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https://github.com/val1813/kwcode.git
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8fd5275b2e
P1: Token budget tracking (llm/llama_backend.py) - Auto-count input/output tokens per LLM call - BudgetExceededError when over limit - OpenAI API uses real usage data, Ollama estimates P2: Guardrails (tools/executor.py) - Block dangerous commands (rm -rf, git push --force, drop database, etc.) - Protect sensitive files (.env, credentials.json, id_rsa) - Confine writes to project_root P3: Execution observability (core/execution_trace.py) - Structured trace per task (steps, timing, tokens, success) - Human-readable summary() output P4: Session continuity (memory/session_md.py) - Auto-save SESSION.md on exit (recent task summaries) - Auto-load on next startup into Gate memory_context - Based on Claude Code 4-Layer Memory + Augment "Session-End Spec Update" Also fixed: apply_patch method accidentally dropped during executor.py rewrite. 311 tests passing. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
92 lines
2.8 KiB
Python
92 lines
2.8 KiB
Python
"""
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Execution Trace: 结构化执行可观测性。
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记录每个任务的完整执行链路（每步耗时、token、结果），任务完成后输出摘要。
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"""
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import logging
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import time
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from dataclasses import dataclass, field
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from typing import Optional
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logger = logging.getLogger(__name__)
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@dataclass
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class TraceStep:
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"""单步执行记录。"""
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name: str
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started_at: float = 0.0
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ended_at: float = 0.0
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success: bool = True
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detail: str = ""
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@property
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def elapsed_ms(self) -> float:
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return (self.ended_at - self.started_at) * 1000
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@dataclass
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class ExecutionTrace:
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"""一次任务的完整执行轨迹。"""
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task_input: str = ""
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steps: list[TraceStep] = field(default_factory=list)
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total_input_tokens: int = 0
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total_output_tokens: int = 0
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llm_calls: int = 0
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retries: int = 0
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success: bool = False
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started_at: float = 0.0
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ended_at: float = 0.0
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@property
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def elapsed_s(self) -> float:
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return self.ended_at - self.started_at
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def begin(self, task_input: str):
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"""开始追踪。"""
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self.task_input = task_input[:100]
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self.started_at = time.time()
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def step_start(self, name: str) -> TraceStep:
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"""记录一步开始。"""
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step = TraceStep(name=name, started_at=time.time())
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self.steps.append(step)
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return step
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def step_end(self, step: TraceStep, success: bool = True, detail: str = ""):
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"""记录一步结束。"""
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step.ended_at = time.time()
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step.success = success
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step.detail = detail[:100]
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def finish(self, success: bool, llm_usage: Optional[dict] = None):
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"""结束追踪，记录最终状态。"""
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self.ended_at = time.time()
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self.success = success
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if llm_usage:
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self.total_input_tokens = llm_usage.get("input_tokens", 0)
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self.total_output_tokens = llm_usage.get("output_tokens", 0)
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self.llm_calls = llm_usage.get("call_count", 0)
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def summary(self) -> str:
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"""生成人类可读的执行摘要。"""
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lines = []
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status = "成功" if self.success else "失败"
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lines.append(f"[{status}] {self.task_input} ({self.elapsed_s:.1f}s)")
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if self.llm_calls > 0:
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total_tokens = self.total_input_tokens + self.total_output_tokens
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lines.append(f" LLM: {self.llm_calls}次调用, {total_tokens} tokens")
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if self.retries > 0:
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lines.append(f" 重试: {self.retries}次")
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# 每步耗时
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for step in self.steps:
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icon = "+" if step.success else "x"
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lines.append(f" {icon} {step.name}: {step.elapsed_ms:.0f}ms")
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if step.detail and not step.success:
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lines.append(f" {step.detail}")
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return "\n".join(lines)
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