Codex Responses stream flag is only allowed in fallback streaming requests.") unexpected = sorted(key for key in api_kwargs.keys() if key not in allowed_keys) if unexpected: raise ValueError( f"Codex Responses request has unsupported field(s): {', '.join(unexpected)}." ) return normalized def _extract_responses_message_text(self, item: Any) -> str: """Extract assistant text from a Responses message output item.""" content = getattr(item, "content", None) if not isinstance(content, list): return "" chunks: List[str] = [] for part in content: ptype = getattr(part, "type", None) if ptype not in {"output_text", "text"}: continue text = getattr(part, "text", None) if isinstance(text, str) and text: chunks.append(text) return "".join(chunks).strip() def _extract_responses_reasoning_text(self, item: Any) -> str: """Extract a compact reasoning text from a Responses reasoning item.""" summary = getattr(item, "summary", None) if isinstance(summary, list): chunks: List[str] = [] for part in summary: text = getattr(part, "text", None) if isinstance(text, str) and text: chunks.append(text) if chunks: return "\n".join(chunks).strip() text = getattr(item, "text", None) if isinstance(text, str) and text: return text.strip() return "" def _normalize_codex_response(self, response: Any) -> tuple[Any, str]: """Normalize a Responses API object to an assistant_message-like object.""" output = getattr(response, "output", None) if not isinstance(output, list) or not output: raise RuntimeError("Responses API returned no output items") response_status = getattr(response, "status", None) if isinstance(response_status, str): response_status = response_status.strip().lower() else: response_status = None if response_status in {"failed", "cancelled"}: error_obj = getattr(response, "error", None) if isinstance(error_obj, dict): error_msg = error_obj.get("message") or str(error_obj) else: error_msg = str(error_obj) if error_obj else f"Responses API returned status '{response_status}'" raise RuntimeError(error_msg) content_parts: List[str] = [] reasoning_parts: List[str] = [] reasoning_items_raw: List[Dict[str, Any]] = [] tool_calls: List[Any] = [] has_incomplete_items = response_status in {"queued", "in_progress", "incomplete"} saw_commentary_phase = False saw_final_answer_phase = False for item in output: item_type = getattr(item, "type", None) item_status = getattr(item, "status", None) if isinstance(item_status, str): item_status = item_status.strip().lower() else: item_status = None if item_status in {"queued", "in_progress", "incomplete"}: has_incomplete_items = True if item_type == "message": item_phase = getattr(item, "phase", None) if isinstance(item_phase, str): normalized_phase = item_phase.strip().lower() if normalized_phase in {"commentary", "analysis"}: saw_commentary_phase = True elif normalized_phase in {"final_answer", "final"}: saw_final_answer_phase = True message_text = self._extract_responses_message_text(item) if message_text: content_parts.append(message_text) elif item_type == "reasoning": reasoning_text = self._extract_responses_reasoning_text(item) if reasoning_text: reasoning_parts.append(reasoning_text) # Capture the full reasoning item for multi-turn continuity. # encrypted_content is an opaque blob the API needs back on # subsequent turns to maintain coherent reasoning chains. encrypted = getattr(item, "encrypted_content", None) if isinstance(encrypted, str) and encrypted: raw_item = {"type": "reasoning", "encrypted_content": encrypted} item_id = getattr(item, "id", None) if isinstance(item_id, str) and item_id: raw_item["id"] = item_id # Capture summary — required by the API when replaying reasoning items summary = getattr(item, "summary", None) if isinstance(summary, list): raw_summary = [] for part in summary: text = getattr(part, "text", None) if isinstance(text, str): raw_summary.append({"type": "summary_text", "text": text}) raw_item["summary"] = raw_summary reasoning_items_raw.append(raw_item) elif item_type == "function_call": if item_status in {"queued", "in_progress", "incomplete"}: continue fn_name = getattr(item, "name", "") or "" arguments = getattr(item, "arguments", "{}") if not isinstance(arguments, str): arguments = json.dumps(arguments, ensure_ascii=False) raw_call_id = getattr(item, "call_id", None) raw_item_id = getattr(item, "id", None) embedded_call_id, _ = self._split_responses_tool_id(raw_item_id) call_id = raw_call_id if isinstance(raw_call_id, str) and raw_call_id.strip() else embedded_call_id if not isinstance(call_id, str) or not call_id.strip(): call_id = f"call_{uuid.uuid4().hex[:12]}" call_id = call_id.strip() response_item_id = raw_item_id if isinstance(raw_item_id, str) else None response_item_id = self._derive_responses_function_call_id(call_id, response_item_id) tool_calls.append(SimpleNamespace( id=call_id, call_id=call_id, response_item_id=response_item_id, type="function", function=SimpleNamespace(name=fn_name, arguments=arguments), )) elif item_type == "custom_tool_call": fn_name = getattr(item, "name", "") or "" arguments = getattr(item, "input", "{}") if not isinstance(arguments, str): arguments = json.dumps(arguments, ensure_ascii=False) raw_call_id = getattr(item, "call_id", None) raw_item_id = getattr(item, "id", None) embedded_call_id, _ = self._split_responses_tool_id(raw_item_id) call_id = raw_call_id if isinstance(raw_call_id, str) and raw_call_id.strip() else embedded_call_id if not isinstance(call_id, str) or not call_id.strip(): call_id = f"call_{uuid.uuid4().hex[:12]}" call_id = call_id.strip() response_item_id = raw_item_id if isinstance(raw_item_id, str) else None response_item_id = self._derive_responses_function_call_id(call_id, response_item_id) tool_calls.append(SimpleNamespace( id=call_id, call_id=call_id, response_item_id=response_item_id, type="function", function=SimpleNamespace(name=fn_name, arguments=arguments), )) final_text = "\n".join([p for p in content_parts if p]).strip() if not final_text and hasattr(response, "output_text"): out_text = getattr(response, "output_text", "") if isinstance(out_text, str): final_text = out_text.strip() assistant_message = SimpleNamespace( content=final_text, tool_calls=tool_calls, reasoning="\n\n".join(reasoning_parts).strip() if reasoning_parts else None, reasoning_content=None, reasoning_details=None, codex_reasoning_items=reasoning_items_raw or None, ) if tool_calls: finish_reason = "tool_calls" elif has_incomplete_items or (saw_commentary_phase and not saw_final_answer_phase): finish_reason = "incomplete" elif reasoning_items_raw and not final_text: # Response contains only reasoning (encrypted thinking state) with # no visible content or tool calls. The model is still thinking and # needs another turn to produce the actual answer. Marking this as # "stop" would send it into the empty-content retry loop which burns # 3 retries then fails — treat it as incomplete instead so the Codex # continuation path handles it correctly. finish_reason = "incomplete" else: finish_reason = "stop" return assistant_message, finish_reason def _thread_identity(self) -> str: thread = threading.current_thread() return f"{thread.name}:{thread.ident}" def _client_log_context(self) -> str: provider = getattr(self, "provider", "unknown") base_url = getattr(self, "base_url", "unknown") model = getattr(self, "model", "unknown") return ( f"thread={self._thread_identity()} provider={provider} " f"base_url={base_url} model={model}" ) def _openai_client_lock(self) -> threading.RLock: lock = getattr(self, "_client_lock", None) if lock is None: lock = threading.RLock() self._client_lock = lock return lock @staticmethod def _is_openai_client_closed(client: Any) -> bool: from unittest.mock import Mock if isinstance(client, Mock): return False if bool(getattr(client, "is_closed", False)): return True http_client = getattr(client, "_client", None) return bool(getattr(http_client, "is_closed", False)) def _create_openai_client(self, client_kwargs: dict, *, reason: str, shared: bool) -> Any: if self.provider == "copilot-acp" or str(client_kwargs.get("base_url", "")).startswith("acp://copilot"): from agent.copilot_acp_client import CopilotACPClient client = CopilotACPClient(**client_kwargs) logger.info( "Copilot ACP client created (%s, shared=%s) %s", reason, shared, self._client_log_context(), ) return client client = OpenAI(**client_kwargs) logger.info( "OpenAI client created (%s, shared=%s) %s", reason, shared, self._client_log_context(), ) return client def _close_openai_client(self, client: Any, *, reason: str, shared: bool) -> None: if client is None: return try: client.close() logger.info( "OpenAI client closed (%s, shared=%s) %s", reason, shared, self._client_log_context(), ) except Exception as exc: logger.debug( "OpenAI client close failed (%s, shared=%s) %s error=%s", reason, shared, self._client_log_context(), exc, ) def _replace_primary_openai_client(self, *, reason: str) -> bool: with self._openai_client_lock(): old_client = getattr(self, "client", None) try: new_client = self._create_openai_client(self._client_kwargs, reason=reason, shared=True) except Exception as exc: logger.warning( "Failed to rebuild shared OpenAI client (%s) %s error=%s", reason, self._client_log_context(), exc, ) return False self.client = new_client self._close_openai_client(old_client, reason=f"replace:{reason}", shared=True) return True def _ensure_primary_openai_client(self, *, reason: str) -> Any: with self._openai_client_lock(): client = getattr(self, "client", None) if client is not None and not self._is_openai_client_closed(client): return client logger.warning( "Detected closed shared OpenAI client; recreating before use (%s) %s", reason, self._client_log_context(), ) if not self._replace_primary_openai_client(reason=f"recreate_closed:{reason}"): raise RuntimeError("Failed to recreate closed OpenAI client") with self._openai_client_lock(): return self.client def _create_request_openai_client(self, *, reason: str) -> Any: from unittest.mock import Mock primary_client = self._ensure_primary_openai_client(reason=reason) if isinstance(primary_client, Mock): return primary_client with self._openai_client_lock(): request_kwargs = dict(self._client_kwargs) return self._create_openai_client(request_kwargs, reason=reason, shared=False) def _close_request_openai_client(self, client: Any, *, reason: str) -> None: self._close_openai_client(client, reason=reason, shared=False) def _run_codex_stream(self, api_kwargs: dict, client: Any = None, on_first_delta: callable = None): """Execute one streaming Responses API request and return the final response.""" active_client = client or self._ensure_primary_openai_client(reason="codex_stream_direct") max_stream_retries = 1 has_tool_calls = False first_delta_fired = False for attempt in range(max_stream_retries + 1): try: with active_client.responses.stream(**api_kwargs) as stream: for event in stream: if self._interrupt_requested: break event_type = getattr(event, "type", "") # Fire callbacks on text content deltas (suppress during tool calls) if "output_text.delta" in event_type or event_type == "response.output_text.delta": delta_text = getattr(event, "delta", "") if delta_text and not has_tool_calls: if not first_delta_fired: first_delta_fired = True if on_first_delta: try: on_first_delta() except Exception: pass self._fire_stream_delta(delta_text) # Track tool calls to suppress text streaming elif "function_call" in event_type: has_tool_calls = True # Fire reasoning callbacks elif "reasoning" in event_type and "delta" in event_type: reasoning_text = getattr(event, "delta", "") if reasoning_text: self._fire_reasoning_delta(reasoning_text) return stream.get_final_response() except RuntimeError as exc: err_text = str(exc) missing_completed = "response.completed" in err_text if missing_completed and attempt < max_stream_retries: logger.debug( "Responses stream closed before completion (attempt %s/%s); retrying. %s", attempt + 1, max_stream_retries + 1, self._client_log_context(), ) continue if missing_completed: logger.debug( "Responses stream did not emit response.completed; falling back to create(stream=True). %s", self._client_log_context(), ) return self._run_codex_create_stream_fallback(api_kwargs, client=active_client) raise def _run_codex_create_stream_fallback(self, api_kwargs: dict, client: Any = None): """Fallback path for stream completion edge cases on Codex-style Responses backends.""" active_client = client or self._ensure_primary_openai_client(reason="codex_create_stream_fallback") fallback_kwargs = dict(api_kwargs) fallback_kwargs["stream"] = True fallback_kwargs = self._preflight_codex_api_kwargs(fallback_kwargs, allow_stream=True) stream_or_response = active_client.responses.create(**fallback_kwargs) # Compatibility shim for mocks or providers that still return a concrete response. if hasattr(stream_or_response, "output"): return stream_or_response if not hasattr(stream_or_response, "__iter__"): return stream_or_response terminal_response = None try: for event in stream_or_response: event_type = getattr(event, "type", None) if not event_type and isinstance(event, dict): event_type = event.get("type") if event_type not in {"response.completed", "response.incomplete", "response.failed"}: continue terminal_response = getattr(event, "response", None) if terminal_response is None and isinstance(event, dict): terminal_response = event.get("response") if terminal_response is not None: return terminal_response finally: close_fn = getattr(stream_or_response, "close", None) if callable(close_fn): try: close_fn() except Exception: pass if terminal_response is not None: return terminal_response raise RuntimeError("Responses create(stream=True) fallback did not emit a terminal response.") def _try_refresh_codex_client_credentials(self, *, force: bool = True) -> bool: if self.api_mode != "codex_responses" or self.provider != "openai-codex": return False try: from hermes_cli.auth import resolve_codex_runtime_credentials creds = resolve_codex_runtime_credentials(force_refresh=force) except Exception as exc: logger.debug("Codex credential refresh failed: %s", exc) return False api_key = creds.get("api_key") base_url = creds.get("base_url") if not isinstance(api_key, str) or not api_key.strip(): return False if not isinstance(base_url, str) or not base_url.strip(): return False self.api_key = api_key.strip() self.base_url = base_url.strip().rstrip("/") self._client_kwargs["api_key"] = self.api_key self._client_kwargs["base_url"] = self.base_url if not self._replace_primary_openai_client(reason="codex_credential_refresh"): return False return True def _try_refresh_nous_client_credentials(self, *, force: bool = True) -> bool: if self.api_mode != "chat_completions" or self.provider != "nous": return False try: from hermes_cli.auth import resolve_nous_runtime_credentials creds = resolve_nous_runtime_credentials( min_key_ttl_seconds=max(60, int(os.getenv("HERMES_NOUS_MIN_KEY_TTL_SECONDS", "1800"))), timeout_seconds=float(os.getenv("HERMES_NOUS_TIMEOUT_SECONDS", "15")), force_mint=force, ) except Exception as exc: logger.debug("Nous credential refresh failed: %s", exc) return False api_key = creds.get("api_key") base_url = creds.get("base_url") if not isinstance(api_key, str) or not api_key.strip(): return False if not isinstance(base_url, str) or not base_url.strip(): return False self.api_key = api_key.strip() self.base_url = base_url.strip().rstrip("/") self._client_kwargs["api_key"] = self.api_key self._client_kwargs["base_url"] = self.base_url # Nous requests should not inherit OpenRouter-only attribution headers. self._client_kwargs.pop("default_headers", None) if not self._replace_primary_openai_client(reason="nous_credential_refresh"): return False return True def _try_refresh_anthropic_client_credentials(self) -> bool: if self.api_mode != "anthropic_messages" or not hasattr(self, "_anthropic_api_key"): return False # Only refresh credentials for the native Anthropic provider. # Other anthropic_messages providers (MiniMax, Alibaba, etc.) use their own keys. if self.provider != "anthropic": return False try: from agent.anthropic_adapter import resolve_anthropic_token, build_anthropic_client new_token = resolve_anthropic_token() except Exception as exc: logger.debug("Anthropic credential refresh failed: %s", exc) return False if not isinstance(new_token, str) or not new_token.strip(): return False new_token = new_token.strip() if new_token == self._anthropic_api_key: return False try: self._anthropic_client.close() except Exception: pass try: self._anthropic_client = build_anthropic_client(new_token, getattr(self, "_anthropic_base_url", None)) except Exception as exc: logger.warning("Failed to rebuild Anthropic client after credential refresh: %s", exc) return False self._anthropic_api_key = new_token # Update OAuth flag — token type may have changed (API key ↔ OAuth) from agent.anthropic_adapter import _is_oauth_token self._is_anthropic_oauth = _is_oauth_token(new_token) return True def _anthropic_messages_create(self, api_kwargs: dict): if self.api_mode == "anthropic_messages": self._try_refresh_anthropic_client_credentials() return self._anthropic_client.messages.create(**api_kwargs) def _interruptible_api_call(self, api_kwargs: dict): """ Run the API call in a background thread so the main conversation loop can detect interrupts without waiting for the full HTTP round-trip. Each worker thread gets its own OpenAI client instance. Interrupts only close that worker-local client, so retries and other requests never inherit a closed transport. """ result = {"response": None, "error": None} request_client_holder = {"client": None} def _call(): try: if self.api_mode == "codex_responses": request_client_holder["client"] = self._create_request_openai_client(reason="codex_stream_request") result["response"] = self._run_codex_stream( api_kwargs, client=request_client_holder["client"], on_first_delta=getattr(self, "_codex_on_first_delta", None), ) elif self.api_mode == "anthropic_messages": result["response"] = self._anthropic_messages_create(api_kwargs) else: request_client_holder["client"] = self._create_request_openai_client(reason="chat_completion_request") result["response"] = request_client_holder["client"].chat.completions.create(**api_kwargs) except Exception as e: result["error"] = e finally: request_client = request_client_holder.get("client") if request_client is not None: self._close_request_openai_client(request_client, reason="request_complete") t = threading.Thread(target=_call, daemon=True) t.start() while t.is_alive(): t.join(timeout=0.3) if self._interrupt_requested: # Force-close the in-flight worker-local HTTP connection to stop # token generation without poisoning the shared client used to # seed future retries. try: if self.api_mode == "anthropic_messages": from agent.anthropic_adapter import build_anthropic_client self._anthropic_client.close() self._anthropic_client = build_anthropic_client( self._anthropic_api_key, getattr(self, "_anthropic_base_url", None), ) else: request_client = request_client_holder.get("client") if request_client is not None: self._close_request_openai_client(request_client, reason="interrupt_abort") except Exception: pass raise InterruptedError("Agent interrupted during API call") if result["error"] is not None: raise result["error"] return result["response"] # ── Unified streaming API call ───────────────────────────────────────── def _fire_stream_delta(self, text: str) -> None: """Fire all registered stream delta callbacks (display + TTS).""" # If a tool iteration set the break flag, prepend a single paragraph # break before the first real text delta. This prevents the original # problem (text concatenation across tool boundaries) without stacking # blank lines when multiple tool iterations run back-to-back. if getattr(self, "_stream_needs_break", False) and text and text.strip(): self._stream_needs_break = False text = "\n\n" + text for cb in (self.stream_delta_callback, self._stream_callback): if cb is not None: try: cb(text) except Exception: pass def _fire_reasoning_delta(self, text: str) -> None: """Fire reasoning callback if registered.""" cb = self.reasoning_callback if cb is not None: try: cb(text) except Exception: pass def _fire_tool_gen_started(self, tool_name: str) -> None: """Notify display layer that the model is generating tool call arguments. Fires once per tool name when the streaming response begins producing tool_call / tool_use tokens. Gives the TUI a chance to show a spinner or status line so the user isn't staring at a frozen screen while a large tool payload (e.g. a 45 KB write_file) is being generated. """ cb = self.tool_gen_callback if cb is not None: try: cb(tool_name) except Exception: pass def _has_stream_consumers(self) -> bool: """Return True if any streaming consumer is registered.""" return ( self.stream_delta_callback is not None or getattr(self, "_stream_callback", None) is not None ) def _interruptible_streaming_api_call( self, api_kwargs: dict, *, on_first_delta: callable = None ): """Streaming variant of _interruptible_api_call for real-time token delivery. Handles all three api_modes: - chat_completions: stream=True on OpenAI-compatible endpoints - anthropic_messages: client.messages.stream() via Anthropic SDK - codex_responses: delegates to _run_codex_stream (already streaming) Fires stream_delta_callback and _stream_callback for each text token. Tool-call turns suppress the callback — only text-only final responses stream to the consumer. Returns a SimpleNamespace that mimics the non-streaming response shape so the rest of the agent loop is unchanged. Falls back to _interruptible_api_call on provider errors indicating streaming is not supported. """ if self.api_mode == "codex_responses": # Codex streams internally via _run_codex_stream. The main dispatch # in _interruptible_api_call already calls it; we just need to # ensure on_first_delta reaches it. Store it on the instance # temporarily so _run_codex_stream can pick it up. self._codex_on_first_delta = on_first_delta try: return self._interruptible_api_call(api_kwargs) finally: self._codex_on_first_delta = None result = {"response": None, "error": None} request_client_holder = {"client": None} first_delta_fired = {"done": False} deltas_were_sent = {"yes": False} # Track if any deltas were fired (for fallback) # Wall-clock timestamp of the last real streaming chunk. The outer # poll loop uses this to detect stale connections that keep receiving # SSE keep-alive pings but no actual data. last_chunk_time = {"t": time.time()} def _fire_first_delta(): if not first_delta_fired["done"] and on_first_delta: first_delta_fired["done"] = True try: on_first_delta() except Exception: pass def _call_chat_completions(): """Stream a chat completions response.""" import httpx as _httpx _base_timeout = float(os.getenv("HERMES_API_TIMEOUT", 900.0)) _stream_read_timeout = float(os.getenv("HERMES_STREAM_READ_TIMEOUT", 60.0)) stream_kwargs = { **api_kwargs, "stream": True, "stream_options": {"include_usage": True}, "timeout": _httpx.Timeout( connect=30.0, read=_stream_read_timeout, write=_base_timeout, pool=30.0, ), } request_client_holder["client"] = self._create_request_openai_client( reason="chat_completion_stream_request" ) stream = request_client_holder["client"].chat.completions.create(**stream_kwargs) content_parts: list = [] tool_calls_acc: dict = {} tool_gen_notified: set = set() finish_reason = None model_name = None role = "assistant" reasoning_parts: list = [] usage_obj = None for chunk in stream: last_chunk_time["t"] = time.time() if self._interrupt_requested: break if not chunk.choices: if hasattr(chunk, "model") and chunk.model: model_name = chunk.model # Usage comes in the final chunk with empty choices if hasattr(chunk, "usage") and chunk.usage: usage_obj = chunk.usage continue delta = chunk.choices[0].delta if hasattr(chunk, "model") and chunk.model: model_name = chunk.model # Accumulate reasoning content reasoning_text = getattr(delta, "reasoning_content", None) or getattr(delta, "reasoning", None) if reasoning_text: reasoning_parts.append(reasoning_text) _fire_first_delta() self._fire_reasoning_delta(reasoning_text) # Accumulate text content — fire callback only when no tool calls if delta and delta.content: content_parts.append(delta.content) if not tool_calls_acc: _fire_first_delta() self._fire_stream_delta(delta.content) deltas_were_sent["yes"] = True # Accumulate tool call deltas — notify display on first name if delta and delta.tool_calls: for tc_delta in delta.tool_calls: idx = tc_delta.index if tc_delta.index is not None else 0 if idx not in tool_calls_acc: tool_calls_acc[idx] = { "id": tc_delta.id or "", "type": "function", "function": {"name": "", "arguments": ""}, "extra_content": None, } entry = tool_calls_acc[idx] if tc_delta.id: entry["id"] = tc_delta.id if tc_delta.function: if tc_delta.function.name: entry["function"]["name"] += tc_delta.function.name if tc_delta.function.arguments: entry["function"]["arguments"] += tc_delta.function.arguments extra = getattr(tc_delta, "extra_content", None) if extra is None and hasattr(tc_delta, "model_extra"): extra = (tc_delta.model_extra or {}).get("extra_content") if extra is not None: if hasattr(extra, "model_dump"): extra = extra.model_dump() entry["extra_content"] = extra # Fire once per tool when the full name is available name = entry["function"]["name"] if name and idx not in tool_gen_notified: tool_gen_notified.add(idx) _fire_first_delta() self._fire_tool_gen_started(name) if chunk.choices[0].finish_reason: finish_reason = chunk.choices[0].finish_reason # Usage in the final chunk if hasattr(chunk, "usage") and chunk.usage: usage_obj = chunk.usage # Build mock response matching non-streaming shape full_content = "".join(content_parts) or None mock_tool_calls = None if tool_calls_acc: mock_tool_calls = [] for idx in sorted(tool_calls_acc): tc = tool_calls_acc[idx] mock_tool_calls.append(SimpleNamespace( id=tc["id"], type=tc["type"], extra_content=tc.get("extra_content"), function=SimpleNamespace( name=tc["function"]["name"], arguments=tc["function"]["arguments"], ), )) full_reasoning = "".join(reasoning_parts) or None mock_message = SimpleNamespace( role=role, content=full_content, tool_calls=mock_tool_calls, reasoning_content=full_reasoning, ) mock_choice = SimpleNamespace( index=0, message=mock_message, finish_reason=finish_reason or "stop", ) return SimpleNamespace( id="stream-" + str(uuid.uuid4()), model=model_name, choices=[mock_choice], usage=usage_obj, ) def _call_anthropic(): """Stream an Anthropic Messages API response. Fires delta callbacks for real-time token delivery, but returns the native Anthropic Message object from get_final_message() so the rest of the agent loop (validation, tool extraction, etc.) works unchanged. """ has_tool_use = False # Use the Anthropic SDK's streaming context manager with self._anthropic_client.messages.stream(**api_kwargs) as stream: for event in stream: if self._interrupt_requested: break event_type = getattr(event, "type", None) if event_type == "content_block_start": block = getattr(event, "content_block", None) if block and getattr(block, "type", None) == "tool_use": has_tool_use = True tool_name = getattr(block, "name", None) if tool_name: _fire_first_delta() self._fire_tool_gen_started(tool_name) elif event_type == "content_block_delta": delta = getattr(event, "delta", None) if delta: delta_type = getattr(delta, "type", None) if delta_type == "text_delta": text = getattr(delta, "text", "") if text and not has_tool_use: _fire_first_delta() self._fire_stream_delta(text) elif delta_type == "thinking_delta": thinking_text = getattr(delta, "thinking", "") if thinking_text: _fire_first_delta() self._fire_reasoning_delta(thinking_text) # Return the native Anthropic Message for downstream processing return stream.get_final_message() def _call(): import httpx as _httpx _max_stream_retries = int(os.getenv("HERMES_STREAM_RETRIES", 2)) try: for _stream_attempt in range(_max_stream_retries + 1): try: if self.api_mode == "anthropic_messages": self._try_refresh_anthropic_client_credentials() result["response"] = _call_anthropic() else: result["response"] = _call_chat_completions() return # success except Exception as e: if deltas_were_sent["yes"]: # Streaming failed AFTER some tokens were already # delivered. Don't retry or fall back — partial # content already reached the user. logger.warning( "Streaming failed after partial delivery, not retrying: %s", e ) result["error"] = e return _is_timeout = isinstance( e, (_httpx.ReadTimeout, _httpx.ConnectTimeout, _httpx.PoolTimeout) ) _is_conn_err = isinstance( e, (_httpx.ConnectError, _httpx.RemoteProtocolError, ConnectionError) ) if _is_timeout or _is_conn_err: # Transient network / timeout error. Retry the # streaming request with a fresh connection first. if _stream_attempt < _max_stream_retries: logger.info( "Streaming attempt %s/%s failed (%s: %s), " "retrying with fresh connection...", _stream_attempt + 1, _max_stream_retries + 1, type(e).__name__, e, ) # Close the stale request client before retry stale = request_client_holder.get("client") if stale is not None: self._close_request_openai_client( stale, reason="stream_retry_cleanup" ) request_client_holder["client"] = None continue logger.warning( "Streaming exhausted %s retries on transient error, " "falling back to non-streaming: %s", _max_stream_retries + 1, e, ) else: _err_lower = str(e).lower() _is_stream_unsupported = ( "stream" in _err_lower and "not supported" in _err_lower ) if _is_stream_unsupported: self._safe_print( "\n⚠ Streaming is not supported for this " "model/provider. Falling back to non-streaming.\n" " To avoid this delay, set display.streaming: false " "in config.yaml\n" ) logger.info( "Streaming failed before delivery, falling back to non-streaming: %s", e, ) try: result["response"] = self._interruptible_api_call(api_kwargs) except Exception as fallback_err: result["error"] = fallback_err return finally: request_client = request_client_holder.get("client") if request_client is not None: self._close_request_openai_client(request_client, reason="stream_request_complete") _stream_stale_timeout = float(os.getenv("HERMES_STREAM_STALE_TIMEOUT", 90.0)) t = threading.Thread(target=_call, daemon=True) t.start() while t.is_alive(): t.join(timeout=0.3) # Detect stale streams: connections kept alive by SSE pings # but delivering no real chunks. Kill the client so the # inner retry loop can start a fresh connection. if time.time() - last_chunk_time["t"] > _stream_stale_timeout: logger.warning( "Stream stale for %.0fs — no chunks received. Killing connection.", _stream_stale_timeout, ) try: rc = request_client_holder.get("client") if rc is not None: self._close_request_openai_client(rc, reason="stale_stream_kill") except Exception: pass # Reset the timer so we don't kill repeatedly while # the inner thread processes the closure. last_chunk_time["t"] = time.time() if self._interrupt_requested: try: if self.api_mode == "anthropic_messages": from agent.anthropic_adapter import build_anthropic_client self._anthropic_client.close() self._anthropic_client = build_anthropic_client( self._anthropic_api_key, getattr(self, "_anthropic_base_url", None), ) else: request_client = request_client_holder.get("client") if request_client is not None: self._close_request_openai_client(request_client, reason="stream_interrupt_abort") except Exception: pass raise InterruptedError("Agent interrupted during streaming API call") if result["error"] is not None: raise result["error"] return result["response"] # ── Provider fallback ────────────────────────────────────────────────── def _try_activate_fallback(self) -> bool: """Switch to the configured fallback model/provider. Called when the primary model is failing after retries. Swaps the OpenAI client, model slug, and provider in-place so the retry loop can continue with the new backend. One-shot: returns False if already activated or not configured. Uses the centralized provider router (resolve_provider_client) for auth resolution and client construction — no duplicated provider→key mappings. """ if self._fallback_activated or not self._fallback_model: return False fb = self._fallback_model fb_provider = (fb.get("provider") or "").strip().lower() fb_model = (fb.get("model") or "").strip() if not fb_provider or not fb_model: return False # Use centralized router for client construction. # raw_codex=True because the main agent needs direct responses.stream() # access for Codex providers. try: from agent.auxiliary_client import resolve_provider_client fb_client, _ = resolve_provider_client( fb_provider, model=fb_model, raw_codex=True) if fb_client is None: logging.warning( "Fallback to %s failed: provider not configured", fb_provider) return False # Determine api_mode from provider / base URL fb_api_mode = "chat_completions" fb_base_url = str(fb_client.base_url) if fb_provider == "openai-codex": fb_api_mode = "codex_responses" elif fb_provider == "anthropic" or fb_base_url.rstrip("/").lower().endswith("/anthropic"): fb_api_mode = "anthropic_messages" elif self._is_direct_openai_url(fb_base_url): fb_api_mode = "codex_responses" old_model = self.model self.model = fb_model self.provider = fb_provider self.base_url = fb_base_url self.api_mode = fb_api_mode self._fallback_activated = True if fb_api_mode == "anthropic_messages": # Build native Anthropic client instead of using OpenAI client from agent.anthropic_adapter import build_anthropic_client, resolve_anthropic_token, _is_oauth_token effective_key = (fb_client.api_key or resolve_anthropic_token() or "") if fb_provider == "anthropic" else (fb_client.api_key or "") self.api_key = effective_key self._anthropic_api_key = effective_key self._anthropic_base_url = getattr(fb_client, "base_url", None) self._anthropic_client = build_anthropic_client(effective_key, self._anthropic_base_url) self._is_anthropic_oauth = _is_oauth_token(effective_key) self.client = None self._client_kwargs = {} else: # Swap OpenAI client and config in-place self.api_key = fb_client.api_key self.client = fb_client self._client_kwargs = { "api_key": fb_client.api_key, "base_url": fb_base_url, } # Re-evaluate prompt caching for the new provider/model is_native_anthropic = fb_api_mode == "anthropic_messages" self._use_prompt_caching = ( ("openrouter" in fb_base_url.lower() and "claude" in fb_model.lower()) or is_native_anthropic ) print( f"{self.log_prefix}🔄 Primary model failed — switching to fallback: " f"{fb_model} via {fb_provider}" ) logging.info( "Fallback activated: %s → %s (%s)", old_model, fb_model, fb_provider, ) return True except Exception as e: logging.error("Failed to activate fallback model: %s", e) return False # ── End provider fallback ────────────────────────────────────────────── @staticmethod def _content_has_image_parts(content: Any) -> bool: if not isinstance(content, list): return False for part in content: if isinstance(part, dict) and part.get("type") in {"image_url", "input_image"}: return True return False @staticmethod def _materialize_data_url_for_vision(image_url: str) -> tuple[str, Optional[Path]]: header, _, data = str(image_url or "").partition(",") mime = "image/jpeg" if header.startswith("data:"): mime_part = header[len("data:"):].split(";", 1)[0].strip() if mime_part.startswith("image/"): mime = mime_part suffix = { "image/png": ".png", "image/gif": ".gif", "image/webp": ".webp", "image/jpeg": ".jpg", "image/jpg": ".jpg", }.get(mime, ".jpg") tmp = tempfile.NamedTemporaryFile(prefix="anthropic_image_", suffix=suffix, delete=False) with tmp: tmp.write(base64.b64decode(data)) path = Path(tmp.name) return str(path), path def _describe_image_for_anthropic_fallback(self, image_url: str, role: str) -> str: cache_key = hashlib.sha256(str(image_url or "").encode("utf-8")).hexdigest() cached = self._anthropic_image_fallback_cache.get(cache_key) if cached: return cached role_label = { "assistant": "assistant", "tool": "tool result", }.get(role, "user") analysis_prompt = ( "Describe everything visible in this image in thorough detail. " "Include any text, code, UI, data, objects, people, layout, colors, " "and any other notable visual information." ) vision_source = str(image_url or "") cleanup_path: Optional[Path] = None if vision_source.startswith("data:"): vision_source, cleanup_path = self._materialize_data_url_for_vision(vision_source) description = "" try: from tools.vision_tools import vision_analyze_tool result_json = asyncio.run( vision_analyze_tool(image_url=vision_source, user_prompt=analysis_prompt) ) result = json.loads(result_json) if isinstance(result_json, str) else {} description = (result.get("analysis") or "").strip() except Exception as e: description = f"Image analysis failed: {e}" finally: if cleanup_path and cleanup_path.exists(): try: cleanup_path.unlink() except OSError: pass if not description: description = "Image analysis failed." note = f"[The {role_label} attached an image. Here's what it contains:\n{description}]" if vision_source and not str(image_url or "").startswith("data:"): note += ( f"\n[If you need a closer look, use vision_analyze with image_url: {vision_source}]" ) self._anthropic_image_fallback_cache[cache_key] = note return note def _preprocess_anthropic_content(self, content: Any, role: str) -> Any: if not self._content_has_image_parts(content): return content text_parts: List[str] = [] image_notes: List[str] = [] for part in content: if isinstance(part, str): if part.strip(): text_parts.append(part.strip()) continue if not isinstance(part, dict): continue ptype = part.get("type") if ptype in {"text", "input_text"}: text = str(part.get("text", "") or "").strip() if text: text_parts.append(text) continue if ptype in {"image_url", "input_image"}: image_data = part.get("image_url", {}) image_url = image_data.get("url", "") if isinstance(image_data, dict) else str(image_data or "") if image_url: image_notes.append(self._describe_image_for_anthropic_fallback(image_url, role)) else: image_notes.append("[An image was attached but no image source was available.]") continue text = str(part.get("text", "") or "").strip() if text: text_parts.append(text) prefix = "\n\n".join(note for note in image_notes if note).strip() suffix = "\n".join(text for text in text_parts if text).strip() if prefix and suffix: return f"{prefix}\n\n{suffix}" if prefix: return prefix if suffix: return suffix return "[A multimodal message was converted to text for Anthropic compatibility.]" def _prepare_anthropic_messages_for_api(self, api_messages: list) -> list: if not any( isinstance(msg, dict) and self._content_has_image_parts(msg.get("content")) for msg in api_messages ): return api_messages transformed = copy.deepcopy(api_messages) for msg in transformed: if not isinstance(msg, dict): continue msg["content"] = self._preprocess_anthropic_content( msg.get("content"), str(msg.get("role", "user") or "user"), ) return transformed def _anthropic_preserve_dots(self) -> bool: """True when using Alibaba/DashScope anthropic-compatible endpoint (model names keep dots, e.g. qwen3.5-plus).""" if (getattr(self, "provider", "") or "").lower() == "alibaba": return True base = (getattr(self, "base_url", "") or "").lower() return "dashscope" in base or "aliyuncs" in base def _build_api_kwargs(self, api_messages: list) -> dict: """Build the keyword arguments dict for the active API mode.""" if self.api_mode == "anthropic_messages": from agent.anthropic_adapter import build_anthropic_kwargs anthropic_messages = self._prepare_anthropic_messages_for_api(api_messages) return build_anthropic_kwargs( model=self.model, messages=anthropic_messages, tools=self.tools, max_tokens=self.max_tokens, reasoning_config=self.reasoning_config, is_oauth=getattr(self, "_is_anthropic_oauth", False), preserve_dots=self._anthropic_preserve_dots(), ) if self.api_mode == "codex_responses": instructions = "" payload_messages = api_messages if api_messages and api_messages[0].get("role") == "system": instructions = str(api_messages[0].get("content") or "").strip() payload_messages = api_messages[1:] if not instructions: instructions = DEFAULT_AGENT_IDENTITY is_github_responses = ( "models.github.ai" in self.base_url.lower() or "api.githubcopilot.com" in self.base_url.lower() ) # Resolve reasoning effort: config > default (medium) reasoning_effort = "medium" reasoning_enabled = True if self.reasoning_config and isinstance(self.reasoning_config, dict): if self.reasoning_config.get("enabled") is False: reasoning_enabled = False elif self.reasoning_config.get("effort"): reasoning_effort = self.reasoning_config["effort"] kwargs = { "model": self.model, "instructions": instructions, "input": self._chat_messages_to_responses_input(payload_messages), "tools": self._responses_tools(), "tool_choice": "auto", "parallel_tool_calls": True, "store": False, } if not is_github_responses: kwargs["prompt_cache_key"] = self.session_id if reasoning_enabled: if is_github_responses: # Copilot's Responses route advertises reasoning-effort support, # but not OpenAI-specific prompt cache or encrypted reasoning # fields. Keep the payload to the documented subset. github_reasoning = self._github_models_reasoning_extra_body() if github_reasoning is not None: kwargs["reasoning"] = github_reasoning else: kwargs["reasoning"] = {"effort": reasoning_effort, "summary": "auto"} kwargs["include"] = ["reasoning.encrypted_content"] elif not is_github_responses: kwargs["include"] = [] if self.max_tokens is not None: kwargs["max_output_tokens"] = self.max_tokens return kwargs sanitized_messages = api_messages needs_sanitization = False for msg in api_messages: if not isinstance(msg, dict): continue if "codex_reasoning_items" in msg: needs_sanitization = True break tool_calls = msg.get("tool_calls") if isinstance(tool_calls, list): for tool_call in tool_calls: if not isinstance(tool_call, dict): continue if "call_id" in tool_call or "response_item_id" in tool_call: needs_sanitization = True break if needs_sanitization: break if needs_sanitization: sanitized_messages = copy.deepcopy(api_messages) for msg in sanitized_messages: if not isinstance(msg, dict): continue # Codex-only replay state must not leak into strict chat-completions APIs. msg.pop("codex_reasoning_items", None) tool_calls = msg.get("tool_calls") if isinstance(tool_calls, list): for tool_call in tool_calls: if isinstance(tool_call, dict): tool_call.pop("call_id", None) tool_call.pop("response_item_id", None) provider_preferences = {} if self.providers_allowed: provider_preferences["only"] = self.providers_allowed if self.providers_ignored: provider_preferences["ignore"] = self.providers_ignored if self.providers_order: provider_preferences["order"] = self.providers_order if self.provider_sort: provider_preferences["sort"] = self.provider_sort if self.provider_require_parameters: provider_preferences["require_parameters"] = True if self.provider_data_collection: provider_preferences["data_collection"] = self.provider_data_collection api_kwargs = { "model": self.model, "messages": sanitized_messages, "tools": self.tools if self.tools else None, "timeout": float(os.getenv("HERMES_API_TIMEOUT", 900.0)), } if self.max_tokens is not None: api_kwargs.update(self._max_tokens_param(self.max_tokens)) extra_body = {} _is_openrouter = "openrouter" in self._base_url_lower _is_github_models = ( "models.github.ai" in self._base_url_lower or "api.githubcopilot.com" in self._base_url_lower ) # Provider preferences (only, ignore, order, sort) are OpenRouter- # specific. Only send to OpenRouter-compatible endpoints. # TODO: Nous Portal will add transparent proxy support — re-enable # for _is_nous when their backend is updated. if provider_preferences and _is_openrouter: extra_body["provider"] = provider_preferences _is_nous = "nousresearch" in self._base_url_lower if self._supports_reasoning_extra_body(): if _is_github_models: github_reasoning = self._github_models_reasoning_extra_body() if github_reasoning is not None: extra_body["reasoning"] = github_reasoning else: if self.reasoning_config is not None: rc = dict(self.reasoning_config) # Nous Portal requires reasoning enabled — don't send # enabled=false to it (would cause 400). if _is_nous and rc.get("enabled") is False: pass # omit reasoning entirely for Nous when disabled else: extra_body["reasoning"] = rc else: extra_body["reasoning"] = { "enabled": True, "effort": "medium" } # Nous Portal product attribution if _is_nous: extra_body["tags"] = ["product=hermes-agent"] if extra_body: api_kwargs["extra_body"] = extra_body return api_kwargs def _supports_reasoning_extra_body(self) -> bool: """Return True when reasoning extra_body is safe to send for this route/model. OpenRouter forwards unknown extra_body fields to upstream providers. Some providers/routes reject `reasoning` with 400s, so gate it to known reasoning-capable model families and direct Nous Portal. """ if "nousresearch" in self._base_url_lower: return True if "ai-gateway.vercel.sh" in self._base_url_lower: return True if "models.github.ai" in self._base_url_lower or "api.githubcopilot.com" in self._base_url_lower: try: from hermes_cli.models import github_model_reasoning_efforts return bool(github_model_reasoning_efforts(self.model)) except Exception: return False if "openrouter" not in self._base_url_lower: return False if "api.mistral.ai" in self._base_url_lower: return False model = (self.model or "").lower() reasoning_model_prefixes = ( "deepseek/", "anthropic/", "openai/", "x-ai/", "google/gemini-2", "qwen/qwen3", ) return any(model.startswith(prefix) for prefix in reasoning_model_prefixes) def _github_models_reasoning_extra_body(self) -> dict | None: """Format reasoning payload for GitHub Models/OpenAI-compatible routes.""" try: from hermes_cli.models import github_model_reasoning_efforts except Exception: return None supported_efforts = github_model_reasoning_efforts(self.model) if not supported_efforts: return None if self.reasoning_config and isinstance(self.reasoning_config, dict): if self.reasoning_config.get("enabled") is False: return None requested_effort = str( self.reasoning_config.get("effort", "medium") ).strip().lower() else: requested_effort = "medium" if requested_effort == "xhigh" and "high" in supported_efforts: requested_effort = "high" elif requested_effort not in supported_efforts: if requested_effort == "minimal" and "low" in supported_efforts: requested_effort = "low" elif "medium" in supported_efforts: requested_effort = "medium" else: requested_effort = supported_efforts[0] return {"effort": requested_effort} def _build_assistant_message(self, assistant_message, finish_reason: str) -> dict: """Build a normalized assistant message dict from an API response message. Handles reasoning extraction, reasoning_details, and optional tool_calls so both the tool-call path and the final-response path share one builder. """ reasoning_text = self._extract_reasoning(assistant_message) # Fallback: extract inline blocks from content when no structured # reasoning fields are present (some models/providers embed thinking # directly in the content rather than returning separate API fields). if not reasoning_text: content = assistant_message.content or "" think_blocks = re.findall(r'(.*?)', content, flags=re.DOTALL) if think_blocks: combined = "\n\n".join(b.strip() for b in think_blocks if b.strip()) reasoning_text = combined or None if reasoning_text and self.verbose_logging: logging.debug(f"Captured reasoning ({len(reasoning_text)} chars): {reasoning_text}") if reasoning_text and self.reasoning_callback: try: self.reasoning_callback(reasoning_text) except Exception: pass msg = { "role": "assistant", "content": assistant_message.content or "", "reasoning": reasoning_text, "finish_reason": finish_reason, } if hasattr(assistant_message, 'reasoning_details') and assistant_message.reasoning_details: # Pass reasoning_details back unmodified so providers (OpenRouter, # Anthropic, OpenAI) can maintain reasoning continuity across turns. # Each provider may include opaque fields (signature, encrypted_content) # that must be preserved exactly. raw_details = assistant_message.reasoning_details preserved = [] for d in raw_details: if isinstance(d, dict): preserved.append(d) elif hasattr(d, "__dict__"): preserved.append(d.__dict__) elif hasattr(d, "model_dump"): preserved.append(d.model_dump()) if preserved: msg["reasoning_details"] = preserved # Codex Responses API: preserve encrypted reasoning items for # multi-turn continuity. These get replayed as input on the next turn. codex_items = getattr(assistant_message, "codex_reasoning_items", None) if codex_items: msg["codex_reasoning_items"] = codex_items if assistant_message.tool_calls: tool_calls = [] for tool_call in assistant_message.tool_calls: raw_id = getattr(tool_call, "id", None) call_id = getattr(tool_call, "call_id", None) if not isinstance(call_id, str) or not call_id.strip(): embedded_call_id, _ = self._split_responses_tool_id(raw_id) call_id = embedded_call_id if not isinstance(call_id, str) or not call_id.strip(): if isinstance(raw_id, str) and raw_id.strip(): call_id = raw_id.strip() else: call_id = f"call_{uuid.uuid4().hex[:12]}" call_id = call_id.strip() response_item_id = getattr(tool_call, "response_item_id", None) if not isinstance(response_item_id, str) or not response_item_id.strip(): _, embedded_response_item_id = self._split_responses_tool_id(raw_id) response_item_id = embedded_response_item_id response_item_id = self._derive_responses_function_call_id( call_id, response_item_id if isinstance(response_item_id, str) else None, ) tc_dict = { "id": call_id, "call_id": call_id, "response_item_id": response_item_id, "type": tool_call.type, "function": { "name": tool_call.function.name, "arguments": tool_call.function.arguments }, } # Preserve extra_content (e.g. Gemini thought_signature) so it # is sent back on subsequent API calls. Without this, Gemini 3 # thinking models reject the request with a 400 error. extra = getattr(tool_call, "extra_content", None) if extra is not None: if hasattr(extra, "model_dump"): extra = extra.model_dump() tc_dict["extra_content"] = extra tool_calls.append(tc_dict) msg["tool_calls"] = tool_calls return msg @staticmethod def _sanitize_tool_calls_for_strict_api(api_msg: dict) -> dict: """Strip Codex Responses API fields from tool_calls for strict providers. Providers like Mistral strictly validate the Chat Completions schema and reject unknown fields (call_id, response_item_id) with 422. These fields are preserved in the internal message history — this method only modifies the outgoing API copy. Creates new tool_call dicts rather than mutating in-place, so the original messages list retains call_id/response_item_id for Codex Responses API compatibility (e.g. if the session falls back to a Codex provider later). """ tool_calls = api_msg.get("tool_calls") if not isinstance(tool_calls, list): return api_msg _STRIP_KEYS = {"call_id", "response_item_id"} api_msg["tool_calls"] = [ {k: v for k, v in tc.items() if k not in _STRIP_KEYS} if isinstance(tc, dict) else tc for tc in tool_calls ] return api_msg def flush_memories(self, messages: list = None, min_turns: int = None): """Give the model one turn to persist memories before context is lost. Called before compression, session reset, or CLI exit. Injects a flush message, makes one API call, executes any memory tool calls, then strips all flush artifacts from the message list. Args: messages: The current conversation messages. If None, uses self._session_messages (last run_conversation state). min_turns: Minimum user turns required to trigger the flush. None = use config value (flush_min_turns). 0 = always flush (used for compression). """ if self._memory_flush_min_turns == 0 and min_turns is None: return if "memory" not in self.valid_tool_names or not self._memory_store: return # honcho-only agent mode: skip local MEMORY.md flush _hcfg = getattr(self, '_honcho_config', None) if _hcfg and _hcfg.peer_memory_mode(_hcfg.ai_peer) == "honcho": return effective_min = min_turns if min_turns is not None else self._memory_flush_min_turns if self._user_turn_count < effective_min: return if messages is None: messages = getattr(self, '_session_messages', None) if not messages or len(messages) < 3: return flush_content = ( "[System: The session is being compressed. " "Save anything worth remembering — prioritize user preferences, " "corrections, and recurring patterns over task-specific details.]" ) _sentinel = f"__flush_{id(self)}_{time.monotonic()}" flush_msg = {"role": "user", "content": flush_content, "_flush_sentinel": _sentinel} messages.append(flush_msg) try: # Build API messages for the flush call _is_strict_api = "api.mistral.ai" in self._base_url_lower api_messages = [] for msg in messages: api_msg = msg.copy() if msg.get("role") == "assistant": reasoning = msg.get("reasoning") if reasoning: api_msg["reasoning_content"] = reasoning api_msg.pop("reasoning", None) api_msg.pop("finish_reason", None) api_msg.pop("_flush_sentinel", None) if _is_strict_api: self._sanitize_tool_calls_for_strict_api(api_msg) api_messages.append(api_msg) if self._cached_system_prompt: api_messages = [{"role": "system", "content": self._cached_system_prompt}] + api_messages # Make one API call with only the memory tool available memory_tool_def = None for t in (self.tools or []): if t.get("function", {}).get("name") == "memory": memory_tool_def = t break if not memory_tool_def: messages.pop() # remove flush msg return # Use auxiliary client for the flush call when available -- # it's cheaper and avoids Codex Responses API incompatibility. from agent.auxiliary_client import call_llm as _call_llm _aux_available = True try: response = _call_llm( task="flush_memories", messages=api_messages, tools=[memory_tool_def], temperature=0.3, max_tokens=5120, timeout=30.0, ) except RuntimeError: _aux_available = False response = None if not _aux_available and self.api_mode == "codex_responses": # No auxiliary client -- use the Codex Responses path directly codex_kwargs = self._build_api_kwargs(api_messages) codex_kwargs["tools"] = self._responses_tools([memory_tool_def]) codex_kwargs["temperature"] = 0.3 if "max_output_tokens" in codex_kwargs: codex_kwargs["max_output_tokens"] = 5120 response = self._run_codex_stream(codex_kwargs) elif not _aux_available and self.api_mode == "anthropic_messages": # Native Anthropic — use the Anthropic client directly from agent.anthropic_adapter import build_anthropic_kwargs as _build_ant_kwargs ant_kwargs = _build_ant_kwargs( model=self.model, messages=api_messages, tools=[memory_tool_def], max_tokens=5120, reasoning_config=None, preserve_dots=self._anthropic_preserve_dots(), ) response = self._anthropic_messages_create(ant_kwargs) elif not _aux_available: api_kwargs = { "model": self.model, "messages": api_messages, "tools": [memory_tool_def], "temperature": 0.3, **self._max_tokens_param(5120), } response = self._ensure_primary_openai_client(reason="flush_memories").chat.completions.create(**api_kwargs, timeout=30.0) # Extract tool calls from the response, handling all API formats tool_calls = [] if self.api_mode == "codex_responses" and not _aux_available: assistant_msg, _ = self._normalize_codex_response(response) if assistant_msg and assistant_msg.tool_calls: tool_calls = assistant_msg.tool_calls elif self.api_mode == "anthropic_messages" and not _aux_available: from agent.anthropic_adapter import normalize_anthropic_response as _nar_flush _flush_msg, _ = _nar_flush(response, strip_tool_prefix=getattr(self, '_is_anthropic_oauth', False)) if _flush_msg and _flush_msg.tool_calls: tool_calls = _flush_msg.tool_calls elif hasattr(response, "choices") and response.choices: assistant_message = response.choices[0].message if assistant_message.tool_calls: tool_calls = assistant_message.tool_calls for tc in tool_calls: if tc.function.name == "memory": try: args = json.loads(tc.function.arguments) flush_target = args.get("target", "memory") from tools.memory_tool import memory_tool as _memory_tool result = _memory_tool( action=args.get("action"), target=flush_target, content=args.get("content"), old_text=args.get("old_text"), store=self._memory_store, ) if self._honcho and flush_target == "user" and args.get("action") == "add": self._honcho_save_user_observation(args.get("content", "")) if not self.quiet_mode: print(f" 🧠 Memory flush: saved to {args.get('target', 'memory')}") except Exception as e: logger.debug("Memory flush tool call failed: %s", e) except Exception as e: logger.debug("Memory flush API call failed: %s", e) finally: # Strip flush artifacts: remove everything from the flush message onward. # Use sentinel marker instead of identity check for robustness. while messages and messages[-1].get("_flush_sentinel") != _sentinel: messages.pop() if not messages: break if messages and messages[-1].get("_flush_sentinel") == _sentinel: messages.pop() def _compress_context(self, messages: list, system_message: str, *, approx_tokens: int = None, task_id: str = "default") -> tuple: """Compress conversation context and split the session in SQLite. Returns: (compressed_messages, new_system_prompt) tuple """ # Pre-compression memory flush: let the model save memories before they're lost self.flush_memories(messages, min_turns=0) compressed = self.context_compressor.compress(messages, current_tokens=approx_tokens) todo_snapshot = self._todo_store.format_for_injection() if todo_snapshot: compressed.append({"role": "user", "content": todo_snapshot}) self._invalidate_system_prompt() new_system_prompt = self._build_system_prompt(system_message) self._cached_system_prompt = new_system_prompt if self._session_db: try: # Propagate title to the new session with auto-numbering old_title = self._session_db.get_session_title(self.session_id) self._session_db.end_session(self.session_id, "compression") old_session_id = self.session_id self.session_id = f"{datetime.now().strftime('%Y%m%d_%H%M%S')}_{uuid.uuid4().hex[:6]}" self._session_db.create_session( session_id=self.session_id, source=self.platform or "cli", model=self.model, parent_session_id=old_session_id, ) # Auto-number the title for the continuation session if old_title: try: new_title = self._session_db.get_next_title_in_lineage(old_title) self._session_db.set_session_title(self.session_id, new_title) except (ValueError, Exception) as e: logger.debug("Could not propagate title on compression: %s", e) self._session_db.update_system_prompt(self.session_id, new_system_prompt) # Reset flush cursor — new session starts with no messages written self._last_flushed_db_idx = 0 except Exception as e: logger.warning("Session DB compression split failed — new session will NOT be indexed: %s", e) # Reset context pressure warning and token estimate — usage drops # after compaction. Without this, the stale last_prompt_tokens from # the previous API call causes the pressure calculation to stay at # >1000% and spam warnings / re-trigger compression in a loop. self._context_pressure_warned = False _compressed_est = ( estimate_tokens_rough(new_system_prompt) + estimate_messages_tokens_rough(compressed) ) self.context_compressor.last_prompt_tokens = _compressed_est self.context_compressor.last_completion_tokens = 0 return compressed, new_system_prompt def _execute_tool_calls(self, assistant_message, messages: list, effective_task_id: str, api_call_count: int = 0) -> None: """Execute tool calls from the assistant message and append results to messages. Dispatches to concurrent execution only for batches that look independent: read-only tools may always share the parallel path, while file reads/writes may do so only when their target paths do not overlap. """ tool_calls = assistant_message.tool_calls # Allow _vprint during tool execution even with stream consumers self._executing_tools = True try: if not _should_parallelize_tool_batch(tool_calls): return self._execute_tool_calls_sequential( assistant_message, messages, effective_task_id, api_call_count ) return self._execute_tool_calls_concurrent( assistant_message, messages, effective_task_id, api_call_count ) finally: self._executing_tools = False def _invoke_tool(self, function_name: str, function_args: dict, effective_task_id: str) -> str: """Invoke a single tool and return the result string. No display logic. Handles both agent-level tools (todo, memory, etc.) and registry-dispatched tools. Used by the concurrent execution path; the sequential path retains its own inline invocation for backward-compatible display handling. """ if function_name == "todo": from tools.todo_tool import todo_tool as _todo_tool return _todo_tool( todos=function_args.get("todos"), merge=function_args.get("merge", False), store=self._todo_store, ) elif function_name == "session_search": if not self._session_db: return json.dumps({"success": False, "error": "Session database not available."}) from tools.session_search_tool import session_search as _session_search return _session_search( query=function_args.get("query", ""), role_filter=function_args.get("role_filter"), limit=function_args.get("limit", 3), db=self._session_db, current_session_id=self.session_id, ) elif function_name == "memory": target = function_args.get("target", "memory") from tools.memory_tool import memory_tool as _memory_tool result = _memory_tool( action=function_args.get("action"), target=target, content=function_args.get("content"), old_text=function_args.get("old_text"), store=self._memory_store, ) # Also send user observations to Honcho when active if self._honcho and target == "user" and function_args.get("action") == "add": self._honcho_save_user_observation(function_args.get("content", "")) return result elif function_name == "clarify": from tools.clarify_tool import clarify_tool as _clarify_tool return _clarify_tool( question=function_args.get("question", ""), choices=function_args.get("choices"), callback=self.clarify_callback, ) elif function_name == "delegate_task": from tools.delegate_tool import delegate_task as _delegate_task return _delegate_task( goal=function_args.get("goal"), context=function_args.get("context"), toolsets=function_args.get("toolsets"), tasks=function_args.get("tasks"), max_iterations=function_args.get("max_iterations"), parent_agent=self, ) else: return handle_function_call( function_name, function_args, effective_task_id, enabled_tools=list(self.valid_tool_names) if self.valid_tool_names else None, honcho_manager=self._honcho, honcho_session_key=self._honcho_session_key, ) def _execute_tool_calls_concurrent(self, assistant_message, messages: list, effective_task_id: str, api_call_count: int = 0) -> None: """Execute multiple tool calls concurrently using a thread pool. Results are collected in the original tool-call order and appended to messages so the API sees them in the expected sequence. """ tool_calls = assistant_message.tool_calls num_tools = len(tool_calls) # ── Pre-flight: interrupt check ────────────────────────────────── if self._interrupt_requested: print(f"{self.log_prefix}⚡ Interrupt: skipping {num_tools} tool call(s)") for tc in tool_calls: messages.append({ "role": "tool", "content": f"[Tool execution cancelled — {tc.function.name} was skipped due to user interrupt]", "tool_call_id": tc.id, }) return # ── Parse args + pre-execution bookkeeping ─────────────────────── parsed_calls = [] # list of (tool_call, function_name, function_args) for tool_call in tool_calls: function_name = tool_call.function.name # Reset nudge counters if function_name == "memory": self._turns_since_memory = 0 elif function_name == "skill_manage": self._iters_since_skill = 0 try: function_args = json.loads(tool_call.function.arguments) except json.JSONDecodeError: function_args = {} if not isinstance(function_args, dict): function_args = {} # Checkpoint for file-mutating tools if function_name in ("write_file", "patch") and self._checkpoint_mgr.enabled: try: file_path = function_args.get("path", "") if file_path: work_dir = self._checkpoint_mgr.get_working_dir_for_path(file_path) self._checkpoint_mgr.ensure_checkpoint(work_dir, f"before {function_name}") except Exception: pass # Checkpoint before destructive terminal commands if function_name == "terminal" and self._checkpoint_mgr.enabled: try: cmd = function_args.get("command", "") if _is_destructive_command(cmd): cwd = function_args.get("workdir") or os.getenv("TERMINAL_CWD", os.getcwd()) self._checkpoint_mgr.ensure_checkpoint( cwd, f"before terminal: {cmd[:60]}" ) except Exception: pass parsed_calls.append((tool_call, function_name, function_args)) # ── Logging / callbacks ────────────────────────────────────────── tool_names_str = ", ".join(name for _, name, _ in parsed_calls) if not self.quiet_mode: print(f" ⚡ Concurrent: {num_tools} tool calls — {tool_names_str}") for i, (tc, name, args) in enumerate(parsed_calls, 1): args_str = json.dumps(args, ensure_ascii=False) if self.verbose_logging: print(f" 📞 Tool {i}: {name}({list(args.keys())})") print(f" Args: {args_str}") else: args_preview = args_str[:self.log_prefix_chars] + "..." if len(args_str) > self.log_prefix_chars else args_str print(f" 📞 Tool {i}: {name}({list(args.keys())}) - {args_preview}") for _, name, args in parsed_calls: if self.tool_progress_callback: try: preview = _build_tool_preview(name, args) self.tool_progress_callback(name, preview, args) except Exception as cb_err: logging.debug(f"Tool progress callback error: {cb_err}") # ── Concurrent execution ───────────────────────────────────────── # Each slot holds (function_name, function_args, function_result, duration, error_flag) results = [None] * num_tools def _run_tool(index, tool_call, function_name, function_args): """Worker function executed in a thread.""" start = time.time() try: result = self._invoke_tool(function_name, function_args, effective_task_id) except Exception as tool_error: result = f"Error executing tool '{function_name}': {tool_error}" logger.error("_invoke_tool raised for %s: %s", function_name, tool_error, exc_info=True) duration = time.time() - start is_error, _ = _detect_tool_failure(function_name, result) results[index] = (function_name, function_args, result, duration, is_error) # Start spinner for CLI mode (skip when TUI handles tool progress) spinner = None if self.quiet_mode and not self.tool_progress_callback: face = random.choice(KawaiiSpinner.KAWAII_WAITING) spinner = KawaiiSpinner(f"{face} ⚡ running {num_tools} tools concurrently", spinner_type='dots') spinner.start() try: max_workers = min(num_tools, _MAX_TOOL_WORKERS) with concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) as executor: futures = [] for i, (tc, name, args) in enumerate(parsed_calls): f = executor.submit(_run_tool, i, tc, name, args) futures.append(f) # Wait for all to complete (exceptions are captured inside _run_tool) concurrent.futures.wait(futures) finally: if spinner: # Build a summary message for the spinner stop completed = sum(1 for r in results if r is not None) total_dur = sum(r[3] for r in results if r is not None) spinner.stop(f"⚡ {completed}/{num_tools} tools completed in {total_dur:.1f}s total") # ── Post-execution: display per-tool results ───────────────────── for i, (tc, name, args) in enumerate(parsed_calls): r = results[i] if r is None: # Shouldn't happen, but safety fallback function_result = f"Error executing tool '{name}': thread did not return a result" tool_duration = 0.0 else: function_name, function_args, function_result, tool_duration, is_error = r if is_error: result_preview = function_result[:200] if len(function_result) > 200 else function_result logger.warning("Tool %s returned error (%.2fs): %s", function_name, tool_duration, result_preview) if self.verbose_logging: logging.debug(f"Tool {function_name} completed in {tool_duration:.2f}s") logging.debug(f"Tool result ({len(function_result)} chars): {function_result}") # Print cute message per tool if self.quiet_mode: cute_msg = _get_cute_tool_message_impl(name, args, tool_duration, result=function_result) print(f" {cute_msg}") elif not self.quiet_mode: if self.verbose_logging: print(f" ✅ Tool {i+1} completed in {tool_duration:.2f}s") print(f" Result: {function_result}") else: response_preview = function_result[:self.log_prefix_chars] + "..." if len(function_result) > self.log_prefix_chars else function_result print(f" ✅ Tool {i+1} completed in {tool_duration:.2f}s - {response_preview}") # Truncate oversized results MAX_TOOL_RESULT_CHARS = 100_000 if len(function_result) > MAX_TOOL_RESULT_CHARS: original_len = len(function_result) function_result = ( function_result[:MAX_TOOL_RESULT_CHARS] + f"\n\n[Truncated: tool response was {original_len:,} chars, " f"exceeding the {MAX_TOOL_RESULT_CHARS:,} char limit]" ) # Append tool result message in order tool_msg = { "role": "tool", "content": function_result, "tool_call_id": tc.id, } messages.append(tool_msg) # ── Budget pressure injection ──────────────────────────────────── budget_warning = self._get_budget_warning(api_call_count) if budget_warning and messages and messages[-1].get("role") == "tool": last_content = messages[-1]["content"] try: parsed = json.loads(last_content) if isinstance(parsed, dict): parsed["_budget_warning"] = budget_warning messages[-1]["content"] = json.dumps(parsed, ensure_ascii=False) else: messages[-1]["content"] = last_content + f"\n\n{budget_warning}" except (json.JSONDecodeError, TypeError): messages[-1]["content"] = last_content + f"\n\n{budget_warning}" if not self.quiet_mode: remaining = self.max_iterations - api_call_count tier = "⚠️ WARNING" if remaining <= self.max_iterations * 0.1 else "💡 CAUTION" print(f"{self.log_prefix}{tier}: {remaining} iterations remaining") def _execute_tool_calls_sequential(self, assistant_message, messages: list, effective_task_id: str, api_call_count: int = 0) -> None: """Execute tool calls sequentially (original behavior). Used for single calls or interactive tools.""" for i, tool_call in enumerate(assistant_message.tool_calls, 1): # SAFETY: check interrupt BEFORE starting each tool. # If the user sent "stop" during a previous tool's execution, # do NOT start any more tools -- skip them all immediately. if self._interrupt_requested: remaining_calls = assistant_message.tool_calls[i-1:] if remaining_calls: self._vprint(f"{self.log_prefix}⚡ Interrupt: skipping {len(remaining_calls)} tool call(s)", force=True) for skipped_tc in remaining_calls: skipped_name = skipped_tc.function.name skip_msg = { "role": "tool", "content": f"[Tool execution cancelled — {skipped_name} was skipped due to user interrupt]", "tool_call_id": skipped_tc.id, } messages.append(skip_msg) break function_name = tool_call.function.name # Reset nudge counters when the relevant tool is actually used if function_name == "memory": self._turns_since_memory = 0 elif function_name == "skill_manage": self._iters_since_skill = 0 try: function_args = json.loads(tool_call.function.arguments) except json.JSONDecodeError as e: logging.warning(f"Unexpected JSON error after validation: {e}") function_args = {} if not isinstance(function_args, dict): function_args = {} if not self.quiet_mode: args_str = json.dumps(function_args, ensure_ascii=False) if self.verbose_logging: print(f" 📞 Tool {i}: {function_name}({list(function_args.keys())})") print(f" Args: {args_str}") else: args_preview = args_str[:self.log_prefix_chars] + "..." if len(args_str) > self.log_prefix_chars else args_str print(f" 📞 Tool {i}: {function_name}({list(function_args.keys())}) - {args_preview}") if self.tool_progress_callback: try: preview = _build_tool_preview(function_name, function_args) self.tool_progress_callback(function_name, preview, function_args) except Exception as cb_err: logging.debug(f"Tool progress callback error: {cb_err}") # Checkpoint: snapshot working dir before file-mutating tools if function_name in ("write_file", "patch") and self._checkpoint_mgr.enabled: try: file_path = function_args.get("path", "") if file_path: work_dir = self._checkpoint_mgr.get_working_dir_for_path(file_path) self._checkpoint_mgr.ensure_checkpoint( work_dir, f"before {function_name}" ) except Exception: pass # never block tool execution # Checkpoint before destructive terminal commands if function_name == "terminal" and self._checkpoint_mgr.enabled: try: cmd = function_args.get("command", "") if _is_destructive_command(cmd): cwd = function_args.get("workdir") or os.getenv("TERMINAL_CWD", os.getcwd()) self._checkpoint_mgr.ensure_checkpoint( cwd, f"before terminal: {cmd[:60]}" ) except Exception: pass # never block tool execution tool_start_time = time.time() if function_name == "todo": from tools.todo_tool import todo_tool as _todo_tool function_result = _todo_tool( todos=function_args.get("todos"), merge=function_args.get("merge", False), store=self._todo_store, ) tool_duration = time.time() - tool_start_time if self.quiet_mode: self._vprint(f" {_get_cute_tool_message_impl('todo', function_args, tool_duration, result=function_result)}") elif function_name == "session_search": if not self._session_db: function_result = json.dumps({"success": False, "error": "Session database not available."}) else: from tools.session_search_tool import session_search as _session_search function_result = _session_search( query=function_args.get("query", ""), role_filter=function_args.get("role_filter"), limit=function_args.get("limit", 3), db=self._session_db, current_session_id=self.session_id, ) tool_duration = time.time() - tool_start_time if self.quiet_mode: self._vprint(f" {_get_cute_tool_message_impl('session_search', function_args, tool_duration, result=function_result)}") elif function_name == "memory": target = function_args.get("target", "memory") from tools.memory_tool import memory_tool as _memory_tool function_result = _memory_tool( action=function_args.get("action"), target=target, content=function_args.get("content"), old_text=function_args.get("old_text"), store=self._memory_store, ) # Also send user observations to Honcho when active if self._honcho and target == "user" and function_args.get("action") == "add": self._honcho_save_user_observation(function_args.get("content", "")) tool_duration = time.time() - tool_start_time if self.quiet_mode: self._vprint(f" {_get_cute_tool_message_impl('memory', function_args, tool_duration, result=function_result)}") elif function_name == "clarify": from tools.clarify_tool import clarify_tool as _clarify_tool function_result = _clarify_tool( question=function_args.get("question", ""), choices=function_args.get("choices"), callback=self.clarify_callback, ) tool_duration = time.time() - tool_start_time if self.quiet_mode: self._vprint(f" {_get_cute_tool_message_impl('clarify', function_args, tool_duration, result=function_result)}") elif function_name == "delegate_task": from tools.delegate_tool import delegate_task as _delegate_task tasks_arg = function_args.get("tasks") if tasks_arg and isinstance(tasks_arg, list): spinner_label = f"🔀 delegating {len(tasks_arg)} tasks" else: goal_preview = (function_args.get("goal") or "")[:30] spinner_label = f"🔀 {goal_preview}" if goal_preview else "🔀 delegating" spinner = None if self.quiet_mode and not self.tool_progress_callback: face = random.choice(KawaiiSpinner.KAWAII_WAITING) spinner = KawaiiSpinner(f"{face} {spinner_label}", spinner_type='dots') spinner.start() self._delegate_spinner = spinner _delegate_result = None try: function_result = _delegate_task( goal=function_args.get("goal"), context=function_args.get("context"), toolsets=function_args.get("toolsets"), tasks=tasks_arg, max_iterations=function_args.get("max_iterations"), parent_agent=self, ) _delegate_result = function_result finally: self._delegate_spinner = None tool_duration = time.time() - tool_start_time cute_msg = _get_cute_tool_message_impl('delegate_task', function_args, tool_duration, result=_delegate_result) if spinner: spinner.stop(cute_msg) elif self.quiet_mode: self._vprint(f" {cute_msg}") elif self.quiet_mode: spinner = None if not self.tool_progress_callback: face = random.choice(KawaiiSpinner.KAWAII_WAITING) emoji = _get_tool_emoji(function_name) preview = _build_tool_preview(function_name, function_args) or function_name if len(preview) > 30: preview = preview[:27] + "..." spinner = KawaiiSpinner(f"{face} {emoji} {preview}", spinner_type='dots') spinner.start() _spinner_result = None try: function_result = handle_function_call( function_name, function_args, effective_task_id, enabled_tools=list(self.valid_tool_names) if self.valid_tool_names else None, honcho_manager=self._honcho, honcho_session_key=self._honcho_session_key, ) _spinner_result = function_result except Exception as tool_error: function_result = f"Error executing tool '{function_name}': {tool_error}" logger.error("handle_function_call raised for %s: %s", function_name, tool_error, exc_info=True) finally: tool_duration = time.time() - tool_start_time cute_msg = _get_cute_tool_message_impl(function_name, function_args, tool_duration, result=_spinner_result) if spinner: spinner.stop(cute_msg) else: self._vprint(f" {cute_msg}") else: try: function_result = handle_function_call( function_name, function_args, effective_task_id, enabled_tools=list(self.valid_tool_names) if self.valid_tool_names else None, honcho_manager=self._honcho, honcho_session_key=self._honcho_session_key, ) except Exception as tool_error: function_result = f"Error executing tool '{function_name}': {tool_error}" logger.error("handle_function_call raised for %s: %s", function_name, tool_error, exc_info=True) tool_duration = time.time() - tool_start_time result_preview = function_result if self.verbose_logging else ( function_result[:200] if len(function_result) > 200 else function_result ) # Log tool errors to the persistent error log so [error] tags # in the UI always have a corresponding detailed entry on disk. _is_error_result, _ = _detect_tool_failure(function_name, function_result) if _is_error_result: logger.warning("Tool %s returned error (%.2fs): %s", function_name, tool_duration, result_preview) if self.verbose_logging: logging.debug(f"Tool {function_name} completed in {tool_duration:.2f}s") logging.debug(f"Tool result ({len(function_result)} chars): {function_result}") # Guard against tools returning absurdly large content that would # blow up the context window. 100K chars ≈ 25K tokens — generous # enough for any reasonable tool output but prevents catastrophic # context explosions (e.g. accidental base64 image dumps). MAX_TOOL_RESULT_CHARS = 100_000 if len(function_result) > MAX_TOOL_RESULT_CHARS: original_len = len(function_result) function_result = ( function_result[:MAX_TOOL_RESULT_CHARS] + f"\n\n[Truncated: tool response was {original_len:,} chars, " f"exceeding the {MAX_TOOL_RESULT_CHARS:,} char limit]" ) tool_msg = { "role": "tool", "content": function_result, "tool_call_id": tool_call.id } messages.append(tool_msg) if not self.quiet_mode: if self.verbose_logging: print(f" ✅ Tool {i} completed in {tool_duration:.2f}s") print(f" Result: {function_result}") else: response_preview = function_result[:self.log_prefix_chars] + "..." if len(function_result) > self.log_prefix_chars else function_result print(f" ✅ Tool {i} completed in {tool_duration:.2f}s - {response_preview}") if self._interrupt_requested and i < len(assistant_message.tool_calls): remaining = len(assistant_message.tool_calls) - i self._vprint(f"{self.log_prefix}⚡ Interrupt: skipping {remaining} remaining tool call(s)", force=True) for skipped_tc in assistant_message.tool_calls[i:]: skipped_name = skipped_tc.function.name skip_msg = { "role": "tool", "content": f"[Tool execution skipped — {skipped_name} was not started. User sent a new message]", "tool_call_id": skipped_tc.id } messages.append(skip_msg) break if self.tool_delay > 0 and i < len(assistant_message.tool_calls): time.sleep(self.tool_delay) # ── Budget pressure injection ───────────────────────────────── # After all tool calls in this turn are processed, check if we're # approaching max_iterations. If so, inject a warning into the LAST # tool result's JSON so the LLM sees it naturally when reading results. budget_warning = self._get_budget_warning(api_call_count) if budget_warning and messages and messages[-1].get("role") == "tool": last_content = messages[-1]["content"] try: parsed = json.loads(last_content) if isinstance(parsed, dict): parsed["_budget_warning"] = budget_warning messages[-1]["content"] = json.dumps(parsed, ensure_ascii=False) else: messages[-1]["content"] = last_content + f"\n\n{budget_warning}" except (json.JSONDecodeError, TypeError): messages[-1]["content"] = last_content + f"\n\n{budget_warning}" if not self.quiet_mode: remaining = self.max_iterations - api_call_count tier = "⚠️ WARNING" if remaining <= self.max_iterations * 0.1 else "💡 CAUTION" print(f"{self.log_prefix}{tier}: {remaining} iterations remaining") def _get_budget_warning(self, api_call_count: int) -> Optional[str]: """Return a budget pressure string, or None if not yet needed. Two-tier system: - Caution (70%): nudge to consolidate work - Warning (90%): urgent, must respond now """ if not self._budget_pressure_enabled or self.max_iterations <= 0: return None progress = api_call_count / self.max_iterations remaining = self.max_iterations - api_call_count if progress >= self._budget_warning_threshold: return ( f"[BUDGET WARNING: Iteration {api_call_count}/{self.max_iterations}. " f"Only {remaining} iteration(s) left. " "Provide your final response NOW. No more tool calls unless absolutely critical.]" ) if progress >= self._budget_caution_threshold: return ( f"[BUDGET: Iteration {api_call_count}/{self.max_iterations}. " f"{remaining} iterations left. Start consolidating your work.]" ) return None def _emit_context_pressure(self, compaction_progress: float, compressor) -> None: """Notify the user that context is approaching the compaction threshold. Args: compaction_progress: How close to compaction (0.0–1.0, where 1.0 = fires). compressor: The ContextCompressor instance (for threshold/context info). Purely user-facing — does NOT modify the message stream. For CLI: prints a formatted line with a progress bar. For gateway: fires status_callback so the platform can send a chat message. """ from agent.display import format_context_pressure, format_context_pressure_gateway threshold_pct = compressor.threshold_tokens / compressor.context_length if compressor.context_length else 0.5 # CLI output — always shown (these are user-facing status notifications, # not verbose debug output, so they bypass quiet_mode). # Gateway users also get the callback below. if self.platform in (None, "cli"): line = format_context_pressure( compaction_progress=compaction_progress, threshold_tokens=compressor.threshold_tokens, threshold_percent=threshold_pct, compression_enabled=self.compression_enabled, ) self._safe_print(line) # Gateway / external consumers if self.status_callback: try: msg = format_context_pressure_gateway( compaction_progress=compaction_progress, threshold_percent=threshold_pct, compression_enabled=self.compression_enabled, ) self.status_callback("context_pressure", msg) except Exception: logger.debug("status_callback error in context pressure", exc_info=True) def _handle_max_iterations(self, messages: list, api_call_count: int) -> str: """Request a summary when max iterations are reached. Returns the final response text.""" print(f"⚠️ Reached maximum iterations ({self.max_iterations}). Requesting summary...") summary_request = ( "You've reached the maximum number of tool-calling iterations allowed. " "Please provide a final response summarizing what you've found and accomplished so far, " "without calling any more tools." ) messages.append({"role": "user", "content": summary_request}) try: # Build API messages, stripping internal-only fields # (finish_reason, reasoning) that strict APIs like Mistral reject with 422 _is_strict_api = "api.mistral.ai" in self._base_url_lower api_messages = [] for msg in messages: api_msg = msg.copy() for internal_field in ("reasoning", "finish_reason"): api_msg.pop(internal_field, None) if _is_strict_api: self._sanitize_tool_calls_for_strict_api(api_msg) api_messages.append(api_msg) effective_system = self._cached_system_prompt or "" if self.ephemeral_system_prompt: effective_system = (effective_system + "\n\n" + self.ephemeral_system_prompt).strip() if effective_system: api_messages = [{"role": "system", "content": effective_system}] + api_messages if self.prefill_messages: sys_offset = 1 if effective_system else 0 for idx, pfm in enumerate(self.prefill_messages): api_messages.insert(sys_offset + idx, pfm.copy()) summary_extra_body = {} _is_nous = "nousresearch" in self._base_url_lower if self._supports_reasoning_extra_body(): if self.reasoning_config is not None: summary_extra_body["reasoning"] = self.reasoning_config else: summary_extra_body["reasoning"] = { "enabled": True, "effort": "medium" } if _is_nous: summary_extra_body["tags"] = ["product=hermes-agent"] if self.api_mode == "codex_responses": codex_kwargs = self._build_api_kwargs(api_messages) codex_kwargs.pop("tools", None) summary_response = self._run_codex_stream(codex_kwargs) assistant_message, _ = self._normalize_codex_response(summary_response) final_response = (assistant_message.content or "").strip() if assistant_message else "" else: summary_kwargs = { "model": self.model, "messages": api_messages, } if self.max_tokens is not None: summary_kwargs.update(self._max_tokens_param(self.max_tokens)) # Include provider routing preferences provider_preferences = {} if self.providers_allowed: provider_preferences["only"] = self.providers_allowed if self.providers_ignored: provider_preferences["ignore"] = self.providers_ignored if self.providers_order: provider_preferences["order"] = self.providers_order if self.provider_sort: provider_preferences["sort"] = self.provider_sort if provider_preferences: summary_extra_body["provider"] = provider_preferences if summary_extra_body: summary_kwargs["extra_body"] = summary_extra_body if self.api_mode == "anthropic_messages": from agent.anthropic_adapter import build_anthropic_kwargs as _bak, normalize_anthropic_response as _nar _ant_kw = _bak(model=self.model, messages=api_messages, tools=None, max_tokens=self.max_tokens, reasoning_config=self.reasoning_config, is_oauth=getattr(self, '_is_anthropic_oauth', False), preserve_dots=self._anthropic_preserve_dots()) summary_response = self._anthropic_messages_create(_ant_kw) _msg, _ = _nar(summary_response, strip_tool_prefix=getattr(self, '_is_anthropic_oauth', False)) final_response = (_msg.content or "").strip() else: summary_response = self._ensure_primary_openai_client(reason="iteration_limit_summary").chat.completions.create(**summary_kwargs) if summary_response.choices and summary_response.choices[0].message.content: final_response = summary_response.choices[0].message.content else: final_response = "" if final_response: if "" in final_response: final_response = re.sub(r'.*?\s*', '', final_response, flags=re.DOTALL).strip() if final_response: messages.append({"role": "assistant", "content": final_response}) else: final_response = "I reached the iteration limit and couldn't generate a summary." else: # Retry summary generation if self.api_mode == "codex_responses": codex_kwargs = self._build_api_kwargs(api_messages) codex_kwargs.pop("tools", None) retry_response = self._run_codex_stream(codex_kwargs) retry_msg, _ = self._normalize_codex_response(retry_response) final_response = (retry_msg.content or "").strip() if retry_msg else "" elif self.api_mode == "anthropic_messages": from agent.anthropic_adapter import build_anthropic_kwargs as _bak2, normalize_anthropic_response as _nar2 _ant_kw2 = _bak2(model=self.model, messages=api_messages, tools=None, is_oauth=getattr(self, '_is_anthropic_oauth', False), max_tokens=self.max_tokens, reasoning_config=self.reasoning_config, preserve_dots=self._anthropic_preserve_dots()) retry_response = self._anthropic_messages_create(_ant_kw2) _retry_msg, _ = _nar2(retry_response, strip_tool_prefix=getattr(self, '_is_anthropic_oauth', False)) final_response = (_retry_msg.content or "").strip() else: summary_kwargs = { "model": self.model, "messages": api_messages, } if self.max_tokens is not None: summary_kwargs.update(self._max_tokens_param(self.max_tokens)) if summary_extra_body: summary_kwargs["extra_body"] = summary_extra_body summary_response = self._ensure_primary_openai_client(reason="iteration_limit_summary_retry").chat.completions.create(**summary_kwargs) if summary_response.choices and summary_response.choices[0].message.content: final_response = summary_response.choices[0].message.content else: final_response = "" if final_response: if "" in final_response: final_response = re.sub(r'.*?\s*', '', final_response, flags=re.DOTALL).strip() if final_response: messages.append({"role": "assistant", "content": final_response}) else: final_response = "I reached the iteration limit and couldn't generate a summary." else: final_response = "I reached the iteration limit and couldn't generate a summary." except Exception as e: logging.warning(f"Failed to get summary response: {e}") final_response = f"I reached the maximum iterations ({self.max_iterations}) but couldn't summarize. Error: {str(e)}" return final_response def run_conversation( self, user_message: str, system_message: str = None, conversation_history: List[Dict[str, Any]] = None, task_id: str = None, stream_callback: Optional[callable] = None, persist_user_message: Optional[str] = None, sync_honcho: bool = True, ) -> Dict[str, Any]: """ Run a complete conversation with tool calling until completion. Args: user_message (str): The user's message/question system_message (str): Custom system message (optional, overrides ephemeral_system_prompt if provided) conversation_history (List[Dict]): Previous conversation messages (optional) task_id (str): Unique identifier for this task to isolate VMs between concurrent tasks (optional, auto-generated if not provided) stream_callback: Optional callback invoked with each text delta during streaming. Used by the TTS pipeline to start audio generation before the full response. When None (default), API calls use the standard non-streaming path. persist_user_message: Optional clean user message to store in transcripts/history when user_message contains API-only synthetic prefixes. sync_honcho: When False, skip writing the final synthetic turn back to Honcho or queuing follow-up prefetch work. Returns: Dict: Complete conversation result with final response and message history """ # Guard stdio against OSError from broken pipes (systemd/headless/daemon). # Installed once, transparent when streams are healthy, prevents crash on write. _install_safe_stdio() # Store stream callback for _interruptible_api_call to pick up self._stream_callback = stream_callback self._persist_user_message_idx = None self._persist_user_message_override = persist_user_message # Generate unique task_id if not provided to isolate VMs between concurrent tasks effective_task_id = task_id or str(uuid.uuid4()) # Reset retry counters and iteration budget at the start of each turn # so subagent usage from a previous turn doesn't eat into the next one. self._invalid_tool_retries = 0 self._invalid_json_retries = 0 self._empty_content_retries = 0 self._incomplete_scratchpad_retries = 0 self._codex_incomplete_retries = 0 self._last_content_with_tools = None self._mute_post_response = False # NOTE: _turns_since_memory and _iters_since_skill are NOT reset here. # They are initialized in __init__ and must persist across run_conversation # calls so that nudge logic accumulates correctly in CLI mode. self.iteration_budget = IterationBudget(self.max_iterations) # Initialize conversation (copy to avoid mutating the caller's list) messages = list(conversation_history) if conversation_history else [] # Hydrate todo store from conversation history (gateway creates a fresh # AIAgent per message, so the in-memory store is empty -- we need to # recover the todo state from the most recent todo tool response in history) if conversation_history and not self._todo_store.has_items(): self._hydrate_todo_store(conversation_history) # Prefill messages (few-shot priming) are injected at API-call time only, # never stored in the messages list. This keeps them ephemeral: they won't # be saved to session DB, session logs, or batch trajectories, but they're # automatically re-applied on every API call (including session continuations). # Track user turns for memory flush and periodic nudge logic self._user_turn_count += 1 # Preserve the original user message (no nudge injection). # Honcho should receive the actual user input, not system nudges. original_user_message = persist_user_message if persist_user_message is not None else user_message # Track memory nudge trigger (turn-based, checked here). # Skill trigger is checked AFTER the agent loop completes, based on # how many tool iterations THIS turn used. _should_review_memory = False if (self._memory_nudge_interval > 0 and "memory" in self.valid_tool_names and self._memory_store): self._turns_since_memory += 1 if self._turns_since_memory >= self._memory_nudge_interval: _should_review_memory = True self._turns_since_memory = 0 # Honcho prefetch consumption: # - First turn: bake into cached system prompt (stable for the session). # - Later turns: attach recall to the current-turn user message at # API-call time only (never persisted to history / session DB). # # This keeps the system-prefix cache stable while still allowing turn N # to consume background prefetch results from turn N-1. self._honcho_context = "" self._honcho_turn_context = "" _recall_mode = (self._honcho_config.recall_mode if self._honcho_config else "hybrid") if self._honcho and self._honcho_session_key and _recall_mode != "tools": try: prefetched_context = self._honcho_prefetch(original_user_message) if prefetched_context: if not conversation_history: self._honcho_context = prefetched_context else: self._honcho_turn_context = prefetched_context except Exception as e: logger.debug("Honcho prefetch failed (non-fatal): %s", e) # Add user message user_msg = {"role": "user", "content": user_message} messages.append(user_msg) current_turn_user_idx = len(messages) - 1 self._persist_user_message_idx = current_turn_user_idx if not self.quiet_mode: self._safe_print(f"💬 Starting conversation: '{user_message[:60]}{'...' if len(user_message) > 60 else ''}'") # ── System prompt (cached per session for prefix caching) ── # Built once on first call, reused for all subsequent calls. # Only rebuilt after context compression events (which invalidate # the cache and reload memory from disk). # # For continuing sessions (gateway creates a fresh AIAgent per # message), we load the stored system prompt from the session DB # instead of rebuilding. Rebuilding would pick up memory changes # from disk that the model already knows about (it wrote them!), # producing a different system prompt and breaking the Anthropic # prefix cache. if self._cached_system_prompt is None: stored_prompt = None if conversation_history and self._session_db: try: session_row = self._session_db.get_session(self.session_id) if session_row: stored_prompt = session_row.get("system_prompt") or None except Exception: pass # Fall through to build fresh if stored_prompt: # Continuing session — reuse the exact system prompt from # the previous turn so the Anthropic cache prefix matches. self._cached_system_prompt = stored_prompt else: # First turn of a new session — build from scratch. self._cached_system_prompt = self._build_system_prompt(system_message) # Bake Honcho context into the prompt so it's stable for # the entire session (not re-fetched per turn). if self._honcho_context: self._cached_system_prompt = ( self._cached_system_prompt + "\n\n" + self._honcho_context ).strip() # Store the system prompt snapshot in SQLite if self._session_db: try: self._session_db.update_system_prompt(self.session_id, self._cached_system_prompt) except Exception as e: logger.debug("Session DB update_system_prompt failed: %s", e) active_system_prompt = self._cached_system_prompt # ── Preflight context compression ── # Before entering the main loop, check if the loaded conversation # history already exceeds the model's context threshold. This handles # cases where a user switches to a model with a smaller context window # while having a large existing session — compress proactively rather # than waiting for an API error (which might be caught as a non-retryable # 4xx and abort the request entirely). if ( self.compression_enabled and len(messages) > self.context_compressor.protect_first_n + self.context_compressor.protect_last_n + 1 ): _sys_tok_est = estimate_tokens_rough(active_system_prompt or "") _msg_tok_est = estimate_messages_tokens_rough(messages) _preflight_tokens = _sys_tok_est + _msg_tok_est if _preflight_tokens >= self.context_compressor.threshold_tokens: logger.info( "Preflight compression: ~%s tokens >= %s threshold (model %s, ctx %s)", f"{_preflight_tokens:,}", f"{self.context_compressor.threshold_tokens:,}", self.model, f"{self.context_compressor.context_length:,}", ) if not self.quiet_mode: self._safe_print( f"📦 Preflight compression: ~{_preflight_tokens:,} tokens " f">= {self.context_compressor.threshold_tokens:,} threshold" ) # May need multiple passes for very large sessions with small # context windows (each pass summarises the middle N turns). for _pass in range(3): _orig_len = len(messages) messages, active_system_prompt = self._compress_context( messages, system_message, approx_tokens=_preflight_tokens, task_id=effective_task_id, ) if len(messages) >= _orig_len: break # Cannot compress further # Re-estimate after compression _sys_tok_est = estimate_tokens_rough(active_system_prompt or "") _msg_tok_est = estimate_messages_tokens_rough(messages) _preflight_tokens = _sys_tok_est + _msg_tok_est if _preflight_tokens < self.context_compressor.threshold_tokens: break # Under threshold # Main conversation loop api_call_count = 0 final_response = None interrupted = False codex_ack_continuations = 0 length_continue_retries = 0 truncated_response_prefix = "" compression_attempts = 0 # Clear any stale interrupt state at start self.clear_interrupt() while api_call_count < self.max_iterations and self.iteration_budget.remaining > 0: # Reset per-turn checkpoint dedup so each iteration can take one snapshot self._checkpoint_mgr.new_turn() # Check for interrupt request (e.g., user sent new message) if self._interrupt_requested: interrupted = True if not self.quiet_mode: self._safe_print(f"\n⚡ Breaking out of tool loop due to interrupt...") break api_call_count += 1 if not self.iteration_budget.consume(): if not self.quiet_mode: self._safe_print(f"\n⚠️ Iteration budget exhausted ({self.iteration_budget.used}/{self.iteration_budget.max_total} iterations used)") break # Fire step_callback for gateway hooks (agent:step event) if self.step_callback is not None: try: prev_tools = [] for _m in reversed(messages): if _m.get("role") == "assistant" and _m.get("tool_calls"): prev_tools = [ tc["function"]["name"] for tc in _m["tool_calls"] if isinstance(tc, dict) ] break self.step_callback(api_call_count, prev_tools) except Exception as _step_err: logger.debug("step_callback error (iteration %s): %s", api_call_count, _step_err) # Track tool-calling iterations for skill nudge. # Counter resets whenever skill_manage is actually used. if (self._skill_nudge_interval > 0 and "skill_manage" in self.valid_tool_names): self._iters_since_skill += 1 # Prepare messages for API call # If we have an ephemeral system prompt, prepend it to the messages # Note: Reasoning is embedded in content via tags for trajectory storage. # However, providers like Moonshot AI require a separate 'reasoning_content' field # on assistant messages with tool_calls. We handle both cases here. api_messages = [] for idx, msg in enumerate(messages): api_msg = msg.copy() if idx == current_turn_user_idx and msg.get("role") == "user" and self._honcho_turn_context: api_msg["content"] = _inject_honcho_turn_context( api_msg.get("content", ""), self._honcho_turn_context ) # For ALL assistant messages, pass reasoning back to the API # This ensures multi-turn reasoning context is preserved if msg.get("role") == "assistant": reasoning_text = msg.get("reasoning") if reasoning_text: # Add reasoning_content for API compatibility (Moonshot AI, Novita, OpenRouter) api_msg["reasoning_content"] = reasoning_text # Remove 'reasoning' field - it's for trajectory storage only # We've copied it to 'reasoning_content' for the API above if "reasoning" in api_msg: api_msg.pop("reasoning") # Remove finish_reason - not accepted by strict APIs (e.g. Mistral) if "finish_reason" in api_msg: api_msg.pop("finish_reason") # Strip Codex Responses API fields (call_id, response_item_id) for # strict providers like Mistral that reject unknown fields with 422. # Uses new dicts so the internal messages list retains the fields # for Codex Responses compatibility. if "api.mistral.ai" in self._base_url_lower: self._sanitize_tool_calls_for_strict_api(api_msg) # Keep 'reasoning_details' - OpenRouter uses this for multi-turn reasoning context # The signature field helps maintain reasoning continuity api_messages.append(api_msg) # Build the final system message: cached prompt + ephemeral system prompt. # Ephemeral additions are API-call-time only (not persisted to session DB). # Honcho later-turn recall is intentionally kept OUT of the system prompt # so the stable cache prefix remains unchanged. effective_system = active_system_prompt or "" if self.ephemeral_system_prompt: effective_system = (effective_system + "\n\n" + self.ephemeral_system_prompt).strip() if effective_system: api_messages = [{"role": "system", "content": effective_system}] + api_messages # Inject ephemeral prefill messages right after the system prompt # but before conversation history. Same API-call-time-only pattern. if self.prefill_messages: sys_offset = 1 if effective_system else 0 for idx, pfm in enumerate(self.prefill_messages): api_messages.insert(sys_offset + idx, pfm.copy()) # Apply Anthropic prompt caching for Claude models via OpenRouter. # Auto-detected: if model name contains "claude" and base_url is OpenRouter, # inject cache_control breakpoints (system + last 3 messages) to reduce # input token costs by ~75% on multi-turn conversations. if self._use_prompt_caching: api_messages = apply_anthropic_cache_control(api_messages, cache_ttl=self._cache_ttl, native_anthropic=(self.api_mode == 'anthropic_messages')) # Safety net: strip orphaned tool results / add stubs for missing # results before sending to the API. Runs unconditionally — not # gated on context_compressor — so orphans from session loading or # manual message manipulation are always caught. api_messages = self._sanitize_api_messages(api_messages) # Calculate approximate request size for logging total_chars = sum(len(str(msg)) for msg in api_messages) approx_tokens = total_chars // 4 # Rough estimate: 4 chars per token # Thinking spinner for quiet mode (animated during API call) thinking_spinner = None if not self.quiet_mode: self._vprint(f"\n{self.log_prefix}🔄 Making API call #{api_call_count}/{self.max_iterations}...") self._vprint(f"{self.log_prefix} 📊 Request size: {len(api_messages)} messages, ~{approx_tokens:,} tokens (~{total_chars:,} chars)") self._vprint(f"{self.log_prefix} 🔧 Available tools: {len(self.tools) if self.tools else 0}") else: # Animated thinking spinner in quiet mode face = random.choice(KawaiiSpinner.KAWAII_THINKING) verb = random.choice(KawaiiSpinner.THINKING_VERBS) if self.thinking_callback: # CLI TUI mode: use prompt_toolkit widget instead of raw spinner # (works in both streaming and non-streaming modes) self.thinking_callback(f"{face} {verb}...") elif not self._has_stream_consumers(): # Raw KawaiiSpinner only when no streaming consumers # (would conflict with streamed token output) spinner_type = random.choice(['brain', 'sparkle', 'pulse', 'moon', 'star']) thinking_spinner = KawaiiSpinner(f"{face} {verb}...", spinner_type=spinner_type) thinking_spinner.start() # Log request details if verbose if self.verbose_logging: logging.debug(f"API Request - Model: {self.model}, Messages: {len(messages)}, Tools: {len(self.tools) if self.tools else 0}") logging.debug(f"Last message role: {messages[-1]['role'] if messages else 'none'}") logging.debug(f"Total message size: ~{approx_tokens:,} tokens") api_start_time = time.time() retry_count = 0 max_retries = 3 max_compression_attempts = 3 codex_auth_retry_attempted = False anthropic_auth_retry_attempted = False nous_auth_retry_attempted = False restart_with_compressed_messages = False restart_with_length_continuation = False finish_reason = "stop" response = None # Guard against UnboundLocalError if all retries fail while retry_count < max_retries: try: api_kwargs = self._build_api_kwargs(api_messages) if self.api_mode == "codex_responses": api_kwargs = self._preflight_codex_api_kwargs(api_kwargs, allow_stream=False) if os.getenv("HERMES_DUMP_REQUESTS", "").strip().lower() in {"1", "true", "yes", "on"}: self._dump_api_request_debug(api_kwargs, reason="preflight") if self._has_stream_consumers(): # Streaming path: fire delta callbacks for real-time # token delivery to CLI display, gateway, or TTS. def _stop_spinner(): nonlocal thinking_spinner if thinking_spinner: thinking_spinner.stop("") thinking_spinner = None if self.thinking_callback: self.thinking_callback("") response = self._interruptible_streaming_api_call( api_kwargs, on_first_delta=_stop_spinner ) else: response = self._interruptible_api_call(api_kwargs) api_duration = time.time() - api_start_time # Stop thinking spinner silently -- the response box or tool # execution messages that follow are more informative. if thinking_spinner: thinking_spinner.stop("") thinking_spinner = None if self.thinking_callback: self.thinking_callback("") if not self.quiet_mode: self._vprint(f"{self.log_prefix}⏱️ API call completed in {api_duration:.2f}s") if self.verbose_logging: # Log response with provider info if available resp_model = getattr(response, 'model', 'N/A') if response else 'N/A' logging.debug(f"API Response received - Model: {resp_model}, Usage: {response.usage if hasattr(response, 'usage') else 'N/A'}") # Validate response shape before proceeding response_invalid = False error_details = [] if self.api_mode == "codex_responses": output_items = getattr(response, "output", None) if response is not None else None if response is None: response_invalid = True error_details.append("response is None") elif not isinstance(output_items, list): response_invalid = True error_details.append("response.output is not a list") elif len(output_items) == 0: response_invalid = True error_details.append("response.output is empty") elif self.api_mode == "anthropic_messages": content_blocks = getattr(response, "content", None) if response is not None else None if response is None: response_invalid = True error_details.append("response is None") elif not isinstance(content_blocks, list): response_invalid = True error_details.append("response.content is not a list") elif len(content_blocks) == 0: response_invalid = True error_details.append("response.content is empty") else: if response is None or not hasattr(response, 'choices') or response.choices is None or len(response.choices) == 0: response_invalid = True if response is None: error_details.append("response is None") elif not hasattr(response, 'choices'): error_details.append("response has no 'choices' attribute") elif response.choices is None: error_details.append("response.choices is None") else: error_details.append("response.choices is empty") if response_invalid: # Stop spinner before printing error messages if thinking_spinner: thinking_spinner.stop(f"(´;ω;`) oops, retrying...") thinking_spinner = None if self.thinking_callback: self.thinking_callback("") # This is often rate limiting or provider returning malformed response retry_count += 1 # Eager fallback: empty/malformed responses are a common # rate-limit symptom. Switch to fallback immediately # rather than retrying with extended backoff. if not self._fallback_activated and self._try_activate_fallback(): retry_count = 0 continue # Check for error field in response (some providers include this) error_msg = "Unknown" provider_name = "Unknown" if response and hasattr(response, 'error') and response.error: error_msg = str(response.error) # Try to extract provider from error metadata if hasattr(response.error, 'metadata') and response.error.metadata: provider_name = response.error.metadata.get('provider_name', 'Unknown') elif response and hasattr(response, 'message') and response.message: error_msg = str(response.message) # Try to get provider from model field (OpenRouter often returns actual model used) if provider_name == "Unknown" and response and hasattr(response, 'model') and response.model: provider_name = f"model={response.model}" # Check for x-openrouter-provider or similar metadata if provider_name == "Unknown" and response: # Log all response attributes for debugging resp_attrs = {k: str(v)[:100] for k, v in vars(response).items() if not k.startswith('_')} if self.verbose_logging: logging.debug(f"Response attributes for invalid response: {resp_attrs}") self._vprint(f"{self.log_prefix}⚠️ Invalid API response (attempt {retry_count}/{max_retries}): {', '.join(error_details)}", force=True) self._vprint(f"{self.log_prefix} 🏢 Provider: {provider_name}", force=True) cleaned_provider_error = self._clean_error_message(error_msg) self._vprint(f"{self.log_prefix} 📝 Provider message: {cleaned_provider_error}", force=True) self._vprint(f"{self.log_prefix} ⏱️ Response time: {api_duration:.2f}s (fast response often indicates rate limiting)", force=True) if retry_count >= max_retries: # Try fallback before giving up if self._try_activate_fallback(): retry_count = 0 continue self._vprint(f"{self.log_prefix}❌ Max retries ({max_retries}) exceeded for invalid responses. Giving up.", force=True) logging.error(f"{self.log_prefix}Invalid API response after {max_retries} retries.") self._persist_session(messages, conversation_history) return { "messages": messages, "completed": False, "api_calls": api_call_count, "error": "Invalid API response shape. Likely rate limited or malformed provider response.", "failed": True # Mark as failure for filtering } # Longer backoff for rate limiting (likely cause of None choices) wait_time = min(5 * (2 ** (retry_count - 1)), 120) # 5s, 10s, 20s, 40s, 80s, 120s self._vprint(f"{self.log_prefix}⏳ Retrying in {wait_time}s (extended backoff for possible rate limit)...", force=True) logging.warning(f"Invalid API response (retry {retry_count}/{max_retries}): {', '.join(error_details)} | Provider: {provider_name}") # Sleep in small increments to stay responsive to interrupts sleep_end = time.time() + wait_time while time.time() < sleep_end: if self._interrupt_requested: self._vprint(f"{self.log_prefix}⚡ Interrupt detected during retry wait, aborting.", force=True) self._persist_session(messages, conversation_history) self.clear_interrupt() return { "final_response": f"Operation interrupted: retrying API call after rate limit (retry {retry_count}/{max_retries}).", "messages": messages, "api_calls": api_call_count, "completed": False, "interrupted": True, } time.sleep(0.2) continue # Retry the API call # Check finish_reason before proceeding if self.api_mode == "codex_responses": status = getattr(response, "status", None) incomplete_details = getattr(response, "incomplete_details", None) incomplete_reason = None if isinstance(incomplete_details, dict): incomplete_reason = incomplete_details.get("reason") else: incomplete_reason = getattr(incomplete_details, "reason", None) if status == "incomplete" and incomplete_reason in {"max_output_tokens", "length"}: finish_reason = "length" else: finish_reason = "stop" elif self.api_mode == "anthropic_messages": stop_reason_map = {"end_turn": "stop", "tool_use": "tool_calls", "max_tokens": "length", "stop_sequence": "stop"} finish_reason = stop_reason_map.get(response.stop_reason, "stop") else: finish_reason = response.choices[0].finish_reason if finish_reason == "length": self._vprint(f"{self.log_prefix}⚠️ Response truncated (finish_reason='length') - model hit max output tokens", force=True) if self.api_mode == "chat_completions": assistant_message = response.choices[0].message if not assistant_message.tool_calls: length_continue_retries += 1 interim_msg = self._build_assistant_message(assistant_message, finish_reason) messages.append(interim_msg) if assistant_message.content: truncated_response_prefix += assistant_message.content if length_continue_retries < 3: self._vprint( f"{self.log_prefix}↻ Requesting continuation " f"({length_continue_retries}/3)..." ) continue_msg = { "role": "user", "content": ( "[System: Your previous response was truncated by the output " "length limit. Continue exactly where you left off. Do not " "restart or repeat prior text. Finish the answer directly.]" ), } messages.append(continue_msg) self._session_messages = messages self._save_session_log(messages) restart_with_length_continuation = True break partial_response = self._strip_think_blocks(truncated_response_prefix).strip() self._cleanup_task_resources(effective_task_id) self._persist_session(messages, conversation_history) return { "final_response": partial_response or None, "messages": messages, "api_calls": api_call_count, "completed": False, "partial": True, "error": "Response remained truncated after 3 continuation attempts", } # If we have prior messages, roll back to last complete state if len(messages) > 1: self._vprint(f"{self.log_prefix} ⏪ Rolling back to last complete assistant turn") rolled_back_messages = self._get_messages_up_to_last_assistant(messages) self._cleanup_task_resources(effective_task_id) self._persist_session(messages, conversation_history) return { "final_response": None, "messages": rolled_back_messages, "api_calls": api_call_count, "completed": False, "partial": True, "error": "Response truncated due to output length limit" } else: # First message was truncated - mark as failed self._vprint(f"{self.log_prefix}❌ First response truncated - cannot recover", force=True) self._persist_session(messages, conversation_history) return { "final_response": None, "messages": messages, "api_calls": api_call_count, "completed": False, "failed": True, "error": "First response truncated due to output length limit" } # Track actual token usage from response for context management if hasattr(response, 'usage') and response.usage: canonical_usage = normalize_usage( response.usage, provider=self.provider, api_mode=self.api_mode, ) prompt_tokens = canonical_usage.prompt_tokens completion_tokens = canonical_usage.output_tokens total_tokens = canonical_usage.total_tokens usage_dict = { "prompt_tokens": prompt_tokens, "completion_tokens": completion_tokens, "total_tokens": total_tokens, } self.context_compressor.update_from_response(usage_dict) # Cache discovered context length after successful call if self.context_compressor._context_probed: ctx = self.context_compressor.context_length save_context_length(self.model, self.base_url, ctx) self._safe_print(f"{self.log_prefix}💾 Cached context length: {ctx:,} tokens for {self.model}") self.context_compressor._context_probed = False self.session_prompt_tokens += prompt_tokens self.session_completion_tokens += completion_tokens self.session_total_tokens += total_tokens self.session_api_calls += 1 self.session_input_tokens += canonical_usage.input_tokens self.session_output_tokens += canonical_usage.output_tokens self.session_cache_read_tokens += canonical_usage.cache_read_tokens self.session_cache_write_tokens += canonical_usage.cache_write_tokens self.session_reasoning_tokens += canonical_usage.reasoning_tokens cost_result = estimate_usage_cost( self.model, canonical_usage, provider=self.provider, base_url=self.base_url, api_key=getattr(self, "api_key", ""), ) if cost_result.amount_usd is not None: self.session_estimated_cost_usd += float(cost_result.amount_usd) self.session_cost_status = cost_result.status self.session_cost_source = cost_result.source # Persist token counts to session DB for /insights. # Gateway sessions persist via session_store.update_session() # after run_conversation returns, so only persist here for # CLI (and other non-gateway) platforms to avoid double-counting. if (self._session_db and self.session_id and getattr(self, 'platform', None) == 'cli'): try: self._session_db.update_token_counts( self.session_id, input_tokens=canonical_usage.input_tokens, output_tokens=canonical_usage.output_tokens, cache_read_tokens=canonical_usage.cache_read_tokens, cache_write_tokens=canonical_usage.cache_write_tokens, reasoning_tokens=canonical_usage.reasoning_tokens, estimated_cost_usd=float(cost_result.amount_usd) if cost_result.amount_usd is not None else None, cost_status=cost_result.status, cost_source=cost_result.source, billing_provider=self.provider, billing_base_url=self.base_url, billing_mode="subscription_included" if cost_result.status == "included" else None, model=self.model, ) except Exception: pass # never block the agent loop if self.verbose_logging: logging.debug(f"Token usage: prompt={usage_dict['prompt_tokens']:,}, completion={usage_dict['completion_tokens']:,}, total={usage_dict['total_tokens']:,}") # Log cache hit stats when prompt caching is active if self._use_prompt_caching: if self.api_mode == "anthropic_messages": # Anthropic uses cache_read_input_tokens / cache_creation_input_tokens cached = getattr(response.usage, 'cache_read_input_tokens', 0) or 0 written = getattr(response.usage, 'cache_creation_input_tokens', 0) or 0 else: # OpenRouter uses prompt_tokens_details.cached_tokens details = getattr(response.usage, 'prompt_tokens_details', None) cached = getattr(details, 'cached_tokens', 0) or 0 if details else 0 written = getattr(details, 'cache_write_tokens', 0) or 0 if details else 0 prompt = usage_dict["prompt_tokens"] hit_pct = (cached / prompt * 100) if prompt > 0 else 0 if not self.quiet_mode: self._vprint(f"{self.log_prefix} 💾 Cache: {cached:,}/{prompt:,} tokens ({hit_pct:.0f}% hit, {written:,} written)") break # Success, exit retry loop except InterruptedError: if thinking_spinner: thinking_spinner.stop("") thinking_spinner = None if self.thinking_callback: self.thinking_callback("") api_elapsed = time.time() - api_start_time self._vprint(f"{self.log_prefix}⚡ Interrupted during API call.", force=True) self._persist_session(messages, conversation_history) interrupted = True final_response = f"Operation interrupted: waiting for model response ({api_elapsed:.1f}s elapsed)." break except Exception as api_error: # Stop spinner before printing error messages if thinking_spinner: thinking_spinner.stop(f"(╥_╥) error, retrying...") thinking_spinner = None if self.thinking_callback: self.thinking_callback("") status_code = getattr(api_error, "status_code", None) if ( self.api_mode == "codex_responses" and self.provider == "openai-codex" and status_code == 401 and not codex_auth_retry_attempted ): codex_auth_retry_attempted = True if self._try_refresh_codex_client_credentials(force=True): self._vprint(f"{self.log_prefix}🔐 Codex auth refreshed after 401. Retrying request...") continue if ( self.api_mode == "chat_completions" and self.provider == "nous" and status_code == 401 and not nous_auth_retry_attempted ): nous_auth_retry_attempted = True if self._try_refresh_nous_client_credentials(force=True): print(f"{self.log_prefix}🔐 Nous agent key refreshed after 401. Retrying request...") continue if ( self.api_mode == "anthropic_messages" and status_code == 401 and hasattr(self, '_anthropic_api_key') and not anthropic_auth_retry_attempted ): anthropic_auth_retry_attempted = True from agent.anthropic_adapter import _is_oauth_token if self._try_refresh_anthropic_client_credentials(): print(f"{self.log_prefix}🔐 Anthropic credentials refreshed after 401. Retrying request...") continue # Credential refresh didn't help — show diagnostic info key = self._anthropic_api_key auth_method = "Bearer (OAuth/setup-token)" if _is_oauth_token(key) else "x-api-key (API key)" print(f"{self.log_prefix}🔐 Anthropic 401 — authentication failed.") print(f"{self.log_prefix} Auth method: {auth_method}") print(f"{self.log_prefix} Token prefix: {key[:12]}..." if key and len(key) > 12 else f"{self.log_prefix} Token: (empty or short)") print(f"{self.log_prefix} Troubleshooting:") print(f"{self.log_prefix} • Check ANTHROPIC_TOKEN in ~/.hermes/.env for Hermes-managed OAuth/setup tokens") print(f"{self.log_prefix} • Check ANTHROPIC_API_KEY in ~/.hermes/.env for API keys or legacy token values") print(f"{self.log_prefix} • For API keys: verify at https://console.anthropic.com/settings/keys") print(f"{self.log_prefix} • For Claude Code: run 'claude /login' to refresh, then retry") print(f"{self.log_prefix} • Clear stale keys: hermes config set ANTHROPIC_TOKEN \"\"") print(f"{self.log_prefix} • Legacy cleanup: hermes config set ANTHROPIC_API_KEY \"\"") retry_count += 1 elapsed_time = time.time() - api_start_time # Enhanced error logging error_type = type(api_error).__name__ error_msg = str(api_error).lower() logger.warning( "API call failed (attempt %s/%s) error_type=%s %s error=%s", retry_count, max_retries, error_type, self._client_log_context(), api_error, ) _provider = getattr(self, "provider", "unknown") _base = getattr(self, "base_url", "unknown") _model = getattr(self, "model", "unknown") _status_code_str = f" [HTTP {status_code}]" if status_code else "" self._vprint(f"{self.log_prefix}⚠️ API call failed (attempt {retry_count}/{max_retries}): {error_type}{_status_code_str}", force=True) self._vprint(f"{self.log_prefix} 🔌 Provider: {_provider} Model: {_model}", force=True) self._vprint(f"{self.log_prefix} 🌐 Endpoint: {_base}", force=True) cleaned_error = self._clean_error_message(str(api_error)) self._vprint(f"{self.log_prefix} 📝 Error: {cleaned_error}", force=True) if status_code == 400: _err_body = getattr(api_error, "body", None) _err_body_str = str(_err_body)[:300] if _err_body else None if _err_body_str: self._vprint(f"{self.log_prefix} 📋 Details: {_err_body_str}", force=True) self._vprint(f"{self.log_prefix} ⏱️ Elapsed: {elapsed_time:.2f}s Context: {len(api_messages)} msgs, ~{approx_tokens:,} tokens") # Check for interrupt before deciding to retry if self._interrupt_requested: self._vprint(f"{self.log_prefix}⚡ Interrupt detected during error handling, aborting retries.", force=True) self._persist_session(messages, conversation_history) self.clear_interrupt() return { "final_response": f"Operation interrupted: handling API error ({error_type}: {self._clean_error_message(str(api_error))}).", "messages": messages, "api_calls": api_call_count, "completed": False, "interrupted": True, } # Check for 413 payload-too-large BEFORE generic 4xx handler. # A 413 is a payload-size error — the correct response is to # compress history and retry, not abort immediately. status_code = getattr(api_error, "status_code", None) # Eager fallback for rate-limit errors (429 or quota exhaustion). # When a fallback model is configured, switch immediately instead # of burning through retries with exponential backoff -- the # primary provider won't recover within the retry window. is_rate_limited = ( status_code == 429 or "rate limit" in error_msg or "too many requests" in error_msg or "rate_limit" in error_msg or "usage limit" in error_msg or "quota" in error_msg ) if is_rate_limited and not self._fallback_activated: if self._try_activate_fallback(): retry_count = 0 continue is_payload_too_large = ( status_code == 413 or 'request entity too large' in error_msg or 'payload too large' in error_msg or 'error code: 413' in error_msg ) if is_payload_too_large: compression_attempts += 1 if compression_attempts > max_compression_attempts: self._vprint(f"{self.log_prefix}❌ Max compression attempts ({max_compression_attempts}) reached for payload-too-large error.", force=True) logging.error(f"{self.log_prefix}413 compression failed after {max_compression_attempts} attempts.") self._persist_session(messages, conversation_history) return { "messages": messages, "completed": False, "api_calls": api_call_count, "error": f"Request payload too large: max compression attempts ({max_compression_attempts}) reached.", "partial": True } self._vprint(f"{self.log_prefix}⚠️ Request payload too large (413) — compression attempt {compression_attempts}/{max_compression_attempts}...") original_len = len(messages) messages, active_system_prompt = self._compress_context( messages, system_message, approx_tokens=approx_tokens, task_id=effective_task_id, ) if len(messages) < original_len: self._vprint(f"{self.log_prefix} 🗜️ Compressed {original_len} → {len(messages)} messages, retrying...") time.sleep(2) # Brief pause between compression retries restart_with_compressed_messages = True break else: self._vprint(f"{self.log_prefix}❌ Payload too large and cannot compress further.", force=True) logging.error(f"{self.log_prefix}413 payload too large. Cannot compress further.") self._persist_session(messages, conversation_history) return { "messages": messages, "completed": False, "api_calls": api_call_count, "error": "Request payload too large (413). Cannot compress further.", "partial": True } # Check for context-length errors BEFORE generic 4xx handler. # Local backends (LM Studio, Ollama, llama.cpp) often return # HTTP 400 with messages like "Context size has been exceeded" # which must trigger compression, not an immediate abort. is_context_length_error = any(phrase in error_msg for phrase in [ 'context length', 'context size', 'maximum context', 'token limit', 'too many tokens', 'reduce the length', 'exceeds the limit', 'context window', 'request entity too large', # OpenRouter/Nous 413 safety net 'prompt is too long', # Anthropic: "prompt is too long: N tokens > M maximum" 'prompt exceeds max length', # Z.AI / GLM: generic 400 overflow wording ]) # Fallback heuristic: Anthropic sometimes returns a generic # 400 invalid_request_error with just "Error" as the message # when the context is too large. If the error message is very # short/generic AND the session is large, treat it as a # probable context-length error and attempt compression rather # than aborting. This prevents an infinite failure loop where # each failed message gets persisted, making the session even # larger. (#1630) if not is_context_length_error and status_code == 400: ctx_len = getattr(getattr(self, 'context_compressor', None), 'context_length', 200000) is_large_session = approx_tokens > ctx_len * 0.4 or len(api_messages) > 80 is_generic_error = len(error_msg.strip()) < 30 # e.g. just "error" if is_large_session and is_generic_error: is_context_length_error = True self._vprint( f"{self.log_prefix}⚠️ Generic 400 with large session " f"(~{approx_tokens:,} tokens, {len(api_messages)} msgs) — " f"treating as probable context overflow.", force=True, ) if is_context_length_error: compressor = self.context_compressor old_ctx = compressor.context_length # Try to parse the actual limit from the error message parsed_limit = parse_context_limit_from_error(error_msg) if parsed_limit and parsed_limit < old_ctx: new_ctx = parsed_limit self._vprint(f"{self.log_prefix}⚠️ Context limit detected from API: {new_ctx:,} tokens (was {old_ctx:,})", force=True) else: # Step down to the next probe tier new_ctx = get_next_probe_tier(old_ctx) if new_ctx and new_ctx < old_ctx: compressor.context_length = new_ctx compressor.threshold_tokens = int(new_ctx * compressor.threshold_percent) compressor._context_probed = True self._vprint(f"{self.log_prefix}⚠️ Context length exceeded — stepping down: {old_ctx:,} → {new_ctx:,} tokens", force=True) else: self._vprint(f"{self.log_prefix}⚠️ Context length exceeded at minimum tier — attempting compression...", force=True) compression_attempts += 1 if compression_attempts > max_compression_attempts: self._vprint(f"{self.log_prefix}❌ Max compression attempts ({max_compression_attempts}) reached.", force=True) logging.error(f"{self.log_prefix}Context compression failed after {max_compression_attempts} attempts.") self._persist_session(messages, conversation_history) return { "messages": messages, "completed": False, "api_calls": api_call_count, "error": f"Context length exceeded: max compression attempts ({max_compression_attempts}) reached.", "partial": True } self._vprint(f"{self.log_prefix} 🗜️ Context compression attempt {compression_attempts}/{max_compression_attempts}...") original_len = len(messages) messages, active_system_prompt = self._compress_context( messages, system_message, approx_tokens=approx_tokens, task_id=effective_task_id, ) if len(messages) < original_len or new_ctx and new_ctx < old_ctx: if len(messages) < original_len: self._vprint(f"{self.log_prefix} 🗜️ Compressed {original_len} → {len(messages)} messages, retrying...") time.sleep(2) # Brief pause between compression retries restart_with_compressed_messages = True break else: # Can't compress further and already at minimum tier self._vprint(f"{self.log_prefix}❌ Context length exceeded and cannot compress further.", force=True) self._vprint(f"{self.log_prefix} 💡 The conversation has accumulated too much content.", force=True) logging.error(f"{self.log_prefix}Context length exceeded: {approx_tokens:,} tokens. Cannot compress further.") self._persist_session(messages, conversation_history) return { "messages": messages, "completed": False, "api_calls": api_call_count, "error": f"Context length exceeded ({approx_tokens:,} tokens). Cannot compress further.", "partial": True } # Check for non-retryable client errors (4xx HTTP status codes). # These indicate a problem with the request itself (bad model ID, # invalid API key, forbidden, etc.) and will never succeed on retry. # Note: 413 and context-length errors are excluded — handled above. # 429 (rate limit) is transient and MUST be retried with backoff. # 529 (Anthropic overloaded) is also transient. # Also catch local validation errors (ValueError, TypeError) — these # are programming bugs, not transient failures. _RETRYABLE_STATUS_CODES = {413, 429, 529} is_local_validation_error = isinstance(api_error, (ValueError, TypeError)) # Detect generic 400s from Anthropic OAuth (transient server-side failures). # Real invalid_request_error responses include a descriptive message; # transient ones contain only "Error" or are empty. (ref: issue #1608) _err_body = getattr(api_error, "body", None) or {} _err_message = (_err_body.get("error", {}).get("message", "") if isinstance(_err_body, dict) else "") _is_generic_400 = (status_code == 400 and _err_message.strip().lower() in ("error", "")) is_client_status_error = isinstance(status_code, int) and 400 <= status_code < 500 and status_code not in _RETRYABLE_STATUS_CODES and not _is_generic_400 is_client_error = (is_local_validation_error or is_client_status_error or any(phrase in error_msg for phrase in [ 'error code: 401', 'error code: 403', 'error code: 404', 'error code: 422', 'is not a valid model', 'invalid model', 'model not found', 'invalid api key', 'invalid_api_key', 'authentication', 'unauthorized', 'forbidden', 'not found', ])) and not is_context_length_error if is_client_error: # Try fallback before aborting — a different provider # may not have the same issue (rate limit, auth, etc.) if self._try_activate_fallback(): retry_count = 0 continue self._dump_api_request_debug( api_kwargs, reason="non_retryable_client_error", error=api_error, ) self._vprint(f"{self.log_prefix}❌ Non-retryable client error (HTTP {status_code}). Aborting.", force=True) self._vprint(f"{self.log_prefix} 🔌 Provider: {_provider} Model: {_model}", force=True) self._vprint(f"{self.log_prefix} 🌐 Endpoint: {_base}", force=True) # Actionable guidance for common auth errors if status_code in (401, 403) or "unauthorized" in error_msg or "forbidden" in error_msg or "permission" in error_msg: self._vprint(f"{self.log_prefix} 💡 Your API key was rejected by the provider. Check:", force=True) self._vprint(f"{self.log_prefix} • Is the key valid? Run: hermes setup", force=True) self._vprint(f"{self.log_prefix} • Does your account have access to {_model}?", force=True) if "openrouter" in str(_base).lower(): self._vprint(f"{self.log_prefix} • Check credits: https://openrouter.ai/settings/credits", force=True) else: self._vprint(f"{self.log_prefix} 💡 This type of error won't be fixed by retrying.", force=True) logging.error(f"{self.log_prefix}Non-retryable client error: {api_error}") # Skip session persistence when the error is likely # context-overflow related (status 400 + large session). # Persisting the failed user message would make the # session even larger, causing the same failure on the # next attempt. (#1630) if status_code == 400 and (approx_tokens > 50000 or len(api_messages) > 80): self._vprint( f"{self.log_prefix}⚠️ Skipping session persistence " f"for large failed session to prevent growth loop.", force=True, ) else: self._persist_session(messages, conversation_history) return { "final_response": None, "messages": messages, "api_calls": api_call_count, "completed": False, "failed": True, "error": str(api_error), } if retry_count >= max_retries: # Try fallback before giving up entirely if self._try_activate_fallback(): retry_count = 0 continue self._vprint(f"{self.log_prefix}❌ Max retries ({max_retries}) exceeded. Giving up.", force=True) logging.error(f"{self.log_prefix}API call failed after {max_retries} retries. Last error: {api_error}") logging.error(f"{self.log_prefix}Request details - Messages: {len(api_messages)}, Approx tokens: {approx_tokens:,}") raise api_error wait_time = min(2 ** retry_count, 60) # Exponential backoff: 2s, 4s, 8s, 16s, 32s, 60s, 60s logger.warning( "Retrying API call in %ss (attempt %s/%s) %s error=%s", wait_time, retry_count, max_retries, self._client_log_context(), api_error, ) # Sleep in small increments so we can respond to interrupts quickly # instead of blocking the entire wait_time in one sleep() call sleep_end = time.time() + wait_time while time.time() < sleep_end: if self._interrupt_requested: self._vprint(f"{self.log_prefix}⚡ Interrupt detected during retry wait, aborting.", force=True) self._persist_session(messages, conversation_history) self.clear_interrupt() return { "final_response": f"Operation interrupted: retrying API call after error (retry {retry_count}/{max_retries}).", "messages": messages, "api_calls": api_call_count, "completed": False, "interrupted": True, } time.sleep(0.2) # Check interrupt every 200ms # If the API call was interrupted, skip response processing if interrupted: break if restart_with_compressed_messages: api_call_count -= 1 self.iteration_budget.refund() # Count compression restarts toward the retry limit to prevent # infinite loops when compression reduces messages but not enough # to fit the context window. retry_count += 1 restart_with_compressed_messages = False continue if restart_with_length_continuation: continue # Guard: if all retries exhausted without a successful response # (e.g. repeated context-length errors that exhausted retry_count), # the `response` variable is still None. Break out cleanly. if response is None: print(f"{self.log_prefix}❌ All API retries exhausted with no successful response.") self._persist_session(messages, conversation_history) break try: if self.api_mode == "codex_responses": assistant_message, finish_reason = self._normalize_codex_response(response) elif self.api_mode == "anthropic_messages": from agent.anthropic_adapter import normalize_anthropic_response assistant_message, finish_reason = normalize_anthropic_response( response, strip_tool_prefix=getattr(self, "_is_anthropic_oauth", False) ) else: assistant_message = response.choices[0].message # Normalize content to string — some OpenAI-compatible servers # (llama-server, etc.) return content as a dict or list instead # of a plain string, which crashes downstream .strip() calls. if assistant_message.content is not None and not isinstance(assistant_message.content, str): raw = assistant_message.content if isinstance(raw, dict): assistant_message.content = raw.get("text", "") or raw.get("content", "") or json.dumps(raw) elif isinstance(raw, list): # Multimodal content list — extract text parts parts = [] for part in raw: if isinstance(part, str): parts.append(part) elif isinstance(part, dict) and part.get("type") == "text": parts.append(part.get("text", "")) elif isinstance(part, dict) and "text" in part: parts.append(str(part["text"])) assistant_message.content = "\n".join(parts) else: assistant_message.content = str(raw) # Handle assistant response if assistant_message.content and not self.quiet_mode: if self.verbose_logging: self._vprint(f"{self.log_prefix}🤖 Assistant: {assistant_message.content}") else: self._vprint(f"{self.log_prefix}🤖 Assistant: {assistant_message.content[:100]}{'...' if len(assistant_message.content) > 100 else ''}") # Notify progress callback of model's thinking (used by subagent # delegation to relay the child's reasoning to the parent display). # Guard: only fire for subagents (_delegate_depth >= 1) to avoid # spamming gateway platforms with the main agent's every thought. if (assistant_message.content and self.tool_progress_callback and getattr(self, '_delegate_depth', 0) > 0): _think_text = assistant_message.content.strip() # Strip reasoning XML tags that shouldn't leak to parent display _think_text = re.sub( r'', '', _think_text ).strip() first_line = _think_text.split('\n')[0][:80] if _think_text else "" if first_line: try: self.tool_progress_callback("_thinking", first_line) except Exception: pass # Check for incomplete (opened but never closed) # This means the model ran out of output tokens mid-reasoning — retry up to 2 times if has_incomplete_scratchpad(assistant_message.content or ""): if not hasattr(self, '_incomplete_scratchpad_retries'): self._incomplete_scratchpad_retries = 0 self._incomplete_scratchpad_retries += 1 self._vprint(f"{self.log_prefix}⚠️ Incomplete detected (opened but never closed)") if self._incomplete_scratchpad_retries <= 2: self._vprint(f"{self.log_prefix}🔄 Retrying API call ({self._incomplete_scratchpad_retries}/2)...") # Don't add the broken message, just retry continue else: # Max retries - discard this turn and save as partial self._vprint(f"{self.log_prefix}❌ Max retries (2) for incomplete scratchpad. Saving as partial.", force=True) self._incomplete_scratchpad_retries = 0 rolled_back_messages = self._get_messages_up_to_last_assistant(messages) self._cleanup_task_resources(effective_task_id) self._persist_session(messages, conversation_history) return { "final_response": None, "messages": rolled_back_messages, "api_calls": api_call_count, "completed": False, "partial": True, "error": "Incomplete REASONING_SCRATCHPAD after 2 retries" } # Reset incomplete scratchpad counter on clean response if hasattr(self, '_incomplete_scratchpad_retries'): self._incomplete_scratchpad_retries = 0 if self.api_mode == "codex_responses" and finish_reason == "incomplete": if not hasattr(self, "_codex_incomplete_retries"): self._codex_incomplete_retries = 0 self._codex_incomplete_retries += 1 interim_msg = self._build_assistant_message(assistant_message, finish_reason) interim_has_content = bool((interim_msg.get("content") or "").strip()) interim_has_reasoning = bool(interim_msg.get("reasoning", "").strip()) if isinstance(interim_msg.get("reasoning"), str) else False interim_has_codex_reasoning = bool(interim_msg.get("codex_reasoning_items")) if interim_has_content or interim_has_reasoning or interim_has_codex_reasoning: last_msg = messages[-1] if messages else None # Duplicate detection: two consecutive incomplete assistant # messages with identical content AND reasoning are collapsed. # For reasoning-only messages (codex_reasoning_items differ but # visible content/reasoning are both empty), we also compare # the encrypted items to avoid silently dropping new state. last_codex_items = last_msg.get("codex_reasoning_items") if isinstance(last_msg, dict) else None interim_codex_items = interim_msg.get("codex_reasoning_items") duplicate_interim = ( isinstance(last_msg, dict) and last_msg.get("role") == "assistant" and last_msg.get("finish_reason") == "incomplete" and (last_msg.get("content") or "") == (interim_msg.get("content") or "") and (last_msg.get("reasoning") or "") == (interim_msg.get("reasoning") or "") and last_codex_items == interim_codex_items ) if not duplicate_interim: messages.append(interim_msg) if self._codex_incomplete_retries < 3: if not self.quiet_mode: self._vprint(f"{self.log_prefix}↻ Codex response incomplete; continuing turn ({self._codex_incomplete_retries}/3)") self._session_messages = messages self._save_session_log(messages) continue self._codex_incomplete_retries = 0 self._persist_session(messages, conversation_history) return { "final_response": None, "messages": messages, "api_calls": api_call_count, "completed": False, "partial": True, "error": "Codex response remained incomplete after 3 continuation attempts", } elif hasattr(self, "_codex_incomplete_retries"): self._codex_incomplete_retries = 0 # Check for tool calls if assistant_message.tool_calls: if not self.quiet_mode: self._vprint(f"{self.log_prefix}🔧 Processing {len(assistant_message.tool_calls)} tool call(s)...") if self.verbose_logging: for tc in assistant_message.tool_calls: logging.debug(f"Tool call: {tc.function.name} with args: {tc.function.arguments[:200]}...") # Validate tool call names - detect model hallucinations # Repair mismatched tool names before validating for tc in assistant_message.tool_calls: if tc.function.name not in self.valid_tool_names: repaired = self._repair_tool_call(tc.function.name) if repaired: print(f"{self.log_prefix}🔧 Auto-repaired tool name: '{tc.function.name}' -> '{repaired}'") tc.function.name = repaired invalid_tool_calls = [ tc.function.name for tc in assistant_message.tool_calls if tc.function.name not in self.valid_tool_names ] if invalid_tool_calls: # Track retries for invalid tool calls if not hasattr(self, '_invalid_tool_retries'): self._invalid_tool_retries = 0 self._invalid_tool_retries += 1 # Return helpful error to model — model can self-correct next turn available = ", ".join(sorted(self.valid_tool_names)) invalid_name = invalid_tool_calls[0] invalid_preview = invalid_name[:80] + "..." if len(invalid_name) > 80 else invalid_name self._vprint(f"{self.log_prefix}⚠️ Unknown tool '{invalid_preview}' — sending error to model for self-correction ({self._invalid_tool_retries}/3)") if self._invalid_tool_retries >= 3: self._vprint(f"{self.log_prefix}❌ Max retries (3) for invalid tool calls exceeded. Stopping as partial.", force=True) self._invalid_tool_retries = 0 self._persist_session(messages, conversation_history) return { "final_response": None, "messages": messages, "api_calls": api_call_count, "completed": False, "partial": True, "error": f"Model generated invalid tool call: {invalid_preview}" } assistant_msg = self._build_assistant_message(assistant_message, finish_reason) messages.append(assistant_msg) for tc in assistant_message.tool_calls: if tc.function.name not in self.valid_tool_names: content = f"Tool '{tc.function.name}' does not exist. Available tools: {available}" else: content = f"Skipped: another tool call in this turn used an invalid name. Please retry this tool call." messages.append({ "role": "tool", "tool_call_id": tc.id, "content": content, }) continue # Reset retry counter on successful tool call validation if hasattr(self, '_invalid_tool_retries'): self._invalid_tool_retries = 0 # Validate tool call arguments are valid JSON # Handle empty strings as empty objects (common model quirk) invalid_json_args = [] for tc in assistant_message.tool_calls: args = tc.function.arguments if isinstance(args, (dict, list)): tc.function.arguments = json.dumps(args) continue if args is not None and not isinstance(args, str): tc.function.arguments = str(args) args = tc.function.arguments # Treat empty/whitespace strings as empty object if not args or not args.strip(): tc.function.arguments = "{}" continue try: json.loads(args) except json.JSONDecodeError as e: invalid_json_args.append((tc.function.name, str(e))) if invalid_json_args: # Track retries for invalid JSON arguments self._invalid_json_retries += 1 tool_name, error_msg = invalid_json_args[0] self._vprint(f"{self.log_prefix}⚠️ Invalid JSON in tool call arguments for '{tool_name}': {error_msg}") if self._invalid_json_retries < 3: self._vprint(f"{self.log_prefix}🔄 Retrying API call ({self._invalid_json_retries}/3)...") # Don't add anything to messages, just retry the API call continue else: # Instead of returning partial, inject tool error results so the model can recover. # Using tool results (not user messages) preserves role alternation. self._vprint(f"{self.log_prefix}⚠️ Injecting recovery tool results for invalid JSON...") self._invalid_json_retries = 0 # Reset for next attempt # Append the assistant message with its (broken) tool_calls recovery_assistant = self._build_assistant_message(assistant_message, finish_reason) messages.append(recovery_assistant) # Respond with tool error results for each tool call invalid_names = {name for name, _ in invalid_json_args} for tc in assistant_message.tool_calls: if tc.function.name in invalid_names: err = next(e for n, e in invalid_json_args if n == tc.function.name) tool_result = ( f"Error: Invalid JSON arguments. {err}. " f"For tools with no required parameters, use an empty object: {{}}. " f"Please retry with valid JSON." ) else: tool_result = "Skipped: other tool call in this response had invalid JSON." messages.append({ "role": "tool", "tool_call_id": tc.id, "content": tool_result, }) continue # Reset retry counter on successful JSON validation self._invalid_json_retries = 0 # ── Post-call guardrails ────────────────────────── assistant_message.tool_calls = self._cap_delegate_task_calls( assistant_message.tool_calls ) assistant_message.tool_calls = self._deduplicate_tool_calls( assistant_message.tool_calls ) assistant_msg = self._build_assistant_message(assistant_message, finish_reason) # If this turn has both content AND tool_calls, capture the content # as a fallback final response. Common pattern: model delivers its # answer and calls memory/skill tools as a side-effect in the same # turn. If the follow-up turn after tools is empty, we use this. turn_content = assistant_message.content or "" if turn_content and self._has_content_after_think_block(turn_content): self._last_content_with_tools = turn_content # The response was already streamed to the user in the # response box. The remaining tool calls (memory, skill, # todo, etc.) are post-response housekeeping — mute all # subsequent CLI output so they run invisibly. if self._has_stream_consumers(): self._mute_post_response = True elif self.quiet_mode: clean = self._strip_think_blocks(turn_content).strip() if clean: self._vprint(f" ┊ 💬 {clean}") messages.append(assistant_msg) # Close any open streaming display (response box, reasoning # box) before tool execution begins. Intermediate turns may # have streamed early content that opened the response box; # flushing here prevents it from wrapping tool feed lines. # Only signal the display callback — TTS (_stream_callback) # should NOT receive None (it uses None as end-of-stream). if self.stream_delta_callback: try: self.stream_delta_callback(None) except Exception: pass _msg_count_before_tools = len(messages) self._execute_tool_calls(assistant_message, messages, effective_task_id, api_call_count) # Signal that a paragraph break is needed before the next # streamed text. We don't emit it immediately because # multiple consecutive tool iterations would stack up # redundant blank lines. Instead, _fire_stream_delta() # will prepend a single "\n\n" the next time real text # arrives. self._stream_needs_break = True # Refund the iteration if the ONLY tool(s) called were # execute_code (programmatic tool calling). These are # cheap RPC-style calls that shouldn't eat the budget. _tc_names = {tc.function.name for tc in assistant_message.tool_calls} if _tc_names == {"execute_code"}: self.iteration_budget.refund() # Estimate next prompt size using real token counts from the # last API response + rough estimate of newly appended tool # results. This catches cases where tool results push the # context past the limit that last_prompt_tokens alone misses # (e.g. large file reads, web extractions). _compressor = self.context_compressor _new_tool_msgs = messages[_msg_count_before_tools:] _new_chars = sum(len(str(m.get("content", "") or "")) for m in _new_tool_msgs) _estimated_next_prompt = ( _compressor.last_prompt_tokens + _compressor.last_completion_tokens + _new_chars // 3 # conservative: JSON-heavy tool results ≈ 3 chars/token ) # ── Context pressure warnings (user-facing only) ────────── # Notify the user (NOT the LLM) as context approaches the # compaction threshold. Thresholds are relative to where # compaction fires, not the raw context window. # Does not inject into messages — just prints to CLI output # and fires status_callback for gateway platforms. if _compressor.threshold_tokens > 0: _compaction_progress = _estimated_next_prompt / _compressor.threshold_tokens if _compaction_progress >= 0.85 and not self._context_pressure_warned: self._context_pressure_warned = True self._emit_context_pressure(_compaction_progress, _compressor) if self.compression_enabled and _compressor.should_compress(_estimated_next_prompt): messages, active_system_prompt = self._compress_context( messages, system_message, approx_tokens=self.context_compressor.last_prompt_tokens, task_id=effective_task_id, ) # Save session log incrementally (so progress is visible even if interrupted) self._session_messages = messages self._save_session_log(messages) # Continue loop for next response continue else: # No tool calls - this is the final response final_response = assistant_message.content or "" # Check if response only has think block with no actual content after it if not self._has_content_after_think_block(final_response): # If the previous turn already delivered real content alongside # tool calls (e.g. "You're welcome!" + memory save), the model # has nothing more to say. Use the earlier content immediately # instead of wasting API calls on retries that won't help. fallback = getattr(self, '_last_content_with_tools', None) if fallback: logger.debug("Empty follow-up after tool calls — using prior turn content as final response") self._last_content_with_tools = None self._empty_content_retries = 0 for i in range(len(messages) - 1, -1, -1): msg = messages[i] if msg.get("role") == "assistant" and msg.get("tool_calls"): tool_names = [] for tc in msg["tool_calls"]: if not tc or not isinstance(tc, dict): continue fn = tc.get("function", {}) tool_names.append(fn.get("name", "unknown")) msg["content"] = f"Calling the {', '.join(tool_names)} tool{'s' if len(tool_names) > 1 else ''}..." break final_response = self._strip_think_blocks(fallback).strip() self._response_was_previewed = True break # No fallback available — this is a genuine empty response. # Retry in case the model just had a bad generation. if not hasattr(self, '_empty_content_retries'): self._empty_content_retries = 0 self._empty_content_retries += 1 reasoning_text = self._extract_reasoning(assistant_message) self._vprint(f"{self.log_prefix}⚠️ Response only contains think block with no content after it") if reasoning_text: reasoning_preview = reasoning_text[:500] + "..." if len(reasoning_text) > 500 else reasoning_text self._vprint(f"{self.log_prefix} Reasoning: {reasoning_preview}") else: content_preview = final_response[:80] + "..." if len(final_response) > 80 else final_response self._vprint(f"{self.log_prefix} Content: '{content_preview}'") if self._empty_content_retries < 3: self._vprint(f"{self.log_prefix}🔄 Retrying API call ({self._empty_content_retries}/3)...") continue else: self._vprint(f"{self.log_prefix}❌ Max retries (3) for empty content exceeded.", force=True) self._empty_content_retries = 0 # If a prior tool_calls turn had real content, salvage it: # rewrite that turn's content to a brief tool description, # and use the original content as the final response here. fallback = getattr(self, '_last_content_with_tools', None) if fallback: self._last_content_with_tools = None # Find the last assistant message with tool_calls and rewrite it for i in range(len(messages) - 1, -1, -1): msg = messages[i] if msg.get("role") == "assistant" and msg.get("tool_calls"): tool_names = [] for tc in msg["tool_calls"]: if not tc or not isinstance(tc, dict): continue fn = tc.get("function", {}) tool_names.append(fn.get("name", "unknown")) msg["content"] = f"Calling the {', '.join(tool_names)} tool{'s' if len(tool_names) > 1 else ''}..." break # Strip blocks from fallback content for user display final_response = self._strip_think_blocks(fallback).strip() self._response_was_previewed = True break # No fallback -- if reasoning_text exists, the model put its # entire response inside tags; use that as the content. if reasoning_text: self._vprint(f"{self.log_prefix}Using reasoning as response content (model wrapped entire response in think tags).", force=True) final_response = reasoning_text empty_msg = { "role": "assistant", "content": final_response, "reasoning": reasoning_text, "finish_reason": finish_reason, } messages.append(empty_msg) break # Truly empty -- no reasoning and no content empty_msg = { "role": "assistant", "content": final_response, "reasoning": reasoning_text, "finish_reason": finish_reason, } messages.append(empty_msg) self._cleanup_task_resources(effective_task_id) self._persist_session(messages, conversation_history) return { "final_response": final_response or None, "messages": messages, "api_calls": api_call_count, "completed": False, "partial": True, "error": "Model generated only think blocks with no actual response after 3 retries" } # Reset retry counter on successful content if hasattr(self, '_empty_content_retries'): self._empty_content_retries = 0 if ( self.api_mode == "codex_responses" and self.valid_tool_names and codex_ack_continuations < 2 and self._looks_like_codex_intermediate_ack( user_message=user_message, assistant_content=final_response, messages=messages, ) ): codex_ack_continuations += 1 interim_msg = self._build_assistant_message(assistant_message, "incomplete") messages.append(interim_msg) continue_msg = { "role": "user", "content": ( "[System: Continue now. Execute the required tool calls and only " "send your final answer after completing the task.]" ), } messages.append(continue_msg) self._session_messages = messages self._save_session_log(messages) continue codex_ack_continuations = 0 if truncated_response_prefix: final_response = truncated_response_prefix + final_response truncated_response_prefix = "" length_continue_retries = 0 # Strip blocks from user-facing response (keep raw in messages for trajectory) final_response = self._strip_think_blocks(final_response).strip() final_msg = self._build_assistant_message(assistant_message, finish_reason) messages.append(final_msg) if not self.quiet_mode: self._safe_print(f"🎉 Conversation completed after {api_call_count} OpenAI-compatible API call(s)") break except Exception as e: error_msg = f"Error during OpenAI-compatible API call #{api_call_count}: {str(e)}" try: print(f"❌ {error_msg}") except OSError: logger.error(error_msg) if self.verbose_logging: logging.exception("Detailed error information:") # If an assistant message with tool_calls was already appended, # the API expects a role="tool" result for every tool_call_id. # Fill in error results for any that weren't answered yet. pending_handled = False for idx in range(len(messages) - 1, -1, -1): msg = messages[idx] if not isinstance(msg, dict): break if msg.get("role") == "tool": continue if msg.get("role") == "assistant" and msg.get("tool_calls"): answered_ids = { m["tool_call_id"] for m in messages[idx + 1:] if isinstance(m, dict) and m.get("role") == "tool" } for tc in msg["tool_calls"]: if not tc or not isinstance(tc, dict): continue if tc["id"] not in answered_ids: err_msg = { "role": "tool", "tool_call_id": tc["id"], "content": f"Error executing tool: {error_msg}", } messages.append(err_msg) pending_handled = True break # Non-tool errors don't need a synthetic message injected. # The error is already printed to the user (line above), and # the retry loop continues. Injecting a fake user/assistant # message pollutes history, burns tokens, and risks violating # role-alternation invariants. # If we're near the limit, break to avoid infinite loops if api_call_count >= self.max_iterations - 1: final_response = f"I apologize, but I encountered repeated errors: {error_msg}" # Append as assistant so the history stays valid for # session resume (avoids consecutive user messages). messages.append({"role": "assistant", "content": final_response}) break if final_response is None and ( api_call_count >= self.max_iterations or self.iteration_budget.remaining <= 0 ): if self.iteration_budget.remaining <= 0 and not self.quiet_mode: print(f"\n⚠️ Iteration budget exhausted ({self.iteration_budget.used}/{self.iteration_budget.max_total} iterations used)") final_response = self._handle_max_iterations(messages, api_call_count) # Determine if conversation completed successfully completed = final_response is not None and api_call_count < self.max_iterations # Save trajectory if enabled self._save_trajectory(messages, user_message, completed) # Clean up VM and browser for this task after conversation completes self._cleanup_task_resources(effective_task_id) # Persist session to both JSON log and SQLite self._persist_session(messages, conversation_history) # Sync conversation to Honcho for user modeling if final_response and not interrupted and sync_honcho: self._honcho_sync(original_user_message, final_response) self._queue_honcho_prefetch(original_user_message) # Extract reasoning from the last assistant message (if any) last_reasoning = None for msg in reversed(messages): if msg.get("role") == "assistant" and msg.get("reasoning"): last_reasoning = msg["reasoning"] break # Build result with interrupt info if applicable result = { "final_response": final_response, "last_reasoning": last_reasoning, "messages": messages, "api_calls": api_call_count, "completed": completed, "partial": False, # True only when stopped due to invalid tool calls "interrupted": interrupted, "response_previewed": getattr(self, "_response_was_previewed", False), "model": self.model, "provider": self.provider, "base_url": self.base_url, "input_tokens": self.session_input_tokens, "output_tokens": self.session_output_tokens, "cache_read_tokens": self.session_cache_read_tokens, "cache_write_tokens": self.session_cache_write_tokens, "reasoning_tokens": self.session_reasoning_tokens, "prompt_tokens": self.session_prompt_tokens, "completion_tokens": self.session_completion_tokens, "total_tokens": self.session_total_tokens, "last_prompt_tokens": getattr(self.context_compressor, "last_prompt_tokens", 0) or 0, "estimated_cost_usd": self.session_estimated_cost_usd, "cost_status": self.session_cost_status, "cost_source": self.session_cost_source, } self._response_was_previewed = False # Include interrupt message if one triggered the interrupt if interrupted and self._interrupt_message: result["interrupt_message"] = self._interrupt_message # Clear interrupt state after handling self.clear_interrupt() # Clear stream callback so it doesn't leak into future calls self._stream_callback = None # Check skill trigger NOW — based on how many tool iterations THIS turn used. _should_review_skills = False if (self._skill_nudge_interval > 0 and self._iters_since_skill >= self._skill_nudge_interval and "skill_manage" in self.valid_tool_names): _should_review_skills = True self._iters_since_skill = 0 # Background memory/skill review — runs AFTER the response is delivered # so it never competes with the user's task for model attention. if final_response and not interrupted and (_should_review_memory or _should_review_skills): try: self._spawn_background_review( messages_snapshot=list(messages), review_memory=_should_review_memory, review_skills=_should_review_skills, ) except Exception: pass # Background review is best-effort return result def chat(self, message: str, stream_callback: Optional[callable] = None) -> str: """ Simple chat interface that returns just the final response. Args: message (str): User message stream_callback: Optional callback invoked with each text delta during streaming. Returns: str: Final assistant response """ result = self.run_conversation(message, stream_callback=stream_callback) return result["final_response"] def main( query: str = None, model: str = "anthropic/claude-opus-4.6", api_key: str = None, base_url: str = "https://openrouter.ai/api/v1", max_turns: int = 10, enabled_toolsets: str = None, disabled_toolsets: str = None, list_tools: bool = False, save_trajectories: bool = False, save_sample: bool = False, verbose: bool = False, log_prefix_chars: int = 20 ): """ Main function for running the agent directly. Args: query (str): Natural language query for the agent. Defaults to Python 3.13 example. model (str): Model name to use (OpenRouter format: provider/model). Defaults to anthropic/claude-sonnet-4.6. api_key (str): API key for authentication. Uses OPENROUTER_API_KEY env var if not provided. base_url (str): Base URL for the model API. Defaults to https://openrouter.ai/api/v1 max_turns (int): Maximum number of API call iterations. Defaults to 10. enabled_toolsets (str): Comma-separated list of toolsets to enable. Supports predefined toolsets (e.g., "research", "development", "safe"). Multiple toolsets can be combined: "web,vision" disabled_toolsets (str): Comma-separated list of toolsets to disable (e.g., "terminal") list_tools (bool): Just list available tools and exit save_trajectories (bool): Save conversation trajectories to JSONL files (appends to trajectory_samples.jsonl). Defaults to False. save_sample (bool): Save a single trajectory sample to a UUID-named JSONL file for inspection. Defaults to False. verbose (bool): Enable verbose logging for debugging. Defaults to False. log_prefix_chars (int): Number of characters to show in log previews for tool calls/responses. Defaults to 20. Toolset Examples: - "research": Web search, extract, crawl + vision tools """ print("🤖 AI Agent with Tool Calling") print("=" * 50) # Handle tool listing if list_tools: from model_tools import get_all_tool_names, get_toolset_for_tool, get_available_toolsets from toolsets import get_all_toolsets, get_toolset_info print("📋 Available Tools & Toolsets:") print("-" * 50) # Show new toolsets system print("\n🎯 Predefined Toolsets (New System):") print("-" * 40) all_toolsets = get_all_toolsets() # Group by category basic_toolsets = [] composite_toolsets = [] scenario_toolsets = [] for name, toolset in all_toolsets.items(): info = get_toolset_info(name) if info: entry = (name, info) if name in ["web", "terminal", "vision", "creative", "reasoning"]: basic_toolsets.append(entry) elif name in ["research", "development", "analysis", "content_creation", "full_stack"]: composite_toolsets.append(entry) else: scenario_toolsets.append(entry) # Print basic toolsets print("\n📌 Basic Toolsets:") for name, info in basic_toolsets: tools_str = ', '.join(info['resolved_tools']) if info['resolved_tools'] else 'none' print(f" • {name:15} - {info['description']}") print(f" Tools: {tools_str}") # Print composite toolsets print("\n📂 Composite Toolsets (built from other toolsets):") for name, info in composite_toolsets: includes_str = ', '.join(info['includes']) if info['includes'] else 'none' print(f" • {name:15} - {info['description']}") print(f" Includes: {includes_str}") print(f" Total tools: {info['tool_count']}") # Print scenario-specific toolsets print("\n🎭 Scenario-Specific Toolsets:") for name, info in scenario_toolsets: print(f" • {name:20} - {info['description']}") print(f" Total tools: {info['tool_count']}") # Show legacy toolset compatibility print("\n📦 Legacy Toolsets (for backward compatibility):") legacy_toolsets = get_available_toolsets() for name, info in legacy_toolsets.items(): status = "✅" if info["available"] else "❌" print(f" {status} {name}: {info['description']}") if not info["available"]: print(f" Requirements: {', '.join(info['requirements'])}") # Show individual tools all_tools = get_all_tool_names() print(f"\n🔧 Individual Tools ({len(all_tools)} available):") for tool_name in sorted(all_tools): toolset = get_toolset_for_tool(tool_name) print(f" 📌 {tool_name} (from {toolset})") print(f"\n💡 Usage Examples:") print(f" # Use predefined toolsets") print(f" python run_agent.py --enabled_toolsets=research --query='search for Python news'") print(f" python run_agent.py --enabled_toolsets=development --query='debug this code'") print(f" python run_agent.py --enabled_toolsets=safe --query='analyze without terminal'") print(f" ") print(f" # Combine multiple toolsets") print(f" python run_agent.py --enabled_toolsets=web,vision --query='analyze website'") print(f" ") print(f" # Disable toolsets") print(f" python run_agent.py --disabled_toolsets=terminal --query='no command execution'") print(f" ") print(f" # Run with trajectory saving enabled") print(f" python run_agent.py --save_trajectories --query='your question here'") return # Parse toolset selection arguments enabled_toolsets_list = None disabled_toolsets_list = None if enabled_toolsets: enabled_toolsets_list = [t.strip() for t in enabled_toolsets.split(",")] print(f"🎯 Enabled toolsets: {enabled_toolsets_list}") if disabled_toolsets: disabled_toolsets_list = [t.strip() for t in disabled_toolsets.split(",")] print(f"🚫 Disabled toolsets: {disabled_toolsets_list}") if save_trajectories: print(f"💾 Trajectory saving: ENABLED") print(f" - Successful conversations → trajectory_samples.jsonl") print(f" - Failed conversations → failed_trajectories.jsonl") # Initialize agent with provided parameters try: agent = AIAgent( base_url=base_url, model=model, api_key=api_key, max_iterations=max_turns, enabled_toolsets=enabled_toolsets_list, disabled_toolsets=disabled_toolsets_list, save_trajectories=save_trajectories, verbose_logging=verbose, log_prefix_chars=log_prefix_chars ) except RuntimeError as e: print(f"❌ Failed to initialize agent: {e}") return # Use provided query or default to Python 3.13 example if query is None: user_query = ( "Tell me about the latest developments in Python 3.13 and what new features " "developers should know about. Please search for current information and try it out." ) else: user_query = query print(f"\n📝 User Query: {user_query}") print("\n" + "=" * 50) # Run conversation result = agent.run_conversation(user_query) print("\n" + "=" * 50) print("📋 CONVERSATION SUMMARY") print("=" * 50) print(f"✅ Completed: {result['completed']}") print(f"📞 API Calls: {result['api_calls']}") print(f"💬 Messages: {len(result['messages'])}") if result['final_response']: print(f"\n🎯 FINAL RESPONSE:") print("-" * 30) print(result['final_response']) # Save sample trajectory to UUID-named file if requested if save_sample: sample_id = str(uuid.uuid4())[:8] sample_filename = f"sample_{sample_id}.json" # Convert messages to trajectory format (same as batch_runner) trajectory = agent._convert_to_trajectory_format( result['messages'], user_query, result['completed'] ) entry = { "conversations": trajectory, "timestamp": datetime.now().isoformat(), "model": model, "completed": result['completed'], "query": user_query } try: with open(sample_filename, "w", encoding="utf-8") as f: # Pretty-print JSON with indent for readability f.write(json.dumps(entry, ensure_ascii=False, indent=2)) print(f"\n💾 Sample trajectory saved to: {sample_filename}") except Exception as e: print(f"\n⚠️ Failed to save sample: {e}") print("\n👋 Agent execution completed!") if __name__ == "__main__": fire.Fire(main)