diff --git a/agent/prompt/builder.py b/agent/prompt/builder.py index f78d4eb..0042d72 100644 --- a/agent/prompt/builder.py +++ b/agent/prompt/builder.py @@ -354,7 +354,7 @@ def _build_workspace_section(workspace_dir: str, language: str) -> List[str]: "", "**路径使用规则** (非常重要):", "", - "- **工作空间内的文件**: 使用相对路径（如 `SOUL.md`、`memory/daily.md`）", + "- **工作空间内的文件**: 可以使用相对路径（如 `SOUL.md`、`MEMORY.md`）", "- **工作空间外的文件**: 必须使用绝对路径（如 `~/project/code.py`、`/etc/config`）", "- **不确定时**: 先用 `bash pwd` 确认当前目录，或用 `ls .` 查看当前位置", "", diff --git a/agent/protocol/agent_stream.py b/agent/protocol/agent_stream.py index 5633d53..d197023 100644 --- a/agent/protocol/agent_stream.py +++ b/agent/protocol/agent_stream.py @@ -102,7 +102,7 @@ class AgentStreamExecutor: try: while turn < self.max_turns: turn += 1 - logger.info(f"\n{'='*50} 第 {turn} 轮 {'='*50}") + logger.info(f"\n🔄 第 {turn} 轮") self._emit_event("turn_start", {"turn": turn}) # Check if memory flush is needed (before calling LLM) @@ -156,9 +156,15 @@ class AgentStreamExecutor: }) break - # Log tool calls in compact format - tool_names = [tc['name'] for tc in tool_calls] - logger.info(f"🔧 调用工具: {', '.join(tool_names)}") + # Log tool calls with arguments + tool_calls_str = [] + for tc in tool_calls: + args_str = ', '.join([f"{k}={v}" for k, v in tc['arguments'].items()]) + if args_str: + tool_calls_str.append(f"{tc['name']}({args_str})") + else: + tool_calls_str.append(tc['name']) + logger.info(f"🔧 {', '.join(tool_calls_str)}") # Execute tools tool_results = [] @@ -179,13 +185,33 @@ class AgentStreamExecutor: logger.info(f" {status_emoji} {tool_call['name']} ({result.get('execution_time', 0):.2f}s): {result_str[:200]}{'...' if len(result_str) > 200 else ''}") # Build tool result block (Claude format) - # Content should be a string representation of the result - result_content = json.dumps(result, ensure_ascii=False) if not isinstance(result, str) else result - tool_result_blocks.append({ + # Format content in a way that's easy for LLM to understand + is_error = result.get("status") == "error" + + if is_error: + # For errors, provide clear error message + result_content = f"Error: {result.get('result', 'Unknown error')}" + elif isinstance(result.get('result'), dict): + # For dict results, use JSON format + result_content = json.dumps(result.get('result'), ensure_ascii=False) + elif isinstance(result.get('result'), str): + # For string results, use directly + result_content = result.get('result') + else: + # Fallback to full JSON + result_content = json.dumps(result, ensure_ascii=False) + + tool_result_block = { "type": "tool_result", "tool_use_id": tool_call["id"], "content": result_content - }) + } + + # Add is_error field for Claude API (helps model understand failures) + if is_error: + tool_result_block["is_error"] = True + + tool_result_blocks.append(tool_result_block) # Add tool results to message history as user message (Claude format) self.messages.append({ @@ -201,6 +227,11 @@ class AgentStreamExecutor: if turn >= self.max_turns: logger.warning(f"⚠️ 已达到最大轮数限制: {self.max_turns}") + if not final_response: + final_response = ( + "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。" + "请尝试简化你的问题，或换一种方式描述。" + ) except Exception as e: logger.error(f"❌ Agent执行错误: {e}") @@ -208,14 +239,19 @@ class AgentStreamExecutor: raise finally: - logger.info(f"{'='*50} 完成({turn}轮) {'='*50}\n") + logger.info(f"🏁 完成({turn}轮)\n") self._emit_event("agent_end", {"final_response": final_response}) return final_response - def _call_llm_stream(self) -> tuple[str, List[Dict]]: + def _call_llm_stream(self, retry_on_empty=True, retry_count=0, max_retries=3) -> tuple[str, List[Dict]]: """ - Call LLM with streaming + Call LLM with streaming and automatic retry on errors + + Args: + retry_on_empty: Whether to retry once if empty response is received + retry_count: Current retry attempt (internal use) + max_retries: Maximum number of retries for API errors Returns: (response_text, tool_calls) @@ -309,8 +345,29 @@ class AgentStreamExecutor: tool_calls_buffer[index]["arguments"] += func["arguments"] except Exception as e: - logger.error(f"LLM call error: {e}") - raise + error_str = str(e).lower() + # Check if error is retryable (timeout, connection, rate limit, etc.) + is_retryable = any(keyword in error_str for keyword in [ + 'timeout', 'timed out', 'connection', 'network', + 'rate limit', 'overloaded', 'unavailable', '429', '500', '502', '503', '504' + ]) + + 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 = [] @@ -328,6 +385,21 @@ class AgentStreamExecutor: "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, retrying once...") + self._emit_event("message_end", { + "content": "", + "tool_calls": [], + "empty_retry": True + }) + # 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": []} diff --git a/bot/gemini/google_gemini_bot.py b/bot/gemini/google_gemini_bot.py index ca01a2d..e166c30 100644 --- a/bot/gemini/google_gemini_bot.py +++ b/bot/gemini/google_gemini_bot.py @@ -255,7 +255,7 @@ class GoogleGeminiBot(Bot): gemini_tools = self._convert_tools_to_gemini_rest_format(tools) if gemini_tools: payload["tools"] = gemini_tools - logger.info(f"[Gemini] Added {len(tools)} tools to request") + logger.debug(f"[Gemini] Added {len(tools)} tools to request") # Make REST API call base_url = f"{self.api_base}/v1beta" @@ -445,6 +445,9 @@ class GoogleGeminiBot(Bot): all_tool_calls = [] has_sent_tool_calls = False has_content = False # Track if any content was sent + chunk_count = 0 + last_finish_reason = None + last_safety_ratings = None for line in response.iter_lines(): if not line: @@ -461,6 +464,7 @@ class GoogleGeminiBot(Bot): try: chunk_data = json.loads(line) + chunk_count += 1 logger.debug(f"[Gemini] Stream chunk: {json.dumps(chunk_data, ensure_ascii=False)[:200]}") candidates = chunk_data.get("candidates", []) @@ -469,6 +473,13 @@ class GoogleGeminiBot(Bot): continue candidate = candidates[0] + + # 记录 finish_reason 和 safety_ratings + if "finishReason" in candidate: + last_finish_reason = candidate["finishReason"] + if "safetyRatings" in candidate: + last_safety_ratings = candidate["safetyRatings"] + content = candidate.get("content", {}) parts = content.get("parts", []) @@ -512,7 +523,7 @@ class GoogleGeminiBot(Bot): # Send tool calls if any were collected if all_tool_calls and not has_sent_tool_calls: - logger.info(f"[Gemini] Stream detected {len(all_tool_calls)} tool calls") + logger.debug(f"[Gemini] Stream detected {len(all_tool_calls)} tool calls") yield { "id": f"chatcmpl-{time.time()}", "object": "chat.completion.chunk", @@ -526,17 +537,17 @@ class GoogleGeminiBot(Bot): } has_sent_tool_calls = True - # Log summary - logger.info(f"[Gemini] Stream complete: has_content={has_content}, tool_calls={len(all_tool_calls)}") + # Log summary (only if there's something interesting) + if not has_content and not all_tool_calls: + logger.debug(f"[Gemini] Stream complete: has_content={has_content}, tool_calls={len(all_tool_calls)}") + elif all_tool_calls: + logger.debug(f"[Gemini] Stream complete: {len(all_tool_calls)} tool calls") + else: + logger.debug(f"[Gemini] Stream complete: text response") # 如果返回空响应，记录详细警告 if not has_content and not all_tool_calls: logger.warning(f"[Gemini] ⚠️ Empty response detected!") - logger.warning(f"[Gemini] Possible reasons:") - logger.warning(f" 1. Model couldn't generate response based on context") - logger.warning(f" 2. Content blocked by safety filters") - logger.warning(f" 3. All previous tool calls failed") - logger.warning(f" 4. API error not properly caught") # Final chunk yield {