feat(evolution): give review agent full context, add knowledge signal, polish UX

agent/evolution/config.py

@@ -14,7 +14,7 @@ from typing import Any
 # until release; enable via ``self_evolution_enabled``.
 DEFAULT_ENABLED = False
 DEFAULT_IDLE_MINUTES = 15
-DEFAULT_MIN_TURNS = 6
+DEFAULT_MIN_TURNS = 8
 # Max review steps for the isolated evolution agent. Kept small (not exposed as
 # config): the review is meant to be cheap and focused, not a long autonomous run.
 DEFAULT_MAX_STEPS = 12

agent/evolution/executor.py

@@ -175,6 +175,9 @@ _WATCH_SUBDIRS = ("MEMORY.md", "skills", "knowledge", "output")
 # Subpaths under memory/ to ignore: evolution's own bookkeeping + the nightly
 # dream diary, none of which count as a user-facing change signal.
 _MEMORY_IGNORE = (".evolution_backups", "dreams", "evolution")
+# Files the skill subsystem maintains automatically (the enable/disable index).
+# Not an evolution result, so a rewrite must not count as a change signal.
+_WATCH_IGNORE_NAMES = ("skills_config.json",)
 
 
 def _workspace_snapshot(workspace_dir) -> dict:
@@ -195,6 +198,8 @@ def _workspace_snapshot(workspace_dir) -> dict:
         for p in root.rglob("*"):
             if not p.is_file():
                 continue
+            if p.name in _WATCH_IGNORE_NAMES:
+                continue
             try:
                 st = p.stat()
                 snap[str(p.relative_to(ws))] = (st.st_mtime, st.st_size)
@@ -269,10 +274,8 @@ def run_evolution_for_session(
         new_messages = all_messages[done:]
         transcript = _build_transcript(new_messages)
         if not transcript.strip():
-            # Routine no-op: the per-minute scan hits every idle session, so keep
-            # this at debug to avoid spamming the log.
-            logger.debug(f"[Evolution] session={session_id}: no new messages, skip")
-            # Advance the cursor anyway so we don't re-scan the same tail.
+            # Routine no-op: the per-minute scan hits every idle session. Advance
+            # the cursor so we don't re-scan the same tail; no log (pure noise).
             agent._evo_done_msg_count = total_msgs
             return False
 
@@ -334,17 +337,23 @@ def run_evolution_for_session(
             str(workspace_dir),
         )
         review_agent = agent_bridge.create_agent(
-            system_prompt=EVOLUTION_SYSTEM_PROMPT,
+            system_prompt="",
             tools=review_tools,
             description="Self-evolution review agent",
             max_steps=cfg.max_steps,
             workspace_dir=str(workspace_dir),
             skill_manager=getattr(agent, "skill_manager", None),
             memory_manager=getattr(agent, "memory_manager", None),
-            enable_skills=False,
+            enable_skills=True,
+            runtime_info=getattr(agent, "runtime_info", None),
         )
         # Reuse the live model so it follows the user's configured model.
         review_agent.model = agent.model
+        # Inject the evolution task brief AFTER the full system prompt: the agent
+        # gets the full context (tools, workspace, user preferences, memory, time)
+        # AND its evolution-specific instructions on top, instead of one
+        # overwriting the other.
+        review_agent.extra_system_suffix = EVOLUTION_SYSTEM_PROMPT
 
         logger.info(
             f"[Evolution] backup {backup_id} ({_backup_n} files) → running review agent"

agent/evolution/prompts.py

@@ -7,7 +7,7 @@ language (instructed at the end of the prompt).
 
 Design goals (see ref/hermes-agent background_review for inspiration):
   - Default to doing NOTHING. Evolution is the exception, not the rule.
-  - Three signal types: memory, skill, unfinished task.
+  - Signal types: skill, unfinished task, memory, knowledge.
   - An explicit "do NOT capture" list to avoid self-poisoning over time.
   - Generic examples only — never bake in domain-specific business terms.
 """
@@ -72,12 +72,11 @@ them. When their signal is clear, act; do not be shy here.
    reply/decision, do NOTHING and stay [SILENT] — do not nag or ping the user.
    You only ever notify the user as a side effect of having actually done work.
 
-3. MEMORY — LAST resort, and you are only a SAFETY NET here, not the primary
-   writer. The main assistant already writes memory DURING the conversation, and
-   a nightly pass consolidates daily notes into long-term memory. Prefer fixing
-   a skill (above) over writing memory whenever the fact belongs in a skill.
-   Act ONLY on something the main assistant clearly MISSED that does not belong
-   in any skill.
+3. MEMORY — RARE, last resort. Default to writing NOTHING here. The main
+   assistant already writes memory during the chat, and a nightly pass plus
+   context-overflow saves are dedicated safety nets — so memory is almost always
+   already covered without you. Skip unless the main assistant clearly missed a
+   durable fact that belongs in no skill AND would visibly change future replies.
    - MEMORY.md is the curated long-term index, auto-loaded into EVERY future
      conversation. Treat it as precious: edit it in place to CORRECT a wrong
      fact, or append a new durable preference/decision/lesson — but do so
@@ -92,6 +91,12 @@ them. When their signal is clear, act; do not be shy here.
    - If it is already captured anywhere (check MEMORY.md AND the daily file
      first), do NOTHING.
 
+4. KNOWLEDGE — only if the conversation produced durable, reusable reference
+   knowledge on a topic (the kind worth looking up again) that the main
+   assistant did NOT already save to `knowledge/`. Add or update the relevant
+   file there. Like memory, this is the exception: skip routine Q&A, and if the
+   topic is already covered in `knowledge/`, do NOTHING rather than duplicate.
+
 # Do NOT capture (these poison future behavior)
 
 - Environment failures: missing binaries, unset credentials, uninstalled
@@ -140,9 +145,6 @@ def build_review_user_message(transcript: str, protected_skills: list = None) ->
     ``protected_skills`` lists skill names that must never be edited (built-in
     skills shipped with the product). Surfaced so the agent avoids them.
     """
-    from datetime import datetime
-    today = datetime.now().strftime("%Y-%m-%d")
-
     protected_note = ""
     if protected_skills:
         names = ", ".join(sorted(protected_skills))
@@ -152,12 +154,10 @@ def build_review_user_message(transcript: str, protected_skills: list = None) ->
         )
     return (
         "Here is the conversation transcript that just went idle. Review it per "
-        "your instructions and act on any clear signal. Prefer fixing a skill at "
-        "its source over writing memory whenever the fact belongs in a skill.\n"
-        f"Today is {today}. Only if a fact genuinely belongs in memory (and not "
-        f"in a skill): append one short bullet to the daily file "
-        f"`memory/{today}.md` for a new fact, or edit MEMORY.md in place to "
-        f"correct an existing wrong fact."
+        "your instructions. Acting is the exception: the main value is fixing or "
+        "creating a skill and finishing promised work. Memory and knowledge are "
+        "rare last resorts — stay [SILENT] unless there is a clear, durable signal "
+        "not already covered."
         f"{protected_note}\n"
         "<transcript>\n"
         f"{transcript}\n"

agent/protocol/agent.py

@@ -52,6 +52,11 @@ class Agent:
         self.workspace_dir = workspace_dir  # Workspace directory
         self.enable_skills = enable_skills  # Skills enabled flag
         self.runtime_info = runtime_info  # Runtime info for dynamic time update
+        # Optional extra instructions appended AFTER the rebuilt full system
+        # prompt. Used by the self-evolution review agent to add its task brief
+        # on top of the full context (tools, workspace, user preferences, time)
+        # so it both follows the user's preferences and knows its evolution job.
+        self.extra_system_suffix = None
         
         # Initialize skill manager
         self.skill_manager = None
@@ -120,15 +125,20 @@ class Agent:
             except Exception:
                 lang = "zh"
             builder = PromptBuilder(workspace_dir=self.workspace_dir or "", language=lang)
-            return builder.build(
+            full = builder.build(
                 tools=self.tools,
                 context_files=context_files,
                 skill_manager=self.skill_manager,
                 memory_manager=self.memory_manager,
                 runtime_info=self.runtime_info,
             )
+            if self.extra_system_suffix:
+                full = f"{full}\n\n{self.extra_system_suffix}"
+            return full
         except Exception as e:
             logger.warning(f"Failed to rebuild system prompt, using cached version: {e}")
+            if self.extra_system_suffix:
+                return f"{self.system_prompt}\n\n{self.extra_system_suffix}"
             return self.system_prompt
 
     def refresh_skills(self):

bridge/agent_initializer.py

@@ -524,6 +524,14 @@ class AgentInitializer:
                         logger.debug("[AgentInitializer] WebSearch skipped - no search provider configured")
                         continue
 
+                # Skip evolution_undo when self-evolution is disabled: with no
+                # evolution there is nothing to roll back, so the tool is dead weight.
+                if tool_name == "evolution_undo":
+                    from agent.evolution.config import get_evolution_config
+                    if not get_evolution_config().enabled:
+                        logger.debug("[AgentInitializer] evolution_undo skipped - self-evolution disabled")
+                        continue
+
                 # Special handling for EnvConfig tool
                 if tool_name == "env_config":
                     from agent.tools import EnvConfig

channel/web/chat.html

@@ -772,7 +772,7 @@
                                         </button>
                                         <button id="memory-tab-dreams" onclick="switchMemoryTab('dreams')"
                                                 class="memory-tab px-3 py-1.5 rounded-md text-xs font-medium cursor-pointer transition-colors duration-150">
-                                            <i class="fas fa-seedling mr-1.5"></i><span data-i18n="memory_tab_dreams">自我进化</span>
+                                            <i class="fas fa-seedling mr-1.5"></i><span data-i18n="memory_tab_dreams">自进化</span>
                                         </button>
                                     </div>
                                 </div>

channel/web/static/js/console.js

@@ -123,7 +123,7 @@ const I18N = {
         config_max_turns: '最大记忆轮次', config_max_turns_hint: '一问一答为一轮，超过后会智能压缩处理',
         config_max_steps: '最大执行步数', config_max_steps_hint: '单次对话中 Agent 最多调用工具的次数',
         config_enable_thinking: '深度思考', config_enable_thinking_hint: '是否启用深度思考模式',
-        config_self_evolution: '自我进化', config_self_evolution_hint: '会话空闲后自动复盘，沉淀记忆、优化技能、处理未完成事项',
+        config_self_evolution: '自进化', config_self_evolution_hint: '会话空闲后自动复盘，沉淀记忆、优化技能、处理未完成事项',
         evolution_badge: '自主学习',
         config_channel_type: '通道类型',
         config_provider: '模型厂商', config_model_name: '模型',
@@ -142,7 +142,7 @@ const I18N = {
         skills_section_title: '技能', skill_enable: '启用', skill_disable: '禁用',
         skill_toggle_error: '操作失败，请稍后再试',
         memory_title: '记忆管理', memory_desc: '查看 Agent 记忆文件和内容',
-        memory_tab_files: '记忆文件', memory_tab_dreams: '自我进化',
+        memory_tab_files: '记忆文件', memory_tab_dreams: '自进化',
         memory_loading: '加载记忆文件中...', memory_loading_desc: '记忆文件将显示在此处',
         memory_back: '返回列表',
         memory_col_name: '文件名', memory_col_type: '类型', memory_col_size: '大小', memory_col_updated: '更新时间',

config.py

@@ -254,7 +254,7 @@ available_setting = {
     # Self-evolution: review idle conversations to learn memory/skills. Flat keys.
     "self_evolution_enabled": False,        # master switch (off until release)
     "self_evolution_idle_minutes": 15,      # idle time before a session is reviewed
-    "self_evolution_min_turns": 6,          # min user turns (or context pressure) to trigger
+    "self_evolution_min_turns": 8,          # min user turns (or context pressure) to trigger
     "skill": {},  # Per-skill runtime config; nested keys flatten to SKILL_<NAME>_<KEY> env vars at startup
     "mcp_servers": [],  # MCP server list; each entry supports type "stdio" (local process) or "sse" (remote URL)
 }

docs/memory/self-evolution.mdx

@@ -18,7 +18,7 @@ Self-Evolution focuses on three things:
 | Goal | Description |
 | --- | --- |
 | **Consolidate memory** | Record important preferences, decisions, and facts from the conversation, filling in what the main chat may have missed |
-| **Improve skills** | When a skill shows a problem in use (such as a wrong setting or a missing step), fix the skill file directly instead of just noting it; create a new skill when one is genuinely needed |
+| **Improve skills** | ① When a skill shows a problem in use (such as a wrong setting or a missing step), fix the skill file directly; ② when a reusable workflow emerges, turn it into a new skill so it can be reused next time |
 | **Follow up on unfinished tasks** | Spot the to-dos left in a conversation and finish them when possible |
 
 Once a review is done, if it actually changed something, the Agent tells you in a single line what it just learned and what it adjusted, so you can decide whether to roll it back.

docs/zh/memory/self-evolution.mdx

@@ -1,5 +1,5 @@
 ---
-title: 自我进化
+title: 自进化
 description: Self-Evolution — 会话空闲后自动复盘，沉淀记忆、优化技能、处理未完成事项
 ---
 
@@ -7,19 +7,20 @@ description: Self-Evolution — 会话空闲后自动复盘，沉淀记忆、优
 
 ### 简介
 
-自我进化（Self-Evolution）让 Agent 不止于"完成单次任务"，而是能在与你的相处中持续成长。每段对话告一段落后，它会自动"回头复盘"一次：把值得记住的沉淀为长期记忆、把使用中暴露的问题修进技能、把没做完的事情接着推进。久而久之，Agent 会越来越懂你的偏好、越来越少重复犯错、越来越主动地把事情收尾，而这一切都在后台静默完成，只有真正做了事情时才会简短地告诉你。
+自进化（Self-Evolution）让 Agent 不止于"完成单次任务"，而是能在与你的相处中持续成长。每段对话告一段落后，它会自动"回头复盘"一次：把使用中暴露的问题修进技能、把没做完的事情接着推进，并把值得记住的沉淀进记忆与知识库。久而久之，Agent 会越来越懂你的偏好、越来越少重复犯错、越来越主动地把事情收尾，而这一切都在后台静默完成，只有真正做了事情时才会简短地告诉你。
 
-> 它与[梦境蒸馏](/zh/memory/deep-dream)互补：梦境蒸馏负责整理记忆本身，自我进化则在记忆之外，进一步优化技能、推进未完成的任务，让 Agent 的能力随使用不断打磨。
+> 它与[梦境蒸馏](/zh/memory/deep-dream)互补：梦境蒸馏负责整理记忆本身，自进化则在记忆之外，进一步优化技能、推进未完成的任务，让 Agent 的能力随使用不断打磨。
 
-### 三个目标
+### 几个目标
 
-自我进化围绕三件事工作：
+自进化围绕以下几件事工作，并以「优化技能、处理未完成事项」为主，「沉淀记忆、知识」作为主对话的查缺补漏：
 
 | 目标 | 说明 |
 | --- | --- |
-| **沉淀记忆** | 把对话中重要的偏好、决策、事实补记到记忆中，作为主对话的查缺补漏 |
-| **优化技能** | 当某个技能在使用中暴露出问题（如配置错误、步骤缺失），直接修正技能文件，而不只是记一笔；也可在需要时创建新技能 |
+| **优化技能** | ① 技能在使用中暴露问题（如配置错误、步骤缺失）时，直接修正技能文件；② 出现一套可复用的流程时，主动固化为新技能，下次直接调用 |
 | **处理未完成事项** | 识别对话中遗留的待办，在能完成时直接完成 |
+| **沉淀记忆** | 把对话中重要的偏好、决策、事实补记到记忆中，作为主对话的查缺补漏 |
+| **沉淀知识** | 把对话中产生的、值得日后查阅的可复用知识补充进知识库（主对话遗漏时） |
 
 复盘完成后，如果确实做了改动，Agent 会在对话中用一句话告诉你"刚刚自主学习了什么、调整了哪里"，方便你判断是否需要回滚。
 
@@ -27,7 +28,7 @@ description: Self-Evolution — 会话空闲后自动复盘，沉淀记忆、优
 
 ### 触发时机
 
-自我进化不是定时执行，而是在**一段对话自然结束、进入空闲后**才触发，避免打断正在进行的交流。需要同时满足：
+自进化不是定时执行，而是在**一段对话自然结束、进入空闲后**才触发，避免打断正在进行的交流。需要同时满足：
 
 - **对话已空闲** — 距离最后一次互动超过设定的空闲时长（默认 15 分钟）
 - **对话有足够内容** — 自上次进化以来累积了足够轮次，或上下文已接近容量上限
@@ -36,11 +37,11 @@ description: Self-Evolution — 会话空闲后自动复盘，沉淀记忆、优
 
 ### 相关配置
 
-自我进化默认关闭，可在 Web 控制台「配置 → Agent 配置」中通过开关启用（位于"深度思考"下方），也可在配置文件中调整：
+自进化默认关闭，可在 Web 控制台「配置 → Agent 配置」中通过开关启用（位于"深度思考"下方），也可在配置文件中调整：
 
 | 参数 | 说明 | 默认值 |
 | --- | --- | --- |
-| `self_evolution_enabled` | 是否启用自我进化 | `false` |
+| `self_evolution_enabled` | 是否启用自进化 | `false` |
 | `self_evolution_idle_minutes` | 对话空闲多久后触发（分钟） | `15` |
 | `self_evolution_min_turns` | 触发所需的最少对话轮次 | `6` |
 
@@ -50,7 +51,7 @@ description: Self-Evolution — 会话空闲后自动复盘，沉淀记忆、优
 
 ### 进化记录
 
-每次进化的过程和结果会按日期记录在 `memory/evolution/YYYY-MM-DD.md` 中，可在 Web 控制台的「记忆管理 → 自我进化」tab 中查看。该 tab 同时汇总了自我进化记录与梦境日记，方便统一回顾 Agent 的成长轨迹。
+每次进化的过程和结果会按日期记录在 `memory/evolution/YYYY-MM-DD.md` 中，可在 Web 控制台的「记忆管理 → 自进化」tab 中查看。该 tab 同时汇总了自进化记录与梦境日记，方便统一回顾 Agent 的成长轨迹。
 
 ### 如何回滚
 
@@ -58,7 +59,7 @@ description: Self-Evolution — 会话空闲后自动复盘，沉淀记忆、优
 
 ## 实现设计
 
-自我进化复用了系统已有的能力，保持轻量：
+自进化复用了系统已有的能力，保持轻量：
 
 - **隔离执行**：每次复盘都启动一个独立的、临时的复盘任务，使用与主对话相同的模型，但拥有受限的工具集（只能读上下文、改记忆与技能文件）。它不会污染主对话的上下文，也不会影响主对话的性能。
 - **基于备份的撤销**：进化前对相关文件做快照备份，撤销时按备份还原，因此每一次改动都可追溯、可逆。
@@ -66,7 +67,7 @@ description: Self-Evolution — 会话空闲后自动复盘，沉淀记忆、优
 
 ### 克制与安全
 
-自我进化的设计原则是"必要时执行，减少打扰"：
+自进化的设计原则是"必要时执行，减少打扰"：
 
 | 机制 | 说明 |
 | --- | --- |

tests/test_evolution.py

@@ -195,6 +195,55 @@ def scenario_silent():
     }
 
 
+def scenario_silent_qa():
+    """A normal knowledge Q&A -> nothing durable, should stay SILENT."""
+    return {
+        "name": "普通问答 (should stay SILENT)",
+        "goal": "none",
+        "turns": [
+            ("Python 里 list 和 tuple 有什么区别？",
+             "主要区别：list 可变、用 []；tuple 不可变、用 ()。tuple 更省内存、可作字典键。"),
+            ("那什么时候该用 tuple？", "当数据不应被修改、或要做字典键/集合元素时用 tuple。"),
+            ("懂了，谢谢", "不客气。"),
+        ],
+        "scripted": "[SILENT]",
+        "on_edit": None,
+        "expect_evolved": False,
+    }
+
+
+def scenario_silent_transient():
+    """User shares transient, non-durable info -> should stay SILENT."""
+    return {
+        "name": "临时信息 (should stay SILENT)",
+        "goal": "none",
+        "turns": [
+            ("帮我看下今天天气适合跑步吗，深圳", "深圳今天多云 26°C，傍晚湿度高，清晨或晚上跑步比较合适。"),
+            ("那我晚上去吧", "好的，记得补水。"),
+            ("行", "👍"),
+        ],
+        "scripted": "[SILENT]",
+        "on_edit": None,
+        "expect_evolved": False,
+    }
+
+
+def scenario_silent_advice():
+    """User asks for one-off advice, no reusable workflow -> should stay SILENT."""
+    return {
+        "name": "一次性建议 (should stay SILENT)",
+        "goal": "none",
+        "turns": [
+            ("给我起三个适合咖啡馆的名字", "可以考虑：① 拾光咖啡 ② 角落 Corner ③ 慢半拍。"),
+            ("第二个不错", "嗯，「角落 Corner」简洁好记。"),
+            ("就用这个了", "好的，祝开业顺利。"),
+        ],
+        "scripted": "[SILENT]",
+        "on_edit": None,
+        "expect_evolved": False,
+    }
+
+
 def scenario_memory_preference():
     """User states a durable working preference -> update MEMORY.md."""
     def edit(ws):
@@ -475,6 +524,9 @@ def scenario_unfinished_task():
 
 SCENARIOS = [
     scenario_silent,
+    scenario_silent_qa,
+    scenario_silent_transient,
+    scenario_silent_advice,
     scenario_memory_preference,
     scenario_memory_correction,
     scenario_skill_gap,
@@ -791,6 +843,12 @@ def run_real():
 
 
 if __name__ == "__main__":
+    if "--debug" in sys.argv:
+        import logging
+        from common.log import logger as _cow_logger
+        _cow_logger.setLevel(logging.DEBUG)
+        for _h in _cow_logger.handlers:
+            _h.setLevel(logging.DEBUG)
     if "--real" in sys.argv:
         run_real()
     else: