60b6abefd9
- Schema v4: unique title index, migration from v2/v3 - set/get/resolve session titles with uniqueness enforcement - Auto-lineage: context compression auto-numbers titles (Task -> Task #2 -> Task #3) - resolve_session_by_title: auto-latest finds most recent continuation - list_sessions_rich: preview (first 60 chars) + last_active timestamp - CLI: -c accepts optional name arg (hermes -c 'my project') - CLI: /title command with deferred mode (set before session exists) - CLI: sessions list shows Title, Preview, Last Active, ID - 27 new tests (1844 total passing)
4331 lines
211 KiB
Python
4331 lines
211 KiB
Python
#!/usr/bin/env python3
|
||
"""
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AI Agent Runner with Tool Calling
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This module provides a clean, standalone agent that can execute AI models
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with tool calling capabilities. It handles the conversation loop, tool execution,
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and response management.
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Features:
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- Automatic tool calling loop until completion
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- Configurable model parameters
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- Error handling and recovery
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- Message history management
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- Support for multiple model providers
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Usage:
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from run_agent import AIAgent
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agent = AIAgent(base_url="http://localhost:30000/v1", model="claude-opus-4-20250514")
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response = agent.run_conversation("Tell me about the latest Python updates")
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"""
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import copy
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import hashlib
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import json
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import logging
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logger = logging.getLogger(__name__)
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import os
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import random
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import re
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import sys
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import time
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import threading
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from types import SimpleNamespace
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import uuid
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from typing import List, Dict, Any, Optional
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from openai import OpenAI
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import fire
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from datetime import datetime
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from pathlib import Path
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# Load .env from ~/.hermes/.env first, then project root as dev fallback
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from dotenv import load_dotenv
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_hermes_home = Path(os.getenv("HERMES_HOME", Path.home() / ".hermes"))
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_user_env = _hermes_home / ".env"
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_project_env = Path(__file__).parent / '.env'
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if _user_env.exists():
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try:
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load_dotenv(dotenv_path=_user_env, encoding="utf-8")
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except UnicodeDecodeError:
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load_dotenv(dotenv_path=_user_env, encoding="latin-1")
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logger.info("Loaded environment variables from %s", _user_env)
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elif _project_env.exists():
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try:
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load_dotenv(dotenv_path=_project_env, encoding="utf-8")
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except UnicodeDecodeError:
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load_dotenv(dotenv_path=_project_env, encoding="latin-1")
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logger.info("Loaded environment variables from %s", _project_env)
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else:
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logger.info("No .env file found. Using system environment variables.")
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# Point mini-swe-agent at ~/.hermes/ so it shares our config
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os.environ.setdefault("MSWEA_GLOBAL_CONFIG_DIR", str(_hermes_home))
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os.environ.setdefault("MSWEA_SILENT_STARTUP", "1")
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# Import our tool system
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from model_tools import get_tool_definitions, handle_function_call, check_toolset_requirements
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from tools.terminal_tool import cleanup_vm
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from tools.interrupt import set_interrupt as _set_interrupt
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from tools.browser_tool import cleanup_browser
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import requests
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from hermes_constants import OPENROUTER_BASE_URL, OPENROUTER_MODELS_URL
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# Agent internals extracted to agent/ package for modularity
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from agent.prompt_builder import (
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DEFAULT_AGENT_IDENTITY, PLATFORM_HINTS,
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MEMORY_GUIDANCE, SESSION_SEARCH_GUIDANCE, SKILLS_GUIDANCE,
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)
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from agent.model_metadata import (
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fetch_model_metadata, get_model_context_length,
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estimate_tokens_rough, estimate_messages_tokens_rough,
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get_next_probe_tier, parse_context_limit_from_error,
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save_context_length,
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)
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from agent.context_compressor import ContextCompressor
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from agent.prompt_caching import apply_anthropic_cache_control
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from agent.prompt_builder import build_skills_system_prompt, build_context_files_prompt
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from agent.display import (
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KawaiiSpinner, build_tool_preview as _build_tool_preview,
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get_cute_tool_message as _get_cute_tool_message_impl,
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_detect_tool_failure,
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)
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from agent.trajectory import (
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convert_scratchpad_to_think, has_incomplete_scratchpad,
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save_trajectory as _save_trajectory_to_file,
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)
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class IterationBudget:
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"""Thread-safe shared iteration counter for parent and child agents.
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Tracks total LLM-call iterations consumed across a parent agent and all
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its subagents. A single ``IterationBudget`` is created by the parent
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and passed to every child so they share the same cap.
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``execute_code`` (programmatic tool calling) iterations are refunded via
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:meth:`refund` so they don't eat into the budget.
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"""
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def __init__(self, max_total: int):
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self.max_total = max_total
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self._used = 0
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self._lock = threading.Lock()
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def consume(self) -> bool:
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"""Try to consume one iteration. Returns True if allowed."""
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with self._lock:
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if self._used >= self.max_total:
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return False
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self._used += 1
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return True
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def refund(self) -> None:
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"""Give back one iteration (e.g. for execute_code turns)."""
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with self._lock:
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if self._used > 0:
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self._used -= 1
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@property
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def used(self) -> int:
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return self._used
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@property
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def remaining(self) -> int:
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with self._lock:
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return max(0, self.max_total - self._used)
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class AIAgent:
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"""
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AI Agent with tool calling capabilities.
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This class manages the conversation flow, tool execution, and response handling
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for AI models that support function calling.
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"""
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def __init__(
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self,
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base_url: str = None,
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api_key: str = None,
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provider: str = None,
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api_mode: str = None,
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model: str = "anthropic/claude-opus-4.6", # OpenRouter format
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max_iterations: int = 90, # Default tool-calling iterations (shared with subagents)
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tool_delay: float = 1.0,
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enabled_toolsets: List[str] = None,
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disabled_toolsets: List[str] = None,
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save_trajectories: bool = False,
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verbose_logging: bool = False,
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quiet_mode: bool = False,
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ephemeral_system_prompt: str = None,
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log_prefix_chars: int = 100,
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log_prefix: str = "",
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providers_allowed: List[str] = None,
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providers_ignored: List[str] = None,
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providers_order: List[str] = None,
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provider_sort: str = None,
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provider_require_parameters: bool = False,
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provider_data_collection: str = None,
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session_id: str = None,
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tool_progress_callback: callable = None,
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clarify_callback: callable = None,
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step_callback: callable = None,
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max_tokens: int = None,
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reasoning_config: Dict[str, Any] = None,
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prefill_messages: List[Dict[str, Any]] = None,
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platform: str = None,
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skip_context_files: bool = False,
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skip_memory: bool = False,
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session_db=None,
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honcho_session_key: str = None,
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iteration_budget: "IterationBudget" = None,
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):
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"""
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Initialize the AI Agent.
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Args:
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base_url (str): Base URL for the model API (optional)
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api_key (str): API key for authentication (optional, uses env var if not provided)
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provider (str): Provider identifier (optional; used for telemetry/routing hints)
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api_mode (str): API mode override: "chat_completions" or "codex_responses"
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model (str): Model name to use (default: "anthropic/claude-opus-4.6")
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max_iterations (int): Maximum number of tool calling iterations (default: 90)
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tool_delay (float): Delay between tool calls in seconds (default: 1.0)
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enabled_toolsets (List[str]): Only enable tools from these toolsets (optional)
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disabled_toolsets (List[str]): Disable tools from these toolsets (optional)
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save_trajectories (bool): Whether to save conversation trajectories to JSONL files (default: False)
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verbose_logging (bool): Enable verbose logging for debugging (default: False)
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quiet_mode (bool): Suppress progress output for clean CLI experience (default: False)
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ephemeral_system_prompt (str): System prompt used during agent execution but NOT saved to trajectories (optional)
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log_prefix_chars (int): Number of characters to show in log previews for tool calls/responses (default: 100)
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log_prefix (str): Prefix to add to all log messages for identification in parallel processing (default: "")
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providers_allowed (List[str]): OpenRouter providers to allow (optional)
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providers_ignored (List[str]): OpenRouter providers to ignore (optional)
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providers_order (List[str]): OpenRouter providers to try in order (optional)
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provider_sort (str): Sort providers by price/throughput/latency (optional)
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session_id (str): Pre-generated session ID for logging (optional, auto-generated if not provided)
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tool_progress_callback (callable): Callback function(tool_name, args_preview) for progress notifications
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clarify_callback (callable): Callback function(question, choices) -> str for interactive user questions.
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Provided by the platform layer (CLI or gateway). If None, the clarify tool returns an error.
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max_tokens (int): Maximum tokens for model responses (optional, uses model default if not set)
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reasoning_config (Dict): OpenRouter reasoning configuration override (e.g. {"effort": "none"} to disable thinking).
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If None, defaults to {"enabled": True, "effort": "medium"} for OpenRouter. Set to disable/customize reasoning.
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prefill_messages (List[Dict]): Messages to prepend to conversation history as prefilled context.
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Useful for injecting a few-shot example or priming the model's response style.
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Example: [{"role": "user", "content": "Hi!"}, {"role": "assistant", "content": "Hello!"}]
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platform (str): The interface platform the user is on (e.g. "cli", "telegram", "discord", "whatsapp").
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Used to inject platform-specific formatting hints into the system prompt.
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skip_context_files (bool): If True, skip auto-injection of SOUL.md, AGENTS.md, and .cursorrules
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into the system prompt. Use this for batch processing and data generation to avoid
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polluting trajectories with user-specific persona or project instructions.
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honcho_session_key (str): Session key for Honcho integration (e.g., "telegram:123456" or CLI session_id).
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When provided and Honcho is enabled in config, enables persistent cross-session user modeling.
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"""
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self.model = model
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self.max_iterations = max_iterations
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# Shared iteration budget — parent creates, children inherit.
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# Consumed by every LLM turn across parent + all subagents.
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self.iteration_budget = iteration_budget or IterationBudget(max_iterations)
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self.tool_delay = tool_delay
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self.save_trajectories = save_trajectories
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self.verbose_logging = verbose_logging
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self.quiet_mode = quiet_mode
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self.ephemeral_system_prompt = ephemeral_system_prompt
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self.platform = platform # "cli", "telegram", "discord", "whatsapp", etc.
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self.skip_context_files = skip_context_files
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self.log_prefix_chars = log_prefix_chars
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self.log_prefix = f"{log_prefix} " if log_prefix else ""
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# Store effective base URL for feature detection (prompt caching, reasoning, etc.)
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# When no base_url is provided, the client defaults to OpenRouter, so reflect that here.
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self.base_url = base_url or OPENROUTER_BASE_URL
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provider_name = provider.strip().lower() if isinstance(provider, str) and provider.strip() else None
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self.provider = provider_name or "openrouter"
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if api_mode in {"chat_completions", "codex_responses"}:
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self.api_mode = api_mode
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elif self.provider == "openai-codex":
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self.api_mode = "codex_responses"
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elif (provider_name is None) and "chatgpt.com/backend-api/codex" in self.base_url.lower():
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self.api_mode = "codex_responses"
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self.provider = "openai-codex"
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else:
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self.api_mode = "chat_completions"
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self.tool_progress_callback = tool_progress_callback
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self.clarify_callback = clarify_callback
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self.step_callback = step_callback
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self._last_reported_tool = None # Track for "new tool" mode
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# Interrupt mechanism for breaking out of tool loops
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self._interrupt_requested = False
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self._interrupt_message = None # Optional message that triggered interrupt
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||
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# Subagent delegation state
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self._delegate_depth = 0 # 0 = top-level agent, incremented for children
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self._active_children = [] # Running child AIAgents (for interrupt propagation)
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||
|
||
# Store OpenRouter provider preferences
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||
self.providers_allowed = providers_allowed
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||
self.providers_ignored = providers_ignored
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||
self.providers_order = providers_order
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||
self.provider_sort = provider_sort
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||
self.provider_require_parameters = provider_require_parameters
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||
self.provider_data_collection = provider_data_collection
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||
|
||
# Store toolset filtering options
|
||
self.enabled_toolsets = enabled_toolsets
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||
self.disabled_toolsets = disabled_toolsets
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||
|
||
# Model response configuration
|
||
self.max_tokens = max_tokens # None = use model default
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self.reasoning_config = reasoning_config # None = use default (medium for OpenRouter)
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||
self.prefill_messages = prefill_messages or [] # Prefilled conversation turns
|
||
|
||
# Anthropic prompt caching: auto-enabled for Claude models via OpenRouter.
|
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# Reduces input costs by ~75% on multi-turn conversations by caching the
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# conversation prefix. Uses system_and_3 strategy (4 breakpoints).
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is_openrouter = "openrouter" in self.base_url.lower()
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||
is_claude = "claude" in self.model.lower()
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||
self._use_prompt_caching = is_openrouter and is_claude
|
||
self._cache_ttl = "5m" # Default 5-minute TTL (1.25x write cost)
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||
|
||
# Persistent error log -- always writes WARNING+ to ~/.hermes/logs/errors.log
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||
# so tool failures, API errors, etc. are inspectable after the fact.
|
||
from agent.redact import RedactingFormatter
|
||
_error_log_dir = Path.home() / ".hermes" / "logs"
|
||
_error_log_dir.mkdir(parents=True, exist_ok=True)
|
||
_error_log_path = _error_log_dir / "errors.log"
|
||
from logging.handlers import RotatingFileHandler
|
||
_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',
|
||
))
|
||
logging.getLogger().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.)
|
||
'minisweagent', # mini-swe-agent execution backend
|
||
'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)
|
||
|
||
# Initialize OpenAI client - defaults to OpenRouter
|
||
client_kwargs = {}
|
||
|
||
# Default to OpenRouter if no base_url provided
|
||
if base_url:
|
||
client_kwargs["base_url"] = base_url
|
||
else:
|
||
client_kwargs["base_url"] = OPENROUTER_BASE_URL
|
||
|
||
# Handle API key - OpenRouter is the primary provider
|
||
if api_key:
|
||
client_kwargs["api_key"] = api_key
|
||
else:
|
||
# Primary: OPENROUTER_API_KEY, fallback to direct provider keys
|
||
client_kwargs["api_key"] = os.getenv("OPENROUTER_API_KEY", "")
|
||
|
||
# OpenRouter app attribution — shows hermes-agent in rankings/analytics
|
||
effective_base = client_kwargs.get("base_url", "")
|
||
if "openrouter" in effective_base.lower():
|
||
client_kwargs["default_headers"] = {
|
||
"HTTP-Referer": "https://github.com/NousResearch/hermes-agent",
|
||
"X-OpenRouter-Title": "Hermes Agent",
|
||
"X-OpenRouter-Categories": "productivity,cli-agent",
|
||
}
|
||
elif "api.kimi.com" in effective_base.lower():
|
||
# Kimi Code API requires a recognized coding-agent User-Agent
|
||
# (see https://github.com/MoonshotAI/kimi-cli)
|
||
client_kwargs["default_headers"] = {
|
||
"User-Agent": "KimiCLI/1.0",
|
||
}
|
||
|
||
self._client_kwargs = client_kwargs # stored for rebuilding after interrupt
|
||
try:
|
||
self.client = OpenAI(**client_kwargs)
|
||
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}")
|
||
|
||
# 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:
|
||
print(f"💾 Prompt caching: ENABLED (Claude via OpenRouter, {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 = Path(os.getenv("HERMES_HOME", Path.home() / ".hermes"))
|
||
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
|
||
|
||
# SQLite session store (optional -- provided by CLI or gateway)
|
||
self._session_db = session_db
|
||
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.debug("Session DB create_session failed: %s", e)
|
||
|
||
# In-memory todo list for task planning (one per agent/session)
|
||
from tools.todo_tool import TodoStore
|
||
self._todo_store = TodoStore()
|
||
|
||
# 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
|
||
if not skip_memory:
|
||
try:
|
||
from hermes_cli.config import load_config as _load_mem_config
|
||
mem_config = _load_mem_config().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 ~/.honcho/config.json as the single source of truth.
|
||
self._honcho = None # HonchoSessionManager | None
|
||
self._honcho_session_key = honcho_session_key
|
||
if not skip_memory:
|
||
try:
|
||
from honcho_integration.client import HonchoClientConfig, get_honcho_client
|
||
hcfg = HonchoClientConfig.from_global_config()
|
||
if hcfg.enabled and hcfg.api_key:
|
||
from honcho_integration.session import HonchoSessionManager
|
||
client = get_honcho_client(hcfg)
|
||
self._honcho = HonchoSessionManager(
|
||
honcho=client,
|
||
config=hcfg,
|
||
context_tokens=hcfg.context_tokens,
|
||
)
|
||
# Resolve session key: explicit arg > global sessions map > fallback
|
||
if not self._honcho_session_key:
|
||
self._honcho_session_key = (
|
||
hcfg.resolve_session_name()
|
||
or "hermes-default"
|
||
)
|
||
# Ensure session exists in Honcho
|
||
self._honcho.get_or_create(self._honcho_session_key)
|
||
# Inject session context into the honcho tool module
|
||
from tools.honcho_tools import set_session_context
|
||
set_session_context(self._honcho, self._honcho_session_key)
|
||
logger.info(
|
||
"Honcho active (session: %s, user: %s, workspace: %s)",
|
||
self._honcho_session_key, hcfg.peer_name, hcfg.workspace_id,
|
||
)
|
||
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")
|
||
except Exception as e:
|
||
logger.debug("Honcho init failed (non-fatal): %s", e)
|
||
self._honcho = None
|
||
|
||
# Skills config: nudge interval for skill creation reminders
|
||
self._skill_nudge_interval = 15
|
||
try:
|
||
from hermes_cli.config import load_config as _load_skills_config
|
||
skills_config = _load_skills_config().get("skills", {})
|
||
self._skill_nudge_interval = int(skills_config.get("creation_nudge_interval", 15))
|
||
except Exception:
|
||
pass
|
||
|
||
# Initialize context compressor for automatic context management
|
||
# Compresses conversation when approaching model's context limit
|
||
# Configuration via config.yaml (compression section) or environment variables
|
||
compression_threshold = float(os.getenv("CONTEXT_COMPRESSION_THRESHOLD", "0.85"))
|
||
compression_enabled = os.getenv("CONTEXT_COMPRESSION_ENABLED", "true").lower() in ("true", "1", "yes")
|
||
compression_summary_model = os.getenv("CONTEXT_COMPRESSION_MODEL") or None
|
||
|
||
self.context_compressor = ContextCompressor(
|
||
model=self.model,
|
||
threshold_percent=compression_threshold,
|
||
protect_first_n=3,
|
||
protect_last_n=4,
|
||
summary_target_tokens=500,
|
||
summary_model_override=compression_summary_model,
|
||
quiet_mode=self.quiet_mode,
|
||
base_url=self.base_url,
|
||
)
|
||
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
|
||
|
||
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 _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'.
|
||
"""
|
||
_is_direct_openai = (
|
||
"api.openai.com" in self.base_url.lower()
|
||
and "openrouter" not in self.base_url.lower()
|
||
)
|
||
if _is_direct_openai:
|
||
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 <think></think> blocks.
|
||
|
||
This detects cases where the model only outputs reasoning but no actual
|
||
response, which indicates an incomplete generation that should be retried.
|
||
|
||
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 <think>...</think> blocks (including nested ones, non-greedy)
|
||
cleaned = re.sub(r'<think>.*?</think>', '', content, flags=re.DOTALL)
|
||
|
||
# Check if there's any non-whitespace content remaining
|
||
return bool(cleaned.strip())
|
||
|
||
def _strip_think_blocks(self, content: str) -> str:
|
||
"""Remove <think>...</think> blocks from content, returning only visible text."""
|
||
if not content:
|
||
return ""
|
||
return re.sub(r'<think>.*?</think>', '', content, flags=re.DOTALL)
|
||
|
||
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)
|
||
|
||
# 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}")
|
||
|
||
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._session_messages = messages
|
||
self._save_session_log(messages)
|
||
self._flush_messages_to_session_db(messages, conversation_history)
|
||
|
||
def _log_msg_to_db(self, msg: Dict):
|
||
"""Log a single message to SQLite immediately. Called after each messages.append()."""
|
||
if not self._session_db:
|
||
return
|
||
try:
|
||
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"),
|
||
)
|
||
except Exception as e:
|
||
logger.debug("Session DB log_msg failed: %s", e)
|
||
|
||
def _flush_messages_to_session_db(self, messages: List[Dict], conversation_history: List[Dict] = None):
|
||
"""Persist any un-logged messages to the SQLite session store.
|
||
|
||
Called both at the normal end of run_conversation and from every early-
|
||
return path so that tool calls, tool responses, and assistant messages
|
||
are never lost even when the conversation errors out.
|
||
"""
|
||
if not self._session_db:
|
||
return
|
||
try:
|
||
start_idx = len(conversation_history) if conversation_history else 0
|
||
for msg in messages[start_idx:]:
|
||
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"),
|
||
)
|
||
except Exception as e:
|
||
logger.debug("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 <tools> </tools> 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 "
|
||
"<tool_response> </tool_response> XML tags. Here are the available tools:\n"
|
||
f"<tools>\n{self._format_tools_for_system_message()}\n</tools>\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 <tool_call> </tool_call> XML tags.\n"
|
||
"Example:\n<tool_call>\n{'name': <function-name>,'arguments': <args-dict>}\n</tool_call>"
|
||
)
|
||
|
||
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 <think> tags around reasoning for trajectory storage
|
||
content = ""
|
||
|
||
# Prepend reasoning in <think> tags if available (native thinking tokens)
|
||
if msg.get("reasoning") and msg["reasoning"].strip():
|
||
content = f"<think>\n{msg['reasoning']}\n</think>\n"
|
||
|
||
if msg.get("content") and msg["content"].strip():
|
||
# Convert any <REASONING_SCRATCHPAD> tags to <think> 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"]:
|
||
# 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"<tool_call>\n{json.dumps(tool_call_json, ensure_ascii=False)}\n</tool_call>\n"
|
||
|
||
# Ensure every gpt turn has a <think> block (empty if no reasoning)
|
||
# so the format is consistent for training data
|
||
if "<think>" not in content:
|
||
content = "<think>\n</think>\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"<tool_response>\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_response += json.dumps({
|
||
"tool_call_id": tool_msg.get("tool_call_id", ""),
|
||
"name": msg["tool_calls"][len(tool_responses)]["function"]["name"] if len(tool_responses) < len(msg["tool_calls"]) else "unknown",
|
||
"content": tool_content
|
||
}, ensure_ascii=False)
|
||
tool_response += "\n</tool_response>"
|
||
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 <think> tags around reasoning for trajectory storage
|
||
content = ""
|
||
|
||
# Prepend reasoning in <think> tags if available (native thinking tokens)
|
||
if msg.get("reasoning") and msg["reasoning"].strip():
|
||
content = f"<think>\n{msg['reasoning']}\n</think>\n"
|
||
|
||
# Convert any <REASONING_SCRATCHPAD> tags to <think> 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 <think> block (empty if no reasoning)
|
||
if "<think>" not in content:
|
||
content = "<think>\n</think>\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 _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 chat.completions.create().
|
||
|
||
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('/')}/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",
|
||
)
|
||
|
||
print(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+(<think>)', r'\n\1', content)
|
||
content = re.sub(r'(</think>)\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 <think> 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,
|
||
}
|
||
|
||
with open(self.session_log_file, "w", encoding="utf-8") as f:
|
||
json.dump(entry, f, indent=2, ensure_ascii=False, 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)
|
||
for child in self._active_children:
|
||
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:
|
||
print(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_prefetch(self, user_message: str) -> str:
|
||
"""Fetch user context from Honcho for system prompt injection.
|
||
|
||
Returns a formatted context block, or empty string if unavailable.
|
||
"""
|
||
if not self._honcho or not self._honcho_session_key:
|
||
return ""
|
||
try:
|
||
ctx = self._honcho.get_prefetch_context(self._honcho_session_key, user_message)
|
||
if not ctx:
|
||
return ""
|
||
parts = []
|
||
rep = ctx.get("representation", "")
|
||
card = ctx.get("card", "")
|
||
if rep:
|
||
parts.append(rep)
|
||
if card:
|
||
parts.append(card)
|
||
if not parts:
|
||
return ""
|
||
return "# Honcho User Context\n" + "\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)
|
||
except Exception as e:
|
||
logger.debug("Honcho sync failed (non-fatal): %s", 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. Default agent identity (always present)
|
||
# 2. User / gateway system prompt (if provided)
|
||
# 3. Persistent memory (frozen snapshot)
|
||
# 4. Skills guidance (if skills tools are loaded)
|
||
# 5. Context files (SOUL.md, AGENTS.md, .cursorrules)
|
||
# 6. Current date & time (frozen at build time)
|
||
# 7. Platform-specific formatting hint
|
||
prompt_parts = [DEFAULT_AGENT_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))
|
||
|
||
# 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'])
|
||
skills_prompt = build_skills_system_prompt() if has_skills_tools else ""
|
||
if skills_prompt:
|
||
prompt_parts.append(skills_prompt)
|
||
|
||
if not self.skip_context_files:
|
||
context_files_prompt = build_context_files_prompt()
|
||
if context_files_prompt:
|
||
prompt_parts.append(context_files_prompt)
|
||
|
||
from hermes_time import now as _hermes_now
|
||
now = _hermes_now()
|
||
prompt_parts.append(
|
||
f"Conversation started: {now.strftime('%A, %B %d, %Y %I:%M %p')}"
|
||
)
|
||
|
||
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)
|
||
|
||
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")
|
||
if isinstance(codex_reasoning, list):
|
||
for ri in codex_reasoning:
|
||
if isinstance(ri, dict) and ri.get("encrypted_content"):
|
||
items.append(ri)
|
||
|
||
if content_text.strip():
|
||
items.append({"role": "assistant", "content": content_text})
|
||
|
||
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",
|
||
}
|
||
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)
|
||
|
||
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 = str(arguments)
|
||
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 = str(arguments)
|
||
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"
|
||
else:
|
||
finish_reason = "stop"
|
||
return assistant_message, finish_reason
|
||
|
||
def _run_codex_stream(self, api_kwargs: dict):
|
||
"""Execute one streaming Responses API request and return the final response."""
|
||
max_stream_retries = 1
|
||
for attempt in range(max_stream_retries + 1):
|
||
try:
|
||
with self.client.responses.stream(**api_kwargs) as stream:
|
||
for _ in stream:
|
||
pass
|
||
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.",
|
||
attempt + 1,
|
||
max_stream_retries + 1,
|
||
)
|
||
continue
|
||
if missing_completed:
|
||
logger.debug(
|
||
"Responses stream did not emit response.completed; falling back to create(stream=True)."
|
||
)
|
||
return self._run_codex_create_stream_fallback(api_kwargs)
|
||
raise
|
||
|
||
def _run_codex_create_stream_fallback(self, api_kwargs: dict):
|
||
"""Fallback path for stream completion edge cases on Codex-style Responses backends."""
|
||
fallback_kwargs = dict(api_kwargs)
|
||
fallback_kwargs["stream"] = True
|
||
fallback_kwargs = self._preflight_codex_api_kwargs(fallback_kwargs, allow_stream=True)
|
||
stream_or_response = self.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
|
||
|
||
try:
|
||
self.client.close()
|
||
except Exception:
|
||
pass
|
||
|
||
try:
|
||
self.client = OpenAI(**self._client_kwargs)
|
||
except Exception as exc:
|
||
logger.warning("Failed to rebuild OpenAI client after Codex refresh: %s", exc)
|
||
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)
|
||
|
||
try:
|
||
self.client.close()
|
||
except Exception:
|
||
pass
|
||
|
||
try:
|
||
self.client = OpenAI(**self._client_kwargs)
|
||
except Exception as exc:
|
||
logger.warning("Failed to rebuild OpenAI client after Nous refresh: %s", exc)
|
||
return False
|
||
|
||
return True
|
||
|
||
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.
|
||
|
||
On interrupt, closes the HTTP client to cancel the in-flight request
|
||
(stops token generation and avoids wasting money), then rebuilds the
|
||
client for future calls.
|
||
"""
|
||
result = {"response": None, "error": None}
|
||
|
||
def _call():
|
||
try:
|
||
if self.api_mode == "codex_responses":
|
||
result["response"] = self._run_codex_stream(api_kwargs)
|
||
else:
|
||
result["response"] = self.client.chat.completions.create(**api_kwargs)
|
||
except Exception as e:
|
||
result["error"] = e
|
||
|
||
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 HTTP connection to stop token generation
|
||
try:
|
||
self.client.close()
|
||
except Exception:
|
||
pass
|
||
# Rebuild the client for future calls (cheap, no network)
|
||
try:
|
||
self.client = OpenAI(**self._client_kwargs)
|
||
except Exception:
|
||
pass
|
||
raise InterruptedError("Agent interrupted during API call")
|
||
if result["error"] is not None:
|
||
raise result["error"]
|
||
return result["response"]
|
||
|
||
def _build_api_kwargs(self, api_messages: list) -> dict:
|
||
"""Build the keyword arguments dict for the active API mode."""
|
||
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
|
||
|
||
# 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(),
|
||
"store": False,
|
||
}
|
||
|
||
if reasoning_enabled:
|
||
kwargs["reasoning"] = {"effort": reasoning_effort, "summary": "auto"}
|
||
kwargs["include"] = ["reasoning.encrypted_content"]
|
||
else:
|
||
kwargs["include"] = []
|
||
|
||
if self.max_tokens is not None:
|
||
kwargs["max_output_tokens"] = self.max_tokens
|
||
|
||
return kwargs
|
||
|
||
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": api_messages,
|
||
"tools": self.tools if self.tools else None,
|
||
"timeout": 900.0,
|
||
}
|
||
|
||
if self.max_tokens is not None:
|
||
api_kwargs.update(self._max_tokens_param(self.max_tokens))
|
||
|
||
extra_body = {}
|
||
|
||
if provider_preferences:
|
||
extra_body["provider"] = provider_preferences
|
||
|
||
_is_openrouter = "openrouter" in self.base_url.lower()
|
||
_is_nous = "nousresearch" in self.base_url.lower()
|
||
|
||
_is_mistral = "api.mistral.ai" in self.base_url.lower()
|
||
if (_is_openrouter or _is_nous) and not _is_mistral:
|
||
if self.reasoning_config is not None:
|
||
extra_body["reasoning"] = self.reasoning_config
|
||
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 _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)
|
||
|
||
if reasoning_text and self.verbose_logging:
|
||
preview = reasoning_text[:100] + "..." if len(reasoning_text) > 100 else reasoning_text
|
||
logging.debug(f"Captured reasoning ({len(reasoning_text)} chars): {preview}")
|
||
|
||
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
|
||
|
||
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
|
||
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. "
|
||
"Please save anything worth remembering to your memories.]"
|
||
)
|
||
_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
|
||
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)
|
||
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 get_text_auxiliary_client
|
||
aux_client, aux_model = get_text_auxiliary_client()
|
||
|
||
if aux_client:
|
||
api_kwargs = {
|
||
"model": aux_model,
|
||
"messages": api_messages,
|
||
"tools": [memory_tool_def],
|
||
"temperature": 0.3,
|
||
"max_tokens": 5120,
|
||
}
|
||
response = aux_client.chat.completions.create(**api_kwargs, timeout=30.0)
|
||
elif 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)
|
||
else:
|
||
api_kwargs = {
|
||
"model": self.model,
|
||
"messages": api_messages,
|
||
"tools": [memory_tool_def],
|
||
"temperature": 0.3,
|
||
**self._max_tokens_param(5120),
|
||
}
|
||
response = self.client.chat.completions.create(**api_kwargs, timeout=30.0)
|
||
|
||
# Extract tool calls from the response, handling both API formats
|
||
tool_calls = []
|
||
if self.api_mode == "codex_responses" and not aux_client:
|
||
assistant_msg, _ = self._normalize_codex_response(response)
|
||
if assistant_msg and assistant_msg.tool_calls:
|
||
tool_calls = assistant_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) -> 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)
|
||
except Exception as e:
|
||
logger.debug("Session DB compression split failed: %s", e)
|
||
|
||
return compressed, new_system_prompt
|
||
|
||
def _execute_tool_calls(self, assistant_message, messages: list, effective_task_id: str) -> None:
|
||
"""Execute tool calls from the assistant message and append results to messages."""
|
||
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:
|
||
print(f"{self.log_prefix}⚡ Interrupt: skipping {len(remaining_calls)} tool call(s)")
|
||
for skipped_tc in remaining_calls:
|
||
skip_msg = {
|
||
"role": "tool",
|
||
"content": "[Tool execution cancelled - user interrupted]",
|
||
"tool_call_id": skipped_tc.id,
|
||
}
|
||
messages.append(skip_msg)
|
||
self._log_msg_to_db(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 self.quiet_mode:
|
||
args_str = json.dumps(function_args, ensure_ascii=False)
|
||
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}")
|
||
|
||
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:
|
||
print(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:
|
||
print(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:
|
||
print(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:
|
||
print(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:
|
||
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:
|
||
print(f" {cute_msg}")
|
||
elif self.quiet_mode:
|
||
face = random.choice(KawaiiSpinner.KAWAII_WAITING)
|
||
tool_emoji_map = {
|
||
'web_search': '🔍', 'web_extract': '📄', 'web_crawl': '🕸️',
|
||
'terminal': '💻', 'process': '⚙️',
|
||
'read_file': '📖', 'write_file': '✍️', 'patch': '🔧', 'search_files': '🔎',
|
||
'browser_navigate': '🌐', 'browser_snapshot': '📸',
|
||
'browser_click': '👆', 'browser_type': '⌨️',
|
||
'browser_scroll': '📜', 'browser_back': '◀️',
|
||
'browser_press': '⌨️', 'browser_close': '🚪',
|
||
'browser_get_images': '🖼️', 'browser_vision': '👁️',
|
||
'image_generate': '🎨', 'text_to_speech': '🔊',
|
||
'vision_analyze': '👁️', 'mixture_of_agents': '🧠',
|
||
'skills_list': '📚', 'skill_view': '📚',
|
||
'schedule_cronjob': '⏰', 'list_cronjobs': '⏰', 'remove_cronjob': '⏰',
|
||
'send_message': '📨', 'todo': '📋', 'memory': '🧠', 'session_search': '🔍',
|
||
'clarify': '❓', 'execute_code': '🐍', 'delegate_task': '🔀',
|
||
}
|
||
emoji = tool_emoji_map.get(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)
|
||
_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)
|
||
spinner.stop(cute_msg)
|
||
else:
|
||
try:
|
||
function_result = handle_function_call(function_name, function_args, effective_task_id)
|
||
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[: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 preview: {result_preview}...")
|
||
|
||
# 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)
|
||
self._log_msg_to_db(tool_msg)
|
||
|
||
if not self.quiet_mode:
|
||
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
|
||
print(f"{self.log_prefix}⚡ Interrupt: skipping {remaining} remaining tool call(s)")
|
||
for skipped_tc in assistant_message.tool_calls[i:]:
|
||
skip_msg = {
|
||
"role": "tool",
|
||
"content": "[Tool execution skipped - user sent a new message]",
|
||
"tool_call_id": skipped_tc.id
|
||
}
|
||
messages.append(skip_msg)
|
||
self._log_msg_to_db(skip_msg)
|
||
break
|
||
|
||
if self.tool_delay > 0 and i < len(assistant_message.tool_calls):
|
||
time.sleep(self.tool_delay)
|
||
|
||
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
|
||
api_messages = []
|
||
for msg in messages:
|
||
api_msg = msg.copy()
|
||
for internal_field in ("reasoning", "finish_reason"):
|
||
api_msg.pop(internal_field, None)
|
||
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_openrouter = "openrouter" in self.base_url.lower()
|
||
_is_nous = "nousresearch" in self.base_url.lower()
|
||
if _is_openrouter or _is_nous:
|
||
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
|
||
|
||
summary_response = self.client.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 "<think>" in final_response:
|
||
final_response = re.sub(r'<think>.*?</think>\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 ""
|
||
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.client.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 "<think>" in final_response:
|
||
final_response = re.sub(r'<think>.*?</think>\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
|
||
) -> 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)
|
||
|
||
Returns:
|
||
Dict: Complete conversation result with final response and message history
|
||
"""
|
||
# 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._last_content_with_tools = None
|
||
self._turns_since_memory = 0
|
||
self._iters_since_skill = 0
|
||
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 before nudge injection.
|
||
# Honcho should receive the actual user input, not system nudges.
|
||
original_user_message = user_message
|
||
|
||
# Periodic memory nudge: remind the model to consider saving memories.
|
||
# Counter resets whenever the memory tool is actually used.
|
||
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:
|
||
user_message += (
|
||
"\n\n[System: You've had several exchanges in this session. "
|
||
"Consider whether there's anything worth saving to your memories.]"
|
||
)
|
||
self._turns_since_memory = 0
|
||
|
||
# Skill creation nudge: fires on the first user message after a long tool loop.
|
||
# The counter increments per API iteration in the tool loop and is checked here.
|
||
if (self._skill_nudge_interval > 0
|
||
and self._iters_since_skill >= self._skill_nudge_interval
|
||
and "skill_manage" in self.valid_tool_names):
|
||
user_message += (
|
||
"\n\n[System: The previous task involved many steps. "
|
||
"If you discovered a reusable workflow, consider saving it as a skill.]"
|
||
)
|
||
self._iters_since_skill = 0
|
||
|
||
# Honcho prefetch: retrieve user context for system prompt injection
|
||
self._honcho_context = ""
|
||
if self._honcho and self._honcho_session_key:
|
||
try:
|
||
self._honcho_context = self._honcho_prefetch(user_message)
|
||
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)
|
||
self._log_msg_to_db(user_msg)
|
||
|
||
if not self.quiet_mode:
|
||
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).
|
||
if self._cached_system_prompt is None:
|
||
self._cached_system_prompt = self._build_system_prompt(system_message)
|
||
# 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:
|
||
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
|
||
)
|
||
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
|
||
|
||
# Clear any stale interrupt state at start
|
||
self.clear_interrupt()
|
||
|
||
while api_call_count < self.max_iterations and self.iteration_budget.remaining > 0:
|
||
# Check for interrupt request (e.g., user sent new message)
|
||
if self._interrupt_requested:
|
||
interrupted = True
|
||
if not self.quiet_mode:
|
||
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:
|
||
print(f"\n⚠️ Session iteration budget exhausted ({self.iteration_budget.max_total} total across agent + subagents)")
|
||
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 <think> 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 msg in messages:
|
||
api_msg = msg.copy()
|
||
|
||
# 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")
|
||
# 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.
|
||
# The ephemeral part is appended here (not baked into the cached prompt)
|
||
# so it stays out of the session DB and logs.
|
||
effective_system = active_system_prompt or ""
|
||
if self.ephemeral_system_prompt:
|
||
effective_system = (effective_system + "\n\n" + self.ephemeral_system_prompt).strip()
|
||
if self._honcho_context:
|
||
effective_system = (effective_system + "\n\n" + self._honcho_context).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)
|
||
|
||
# Safety net: strip orphaned tool results / add stubs for missing
|
||
# results before sending to the API. The compressor handles this
|
||
# during compression, but orphans can also sneak in from session
|
||
# loading or manual message manipulation.
|
||
if hasattr(self, 'context_compressor') and self.context_compressor:
|
||
api_messages = self.context_compressor._sanitize_tool_pairs(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:
|
||
print(f"\n{self.log_prefix}🔄 Making API call #{api_call_count}/{self.max_iterations}...")
|
||
print(f"{self.log_prefix} 📊 Request size: {len(api_messages)} messages, ~{approx_tokens:,} tokens (~{total_chars:,} chars)")
|
||
print(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)
|
||
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 = 6 # Increased to allow longer backoff periods
|
||
compression_attempts = 0
|
||
max_compression_attempts = 3
|
||
codex_auth_retry_attempted = False
|
||
nous_auth_retry_attempted = 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")
|
||
|
||
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 not self.quiet_mode:
|
||
print(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")
|
||
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
|
||
|
||
# This is often rate limiting or provider returning malformed response
|
||
retry_count += 1
|
||
|
||
# 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}")
|
||
|
||
print(f"{self.log_prefix}⚠️ Invalid API response (attempt {retry_count}/{max_retries}): {', '.join(error_details)}")
|
||
print(f"{self.log_prefix} 🏢 Provider: {provider_name}")
|
||
print(f"{self.log_prefix} 📝 Provider message: {error_msg[:200]}")
|
||
print(f"{self.log_prefix} ⏱️ Response time: {api_duration:.2f}s (fast response often indicates rate limiting)")
|
||
|
||
if retry_count >= max_retries:
|
||
print(f"{self.log_prefix}❌ Max retries ({max_retries}) exceeded for invalid responses. Giving up.")
|
||
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
|
||
print(f"{self.log_prefix}⏳ Retrying in {wait_time}s (extended backoff for possible rate limit)...")
|
||
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:
|
||
print(f"{self.log_prefix}⚡ Interrupt detected during retry wait, aborting.")
|
||
self._persist_session(messages, conversation_history)
|
||
self.clear_interrupt()
|
||
return {
|
||
"final_response": "Operation interrupted.",
|
||
"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"
|
||
else:
|
||
finish_reason = response.choices[0].finish_reason
|
||
|
||
# Handle "length" finish_reason - response was truncated
|
||
if finish_reason == "length":
|
||
print(f"{self.log_prefix}⚠️ Response truncated (finish_reason='length') - model hit max output tokens")
|
||
|
||
# If we have prior messages, roll back to last complete state
|
||
if len(messages) > 1:
|
||
print(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
|
||
print(f"{self.log_prefix}❌ First response truncated - cannot recover")
|
||
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:
|
||
if self.api_mode == "codex_responses":
|
||
prompt_tokens = getattr(response.usage, 'input_tokens', 0) or 0
|
||
completion_tokens = getattr(response.usage, 'output_tokens', 0) or 0
|
||
total_tokens = (
|
||
getattr(response.usage, 'total_tokens', None)
|
||
or (prompt_tokens + completion_tokens)
|
||
)
|
||
else:
|
||
prompt_tokens = getattr(response.usage, 'prompt_tokens', 0) or 0
|
||
completion_tokens = getattr(response.usage, 'completion_tokens', 0) or 0
|
||
total_tokens = getattr(response.usage, 'total_tokens', 0) or 0
|
||
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)
|
||
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
|
||
|
||
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:
|
||
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:
|
||
print(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
|
||
print(f"{self.log_prefix}⚡ Interrupted during API call.")
|
||
self._persist_session(messages, conversation_history)
|
||
interrupted = True
|
||
final_response = "Operation interrupted."
|
||
break
|
||
|
||
except Exception as api_error:
|
||
# Stop spinner before printing error messages
|
||
if thinking_spinner:
|
||
thinking_spinner.stop(f"(╥_╥) error, retrying...")
|
||
thinking_spinner = None
|
||
|
||
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):
|
||
print(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
|
||
|
||
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()
|
||
|
||
print(f"{self.log_prefix}⚠️ API call failed (attempt {retry_count}/{max_retries}): {error_type}")
|
||
print(f"{self.log_prefix} ⏱️ Time elapsed before failure: {elapsed_time:.2f}s")
|
||
print(f"{self.log_prefix} 📝 Error: {str(api_error)[:200]}")
|
||
print(f"{self.log_prefix} 📊 Request context: {len(api_messages)} messages, ~{approx_tokens:,} tokens, {len(self.tools) if self.tools else 0} tools")
|
||
|
||
# Check for interrupt before deciding to retry
|
||
if self._interrupt_requested:
|
||
print(f"{self.log_prefix}⚡ Interrupt detected during error handling, aborting retries.")
|
||
self._persist_session(messages, conversation_history)
|
||
self.clear_interrupt()
|
||
return {
|
||
"final_response": "Operation interrupted.",
|
||
"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)
|
||
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:
|
||
print(f"{self.log_prefix}❌ Max compression attempts ({max_compression_attempts}) reached for payload-too-large error.")
|
||
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
|
||
}
|
||
print(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
|
||
)
|
||
|
||
if len(messages) < original_len:
|
||
print(f"{self.log_prefix} 🗜️ Compressed {original_len} → {len(messages)} messages, retrying...")
|
||
time.sleep(2) # Brief pause between compression retries
|
||
continue # Retry with compressed messages
|
||
else:
|
||
print(f"{self.log_prefix}❌ Payload too large and cannot compress further.")
|
||
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
|
||
])
|
||
|
||
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
|
||
print(f"{self.log_prefix}⚠️ Context limit detected from API: {new_ctx:,} tokens (was {old_ctx:,})")
|
||
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
|
||
print(f"{self.log_prefix}⚠️ Context length exceeded — stepping down: {old_ctx:,} → {new_ctx:,} tokens")
|
||
else:
|
||
print(f"{self.log_prefix}⚠️ Context length exceeded at minimum tier — attempting compression...")
|
||
|
||
compression_attempts += 1
|
||
if compression_attempts > max_compression_attempts:
|
||
print(f"{self.log_prefix}❌ Max compression attempts ({max_compression_attempts}) reached.")
|
||
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
|
||
}
|
||
print(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
|
||
)
|
||
|
||
if len(messages) < original_len or new_ctx and new_ctx < old_ctx:
|
||
if len(messages) < original_len:
|
||
print(f"{self.log_prefix} 🗜️ Compressed {original_len} → {len(messages)} messages, retrying...")
|
||
time.sleep(2) # Brief pause between compression retries
|
||
continue # Retry with compressed messages or new tier
|
||
else:
|
||
# Can't compress further and already at minimum tier
|
||
print(f"{self.log_prefix}❌ Context length exceeded and cannot compress further.")
|
||
print(f"{self.log_prefix} 💡 The conversation has accumulated too much content.")
|
||
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.
|
||
is_client_status_error = isinstance(status_code, int) and 400 <= status_code < 500 and status_code != 413
|
||
is_client_error = (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:
|
||
self._dump_api_request_debug(
|
||
api_kwargs, reason="non_retryable_client_error", error=api_error,
|
||
)
|
||
print(f"{self.log_prefix}❌ Non-retryable client error detected. Aborting immediately.")
|
||
print(f"{self.log_prefix} 💡 This type of error won't be fixed by retrying.")
|
||
logging.error(f"{self.log_prefix}Non-retryable client error: {api_error}")
|
||
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:
|
||
print(f"{self.log_prefix}❌ Max retries ({max_retries}) exceeded. Giving up.")
|
||
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
|
||
logging.warning(f"API retry {retry_count}/{max_retries} after error: {api_error}")
|
||
if retry_count >= max_retries:
|
||
print(f"{self.log_prefix}⚠️ API call failed after {retry_count} attempts: {str(api_error)[:100]}")
|
||
print(f"{self.log_prefix}⏳ Final retry in {wait_time}s...")
|
||
|
||
# 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:
|
||
print(f"{self.log_prefix}⚡ Interrupt detected during retry wait, aborting.")
|
||
self._persist_session(messages, conversation_history)
|
||
self.clear_interrupt()
|
||
return {
|
||
"final_response": "Operation interrupted.",
|
||
"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
|
||
|
||
# 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)
|
||
else:
|
||
assistant_message = response.choices[0].message
|
||
|
||
# Handle assistant response
|
||
if assistant_message.content and not self.quiet_mode:
|
||
print(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'</?(?:REASONING_SCRATCHPAD|think|reasoning)>', '', _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 <REASONING_SCRATCHPAD> (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
|
||
|
||
print(f"{self.log_prefix}⚠️ Incomplete <REASONING_SCRATCHPAD> detected (opened but never closed)")
|
||
|
||
if self._incomplete_scratchpad_retries <= 2:
|
||
print(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
|
||
print(f"{self.log_prefix}❌ Max retries (2) for incomplete scratchpad. Saving as partial.")
|
||
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
|
||
|
||
if interim_has_content or interim_has_reasoning:
|
||
last_msg = messages[-1] if messages else None
|
||
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 "")
|
||
)
|
||
if not duplicate_interim:
|
||
messages.append(interim_msg)
|
||
self._log_msg_to_db(interim_msg)
|
||
|
||
if self._codex_incomplete_retries < 3:
|
||
if not self.quiet_mode:
|
||
print(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:
|
||
print(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
|
||
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
|
||
|
||
invalid_preview = invalid_tool_calls[0][:80] + "..." if len(invalid_tool_calls[0]) > 80 else invalid_tool_calls[0]
|
||
print(f"{self.log_prefix}⚠️ Invalid tool call detected: '{invalid_preview}'")
|
||
print(f"{self.log_prefix} Valid tools: {sorted(self.valid_tool_names)}")
|
||
|
||
if self._invalid_tool_retries < 3:
|
||
print(f"{self.log_prefix}🔄 Retrying API call ({self._invalid_tool_retries}/3)...")
|
||
# Don't add anything to messages, just retry the API call
|
||
continue
|
||
else:
|
||
print(f"{self.log_prefix}❌ Max retries (3) for invalid tool calls exceeded. Stopping as partial.")
|
||
# Return partial result - don't include the bad tool call in messages
|
||
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}"
|
||
}
|
||
|
||
# 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
|
||
# 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]
|
||
print(f"{self.log_prefix}⚠️ Invalid JSON in tool call arguments for '{tool_name}': {error_msg}")
|
||
|
||
if self._invalid_json_retries < 3:
|
||
print(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 a helpful message and let model recover
|
||
print(f"{self.log_prefix}⚠️ Injecting recovery message for invalid JSON...")
|
||
self._invalid_json_retries = 0 # Reset for next attempt
|
||
|
||
# Add a user message explaining the issue
|
||
recovery_msg = (
|
||
f"Your tool call to '{tool_name}' had invalid JSON arguments. "
|
||
f"Error: {error_msg}. "
|
||
f"For tools with no required parameters, use an empty object: {{}}. "
|
||
f"Please either retry the tool call with valid JSON, or respond without using that tool."
|
||
)
|
||
recovery_dict = {"role": "user", "content": recovery_msg}
|
||
messages.append(recovery_dict)
|
||
self._log_msg_to_db(recovery_dict)
|
||
continue
|
||
|
||
# Reset retry counter on successful JSON validation
|
||
self._invalid_json_retries = 0
|
||
|
||
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
|
||
# Show intermediate commentary so the user can follow along
|
||
if self.quiet_mode:
|
||
clean = self._strip_think_blocks(turn_content).strip()
|
||
if clean:
|
||
print(f" ┊ 💬 {clean}")
|
||
|
||
messages.append(assistant_msg)
|
||
self._log_msg_to_db(assistant_msg)
|
||
|
||
self._execute_tool_calls(assistant_message, messages, effective_task_id)
|
||
|
||
# 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()
|
||
|
||
if self.compression_enabled and self.context_compressor.should_compress():
|
||
messages, active_system_prompt = self._compress_context(
|
||
messages, system_message,
|
||
approx_tokens=self.context_compressor.last_prompt_tokens
|
||
)
|
||
|
||
# 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"]:
|
||
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()
|
||
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)
|
||
print(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
|
||
print(f"{self.log_prefix} Reasoning: {reasoning_preview}")
|
||
else:
|
||
content_preview = final_response[:80] + "..." if len(final_response) > 80 else final_response
|
||
print(f"{self.log_prefix} Content: '{content_preview}'")
|
||
|
||
if self._empty_content_retries < 3:
|
||
print(f"{self.log_prefix}🔄 Retrying API call ({self._empty_content_retries}/3)...")
|
||
continue
|
||
else:
|
||
print(f"{self.log_prefix}❌ Max retries (3) for empty content exceeded.")
|
||
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"]:
|
||
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 <think> blocks from fallback content for user display
|
||
final_response = self._strip_think_blocks(fallback).strip()
|
||
break
|
||
|
||
# No fallback -- append the empty message as-is
|
||
empty_msg = {
|
||
"role": "assistant",
|
||
"content": final_response,
|
||
"reasoning": reasoning_text,
|
||
"finish_reason": finish_reason,
|
||
}
|
||
messages.append(empty_msg)
|
||
self._log_msg_to_db(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)
|
||
self._log_msg_to_db(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._log_msg_to_db(continue_msg)
|
||
self._session_messages = messages
|
||
self._save_session_log(messages)
|
||
continue
|
||
|
||
codex_ack_continuations = 0
|
||
|
||
# Strip <think> 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)
|
||
self._log_msg_to_db(final_msg)
|
||
|
||
if not self.quiet_mode:
|
||
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)}"
|
||
print(f"❌ {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 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)
|
||
self._log_msg_to_db(err_msg)
|
||
pending_handled = True
|
||
break
|
||
|
||
if not pending_handled:
|
||
# Error happened before tool processing (e.g. response parsing).
|
||
# Use a user-role message so the model can see what went wrong
|
||
# without confusing the API with a fabricated assistant turn.
|
||
sys_err_msg = {
|
||
"role": "user",
|
||
"content": f"[System error during processing: {error_msg}]",
|
||
}
|
||
messages.append(sys_err_msg)
|
||
self._log_msg_to_db(sys_err_msg)
|
||
|
||
# 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}"
|
||
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⚠️ Session iteration budget exhausted ({self.iteration_budget.used}/{self.iteration_budget.max_total} used, including subagents)")
|
||
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:
|
||
self._honcho_sync(original_user_message, final_response)
|
||
|
||
# Build result with interrupt info if applicable
|
||
result = {
|
||
"final_response": final_response,
|
||
"messages": messages,
|
||
"api_calls": api_call_count,
|
||
"completed": completed,
|
||
"partial": False, # True only when stopped due to invalid tool calls
|
||
"interrupted": interrupted,
|
||
}
|
||
|
||
# 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()
|
||
|
||
return result
|
||
|
||
def chat(self, message: str) -> str:
|
||
"""
|
||
Simple chat interface that returns just the final response.
|
||
|
||
Args:
|
||
message (str): User message
|
||
|
||
Returns:
|
||
str: Final assistant response
|
||
"""
|
||
result = self.run_conversation(message)
|
||
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)
|