mirror of
https://github.com/NousResearch/hermes-agent.git
synced 2026-04-25 00:51:20 +00:00
refactor: enhance API interaction and message handling in AIAgent
- Introduced new methods in run_agent.py for building API keyword arguments and normalizing assistant messages from API responses. - Added functionality for compressing conversation context and managing session state in SQLite. - Improved tool call execution handling, including enhanced logging and error management. - Updated path handling in multiple platform files to utilize pathlib for better compatibility and readability.
This commit is contained in:
teknium1
parent
7ee7221af1
commit
ecb430effe
6 changed files with 351 additions and 401 deletions
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@ -17,7 +17,8 @@ from typing import Dict, List, Optional, Any, Callable, Awaitable, Tuple
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from enum import Enum
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import sys
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sys.path.insert(0, str(__file__).rsplit("/", 3)[0])
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from pathlib import Path as _Path
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sys.path.insert(0, str(_Path(__file__).resolve().parents[2]))
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from gateway.config import Platform, PlatformConfig
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from gateway.session import SessionSource
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@ -27,7 +27,8 @@ except ImportError:
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commands = None
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import sys
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sys.path.insert(0, str(__file__).rsplit("/", 3)[0])
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from pathlib import Path as _Path
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sys.path.insert(0, str(_Path(__file__).resolve().parents[2]))
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from gateway.config import Platform, PlatformConfig
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from gateway.platforms.base import (
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@ -24,7 +24,8 @@ except ImportError:
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AsyncWebClient = Any
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import sys
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sys.path.insert(0, str(__file__).rsplit("/", 3)[0])
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from pathlib import Path as _Path
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sys.path.insert(0, str(_Path(__file__).resolve().parents[2]))
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from gateway.config import Platform, PlatformConfig
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from gateway.platforms.base import (
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@ -30,7 +30,8 @@ except ImportError:
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ContextTypes = Any
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import sys
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sys.path.insert(0, str(__file__).rsplit("/", 3)[0])
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from pathlib import Path as _Path
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sys.path.insert(0, str(_Path(__file__).resolve().parents[2]))
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from gateway.config import Platform, PlatformConfig
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from gateway.platforms.base import (
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@ -25,7 +25,7 @@ from typing import Dict, List, Optional, Any
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logger = logging.getLogger(__name__)
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import sys
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sys.path.insert(0, str(__file__).rsplit("/", 3)[0])
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sys.path.insert(0, str(Path(__file__).resolve().parents[2]))
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from gateway.config import Platform, PlatformConfig
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from gateway.platforms.base import (
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738
run_agent.py
738
run_agent.py
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@ -2284,7 +2284,337 @@ class AIAgent:
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self._cached_system_prompt = None
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if self._memory_store:
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self._memory_store.load_from_disk()
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def _build_api_kwargs(self, api_messages: list) -> dict:
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"""Build the keyword arguments dict for the chat completions API call."""
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provider_preferences = {}
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if self.providers_allowed:
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provider_preferences["only"] = self.providers_allowed
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if self.providers_ignored:
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provider_preferences["ignore"] = self.providers_ignored
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if self.providers_order:
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provider_preferences["order"] = self.providers_order
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if self.provider_sort:
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provider_preferences["sort"] = self.provider_sort
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api_kwargs = {
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"model": self.model,
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"messages": api_messages,
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"tools": self.tools if self.tools else None,
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"timeout": 600.0,
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}
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if self.max_tokens is not None:
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api_kwargs["max_tokens"] = self.max_tokens
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extra_body = {}
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if provider_preferences:
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extra_body["provider"] = provider_preferences
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if "openrouter" in self.base_url.lower():
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if self.reasoning_config is not None:
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extra_body["reasoning"] = self.reasoning_config
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else:
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extra_body["reasoning"] = {
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"enabled": True,
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"effort": "xhigh"
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}
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if extra_body:
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api_kwargs["extra_body"] = extra_body
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return api_kwargs
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def _build_assistant_message(self, assistant_message, finish_reason: str) -> dict:
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"""Build a normalized assistant message dict from an API response message.
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Handles reasoning extraction, reasoning_details, and optional tool_calls
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so both the tool-call path and the final-response path share one builder.
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"""
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reasoning_text = self._extract_reasoning(assistant_message)
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if reasoning_text and self.verbose_logging:
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preview = reasoning_text[:100] + "..." if len(reasoning_text) > 100 else reasoning_text
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logging.debug(f"Captured reasoning ({len(reasoning_text)} chars): {preview}")
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msg = {
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"role": "assistant",
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"content": assistant_message.content or "",
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"reasoning": reasoning_text,
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"finish_reason": finish_reason,
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}
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if hasattr(assistant_message, 'reasoning_details') and assistant_message.reasoning_details:
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msg["reasoning_details"] = [
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{"type": d.get("type"), "text": d.get("text"), "signature": d.get("signature")}
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for d in assistant_message.reasoning_details
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if isinstance(d, dict)
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]
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if assistant_message.tool_calls:
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msg["tool_calls"] = [
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{
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"id": tool_call.id,
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"type": tool_call.type,
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"function": {
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"name": tool_call.function.name,
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"arguments": tool_call.function.arguments
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}
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}
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for tool_call in assistant_message.tool_calls
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]
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return msg
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def _compress_context(self, messages: list, system_message: str, *, approx_tokens: int = None) -> tuple:
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"""Compress conversation context and split the session in SQLite.
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Returns:
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(compressed_messages, new_system_prompt) tuple
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"""
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compressed = self.context_compressor.compress(messages, current_tokens=approx_tokens)
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todo_snapshot = self._todo_store.format_for_injection()
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if todo_snapshot:
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compressed.append({"role": "user", "content": todo_snapshot})
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self._invalidate_system_prompt()
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new_system_prompt = self._build_system_prompt(system_message)
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self._cached_system_prompt = new_system_prompt
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if self._session_db:
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try:
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self._session_db.end_session(self.session_id, "compression")
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old_session_id = self.session_id
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self.session_id = f"{datetime.now().strftime('%Y%m%d_%H%M%S')}_{uuid.uuid4().hex[:6]}"
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self._session_db.create_session(
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session_id=self.session_id,
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source=self.platform or "cli",
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model=self.model,
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parent_session_id=old_session_id,
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)
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self._session_db.update_system_prompt(self.session_id, new_system_prompt)
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except Exception as e:
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logger.debug("Session DB compression split failed: %s", e)
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return compressed, new_system_prompt
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def _execute_tool_calls(self, assistant_message, messages: list, effective_task_id: str) -> None:
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"""Execute tool calls from the assistant message and append results to messages."""
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for i, tool_call in enumerate(assistant_message.tool_calls, 1):
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function_name = tool_call.function.name
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try:
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function_args = json.loads(tool_call.function.arguments)
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except json.JSONDecodeError as e:
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logging.warning(f"Unexpected JSON error after validation: {e}")
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function_args = {}
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if not self.quiet_mode:
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args_str = json.dumps(function_args, ensure_ascii=False)
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args_preview = args_str[:self.log_prefix_chars] + "..." if len(args_str) > self.log_prefix_chars else args_str
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print(f" 📞 Tool {i}: {function_name}({list(function_args.keys())}) - {args_preview}")
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if self.tool_progress_callback:
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try:
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preview = _build_tool_preview(function_name, function_args)
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self.tool_progress_callback(function_name, preview)
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except Exception as cb_err:
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logging.debug(f"Tool progress callback error: {cb_err}")
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tool_start_time = time.time()
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if function_name == "todo":
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from tools.todo_tool import todo_tool as _todo_tool
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function_result = _todo_tool(
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todos=function_args.get("todos"),
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merge=function_args.get("merge", False),
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store=self._todo_store,
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)
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tool_duration = time.time() - tool_start_time
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if self.quiet_mode:
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print(f" {self._get_cute_tool_message('todo', function_args, tool_duration)}")
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elif function_name == "session_search" and self._session_db:
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from tools.session_search_tool import session_search as _session_search
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function_result = _session_search(
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query=function_args.get("query", ""),
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role_filter=function_args.get("role_filter"),
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limit=function_args.get("limit", 3),
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db=self._session_db,
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)
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tool_duration = time.time() - tool_start_time
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if self.quiet_mode:
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print(f" {self._get_cute_tool_message('session_search', function_args, tool_duration)}")
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elif function_name == "memory":
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from tools.memory_tool import memory_tool as _memory_tool
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function_result = _memory_tool(
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action=function_args.get("action"),
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target=function_args.get("target", "memory"),
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content=function_args.get("content"),
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old_text=function_args.get("old_text"),
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store=self._memory_store,
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)
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tool_duration = time.time() - tool_start_time
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if self.quiet_mode:
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print(f" {self._get_cute_tool_message('memory', function_args, tool_duration)}")
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elif function_name == "clarify":
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from tools.clarify_tool import clarify_tool as _clarify_tool
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function_result = _clarify_tool(
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question=function_args.get("question", ""),
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choices=function_args.get("choices"),
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callback=self.clarify_callback,
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)
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tool_duration = time.time() - tool_start_time
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if self.quiet_mode:
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print(f" {self._get_cute_tool_message('clarify', function_args, tool_duration)}")
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elif function_name == "delegate_task":
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from tools.delegate_tool import delegate_task as _delegate_task
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tasks_arg = function_args.get("tasks")
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if tasks_arg and isinstance(tasks_arg, list):
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spinner_label = f"🔀 delegating {len(tasks_arg)} tasks"
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else:
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goal_preview = (function_args.get("goal") or "")[:30]
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spinner_label = f"🔀 {goal_preview}" if goal_preview else "🔀 delegating"
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spinner = None
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if self.quiet_mode:
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face = random.choice(KawaiiSpinner.KAWAII_WAITING)
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spinner = KawaiiSpinner(f"{face} {spinner_label}", spinner_type='dots')
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spinner.start()
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self._delegate_spinner = spinner
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try:
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function_result = _delegate_task(
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goal=function_args.get("goal"),
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context=function_args.get("context"),
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toolsets=function_args.get("toolsets"),
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tasks=tasks_arg,
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model=function_args.get("model"),
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max_iterations=function_args.get("max_iterations"),
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parent_agent=self,
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)
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finally:
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self._delegate_spinner = None
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tool_duration = time.time() - tool_start_time
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cute_msg = self._get_cute_tool_message('delegate_task', function_args, tool_duration)
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if spinner:
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spinner.stop(cute_msg)
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elif self.quiet_mode:
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print(f" {cute_msg}")
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elif self.quiet_mode:
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face = random.choice(KawaiiSpinner.KAWAII_WAITING)
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tool_emoji_map = {
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'web_search': '🔍', 'web_extract': '📄', 'web_crawl': '🕸️',
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'terminal': '💻', 'process': '⚙️',
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'read_file': '📖', 'write_file': '✍️', 'patch': '🔧', 'search_files': '🔎',
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'browser_navigate': '🌐', 'browser_snapshot': '📸',
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'browser_click': '👆', 'browser_type': '⌨️',
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'browser_scroll': '📜', 'browser_back': '◀️',
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'browser_press': '⌨️', 'browser_close': '🚪',
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'browser_get_images': '🖼️', 'browser_vision': '👁️',
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'image_generate': '🎨', 'text_to_speech': '🔊',
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'vision_analyze': '👁️', 'mixture_of_agents': '🧠',
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'skills_list': '📚', 'skill_view': '📚',
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'schedule_cronjob': '⏰', 'list_cronjobs': '⏰', 'remove_cronjob': '⏰',
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'send_message': '📨', 'todo': '📋', 'memory': '🧠', 'session_search': '🔍',
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'clarify': '❓', 'execute_code': '🐍', 'delegate_task': '🔀',
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}
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emoji = tool_emoji_map.get(function_name, '⚡')
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preview = _build_tool_preview(function_name, function_args) or function_name
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if len(preview) > 30:
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preview = preview[:27] + "..."
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spinner = KawaiiSpinner(f"{face} {emoji} {preview}", spinner_type='dots')
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spinner.start()
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try:
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function_result = handle_function_call(function_name, function_args, effective_task_id)
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finally:
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tool_duration = time.time() - tool_start_time
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cute_msg = self._get_cute_tool_message(function_name, function_args, tool_duration)
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spinner.stop(cute_msg)
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else:
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function_result = handle_function_call(function_name, function_args, effective_task_id)
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tool_duration = time.time() - tool_start_time
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result_preview = function_result[:200] if len(function_result) > 200 else function_result
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if self.verbose_logging:
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logging.debug(f"Tool {function_name} completed in {tool_duration:.2f}s")
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logging.debug(f"Tool result preview: {result_preview}...")
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messages.append({
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"role": "tool",
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"content": function_result,
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"tool_call_id": tool_call.id
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})
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if not self.quiet_mode:
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response_preview = function_result[:self.log_prefix_chars] + "..." if len(function_result) > self.log_prefix_chars else function_result
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print(f" ✅ Tool {i} completed in {tool_duration:.2f}s - {response_preview}")
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if self._interrupt_requested and i < len(assistant_message.tool_calls):
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remaining = len(assistant_message.tool_calls) - i
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print(f"{self.log_prefix}⚡ Interrupt: skipping {remaining} remaining tool call(s)")
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for skipped_tc in assistant_message.tool_calls[i:]:
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messages.append({
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"role": "tool",
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"content": "[Tool execution skipped - user sent a new message]",
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"tool_call_id": skipped_tc.id
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})
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break
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if self.tool_delay > 0 and i < len(assistant_message.tool_calls):
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time.sleep(self.tool_delay)
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def _handle_max_iterations(self, messages: list, api_call_count: int) -> str:
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"""Request a summary when max iterations are reached. Returns the final response text."""
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print(f"⚠️ Reached maximum iterations ({self.max_iterations}). Requesting summary...")
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summary_request = (
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"You've reached the maximum number of tool-calling iterations allowed. "
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"Please provide a final response summarizing what you've found and accomplished so far, "
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"without calling any more tools."
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)
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messages.append({"role": "user", "content": summary_request})
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try:
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api_messages = messages.copy()
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if self.ephemeral_system_prompt:
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api_messages = [{"role": "system", "content": self.ephemeral_system_prompt}] + api_messages
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summary_extra_body = {}
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if "openrouter" in self.base_url.lower():
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if self.reasoning_config is not None:
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summary_extra_body["reasoning"] = self.reasoning_config
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else:
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summary_extra_body["reasoning"] = {
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"enabled": True,
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"effort": "xhigh"
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}
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summary_kwargs = {
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"model": self.model,
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"messages": api_messages,
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}
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if self.max_tokens is not None:
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summary_kwargs["max_tokens"] = self.max_tokens
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if summary_extra_body:
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summary_kwargs["extra_body"] = summary_extra_body
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summary_response = self.client.chat.completions.create(**summary_kwargs)
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if summary_response.choices and summary_response.choices[0].message.content:
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final_response = summary_response.choices[0].message.content
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if "<think>" in final_response:
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final_response = re.sub(r'<think>.*?</think>\s*', '', final_response, flags=re.DOTALL).strip()
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messages.append({"role": "assistant", "content": final_response})
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else:
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final_response = "I reached the iteration limit and couldn't generate a summary."
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except Exception as e:
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logging.warning(f"Failed to get summary response: {e}")
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final_response = f"I reached the maximum iterations ({self.max_iterations}) but couldn't summarize. Error: {str(e)}"
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return final_response
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def run_conversation(
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self,
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user_message: str,
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|
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@ -2443,50 +2773,7 @@ class AIAgent:
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while retry_count <= max_retries:
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try:
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# Build OpenRouter provider preferences if specified
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provider_preferences = {}
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if self.providers_allowed:
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provider_preferences["only"] = self.providers_allowed
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if self.providers_ignored:
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provider_preferences["ignore"] = self.providers_ignored
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if self.providers_order:
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provider_preferences["order"] = self.providers_order
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if self.provider_sort:
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provider_preferences["sort"] = self.provider_sort
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# Make API call with tools - increased timeout for long responses
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api_kwargs = {
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"model": self.model,
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"messages": api_messages,
|
||||
"tools": self.tools if self.tools else None,
|
||||
"timeout": 600.0 # 10 minute timeout for very long responses
|
||||
}
|
||||
|
||||
# Add max_tokens if configured (overrides model default)
|
||||
if self.max_tokens is not None:
|
||||
api_kwargs["max_tokens"] = self.max_tokens
|
||||
|
||||
# Add extra_body for OpenRouter (provider preferences + reasoning)
|
||||
extra_body = {}
|
||||
|
||||
# Add provider preferences if specified
|
||||
if provider_preferences:
|
||||
extra_body["provider"] = provider_preferences
|
||||
|
||||
# Configure reasoning for OpenRouter
|
||||
# If reasoning_config is explicitly provided, use it (allows disabling/customizing)
|
||||
# Otherwise, default to xhigh effort for OpenRouter models
|
||||
if "openrouter" in self.base_url.lower():
|
||||
if self.reasoning_config is not None:
|
||||
extra_body["reasoning"] = self.reasoning_config
|
||||
else:
|
||||
extra_body["reasoning"] = {
|
||||
"enabled": True,
|
||||
"effort": "xhigh"
|
||||
}
|
||||
|
||||
if extra_body:
|
||||
api_kwargs["extra_body"] = extra_body
|
||||
api_kwargs = self._build_api_kwargs(api_messages)
|
||||
|
||||
if os.getenv("HERMES_DUMP_REQUESTS", "").strip().lower() in {"1", "true", "yes", "on"}:
|
||||
self._dump_api_request_debug(api_kwargs, reason="preflight")
|
||||
|
|
@ -2717,34 +3004,12 @@ class AIAgent:
|
|||
if is_context_length_error:
|
||||
print(f"{self.log_prefix}⚠️ Context length exceeded - attempting compression...")
|
||||
|
||||
# Try to compress and retry
|
||||
original_len = len(messages)
|
||||
messages = self.context_compressor.compress(messages, current_tokens=approx_tokens)
|
||||
messages, active_system_prompt = self._compress_context(
|
||||
messages, system_message, approx_tokens=approx_tokens
|
||||
)
|
||||
|
||||
if len(messages) < original_len:
|
||||
# Compression was possible -- re-inject todo state
|
||||
todo_snapshot = self._todo_store.format_for_injection()
|
||||
if todo_snapshot:
|
||||
messages.append({"role": "user", "content": todo_snapshot})
|
||||
# Rebuild system prompt with fresh date/time + memory
|
||||
self._invalidate_system_prompt()
|
||||
active_system_prompt = self._build_system_prompt(system_message)
|
||||
self._cached_system_prompt = active_system_prompt
|
||||
# Split session in SQLite (close old, open new with parent link)
|
||||
if self._session_db:
|
||||
try:
|
||||
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,
|
||||
)
|
||||
self._session_db.update_system_prompt(self.session_id, active_system_prompt)
|
||||
except Exception as e:
|
||||
logger.debug("Session DB compression split failed: %s", e)
|
||||
print(f"{self.log_prefix} 🗜️ Compressed {original_len} → {len(messages)} messages, retrying...")
|
||||
continue # Retry with compressed messages
|
||||
else:
|
||||
|
|
@ -2921,262 +3186,17 @@ class AIAgent:
|
|||
# Reset retry counter on successful JSON validation
|
||||
self._invalid_json_retries = 0
|
||||
|
||||
# Extract reasoning from response if available
|
||||
# OpenRouter can return reasoning in multiple formats:
|
||||
# 1. message.reasoning - direct reasoning field
|
||||
# 2. message.reasoning_content - alternative field (some providers)
|
||||
# 3. message.reasoning_details - array with {summary: "..."} objects
|
||||
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}")
|
||||
|
||||
# Build assistant message with tool calls
|
||||
# Content stays as-is; reasoning is stored separately and will be passed
|
||||
# to the API via reasoning_content field when preparing api_messages
|
||||
assistant_msg = {
|
||||
"role": "assistant",
|
||||
"content": assistant_message.content or "",
|
||||
"reasoning": reasoning_text,
|
||||
"finish_reason": finish_reason,
|
||||
"tool_calls": [
|
||||
{
|
||||
"id": tool_call.id,
|
||||
"type": tool_call.type,
|
||||
"function": {
|
||||
"name": tool_call.function.name,
|
||||
"arguments": tool_call.function.arguments
|
||||
}
|
||||
}
|
||||
for tool_call in assistant_message.tool_calls
|
||||
]
|
||||
}
|
||||
|
||||
# Store reasoning_details for multi-turn reasoning context (OpenRouter)
|
||||
if hasattr(assistant_message, 'reasoning_details') and assistant_message.reasoning_details:
|
||||
assistant_msg["reasoning_details"] = [
|
||||
{"type": d.get("type"), "text": d.get("text"), "signature": d.get("signature")}
|
||||
for d in assistant_message.reasoning_details
|
||||
if isinstance(d, dict)
|
||||
]
|
||||
assistant_msg = self._build_assistant_message(assistant_message, finish_reason)
|
||||
|
||||
messages.append(assistant_msg)
|
||||
|
||||
# Execute each tool call
|
||||
for i, tool_call in enumerate(assistant_message.tool_calls, 1):
|
||||
function_name = tool_call.function.name
|
||||
|
||||
# Parse arguments - should always succeed since we validated above
|
||||
try:
|
||||
function_args = json.loads(tool_call.function.arguments)
|
||||
except json.JSONDecodeError as e:
|
||||
# This shouldn't happen since we validate and retry above
|
||||
logging.warning(f"Unexpected JSON error after validation: {e}")
|
||||
function_args = {}
|
||||
|
||||
# Preview tool call - cleaner format for quiet mode
|
||||
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}")
|
||||
|
||||
# Fire progress callback if registered (for messaging platforms)
|
||||
if self.tool_progress_callback:
|
||||
try:
|
||||
# Build a short preview of the primary argument
|
||||
preview = _build_tool_preview(function_name, function_args)
|
||||
self.tool_progress_callback(function_name, preview)
|
||||
except Exception as cb_err:
|
||||
logging.debug(f"Tool progress callback error: {cb_err}")
|
||||
|
||||
tool_start_time = time.time()
|
||||
|
||||
# Todo tool -- handle directly (needs agent's TodoStore instance)
|
||||
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" {self._get_cute_tool_message('todo', function_args, tool_duration)}")
|
||||
# Session search -- handle directly (needs SessionDB instance)
|
||||
elif function_name == "session_search" and self._session_db:
|
||||
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,
|
||||
)
|
||||
tool_duration = time.time() - tool_start_time
|
||||
if self.quiet_mode:
|
||||
print(f" {self._get_cute_tool_message('session_search', function_args, tool_duration)}")
|
||||
# Memory tool -- handle directly (needs agent's MemoryStore instance)
|
||||
elif function_name == "memory":
|
||||
from tools.memory_tool import memory_tool as _memory_tool
|
||||
function_result = _memory_tool(
|
||||
action=function_args.get("action"),
|
||||
target=function_args.get("target", "memory"),
|
||||
content=function_args.get("content"),
|
||||
old_text=function_args.get("old_text"),
|
||||
store=self._memory_store,
|
||||
)
|
||||
tool_duration = time.time() - tool_start_time
|
||||
if self.quiet_mode:
|
||||
print(f" {self._get_cute_tool_message('memory', function_args, tool_duration)}")
|
||||
# Clarify tool -- delegates to platform-provided callback
|
||||
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" {self._get_cute_tool_message('clarify', function_args, tool_duration)}")
|
||||
# Delegate task -- spawn child agent(s) with isolated context
|
||||
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()
|
||||
# Store spinner on self so delegate_tool can update its text
|
||||
self._delegate_spinner = spinner
|
||||
try:
|
||||
function_result = _delegate_task(
|
||||
goal=function_args.get("goal"),
|
||||
context=function_args.get("context"),
|
||||
toolsets=function_args.get("toolsets"),
|
||||
tasks=tasks_arg,
|
||||
model=function_args.get("model"),
|
||||
max_iterations=function_args.get("max_iterations"),
|
||||
parent_agent=self,
|
||||
)
|
||||
finally:
|
||||
self._delegate_spinner = None
|
||||
tool_duration = time.time() - tool_start_time
|
||||
cute_msg = self._get_cute_tool_message('delegate_task', function_args, tool_duration)
|
||||
if spinner:
|
||||
spinner.stop(cute_msg)
|
||||
elif self.quiet_mode:
|
||||
print(f" {cute_msg}")
|
||||
# Execute other tools - with animated kawaii spinner in quiet mode
|
||||
# The face is "alive" while the tool works, then vanishes
|
||||
# and is replaced by the clean result line.
|
||||
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()
|
||||
try:
|
||||
function_result = handle_function_call(function_name, function_args, effective_task_id)
|
||||
finally:
|
||||
tool_duration = time.time() - tool_start_time
|
||||
cute_msg = self._get_cute_tool_message(function_name, function_args, tool_duration)
|
||||
spinner.stop(cute_msg)
|
||||
else:
|
||||
function_result = handle_function_call(function_name, function_args, effective_task_id)
|
||||
tool_duration = time.time() - tool_start_time
|
||||
|
||||
result_preview = function_result[:200] if len(function_result) > 200 else function_result
|
||||
|
||||
if self.verbose_logging:
|
||||
logging.debug(f"Tool {function_name} completed in {tool_duration:.2f}s")
|
||||
logging.debug(f"Tool result preview: {result_preview}...")
|
||||
|
||||
# Add tool result to conversation
|
||||
messages.append({
|
||||
"role": "tool",
|
||||
"content": function_result,
|
||||
"tool_call_id": tool_call.id
|
||||
})
|
||||
|
||||
# Preview tool response (only in non-quiet mode)
|
||||
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}")
|
||||
|
||||
# Check for interrupt between tool calls - skip remaining
|
||||
# tools so the agent can respond to the user immediately
|
||||
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)")
|
||||
# Add placeholder results for skipped tool calls so the
|
||||
# message sequence stays valid (assistant tool_calls need matching tool results)
|
||||
for skipped_tc in assistant_message.tool_calls[i:]:
|
||||
messages.append({
|
||||
"role": "tool",
|
||||
"content": "[Tool execution skipped - user sent a new message]",
|
||||
"tool_call_id": skipped_tc.id
|
||||
})
|
||||
break
|
||||
|
||||
# Delay between tool calls
|
||||
if self.tool_delay > 0 and i < len(assistant_message.tool_calls):
|
||||
time.sleep(self.tool_delay)
|
||||
self._execute_tool_calls(assistant_message, messages, effective_task_id)
|
||||
|
||||
# Check if context compression is needed before next API call
|
||||
# Uses actual token count from last API response
|
||||
if self.compression_enabled and self.context_compressor.should_compress():
|
||||
messages = self.context_compressor.compress(
|
||||
messages,
|
||||
current_tokens=self.context_compressor.last_prompt_tokens
|
||||
messages, active_system_prompt = self._compress_context(
|
||||
messages, system_message,
|
||||
approx_tokens=self.context_compressor.last_prompt_tokens
|
||||
)
|
||||
# Re-inject todo state after compression
|
||||
todo_snapshot = self._todo_store.format_for_injection()
|
||||
if todo_snapshot:
|
||||
messages.append({"role": "user", "content": todo_snapshot})
|
||||
# Rebuild system prompt with fresh date/time + memory
|
||||
self._invalidate_system_prompt()
|
||||
active_system_prompt = self._build_system_prompt(system_message)
|
||||
self._cached_system_prompt = active_system_prompt
|
||||
# Split session in SQLite (close old, open new with parent link)
|
||||
if self._session_db:
|
||||
try:
|
||||
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,
|
||||
)
|
||||
self._session_db.update_system_prompt(self.session_id, active_system_prompt)
|
||||
except Exception as e:
|
||||
logger.debug("Session DB compression split failed: %s", e)
|
||||
|
||||
# Save session log incrementally (so progress is visible even if interrupted)
|
||||
self._session_messages = messages
|
||||
|
|
@ -3242,28 +3262,7 @@ class AIAgent:
|
|||
if hasattr(self, '_empty_content_retries'):
|
||||
self._empty_content_retries = 0
|
||||
|
||||
# Extract reasoning from response if available
|
||||
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 final reasoning ({len(reasoning_text)} chars): {preview}")
|
||||
|
||||
# Build final assistant message
|
||||
final_msg = {
|
||||
"role": "assistant",
|
||||
"content": final_response,
|
||||
"reasoning": reasoning_text,
|
||||
"finish_reason": finish_reason,
|
||||
}
|
||||
|
||||
# Store reasoning_details for multi-turn reasoning context (OpenRouter)
|
||||
if hasattr(assistant_message, 'reasoning_details') and assistant_message.reasoning_details:
|
||||
final_msg["reasoning_details"] = [
|
||||
{"type": d.get("type"), "text": d.get("text"), "signature": d.get("signature")}
|
||||
for d in assistant_message.reasoning_details
|
||||
if isinstance(d, dict)
|
||||
]
|
||||
final_msg = self._build_assistant_message(assistant_message, finish_reason)
|
||||
|
||||
messages.append(final_msg)
|
||||
|
||||
|
|
@ -3289,61 +3288,8 @@ class AIAgent:
|
|||
final_response = f"I apologize, but I encountered repeated errors: {error_msg}"
|
||||
break
|
||||
|
||||
# Handle max iterations reached - ask model to summarize what it found
|
||||
if api_call_count >= self.max_iterations and final_response is None:
|
||||
print(f"⚠️ Reached maximum iterations ({self.max_iterations}). Requesting summary...")
|
||||
|
||||
# Inject a user message asking for a 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})
|
||||
|
||||
# Make one final API call WITHOUT tools to force a text response
|
||||
try:
|
||||
api_messages = messages.copy()
|
||||
if self.ephemeral_system_prompt:
|
||||
api_messages = [{"role": "system", "content": self.ephemeral_system_prompt}] + api_messages
|
||||
|
||||
# Build extra_body for summary call (same reasoning config as main loop)
|
||||
summary_extra_body = {}
|
||||
if "openrouter" in self.base_url.lower():
|
||||
if self.reasoning_config is not None:
|
||||
summary_extra_body["reasoning"] = self.reasoning_config
|
||||
else:
|
||||
summary_extra_body["reasoning"] = {
|
||||
"enabled": True,
|
||||
"effort": "xhigh"
|
||||
}
|
||||
|
||||
summary_kwargs = {
|
||||
"model": self.model,
|
||||
"messages": api_messages,
|
||||
# No tools parameter - forces text response
|
||||
}
|
||||
if self.max_tokens is not None:
|
||||
summary_kwargs["max_tokens"] = 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
|
||||
# Strip think blocks from final response
|
||||
if "<think>" in final_response:
|
||||
final_response = re.sub(r'<think>.*?</think>\s*', '', final_response, flags=re.DOTALL).strip()
|
||||
|
||||
# Add to messages for session continuity
|
||||
messages.append({"role": "assistant", "content": final_response})
|
||||
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)}"
|
||||
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
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue