feat: 自进化经验记忆——任务成功后用模型把工具序列提炼成「操作配方」存入 agent_experience 表；下次相似任务（bigram 相似度检索 top2）自动注入 system prompt 参考，AI 越用越聪明（同类操作不再摸索）

mcp_agent/agent.py

@@ -135,7 +135,7 @@ class Agent:
     # ---------- 主循环（流式） ----------
     async def run_stream(self, prompt: str, serial: str = "",
                          history=None, on_delta=None, on_tool=None,
-                         should_stop=None):
+                         should_stop=None, extra_context=None):
         """流式执行一轮指令，返回最终完整文本。
 
         history：上一轮的 [{"role": "user"|"assistant", "content": 文本}] 列表，
@@ -143,6 +143,7 @@ class Agent:
         on_delta(text, kind)：content/reasoning 文本增量（实时推给前端）
         on_tool(step)：工具调用完成（实时显示 MCP 步骤）
         should_stop：可调用 fn() -> bool，每轮模型调用前检查（用户中断用）
+        extra_context：附加文本（经验记忆注入，放在 system prompt 末尾）
         """
         self.on_delta = on_delta
         self.on_tool = on_tool
@@ -150,6 +151,8 @@ class Agent:
         sys_txt = SYSTEM_PROMPT
         if target:
             sys_txt += f"\n\n本次默认目标设备 serial：{target}（未指定设备时用它）。"
+        if extra_context:
+            sys_txt += f"\n\n## 过往成功经验参考（同类任务，可参考其中的操作套路，但要根据当前界面灵活调整）\n{extra_context}"
         self.messages = [{"role": "system", "content": sys_txt}]
         for h in (history or []):
             if h.get("role") in ("user", "assistant") and h.get("content"):

web/agent_api.py

@@ -60,6 +60,101 @@ def _read_cfg():
     return {k: _meta_get(v) for k, v in _CFG_KEYS.items()}
 
 
+# ================== 经验记忆（自进化） ==================
+# agent_experience：任务成功后的操作配方，下次相似任务检索注入 system prompt。
+# 原始 SQLite（CREATE IF NOT EXISTS 幂等），不进模型层迁移。
+_EXP_TABLE = """
+CREATE TABLE IF NOT EXISTS agent_experience (
+  id INTEGER PRIMARY KEY AUTOINCREMENT,
+  task_prompt TEXT DEFAULT '',
+  recipe TEXT DEFAULT '',
+  tool_seq TEXT DEFAULT '',
+  hits INTEGER DEFAULT 0,
+  created_at VARCHAR(20) DEFAULT '')"""
+
+
+def _ensure_exp_table():
+    try:
+        db.session.execute(db.text(_EXP_TABLE))
+        db.session.commit()
+    except Exception:
+        pass
+
+
+def _bigrams(text):
+    """中文/英文文本 bigram 集合（无空格分词，粗粒度相似度）。"""
+    t = "".join(c for c in (text or "").lower() if c.isalnum() or "\u4e00" <= c <= "\u9fff")
+    return {t[i:i + 2] for i in range(len(t) - 1)}
+
+
+def _find_experiences(prompt, limit=2, threshold=0.10):
+    """按 bigram 重叠检索相似历史经验（prompt 与任务描述的字符相似度）。"""
+    try:
+        _ensure_exp_table()
+        rows = db.session.execute(db.text(
+            "SELECT task_prompt, recipe, hits FROM agent_experience "
+            "WHERE recipe != '' ORDER BY hits DESC, id DESC LIMIT 50")).fetchall()
+    except Exception:
+        return ""
+    if not rows:
+        return ""
+    cur = _bigrams(prompt)
+    if not cur:
+        return ""
+    scored = []
+    for task_prompt, recipe, hits in rows:
+        sim = len(cur & _bigrams(task_prompt)) / len(cur)
+        if sim >= threshold:
+            scored.append((sim, hits or 0, recipe))
+    scored.sort(key=lambda x: (-x[0], -x[1]))
+    parts = []
+    for sim, _hits, recipe in scored[:limit]:
+        parts.append(f"- {recipe[:600]}")
+    return "\n".join(parts)
+
+
+def _distill_experience(cfg, prompt, tool_seq):
+    """任务完成后用模型把操作序列提炼为可复用配方（失败静默，不阻塞）。"""
+    try:
+        import httpx
+        body = {
+            "model": cfg.get("model") or "deepseek-v4-flash-vision-exp",
+            "messages": [{"role": "user",
+                          "content": "以下是一次成功的手机自动化操作记录。请提炼成简洁的"
+                                     "「操作配方」（2-6 步，每步：目标 → 用哪个工具），"
+                                     "供下次同类任务参考。不要解释，直接输出配方。\n"
+                                     f"任务：{prompt[:300]}\n操作序列：{tool_seq[:800]}"}],
+            "max_tokens": 600,
+        }
+        headers = {"Authorization": f"Bearer {cfg.get('api_key', '')}",
+                   "Content-Type": "application/json"}
+        r = httpx.post(f"{(cfg.get('api_base') or 'https://api.deepseek.com').rstrip('/')}/chat/completions",
+                       json=body, headers=headers, timeout=60)
+        if r.status_code != 200:
+            return ""
+        j = r.json()
+        recipe = ((j.get("choices") or [{}])[0].get("message") or {}).get("content") or ""
+        return recipe.strip()[:1500]
+    except Exception as e:
+        _log.warning(f"经验提炼失败: {e}")
+        return ""
+
+
+def _save_experience(prompt, recipe, tool_seq):
+    try:
+        _ensure_exp_table()
+        from datetime import datetime
+        db.session.execute(db.text(
+            "INSERT INTO agent_experience(task_prompt, recipe, tool_seq, hits, created_at) "
+            "VALUES(:p, :r, :t, 0, :c)"),
+            {"p": prompt[:500], "r": recipe, "t": tool_seq[:1000],
+             "c": datetime.now().strftime("%Y-%m-%d %H:%M")})
+        db.session.commit()
+        _log.info("经验已保存（配方 %d 字符）", len(recipe))
+    except Exception as e:
+        _log.warning(f"经验保存失败: {e}")
+
+
 # ================== 配置 ==================
 @bp.route("/api/agent/config", methods=["GET"])
 @admin_required
@@ -193,12 +288,21 @@ def _agent_thread(run_id, prompt, serial, cfg):
         def on_delta(text, kind):
             q.put(("delta", {"text": text, "kind": kind}))
 
+        tool_seq = []   # 本轮工具序列（经验提炼用）
+
         def on_tool(step):
             rec = {"tool": step.get("tool"),
                    "args": str(step.get("args"))[:200]}
             if step.get("image"):
                 rec["image"] = _shrink_image(step["image"])
             q.put(("step", rec))
+            # 记录精简工具序列
+            try:
+                args = step.get("args") or {}
+                brief = {k: v for k, v in args.items() if k != "serial"}
+                tool_seq.append(f"{step.get('tool')}({str(brief)[:60]})")
+            except Exception:
+                pass
 
         agent = Agent()
         agent.s.api_base = cfg.get("api_base") or agent.s.api_base
@@ -211,13 +315,22 @@ def _agent_thread(run_id, prompt, serial, cfg):
         target = serial or cfg.get("default_serial") or ""
         stop_evt = _stop_events.get(run_id)
 
+        # 经验检索：相似历史任务的操作配方注入 system（自进化记忆）
+        exp_ctx = _find_experiences(prompt)
+        if exp_ctx:
+            _log.info("命中历史经验，注入参考配方")
+            q.put(("step", {"tool": "🧠 经验记忆",
+                            "args": f"命中 {exp_ctx.count(chr(10) + '- ')} 条同类历史经验，已注入参考",
+                            "image": None}))
+
         async def _execute():
             await agent._load_tools()
             return await agent.run_stream(prompt, target,
                                           history=history,
                                           on_delta=on_delta, on_tool=on_tool,
                                           should_stop=lambda: bool(
-                                              stop_evt and stop_evt.is_set()))
+                                              stop_evt and stop_evt.is_set()),
+                                          extra_context=exp_ctx)
 
         # 整体超时保护：卡死时结束，释放单实例
         answer = asyncio.run(asyncio.wait_for(_execute(), timeout=900))
@@ -230,6 +343,15 @@ def _agent_thread(run_id, prompt, serial, cfg):
             hist.append({"role": "assistant", "content": (answer or "")[:4000]})
             _run["history"] = hist[-24:]
         q.put(("done", {"answer": answer}))
+
+        # 自进化：成功后提炼操作配方存为经验（尽力而为，不阻塞/不影响结果）
+        if tool_seq:
+            try:
+                recipe = _distill_experience(cfg, prompt, " -> ".join(tool_seq))
+                if recipe and "配方" not in recipe[:50]:
+                    _save_experience(prompt, recipe, " -> ".join(tool_seq))
+            except Exception as e:
+                _log.warning(f"经验保存异常: {e}")
     except Exception as e:
         _log.warning(f"Agent 运行异常: {e}")
         with _lock: