feat: 实现 Agent 自主学习 — 从历史执行中优化工具选择

- 新增 AgentLearningPattern 模型和 agent_learning_service 服务
- 执行前注入历史学习模式到 system prompt 作为工具选择建议
- 执行后自动提取工具序列并保存/累计学习模式
- 支持任务分类(11类)、关键词提取、工具序列合并、有效性评分
- 集成到 AgentRuntime.run()/run_stream()，支持 bare chat 和 Agent 模式

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

backend/app/agent_runtime/core.py

@@ -24,6 +24,12 @@ from app.agent_runtime.context import AgentContext
 from app.agent_runtime.memory import AgentMemory
 from app.agent_runtime.tool_manager import AgentToolManager
 from app.core.exceptions import WorkflowExecutionError
+from app.services.agent_learning_service import (
+    extract_pattern_from_result,
+    format_pattern_hint,
+    load_relevant_patterns,
+    save_learning_pattern,
+)
 
 logger = logging.getLogger(__name__)
 
@@ -98,6 +104,8 @@ class AgentRuntime:
         self.on_llm_call = on_llm_call
         self._memory_context_loaded = False
         self._llm_invocations = 0
+        # 自主学习作用域：bare 聊天用 "bare"，Agent 用 "agent"
+        self._learning_scope_kind = "bare" if "bare" in str(_mem_scope) else "agent"
 
         # 预算回调：供 WorkflowEngine 注入，使 Agent 内部计数计入工作流预算
         # 返回 True 表示预算充足；返回 False 或抛出异常表示超限
@@ -221,6 +229,13 @@ class AgentRuntime:
                 ))
                 # 保存记忆
                 await self.memory.save_context(user_input, final_text, self.context.messages)
+                # 保存学习模式
+                if self.config.memory.learning_enabled:
+                    await self._save_learning_pattern(
+                        user_input, steps, success=True,
+                        iterations_used=self.context.iteration,
+                        tool_calls_made=self.context.tool_calls_made,
+                    )
                 return AgentResult(
                     success=True,
                     content=final_text,
@@ -318,6 +333,13 @@ class AgentRuntime:
 
         logger.warning("Agent 达到最大迭代次数 (%s)", max_iter)
         await self.memory.save_context(user_input, last_content or "（已达最大迭代次数）", self.context.messages)
+        # 保存学习模式（即便截断，工具调用模式仍有参考价值）
+        if self.config.memory.learning_enabled:
+            await self._save_learning_pattern(
+                user_input, steps, success=True,
+                iterations_used=self.context.iteration,
+                tool_calls_made=self.context.tool_calls_made,
+            )
         if last_content:
             steps.append(AgentStep(
                 iteration=self.context.iteration,
@@ -446,6 +468,13 @@ class AgentRuntime:
                     "session_id": self.context.session_id,
                 }
                 await self.memory.save_context(user_input, final_text, self.context.messages)
+                # 保存学习模式
+                if self.config.memory.learning_enabled:
+                    await self._save_learning_pattern(
+                        user_input, steps, success=True,
+                        iterations_used=self.context.iteration,
+                        tool_calls_made=self.context.tool_calls_made,
+                    )
                 return
 
             # 有工具调用 → 先记录 assistant 消息
@@ -557,6 +586,13 @@ class AgentRuntime:
 
         logger.warning("Agent 达到最大迭代次数 (%s)", max_iter)
         await self.memory.save_context(user_input, last_content or "（已达最大迭代次数）", self.context.messages)
+        # 保存学习模式（即便截断，工具调用模式仍有参考价值）
+        if self.config.memory.learning_enabled:
+            await self._save_learning_pattern(
+                user_input, steps, success=True,
+                iterations_used=self.context.iteration,
+                tool_calls_made=self.context.tool_calls_made,
+            )
         yield {
             "type": "final",
             "content": last_content or "已达最大迭代次数，但模型未返回最终回答。",
@@ -570,14 +606,62 @@ class AgentRuntime:
     async def _inject_memory_context(self, query: str = "") -> None:
         """加载长期记忆并注入 system prompt。"""
         mem_text = await self.memory.initialize(query=query)
+        enriched = self.config.system_prompt.rstrip("\n")
+
         if mem_text:
-            enriched = (
-                self.config.system_prompt.rstrip("\n")
-                + "\n\n"
-                + mem_text
+            enriched += "\n\n" + mem_text
+
+        # 注入学习模式提示（历史工具使用建议）
+        if self.config.memory.learning_enabled:
+            pattern_hint = await self._inject_learning_patterns(query)
+            if pattern_hint:
+                enriched += "\n\n" + pattern_hint
+
+        self.context.set_system_prompt(enriched)
+        logger.info("Agent 已注入长期记忆上下文")
+
+    async def _inject_learning_patterns(self, query: str) -> str:
+        """查询学习模式，返回格式化的提示文本。"""
+        from app.core.database import SessionLocal
+        db = None
+        try:
+            db = SessionLocal()
+            patterns = load_relevant_patterns(
+                db, self._learning_scope_kind, self.memory.scope_id, query
             )
-            self.context.set_system_prompt(enriched)
-            logger.info("Agent 已注入长期记忆上下文")
+            return format_pattern_hint(patterns, query)
+        except Exception as e:
+            logger.warning("加载学习模式失败: %s", e)
+            return ""
+        finally:
+            if db:
+                db.close()
+
+    async def _save_learning_pattern(
+        self, query: str, steps: List[AgentStep],
+        success: bool, iterations_used: int, tool_calls_made: int,
+    ) -> None:
+        """从执行结果中提取模式并保存。"""
+        from app.core.database import SessionLocal
+        db = None
+        try:
+            db = SessionLocal()
+            pattern_data = extract_pattern_from_result(
+                query=query,
+                steps=steps,
+                success=success,
+                iterations_used=iterations_used,
+                tool_calls_made=tool_calls_made,
+            )
+            save_learning_pattern(
+                db, self._learning_scope_kind,
+                self.memory.scope_id, pattern_data,
+            )
+        except Exception as e:
+            logger.warning("保存学习模式失败: %s", e)
+        finally:
+            if db:
+                db.close()
 
     @staticmethod
     def _extract_tool_calls(response: Any) -> List[Dict[str, Any]]: