mirror of
https://github.com/zhayujie/chatgpt-on-wechat.git
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844 lines
37 KiB
Python
844 lines
37 KiB
Python
"""
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Agent Stream Execution Module - Multi-turn reasoning based on tool-call
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Provides streaming output, event system, and complete tool-call loop
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"""
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import json
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import time
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from typing import List, Dict, Any, Optional, Callable
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from agent.protocol.models import LLMRequest, LLMModel
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from agent.tools.base_tool import BaseTool, ToolResult
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from common.log import logger
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class AgentStreamExecutor:
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"""
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Agent Stream Executor
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Handles multi-turn reasoning loop based on tool-call:
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1. LLM generates response (may include tool calls)
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2. Execute tools
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3. Return results to LLM
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4. Repeat until no more tool calls
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"""
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def __init__(
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self,
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agent, # Agent instance
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model: LLMModel,
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system_prompt: str,
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tools: List[BaseTool],
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max_turns: int = 50,
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on_event: Optional[Callable] = None,
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messages: Optional[List[Dict]] = None
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):
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"""
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Initialize stream executor
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Args:
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agent: Agent instance (for accessing context)
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model: LLM model
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system_prompt: System prompt
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tools: List of available tools
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max_turns: Maximum number of turns
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on_event: Event callback function
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messages: Optional existing message history (for persistent conversations)
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"""
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self.agent = agent
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self.model = model
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self.system_prompt = system_prompt
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# Convert tools list to dict
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self.tools = {tool.name: tool for tool in tools} if isinstance(tools, list) else tools
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self.max_turns = max_turns
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self.on_event = on_event
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# Message history - use provided messages or create new list
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self.messages = messages if messages is not None else []
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# Tool failure tracking for retry protection
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self.tool_failure_history = [] # List of (tool_name, args_hash, success) tuples
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# Track files to send (populated by read tool)
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self.files_to_send = [] # List of file metadata dicts
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def _emit_event(self, event_type: str, data: dict = None):
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"""Emit event"""
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if self.on_event:
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try:
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self.on_event({
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"type": event_type,
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"timestamp": time.time(),
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"data": data or {}
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})
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except Exception as e:
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logger.error(f"Event callback error: {e}")
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def _hash_args(self, args: dict) -> str:
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"""Generate a simple hash for tool arguments"""
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import hashlib
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# Sort keys for consistent hashing
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args_str = json.dumps(args, sort_keys=True, ensure_ascii=False)
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return hashlib.md5(args_str.encode()).hexdigest()[:8]
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def _check_consecutive_failures(self, tool_name: str, args: dict) -> tuple[bool, str, bool]:
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"""
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Check if tool has failed too many times consecutively
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Returns:
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(should_stop, reason, is_critical)
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- should_stop: Whether to stop tool execution
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- reason: Reason for stopping
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- is_critical: Whether to abort entire conversation (True for 8+ failures)
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"""
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args_hash = self._hash_args(args)
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# Count consecutive failures for same tool + args
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same_args_failures = 0
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for name, ahash, success in reversed(self.tool_failure_history):
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if name == tool_name and ahash == args_hash:
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if not success:
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same_args_failures += 1
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else:
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break # Stop at first success
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else:
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break # Different tool or args, stop counting
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if same_args_failures >= 3:
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return True, f"工具 '{tool_name}' 使用相同参数连续失败 {same_args_failures} 次，停止执行以防止无限循环", False
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# Count consecutive failures for same tool (any args)
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same_tool_failures = 0
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for name, ahash, success in reversed(self.tool_failure_history):
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if name == tool_name:
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if not success:
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same_tool_failures += 1
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else:
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break # Stop at first success
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else:
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break # Different tool, stop counting
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# Hard stop at 8 failures - abort with critical message
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if same_tool_failures >= 8:
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return True, f"抱歉，我没能完成这个任务。可能是我理解有误或者当前方法不太合适。\n\n建议你：\n• 换个方式描述需求试试\n• 把任务拆分成更小的步骤\n• 或者换个思路来解决", True
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# Warning at 6 failures
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if same_tool_failures >= 6:
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return True, f"工具 '{tool_name}' 连续失败 {same_tool_failures} 次（使用不同参数），停止执行以防止无限循环", False
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return False, "", False
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def _record_tool_result(self, tool_name: str, args: dict, success: bool):
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"""Record tool execution result for failure tracking"""
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args_hash = self._hash_args(args)
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self.tool_failure_history.append((tool_name, args_hash, success))
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# Keep only last 50 records to avoid memory bloat
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if len(self.tool_failure_history) > 50:
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self.tool_failure_history = self.tool_failure_history[-50:]
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def run_stream(self, user_message: str) -> str:
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"""
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Execute streaming reasoning loop
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Args:
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user_message: User message
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Returns:
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Final response text
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"""
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# Log user message with model info
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logger.info(f"{'='*50}")
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logger.info(f"🤖 Model: {self.model.model}")
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logger.info(f"👤 用户: {user_message}")
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logger.info(f"{'='*50}")
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# Add user message (Claude format - use content blocks for consistency)
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self.messages.append({
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"role": "user",
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"content": [
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{
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"type": "text",
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"text": user_message
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}
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]
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})
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self._emit_event("agent_start")
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final_response = ""
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turn = 0
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try:
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while turn < self.max_turns:
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turn += 1
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logger.info(f"第 {turn} 轮")
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self._emit_event("turn_start", {"turn": turn})
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# Check if memory flush is needed (before calling LLM)
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# 使用独立的 flush 阈值（50K tokens 或 20 轮）
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if self.agent.memory_manager and hasattr(self.agent, 'last_usage'):
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usage = self.agent.last_usage
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if usage and 'input_tokens' in usage:
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current_tokens = usage.get('input_tokens', 0)
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if self.agent.memory_manager.should_flush_memory(
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current_tokens=current_tokens
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):
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self._emit_event("memory_flush_start", {
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"current_tokens": current_tokens,
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"turn_count": self.agent.memory_manager.flush_manager.turn_count
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})
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# TODO: Execute memory flush in background
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# This would require async support
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logger.info(
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f"Memory flush recommended: tokens={current_tokens}, turns={self.agent.memory_manager.flush_manager.turn_count}")
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# Call LLM (enable retry_on_empty for better reliability)
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assistant_msg, tool_calls = self._call_llm_stream(retry_on_empty=True)
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final_response = assistant_msg
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# No tool calls, end loop
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if not tool_calls:
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# 检查是否返回了空响应
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if not assistant_msg:
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logger.warning(f"[Agent] LLM returned empty response after retry (no content and no tool calls)")
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logger.info(f"[Agent] This usually happens when LLM thinks the task is complete after tool execution")
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# 如果之前有工具调用，强制要求 LLM 生成文本回复
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if turn > 1:
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logger.info(f"[Agent] Requesting explicit response from LLM...")
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# 添加一条消息，明确要求回复用户
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self.messages.append({
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"role": "user",
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"content": [{
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"type": "text",
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"text": "请向用户说明刚才工具执行的结果或回答用户的问题。"
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}]
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})
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# 再调用一次 LLM
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assistant_msg, tool_calls = self._call_llm_stream(retry_on_empty=False)
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final_response = assistant_msg
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# 如果还是空，才使用 fallback
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if not assistant_msg and not tool_calls:
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logger.warning(f"[Agent] Still empty after explicit request")
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final_response = (
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"抱歉，我暂时无法生成回复。请尝试换一种方式描述你的需求，或稍后再试。"
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)
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logger.info(f"Generated fallback response for empty LLM output")
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else:
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# 第一轮就空回复，直接 fallback
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final_response = (
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"抱歉，我暂时无法生成回复。请尝试换一种方式描述你的需求，或稍后再试。"
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)
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logger.info(f"Generated fallback response for empty LLM output")
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else:
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logger.info(f"💭 {assistant_msg[:150]}{'...' if len(assistant_msg) > 150 else ''}")
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logger.info(f"✅ 完成 (无工具调用)")
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self._emit_event("turn_end", {
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"turn": turn,
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"has_tool_calls": False
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})
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break
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# Log tool calls with arguments
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tool_calls_str = []
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for tc in tool_calls:
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args_str = ', '.join([f"{k}={v}" for k, v in tc['arguments'].items()])
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if args_str:
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tool_calls_str.append(f"{tc['name']}({args_str})")
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else:
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tool_calls_str.append(tc['name'])
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logger.info(f"🔧 {', '.join(tool_calls_str)}")
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# Execute tools
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tool_results = []
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tool_result_blocks = []
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try:
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for tool_call in tool_calls:
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result = self._execute_tool(tool_call)
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tool_results.append(result)
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# Check if this is a file to send (from read tool)
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if result.get("status") == "success" and isinstance(result.get("result"), dict):
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result_data = result.get("result")
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if result_data.get("type") == "file_to_send":
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# Store file metadata for later sending
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self.files_to_send.append(result_data)
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logger.info(f"📎 检测到待发送文件: {result_data.get('file_name', result_data.get('path'))}")
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# Check for critical error - abort entire conversation
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if result.get("status") == "critical_error":
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logger.error(f"💥 检测到严重错误，终止对话")
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final_response = result.get('result', '任务执行失败')
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return final_response
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# Log tool result in compact format
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status_emoji = "✅" if result.get("status") == "success" else "❌"
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result_data = result.get('result', '')
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# Format result string with proper Chinese character support
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if isinstance(result_data, (dict, list)):
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result_str = json.dumps(result_data, ensure_ascii=False)
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else:
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result_str = str(result_data)
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logger.info(f" {status_emoji} {tool_call['name']} ({result.get('execution_time', 0):.2f}s): {result_str[:200]}{'...' if len(result_str) > 200 else ''}")
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# Build tool result block (Claude format)
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# Format content in a way that's easy for LLM to understand
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is_error = result.get("status") == "error"
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if is_error:
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# For errors, provide clear error message
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result_content = f"Error: {result.get('result', 'Unknown error')}"
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elif isinstance(result.get('result'), dict):
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# For dict results, use JSON format
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result_content = json.dumps(result.get('result'), ensure_ascii=False)
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elif isinstance(result.get('result'), str):
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# For string results, use directly
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result_content = result.get('result')
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else:
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# Fallback to full JSON
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result_content = json.dumps(result, ensure_ascii=False)
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tool_result_block = {
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"type": "tool_result",
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"tool_use_id": tool_call["id"],
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"content": result_content
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}
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# Add is_error field for Claude API (helps model understand failures)
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if is_error:
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tool_result_block["is_error"] = True
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tool_result_blocks.append(tool_result_block)
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finally:
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# CRITICAL: Always add tool_result to maintain message history integrity
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# Even if tool execution fails, we must add error results to match tool_use
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if tool_result_blocks:
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# Add tool results to message history as user message (Claude format)
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self.messages.append({
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"role": "user",
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"content": tool_result_blocks
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})
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elif tool_calls:
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# If we have tool_calls but no tool_result_blocks (unexpected error),
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# create error results for all tool calls to maintain message integrity
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logger.warning("⚠️ Tool execution interrupted, adding error results to maintain message history")
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emergency_blocks = []
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for tool_call in tool_calls:
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emergency_blocks.append({
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"type": "tool_result",
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"tool_use_id": tool_call["id"],
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"content": "Error: Tool execution was interrupted",
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"is_error": True
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})
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self.messages.append({
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"role": "user",
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"content": emergency_blocks
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})
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self._emit_event("turn_end", {
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"turn": turn,
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"has_tool_calls": True,
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"tool_count": len(tool_calls)
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})
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if turn >= self.max_turns:
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logger.warning(f"⚠️ 已达到最大轮数限制: {self.max_turns}")
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if not final_response:
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final_response = (
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"抱歉，我在处理你的请求时遇到了一些困难，尝试了多次仍未能完成。"
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"请尝试简化你的问题，或换一种方式描述。"
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)
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except Exception as e:
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logger.error(f"❌ Agent执行错误: {e}")
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self._emit_event("error", {"error": str(e)})
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raise
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finally:
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logger.info(f"🏁 完成({turn}轮)")
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self._emit_event("agent_end", {"final_response": final_response})
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# 每轮对话结束后增加计数（用户消息+AI回复=1轮）
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if self.agent.memory_manager:
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self.agent.memory_manager.increment_turn()
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return final_response
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def _call_llm_stream(self, retry_on_empty=True, retry_count=0, max_retries=3) -> tuple[str, List[Dict]]:
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"""
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Call LLM with streaming and automatic retry on errors
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Args:
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retry_on_empty: Whether to retry once if empty response is received
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retry_count: Current retry attempt (internal use)
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max_retries: Maximum number of retries for API errors
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Returns:
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(response_text, tool_calls)
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"""
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# Validate and fix message history first
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self._validate_and_fix_messages()
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# Trim messages if needed (using agent's context management)
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self._trim_messages()
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# Prepare messages
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messages = self._prepare_messages()
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# Debug: log message structure
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logger.debug(f"Sending {len(messages)} messages to LLM")
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for i, msg in enumerate(messages):
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role = msg.get("role", "unknown")
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content = msg.get("content", "")
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if isinstance(content, list):
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content_types = [c.get("type") for c in content if isinstance(c, dict)]
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logger.debug(f" Message {i}: role={role}, content_blocks={content_types}")
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else:
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logger.debug(f" Message {i}: role={role}, content_length={len(str(content))}")
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# Prepare tool definitions (OpenAI/Claude format)
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tools_schema = None
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if self.tools:
|
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tools_schema = []
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for tool in self.tools.values():
|
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tools_schema.append({
|
||
"name": tool.name,
|
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"description": tool.description,
|
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"input_schema": tool.params # Claude uses input_schema
|
||
})
|
||
|
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# Create request
|
||
request = LLMRequest(
|
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messages=messages,
|
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temperature=0,
|
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stream=True,
|
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tools=tools_schema,
|
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system=self.system_prompt # Pass system prompt separately for Claude API
|
||
)
|
||
|
||
self._emit_event("message_start", {"role": "assistant"})
|
||
|
||
# Streaming response
|
||
full_content = ""
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||
tool_calls_buffer = {} # {index: {id, name, arguments}}
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stop_reason = None # Track why the stream stopped
|
||
|
||
try:
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stream = self.model.call_stream(request)
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||
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||
for chunk in stream:
|
||
# Check for errors
|
||
if isinstance(chunk, dict) and chunk.get("error"):
|
||
# Extract error message from nested structure
|
||
error_data = chunk.get("error", {})
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||
if isinstance(error_data, dict):
|
||
error_msg = error_data.get("message", chunk.get("message", "Unknown error"))
|
||
error_code = error_data.get("code", "")
|
||
error_type = error_data.get("type", "")
|
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else:
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error_msg = chunk.get("message", str(error_data))
|
||
error_code = ""
|
||
error_type = ""
|
||
|
||
status_code = chunk.get("status_code", "N/A")
|
||
|
||
# Log error with all available information
|
||
logger.error(f"🔴 Stream API Error:")
|
||
logger.error(f" Message: {error_msg}")
|
||
logger.error(f" Status Code: {status_code}")
|
||
logger.error(f" Error Code: {error_code}")
|
||
logger.error(f" Error Type: {error_type}")
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||
logger.error(f" Full chunk: {chunk}")
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||
|
||
# Check if this is a context overflow error (keyword-based, works for all models)
|
||
# Don't rely on specific status codes as different providers use different codes
|
||
error_msg_lower = error_msg.lower()
|
||
is_overflow = any(keyword in error_msg_lower for keyword in [
|
||
'context length exceeded', 'maximum context length', 'prompt is too long',
|
||
'context overflow', 'context window', 'too large', 'exceeds model context',
|
||
'request_too_large', 'request exceeds the maximum size', 'tokens exceed'
|
||
])
|
||
|
||
if is_overflow:
|
||
# Mark as context overflow for special handling
|
||
raise Exception(f"[CONTEXT_OVERFLOW] {error_msg} (Status: {status_code})")
|
||
else:
|
||
# Raise exception with full error message for retry logic
|
||
raise Exception(f"{error_msg} (Status: {status_code}, Code: {error_code}, Type: {error_type})")
|
||
|
||
# Parse chunk
|
||
if isinstance(chunk, dict) and "choices" in chunk:
|
||
choice = chunk["choices"][0]
|
||
delta = choice.get("delta", {})
|
||
|
||
# Capture finish_reason if present
|
||
finish_reason = choice.get("finish_reason")
|
||
if finish_reason:
|
||
stop_reason = finish_reason
|
||
|
||
# Handle text content
|
||
if "content" in delta and delta["content"]:
|
||
content_delta = delta["content"]
|
||
full_content += content_delta
|
||
self._emit_event("message_update", {"delta": content_delta})
|
||
|
||
# Handle tool calls
|
||
if "tool_calls" in delta:
|
||
for tc_delta in delta["tool_calls"]:
|
||
index = tc_delta.get("index", 0)
|
||
|
||
if index not in tool_calls_buffer:
|
||
tool_calls_buffer[index] = {
|
||
"id": "",
|
||
"name": "",
|
||
"arguments": ""
|
||
}
|
||
|
||
if "id" in tc_delta:
|
||
tool_calls_buffer[index]["id"] = tc_delta["id"]
|
||
|
||
if "function" in tc_delta:
|
||
func = tc_delta["function"]
|
||
if "name" in func:
|
||
tool_calls_buffer[index]["name"] = func["name"]
|
||
if "arguments" in func:
|
||
tool_calls_buffer[index]["arguments"] += func["arguments"]
|
||
|
||
except Exception as e:
|
||
error_str = str(e)
|
||
error_str_lower = error_str.lower()
|
||
|
||
# Check if error is context overflow (non-retryable, needs session reset)
|
||
# Method 1: Check for special marker (set in stream error handling above)
|
||
is_context_overflow = '[context_overflow]' in error_str_lower
|
||
|
||
# Method 2: Fallback to keyword matching for non-stream errors
|
||
if not is_context_overflow:
|
||
is_context_overflow = any(keyword in error_str_lower for keyword in [
|
||
'context length exceeded', 'maximum context length', 'prompt is too long',
|
||
'context overflow', 'context window', 'too large', 'exceeds model context',
|
||
'request_too_large', 'request exceeds the maximum size'
|
||
])
|
||
|
||
# Check if error is message format error (incomplete tool_use/tool_result pairs)
|
||
# This happens when previous conversation had tool failures
|
||
is_message_format_error = any(keyword in error_str_lower for keyword in [
|
||
'tool_use', 'tool_result', 'without', 'immediately after',
|
||
'corresponding', 'must have', 'each'
|
||
]) and 'status: 400' in error_str_lower
|
||
|
||
if is_context_overflow or is_message_format_error:
|
||
error_type = "context overflow" if is_context_overflow else "message format error"
|
||
logger.error(f"💥 {error_type} detected: {e}")
|
||
# Clear message history to recover
|
||
logger.warning("🔄 Clearing conversation history to recover")
|
||
self.messages.clear()
|
||
# Raise special exception with user-friendly message
|
||
if is_context_overflow:
|
||
raise Exception(
|
||
"抱歉，对话历史过长导致上下文溢出。我已清空历史记录，请重新描述你的需求。"
|
||
)
|
||
else:
|
||
raise Exception(
|
||
"抱歉，之前的对话出现了问题。我已清空历史记录，请重新发送你的消息。"
|
||
)
|
||
|
||
# Check if error is retryable (timeout, connection, rate limit, server busy, etc.)
|
||
is_retryable = any(keyword in error_str_lower for keyword in [
|
||
'timeout', 'timed out', 'connection', 'network',
|
||
'rate limit', 'overloaded', 'unavailable', 'busy', 'retry',
|
||
'429', '500', '502', '503', '504', '512'
|
||
])
|
||
|
||
if is_retryable and retry_count < max_retries:
|
||
wait_time = (retry_count + 1) * 2 # Exponential backoff: 2s, 4s, 6s
|
||
logger.warning(f"⚠️ LLM API error (attempt {retry_count + 1}/{max_retries}): {e}")
|
||
logger.info(f"Retrying in {wait_time}s...")
|
||
time.sleep(wait_time)
|
||
return self._call_llm_stream(
|
||
retry_on_empty=retry_on_empty,
|
||
retry_count=retry_count + 1,
|
||
max_retries=max_retries
|
||
)
|
||
else:
|
||
if retry_count >= max_retries:
|
||
logger.error(f"❌ LLM API error after {max_retries} retries: {e}")
|
||
else:
|
||
logger.error(f"❌ LLM call error (non-retryable): {e}")
|
||
raise
|
||
|
||
# Parse tool calls
|
||
tool_calls = []
|
||
for idx in sorted(tool_calls_buffer.keys()):
|
||
tc = tool_calls_buffer[idx]
|
||
try:
|
||
arguments = json.loads(tc["arguments"]) if tc["arguments"] else {}
|
||
except json.JSONDecodeError as e:
|
||
args_preview = tc['arguments'][:200] if len(tc['arguments']) > 200 else tc['arguments']
|
||
logger.error(f"Failed to parse tool arguments for {tc['name']}")
|
||
logger.error(f"Arguments length: {len(tc['arguments'])} chars")
|
||
logger.error(f"Arguments preview: {args_preview}...")
|
||
logger.error(f"JSON decode error: {e}")
|
||
|
||
# Return a clear error message to the LLM instead of empty dict
|
||
# This helps the LLM understand what went wrong
|
||
tool_calls.append({
|
||
"id": tc["id"],
|
||
"name": tc["name"],
|
||
"arguments": {},
|
||
"_parse_error": f"Invalid JSON in tool arguments: {args_preview}... Error: {str(e)}. Tip: For large content, consider splitting into smaller chunks or using a different approach."
|
||
})
|
||
continue
|
||
|
||
tool_calls.append({
|
||
"id": tc["id"],
|
||
"name": tc["name"],
|
||
"arguments": arguments
|
||
})
|
||
|
||
# Check for empty response and retry once if enabled
|
||
if retry_on_empty and not full_content and not tool_calls:
|
||
logger.warning(f"⚠️ LLM returned empty response (stop_reason: {stop_reason}), retrying once...")
|
||
self._emit_event("message_end", {
|
||
"content": "",
|
||
"tool_calls": [],
|
||
"empty_retry": True,
|
||
"stop_reason": stop_reason
|
||
})
|
||
# Retry without retry flag to avoid infinite loop
|
||
return self._call_llm_stream(
|
||
retry_on_empty=False,
|
||
retry_count=retry_count,
|
||
max_retries=max_retries
|
||
)
|
||
|
||
# Add assistant message to history (Claude format uses content blocks)
|
||
assistant_msg = {"role": "assistant", "content": []}
|
||
|
||
# Add text content block if present
|
||
if full_content:
|
||
assistant_msg["content"].append({
|
||
"type": "text",
|
||
"text": full_content
|
||
})
|
||
|
||
# Add tool_use blocks if present
|
||
if tool_calls:
|
||
for tc in tool_calls:
|
||
assistant_msg["content"].append({
|
||
"type": "tool_use",
|
||
"id": tc["id"],
|
||
"name": tc["name"],
|
||
"input": tc["arguments"]
|
||
})
|
||
|
||
# Only append if content is not empty
|
||
if assistant_msg["content"]:
|
||
self.messages.append(assistant_msg)
|
||
|
||
self._emit_event("message_end", {
|
||
"content": full_content,
|
||
"tool_calls": tool_calls
|
||
})
|
||
|
||
return full_content, tool_calls
|
||
|
||
def _execute_tool(self, tool_call: Dict) -> Dict[str, Any]:
|
||
"""
|
||
Execute tool
|
||
|
||
Args:
|
||
tool_call: {"id": str, "name": str, "arguments": dict}
|
||
|
||
Returns:
|
||
Tool execution result
|
||
"""
|
||
tool_name = tool_call["name"]
|
||
tool_id = tool_call["id"]
|
||
arguments = tool_call["arguments"]
|
||
|
||
# Check if there was a JSON parse error
|
||
if "_parse_error" in tool_call:
|
||
parse_error = tool_call["_parse_error"]
|
||
logger.error(f"Skipping tool execution due to parse error: {parse_error}")
|
||
result = {
|
||
"status": "error",
|
||
"result": f"Failed to parse tool arguments. {parse_error}. Please ensure your tool call uses valid JSON format with all required parameters.",
|
||
"execution_time": 0
|
||
}
|
||
self._record_tool_result(tool_name, arguments, False)
|
||
return result
|
||
|
||
# Check for consecutive failures (retry protection)
|
||
should_stop, stop_reason, is_critical = self._check_consecutive_failures(tool_name, arguments)
|
||
if should_stop:
|
||
logger.error(f"🛑 {stop_reason}")
|
||
self._record_tool_result(tool_name, arguments, False)
|
||
|
||
if is_critical:
|
||
# Critical failure - abort entire conversation
|
||
result = {
|
||
"status": "critical_error",
|
||
"result": stop_reason,
|
||
"execution_time": 0
|
||
}
|
||
else:
|
||
# Normal failure - let LLM try different approach
|
||
result = {
|
||
"status": "error",
|
||
"result": f"{stop_reason}\n\n当前方法行不通，请尝试完全不同的方法或向用户询问更多信息。",
|
||
"execution_time": 0
|
||
}
|
||
return result
|
||
|
||
self._emit_event("tool_execution_start", {
|
||
"tool_call_id": tool_id,
|
||
"tool_name": tool_name,
|
||
"arguments": arguments
|
||
})
|
||
|
||
try:
|
||
tool = self.tools.get(tool_name)
|
||
if not tool:
|
||
raise ValueError(f"Tool '{tool_name}' not found")
|
||
|
||
# Set tool context
|
||
tool.model = self.model
|
||
tool.context = self.agent
|
||
|
||
# Execute tool
|
||
start_time = time.time()
|
||
result: ToolResult = tool.execute_tool(arguments)
|
||
execution_time = time.time() - start_time
|
||
|
||
result_dict = {
|
||
"status": result.status,
|
||
"result": result.result,
|
||
"execution_time": execution_time
|
||
}
|
||
|
||
# Record tool result for failure tracking
|
||
success = result.status == "success"
|
||
self._record_tool_result(tool_name, arguments, success)
|
||
|
||
# Auto-refresh skills after skill creation
|
||
if tool_name == "bash" and result.status == "success":
|
||
command = arguments.get("command", "")
|
||
if "init_skill.py" in command and self.agent.skill_manager:
|
||
logger.info("Detected skill creation, refreshing skills...")
|
||
self.agent.refresh_skills()
|
||
logger.info(f"Skills refreshed! Now have {len(self.agent.skill_manager.skills)} skills")
|
||
|
||
self._emit_event("tool_execution_end", {
|
||
"tool_call_id": tool_id,
|
||
"tool_name": tool_name,
|
||
**result_dict
|
||
})
|
||
|
||
return result_dict
|
||
|
||
except Exception as e:
|
||
logger.error(f"Tool execution error: {e}")
|
||
error_result = {
|
||
"status": "error",
|
||
"result": str(e),
|
||
"execution_time": 0
|
||
}
|
||
# Record failure
|
||
self._record_tool_result(tool_name, arguments, False)
|
||
|
||
self._emit_event("tool_execution_end", {
|
||
"tool_call_id": tool_id,
|
||
"tool_name": tool_name,
|
||
**error_result
|
||
})
|
||
return error_result
|
||
|
||
def _validate_and_fix_messages(self):
|
||
"""
|
||
Validate message history and fix incomplete tool_use/tool_result pairs.
|
||
Claude API requires each tool_use to have a corresponding tool_result immediately after.
|
||
"""
|
||
if not self.messages:
|
||
return
|
||
|
||
# Check last message for incomplete tool_use
|
||
if len(self.messages) > 0:
|
||
last_msg = self.messages[-1]
|
||
if last_msg.get("role") == "assistant":
|
||
# Check if assistant message has tool_use blocks
|
||
content = last_msg.get("content", [])
|
||
if isinstance(content, list):
|
||
has_tool_use = any(block.get("type") == "tool_use" for block in content)
|
||
if has_tool_use:
|
||
# This is incomplete - remove it
|
||
logger.warning(f"⚠️ Removing incomplete tool_use message from history")
|
||
self.messages.pop()
|
||
|
||
def _trim_messages(self):
|
||
"""
|
||
Trim message history to stay within context limits.
|
||
Uses agent's context management configuration.
|
||
"""
|
||
if not self.messages or not self.agent:
|
||
return
|
||
|
||
# Get context window from agent (based on model)
|
||
context_window = self.agent._get_model_context_window()
|
||
|
||
# Reserve 10% for response generation
|
||
reserve_tokens = int(context_window * 0.1)
|
||
max_tokens = context_window - reserve_tokens
|
||
|
||
# Estimate current tokens
|
||
current_tokens = sum(self.agent._estimate_message_tokens(msg) for msg in self.messages)
|
||
|
||
# Add system prompt tokens
|
||
system_tokens = self.agent._estimate_message_tokens({"role": "system", "content": self.system_prompt})
|
||
current_tokens += system_tokens
|
||
|
||
# If under limit, no need to trim
|
||
if current_tokens <= max_tokens:
|
||
return
|
||
|
||
# Keep messages from newest, accumulating tokens
|
||
available_tokens = max_tokens - system_tokens
|
||
kept_messages = []
|
||
accumulated_tokens = 0
|
||
|
||
for msg in reversed(self.messages):
|
||
msg_tokens = self.agent._estimate_message_tokens(msg)
|
||
if accumulated_tokens + msg_tokens <= available_tokens:
|
||
kept_messages.insert(0, msg)
|
||
accumulated_tokens += msg_tokens
|
||
else:
|
||
break
|
||
|
||
old_count = len(self.messages)
|
||
self.messages = kept_messages
|
||
new_count = len(self.messages)
|
||
|
||
if old_count > new_count:
|
||
logger.info(
|
||
f"Context trimmed: {old_count} -> {new_count} messages "
|
||
f"(~{current_tokens} -> ~{system_tokens + accumulated_tokens} tokens, "
|
||
f"limit: {max_tokens})"
|
||
)
|
||
|
||
def _prepare_messages(self) -> List[Dict[str, Any]]:
|
||
"""
|
||
Prepare messages to send to LLM
|
||
|
||
Note: For Claude API, system prompt should be passed separately via system parameter,
|
||
not as a message. The AgentLLMModel will handle this.
|
||
"""
|
||
# Don't add system message here - it will be handled separately by the LLM adapter
|
||
return self.messages |