From cc2cd414c97c0c9c5059458ba2b6e95e1f56e988 Mon Sep 17 00:00:00 2001 From: chiguyong Date: Thu, 11 Jun 2026 06:22:35 +0800 Subject: [PATCH] fix: resolve all code review issues from cross-validation MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 1. Critical: Add missing TaskResult import in plan_exec_engine.py 2. Critical: Fix ReWOOEngine param name (max_steps → max_plan_steps) 3. Major: Remove duplicate token counting in reflexion.py 4. Major: LLM audit failure now passes (trusts rule check) instead of failing 5. Major: Fix dict iteration with del using list() copy in lifecycle.py 6. Major: Fix Chinese content tokenization using regex split instead of space split 7. Minor: _is_positive_mention now checks all occurrences, not just the first --- src/agentkit/chat/skill_routing.py | 5 +++- src/agentkit/core/config_driven.py | 2 +- src/agentkit/core/plan_exec_engine.py | 2 +- src/agentkit/core/reflexion.py | 23 ----------------- src/agentkit/evolution/lifecycle.py | 2 +- src/agentkit/quality/alignment.py | 36 +++++++++++++++------------ 6 files changed, 27 insertions(+), 43 deletions(-) diff --git a/src/agentkit/chat/skill_routing.py b/src/agentkit/chat/skill_routing.py index 937a07a..3b6659d 100644 --- a/src/agentkit/chat/skill_routing.py +++ b/src/agentkit/chat/skill_routing.py @@ -281,7 +281,10 @@ class CostAwareRouter: try: # Extract capability-like keywords from content for matching # find_best_agent expects list[str] of required capabilities - content_words = [w for w in content.split() if len(w) > 2][:5] + # Support both space-separated (English) and punctuation-separated (Chinese) content + import re + tokens = re.split(r'[\s,，。！？、；：\n]+', content) + content_words = [t for t in tokens if len(t) > 1][:5] best_agent = self._org_context.find_best_agent(required_capabilities=content_words) if best_agent is not None: agent_name = best_agent if isinstance(best_agent, str) else getattr(best_agent, "name", str(best_agent)) diff --git a/src/agentkit/core/config_driven.py b/src/agentkit/core/config_driven.py index fa8c653..ca61122 100644 --- a/src/agentkit/core/config_driven.py +++ b/src/agentkit/core/config_driven.py @@ -700,7 +700,7 @@ class ConfigDrivenAgent(BaseAgent, EvolutionMixin): rewoo_engine = ReWOOEngine( llm_gateway=self._llm_gateway, - max_steps=self._skill_config.max_steps if self._skill_config else 5, + max_plan_steps=self._skill_config.max_steps if self._skill_config else 5, default_timeout=300.0, ) diff --git a/src/agentkit/core/plan_exec_engine.py b/src/agentkit/core/plan_exec_engine.py index 3d6e990..db380b7 100644 --- a/src/agentkit/core/plan_exec_engine.py +++ b/src/agentkit/core/plan_exec_engine.py @@ -23,7 +23,7 @@ from agentkit.core.exceptions import TaskCancelledError, TaskTimeoutError from agentkit.core.goal_planner import GoalPlanner from agentkit.core.plan_executor import PlanExecutor, PlanExecutionResult, StepExecutionResult from agentkit.core.plan_schema import ExecutionPlan, PlanStep, PlanStepStatus -from agentkit.core.protocol import CancellationToken, TaskMessage, TaskStatus +from agentkit.core.protocol import CancellationToken, TaskMessage, TaskResult, TaskStatus from agentkit.core.react import ReActEvent, ReActResult, ReActStep from agentkit.orchestrator.reflection import PipelineReflector, PipelineReplanner from agentkit.orchestrator.pipeline_schema import Pipeline, PipelineResult, ReflectionReport, StageResult, StageStatus diff --git a/src/agentkit/core/reflexion.py b/src/agentkit/core/reflexion.py index 2b26e54..100ecd2 100644 --- a/src/agentkit/core/reflexion.py +++ b/src/agentkit/core/reflexion.py @@ -237,7 +237,6 @@ class ReflexionEngine: agent_name=agent_name, task_type=task_type, ) - total_tokens += self._extract_usage_tokens(react_result) # Track best result if score > best_score: @@ -269,7 +268,6 @@ class ReflexionEngine: agent_name=agent_name, task_type=task_type, ) - total_tokens += self._extract_usage_tokens(react_result) if reflection_text is None: # 反思失败，返回当前最佳结果 @@ -672,27 +670,6 @@ class ReflexionEngine: logger.warning(f"Reflection LLM call failed, skipping reflection: {e}") return None - @staticmethod - def _extract_usage_tokens(result: ReActResult) -> int: - """从 LLM 响应中提取实际 token 用量，降级时估算 - - 尝试从 ReActResult 的 trajectory 中获取最后一步的 usage 信息。 - 如果不可用，基于输出长度估算。 - """ - # 尝试从 trajectory 中获取 usage - if result.trajectory: - last_step = result.trajectory[-1] - # ReActStep 可能携带 usage 信息 - usage = getattr(last_step, "usage", None) or getattr(last_step, "token_usage", None) - if usage and isinstance(usage, dict): - total = usage.get("total_tokens", 0) - if total > 0: - return total - - # 降级：基于输出长度估算（约 4 字符 = 1 token） - estimated = max(1, len(result.output) // 4) - return estimated - def _build_reflection_prompt( self, original_prompt: str | None, diff --git a/src/agentkit/evolution/lifecycle.py b/src/agentkit/evolution/lifecycle.py index 582d82b..e165068 100644 --- a/src/agentkit/evolution/lifecycle.py +++ b/src/agentkit/evolution/lifecycle.py @@ -409,7 +409,7 @@ class EvolutionMixin: self.pending_soul_updates[pattern].append(reflection) # 检查是否有同一类别累积 >= 3 次反思 - for category, reflections in self.pending_soul_updates.items(): + for category, reflections in list(self.pending_soul_updates.items()): if len(reflections) >= 3: # 触发 soul 更新 from agentkit.tools.memory_tool import MemoryTool diff --git a/src/agentkit/quality/alignment.py b/src/agentkit/quality/alignment.py index 86faddd..56fa347 100644 --- a/src/agentkit/quality/alignment.py +++ b/src/agentkit/quality/alignment.py @@ -156,23 +156,27 @@ class AlignmentGuard: """判断 keyword 在 content 中是否为肯定性提及（实际执行/输出） 如果 keyword 出现在否定语境中（如"我们不会存储X"），不算违规。 + 遍历所有出现位置，只要有一次肯定性提及即返回 True。 """ - # 找到 keyword 在 content 中的位置 - idx = content.find(keyword) - if idx == -1: - return False - - # 检查 keyword 前面是否有否定词 - prefix = content[max(0, idx - 20) : idx] - neg_prefixes = [ - "不会", "不能", "不要", "没有", "并未", "并未", "无法", - "won't", "don't", "not ", "never ", "no ", - ] - for neg in neg_prefixes: - if neg in prefix: + start = 0 + while True: + idx = content.find(keyword, start) + if idx == -1: return False - return True + # 检查 keyword 前面是否有否定词 + prefix = content[max(0, idx - 20) : idx] + neg_prefixes = [ + "不会", "不能", "不要", "没有", "并未", "并未", "无法", + "won't", "don't", "not ", "never ", "no ", + ] + is_negated = any(neg in prefix for neg in neg_prefixes) + + if not is_negated: + return True + + # 继续搜索下一个出现位置 + start = idx + len(keyword) @staticmethod def _extract_text(output: dict[str, Any]) -> str: @@ -223,8 +227,8 @@ class AlignmentGuard: except Exception as e: logger.warning(f"LLM audit failed: {e}") return AlignmentCheckResult( - passed=False, - violations=[f"LLM audit unavailable: {e}"], + passed=True, + violations=[f"LLM audit unavailable (delegated to rule check): {e}"], checked_by="rule", )