feat(evolution): add self-evolution subsystem

Add a self-evolution subsystem that reviews idle conversations in an
isolated agent and durably learns from them — patching/creating skills,
finishing unfinished tasks, and backfilling missed memory.

- Trigger: background idle scan, fires when a session is idle >= N min AND
  (>= N turns OR context usage > 80%). In-memory cursor reviews only new
  messages so a session never re-learns old content.
- Isolated review agent: same model, restricted toolset, hard write-guard
  confining edits to the workspace (built-in skills are protected).
- Safety: file-level backup before edits + evolution_undo tool; notify the
  user ONLY when a workspace file actually changed (no-nag rule); capped
  concurrency.
- Records to memory/evolution/<date>.md, surfaced in the memory UI's
  renamed "Self-Evolution" tab (merged with dream diaries).
- Hide internal [SCHEDULED]/[EVOLUTION]/backup_id markers from chat history
  display (also fixes scheduler marker leakage) while keeping them in stored
  content for undo.
- Flat config: self_evolution_enabled (default off until release),
  self_evolution_idle_minutes (15), self_evolution_min_turns (6).
- Tests: tests/test_evolution.py (stub + real model modes, 7 scenarios).

agent/evolution/__init__.py

@@ -0,0 +1,19 @@
+"""
+Self-evolution subsystem for CowAgent.
+
+Runs a lightweight, isolated review pass after a conversation goes idle to
+decide whether anything is worth durably learning (memory / skill) or whether
+an unfinished task can be pushed forward. Conservative by design: most
+conversations should produce no change at all.
+
+Public entry points:
+    from agent.evolution import get_evolution_config
+    from agent.evolution.trigger import start_evolution_trigger, note_user_turn
+"""
+
+from agent.evolution.config import EvolutionConfig, get_evolution_config
+
+__all__ = [
+    "EvolutionConfig",
+    "get_evolution_config",
+]

agent/evolution/backup.py

@@ -0,0 +1,102 @@
+"""File backup / rollback support for self-evolution.
+
+Before the evolution agent edits MEMORY.md or a skill file, we snapshot the
+current state into ``memory/.evolution_backups/<backup_id>/`` so a later "undo"
+can restore it. File-level restore only — simple and reliable.
+"""
+
+from __future__ import annotations
+
+import json
+import shutil
+import time
+from datetime import datetime
+from pathlib import Path
+from typing import List, Optional
+
+from common.log import logger
+
+_BACKUP_DIRNAME = ".evolution_backups"
+_MANIFEST_NAME = "manifest.json"
+# Keep only the most recent N backups to bound disk usage.
+_MAX_BACKUPS = 10
+
+
+def _backups_root(workspace_dir: Path) -> Path:
+    return Path(workspace_dir) / "memory" / _BACKUP_DIRNAME
+
+
+def create_backup(workspace_dir: Path, files: List[Path]) -> Optional[str]:
+    """Snapshot ``files`` (those that exist) under a new backup id.
+
+    Returns the backup_id, or None when there is nothing to back up.
+    """
+    existing = [Path(f) for f in files if Path(f).exists()]
+    if not existing:
+        return None
+
+    backup_id = datetime.now().strftime("%Y%m%d-%H%M%S-") + str(int(time.time() * 1000) % 1000)
+    root = _backups_root(workspace_dir)
+    target = root / backup_id
+    try:
+        target.mkdir(parents=True, exist_ok=True)
+        ws = Path(workspace_dir)
+        manifest = []
+        for idx, src in enumerate(existing):
+            # Store under a flat index plus the relative path so restore knows
+            # where it came from, even for nested skill files.
+            try:
+                rel = str(src.relative_to(ws))
+            except ValueError:
+                rel = src.name
+            dst = target / f"{idx}.bak"
+            shutil.copy2(src, dst)
+            manifest.append({"rel": rel, "bak": f"{idx}.bak"})
+        (target / _MANIFEST_NAME).write_text(
+            json.dumps(manifest, ensure_ascii=False, indent=2), encoding="utf-8"
+        )
+        _prune_old_backups(root)
+        # Caller logs a combined backup+review line; keep this at debug.
+        logger.debug(f"[Evolution] Created backup {backup_id} ({len(manifest)} file(s))")
+        return backup_id
+    except Exception as e:
+        logger.warning(f"[Evolution] Failed to create backup: {e}")
+        return None
+
+
+def restore_backup(workspace_dir: Path, backup_id: str) -> bool:
+    """Restore all files captured under ``backup_id``. Returns success."""
+    if not backup_id:
+        return False
+    target = _backups_root(workspace_dir) / backup_id
+    manifest_path = target / _MANIFEST_NAME
+    if not manifest_path.exists():
+        logger.warning(f"[Evolution] Backup not found: {backup_id}")
+        return False
+    try:
+        manifest = json.loads(manifest_path.read_text(encoding="utf-8"))
+        ws = Path(workspace_dir)
+        for entry in manifest:
+            bak = target / entry["bak"]
+            dst = ws / entry["rel"]
+            if bak.exists():
+                dst.parent.mkdir(parents=True, exist_ok=True)
+                shutil.copy2(bak, dst)
+        logger.info(f"[Evolution] Restored backup {backup_id} ({len(manifest)} file(s))")
+        return True
+    except Exception as e:
+        logger.warning(f"[Evolution] Failed to restore backup {backup_id}: {e}")
+        return False
+
+
+def _prune_old_backups(root: Path) -> None:
+    """Drop the oldest backups beyond _MAX_BACKUPS (sorted by name = chronological)."""
+    try:
+        dirs = sorted(
+            [d for d in root.iterdir() if d.is_dir()],
+            key=lambda p: p.name,
+        )
+        for old in dirs[:-_MAX_BACKUPS]:
+            shutil.rmtree(old, ignore_errors=True)
+    except Exception as e:
+        logger.debug(f"[Evolution] Backup prune skipped: {e}")

agent/evolution/config.py

@@ -0,0 +1,76 @@
+"""Configuration for the self-evolution subsystem.
+
+Reads flat ``self_evolution_*`` keys from config.json. All fields have safe
+defaults so the feature degrades gracefully when keys are absent.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import Any
+
+
+# Defaults — conservative (see executor module docstring). Disabled by default
+# until release; enable via ``self_evolution_enabled``.
+DEFAULT_ENABLED = False
+DEFAULT_IDLE_MINUTES = 15
+DEFAULT_MIN_TURNS = 6
+# 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
+
+
+@dataclass
+class EvolutionConfig:
+    """Resolved self-evolution settings."""
+
+    enabled: bool = DEFAULT_ENABLED
+    idle_minutes: int = DEFAULT_IDLE_MINUTES
+    min_turns: int = DEFAULT_MIN_TURNS
+    max_steps: int = DEFAULT_MAX_STEPS
+
+    @property
+    def idle_seconds(self) -> int:
+        return max(60, self.idle_minutes * 60)
+
+
+def _as_bool(value: Any, fallback: bool) -> bool:
+    if isinstance(value, bool):
+        return value
+    if isinstance(value, str):
+        v = value.strip().lower()
+        if v in ("true", "1", "yes", "on"):
+            return True
+        if v in ("false", "0", "no", "off"):
+            return False
+    return fallback
+
+
+def _as_pos_int(value: Any, fallback: int) -> int:
+    try:
+        n = int(value)
+        return n if n > 0 else fallback
+    except (TypeError, ValueError):
+        return fallback
+
+
+def get_evolution_config() -> EvolutionConfig:
+    """Build EvolutionConfig from the live config.json ``self_evolution_*`` keys."""
+    try:
+        from config import conf
+        c = conf()
+    except Exception:
+        c = {}
+
+    def _get(key, default):
+        try:
+            return c.get(key, default)
+        except Exception:
+            return default
+
+    return EvolutionConfig(
+        enabled=_as_bool(_get("self_evolution_enabled", None), DEFAULT_ENABLED),
+        idle_minutes=_as_pos_int(_get("self_evolution_idle_minutes", None), DEFAULT_IDLE_MINUTES),
+        min_turns=_as_pos_int(_get("self_evolution_min_turns", None), DEFAULT_MIN_TURNS),
+        max_steps=DEFAULT_MAX_STEPS,
+    )

agent/evolution/executor.py

@@ -0,0 +1,449 @@
+"""Self-evolution executor.
+
+Runs an isolated review agent over an idle conversation's transcript and, if a
+clear signal is found, lets it edit memory / skills via a restricted toolset.
+Conservative by design: most runs return ``[SILENT]`` and change nothing.
+
+Flow:
+    1. Build a transcript from the session's new (since last pass) messages.
+    2. Snapshot MEMORY.md + daily file + editable skills (for undo) -> backup_id.
+    3. Run an isolated agent (same model, restricted tools, evolution prompt).
+    4. If output is [SILENT], or no workspace file actually changed -> done.
+    5. Otherwise -> record to the evolution log, inject an [EVOLUTION] note into
+       the user session (so the main agent can honor "undo"), and push the
+       summary to the user's channel.
+
+Reuses existing infrastructure (AgentBridge.create_agent, ToolManager,
+remember_scheduled_output, channel_factory) rather than introducing a fork.
+"""
+
+from __future__ import annotations
+
+import threading
+from datetime import datetime
+from pathlib import Path
+from typing import List, Optional
+
+from common.log import logger
+
+from agent.evolution.backup import create_backup
+from agent.evolution.config import get_evolution_config
+from agent.evolution.prompts import (
+    EVOLUTION_MARKER,
+    EVOLUTION_SYSTEM_PROMPT,
+    SILENT_TOKEN,
+    build_review_user_message,
+)
+from agent.evolution.record import append_session_evolution
+
+# Tools the isolated evolution agent is allowed to use. Everything else is
+# withheld so a review pass can only read context and edit memory/skill files.
+_ALLOWED_TOOLS = {"read", "write", "edit", "ls", "memory_search", "memory_get"}
+
+# Cap concurrent evolution passes so a burst of idle sessions can't spawn many
+# background model runs at once. Extra sessions simply wait for the next scan.
+_MAX_CONCURRENT = 2
+_running_lock = threading.Lock()
+_running_count = 0
+
+
+def _builtin_skill_names() -> set:
+    """Names of skills shipped with the product (project-root ``skills/``).
+
+    These are protected: the evolution agent must never edit them, even though
+    a same-named copy exists in the workspace at runtime. The project dir is the
+    authoritative list of what counts as built-in.
+    """
+    try:
+        # executor.py -> agent/evolution -> agent -> project root
+        project_root = Path(__file__).resolve().parents[2]
+        builtin_dir = project_root / "skills"
+        if not builtin_dir.is_dir():
+            return set()
+        names = set()
+        for entry in builtin_dir.iterdir():
+            if entry.is_dir() and not entry.name.startswith("."):
+                names.add(entry.name)
+        return names
+    except Exception:
+        return set()
+
+
+def _build_transcript(messages: List[dict], max_chars: int = 12000) -> str:
+    """Render the session messages into a compact text transcript."""
+    lines: List[str] = []
+    for msg in messages:
+        role = msg.get("role", "")
+        if role not in ("user", "assistant"):
+            continue
+        content = msg.get("content", "")
+        text = _extract_text(content)
+        if not text.strip():
+            continue
+        speaker = "User" if role == "user" else "Assistant"
+        lines.append(f"{speaker}: {text.strip()}")
+    transcript = "\n".join(lines)
+    # Keep the most RECENT context if oversized (tail is most relevant).
+    if len(transcript) > max_chars:
+        transcript = "...(earlier omitted)...\n" + transcript[-max_chars:]
+    return transcript
+
+
+def _extract_text(content) -> str:
+    if isinstance(content, str):
+        return content
+    if isinstance(content, list):
+        parts = []
+        for block in content:
+            if isinstance(block, dict) and block.get("type") == "text":
+                parts.append(block.get("text", ""))
+            elif isinstance(block, str):
+                parts.append(block)
+        return "\n".join(parts)
+    return ""
+
+
+def _select_tools(all_tools: list) -> list:
+    return [t for t in all_tools if getattr(t, "name", None) in _ALLOWED_TOOLS]
+
+
+# Tools whose writes must be confined to the workspace during evolution.
+_WRITE_TOOLS = {"write", "edit"}
+
+
+class _WorkspaceWriteGuard:
+    """Wraps a write/edit tool so it can ONLY write inside the workspace.
+
+    Hard engineering guard (not prompt-based): any write resolving outside the
+    workspace — e.g. the project's bundled ``skills/`` dir — is rejected. This
+    protects built-in skills regardless of what the model attempts.
+    """
+
+    def __init__(self, inner, workspace_dir: str):
+        self._inner = inner
+        self._ws = Path(workspace_dir).resolve()
+        # Mirror the attributes the agent runtime reads off a tool.
+        self.name = inner.name
+        self.description = inner.description
+        self.params = inner.params
+
+    def __getattr__(self, item):
+        return getattr(self._inner, item)
+
+    def execute_tool(self, params):
+        # The agent runtime calls execute_tool (not execute); route it through
+        # our guarded execute so the path checks always run.
+        try:
+            return self.execute(params)
+        except Exception as e:
+            logger.error(f"[Evolution] guarded tool error: {e}")
+            from agent.tools.base_tool import ToolResult
+            return ToolResult.fail(f"Error: {e}")
+
+    def execute(self, args):
+        path = (args.get("path") or "").strip()
+        if path:
+            try:
+                resolved = Path(self._inner._resolve_path(path)).resolve()
+                from agent.tools.base_tool import ToolResult
+                # Confine writes to the workspace. This protects the product's
+                # bundled skills (which live outside the workspace) from ever
+                # being modified, no matter what path the model attempts.
+                if self._ws not in resolved.parents and resolved != self._ws:
+                    return ToolResult.fail(
+                        "Error: evolution may only write inside the workspace; "
+                        f"path '{path}' is outside and was blocked."
+                    )
+            except Exception:
+                pass
+        return self._inner.execute(args)
+
+
+def _guard_tools(tools: list, workspace_dir: str) -> list:
+    """Wrap write/edit tools with the workspace guard; leave others as-is."""
+    guarded = []
+    for t in tools:
+        if getattr(t, "name", None) in _WRITE_TOOLS:
+            guarded.append(_WorkspaceWriteGuard(t, workspace_dir))
+        else:
+            guarded.append(t)
+    return guarded
+
+
+# Workspace subtrees worth watching for evolution-induced changes.
+_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")
+
+
+def _workspace_snapshot(workspace_dir) -> dict:
+    """Map relative path -> (mtime, size) for watched files. Cheap, no reads."""
+    ws = Path(workspace_dir)
+    snap: dict = {}
+    for name in _WATCH_SUBDIRS:
+        root = ws / name
+        if root.is_file():
+            try:
+                st = root.stat()
+                snap[name] = (st.st_mtime, st.st_size)
+            except OSError:
+                pass
+            continue
+        if not root.is_dir():
+            continue
+        for p in root.rglob("*"):
+            if not p.is_file():
+                continue
+            try:
+                st = p.stat()
+                snap[str(p.relative_to(ws))] = (st.st_mtime, st.st_size)
+            except OSError:
+                pass
+
+    # Watch the daily memory files (memory/*.md and per-user dailies) since
+    # evolution now records learnings there. Skip backups/dreams bookkeeping.
+    mem_dir = ws / "memory"
+    if mem_dir.is_dir():
+        for p in mem_dir.rglob("*.md"):
+            rel_parts = p.relative_to(mem_dir).parts
+            if rel_parts and rel_parts[0] in _MEMORY_IGNORE:
+                continue
+            try:
+                st = p.stat()
+                snap[str(p.relative_to(ws))] = (st.st_mtime, st.st_size)
+            except OSError:
+                pass
+    return snap
+
+
+def _workspace_changed(workspace_dir, pre: dict) -> bool:
+    """True if any watched file was added, removed, or modified since ``pre``."""
+    return _workspace_snapshot(workspace_dir) != pre
+
+
+def run_evolution_for_session(
+    agent_bridge,
+    session_id: str,
+    channel_type: str = "",
+    receiver: str = "",
+    user_id: Optional[str] = None,
+    idle_minutes: float = 0.0,
+) -> bool:
+    """Run one evolution pass for a session. Returns True if it changed anything.
+
+    Safe to call from a background thread. All failures are swallowed and
+    logged — evolution must never disrupt the main pipeline.
+    """
+    cfg = get_evolution_config()
+    if not cfg.enabled:
+        return False
+
+    # Concurrency gate: bound how many evolution passes run at once.
+    global _running_count
+    with _running_lock:
+        if _running_count >= _MAX_CONCURRENT:
+            logger.info(
+                f"[Evolution] busy ({_running_count}/{_MAX_CONCURRENT} running); "
+                f"skipping session={session_id} this scan"
+            )
+            return False
+        _running_count += 1
+
+    try:
+        agent = agent_bridge.agents.get(session_id) or agent_bridge.default_agent
+        if not agent:
+            return False
+
+        with agent.messages_lock:
+            all_messages = list(agent.messages)
+        total_msgs = len(all_messages)
+        # In-memory evolution cursor: only review messages added since the last
+        # pass so a long session doesn't re-judge (and re-write) old content.
+        # Stored on the agent instance; lost on restart (acceptable — at worst
+        # one redundant pass right after a restart, gated by the file-change
+        # check downstream so it won't double-write identical memory).
+        done = int(getattr(agent, "_evo_done_msg_count", 0))
+        if done > total_msgs:
+            done = 0  # history was trimmed/reset; start fresh
+        new_messages = all_messages[done:]
+        transcript = _build_transcript(new_messages)
+        if not transcript.strip():
+            logger.info(f"[Evolution] session={session_id}: no new messages, skip")
+            # Advance the cursor anyway so we don't re-scan the same tail.
+            agent._evo_done_msg_count = total_msgs
+            return False
+
+        logger.info(
+            f"[Evolution] ▶ Reviewing session={session_id} "
+            f"(idle {idle_minutes:.1f}min, {len(new_messages)} new/{total_msgs} msgs, "
+            f"~{len(transcript)} chars)"
+        )
+
+        # Resolve workspace + files to snapshot for undo.
+        from agent.memory.config import get_default_memory_config
+        mem_cfg = get_default_memory_config()
+        workspace_dir = mem_cfg.get_workspace()
+        if user_id:
+            memory_file = Path(workspace_dir) / "memory" / "users" / user_id / "MEMORY.md"
+        else:
+            memory_file = Path(workspace_dir) / "MEMORY.md"
+        skills_dir = mem_cfg.get_skills_dir()
+
+        # Snapshot MEMORY.md + every NON-protected skill's SKILL.md. Protected
+        # built-in skills are excluded from backup because they must never be
+        # edited in the first place.
+        protected_names = _builtin_skill_names()
+        # Back up both MEMORY.md and today's daily file: evolution now writes to
+        # the daily file, but MEMORY.md is cheap to snapshot and keeps undo safe
+        # if the model ever edits it.
+        today_daily = Path(workspace_dir) / "memory" / (
+            datetime.now().strftime("%Y-%m-%d") + ".md"
+        )
+        if user_id:
+            today_daily = Path(workspace_dir) / "memory" / "users" / user_id / (
+                datetime.now().strftime("%Y-%m-%d") + ".md"
+            )
+        backup_files = [Path(memory_file), today_daily]
+        if skills_dir.exists():
+            for skill_md in skills_dir.rglob("SKILL.md"):
+                # The skill dir is the SKILL.md's parent (or an ancestor for
+                # collections); guard by checking the immediate top-level dir.
+                try:
+                    top = skill_md.relative_to(skills_dir).parts[0]
+                except (ValueError, IndexError):
+                    continue
+                if top in protected_names:
+                    continue
+                backup_files.append(skill_md)
+        backup_id = create_backup(workspace_dir, backup_files)
+        _backup_n = sum(1 for f in backup_files if Path(f).exists())
+
+        # Snapshot the whole workspace (path -> mtime/size) so we can reliably
+        # detect ANY file change — including new output files written when
+        # finishing an unfinished task, which are not in backup_files.
+        pre_snapshot = _workspace_snapshot(workspace_dir)
+
+        # Build the isolated review agent: same model, restricted tools, with a
+        # hard guard that confines all writes to the workspace (protects the
+        # project's bundled skills from ever being modified).
+        review_tools = _guard_tools(
+            _select_tools(list(getattr(agent, "tools", []) or [])),
+            str(workspace_dir),
+        )
+        review_agent = agent_bridge.create_agent(
+            system_prompt=EVOLUTION_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,
+        )
+        # Reuse the live model so it follows the user's configured model.
+        review_agent.model = agent.model
+
+        logger.info(
+            f"[Evolution] backup {backup_id} ({_backup_n} files) → running review agent"
+        )
+        user_msg = build_review_user_message(transcript, protected_skills=list(protected_names))
+        result = review_agent.run_stream(user_msg, clear_history=True)
+        result = (result or "").strip()
+
+        # These messages are now reviewed; advance the cursor so the next pass
+        # only looks at messages added after this point (silent or not).
+        agent._evo_done_msg_count = total_msgs
+
+        if not result or SILENT_TOKEN in result:
+            logger.info(f"[Evolution] ✗ No change for session={session_id} ([SILENT])")
+            return False
+
+        # Hard gate: an evolution only counts (and only notifies) if a workspace
+        # file ACTUALLY changed. If the model did real work (wrote memory /
+        # patched a skill / finished a task) the user is told; if it merely
+        # produced text without changing anything, we stay silent. This is the
+        # key anti-nag rule — no notification unless something was actually done.
+        if not _workspace_changed(workspace_dir, pre_snapshot):
+            logger.info(
+                f"[Evolution] ✗ session={session_id}: model produced text but "
+                f"changed no file — treating as silent"
+            )
+            return False
+
+        logger.info(f"[Evolution] ✓ session={session_id} evolved:\n{result}")
+        append_session_evolution(workspace_dir, result, backup_id=backup_id, user_id=user_id)
+        # Inject an [EVOLUTION] note so the main agent can honor "undo".
+        _inject_evolution_record(agent_bridge, session_id, channel_type, result, backup_id)
+
+        # Push the summary to the user's channel. The "did a file actually
+        # change" gate above is the only throttle we need: real evolutions are
+        # rare, so no extra opt-in switch or daily-count limit is required.
+        if channel_type and receiver:
+            _notify_user(channel_type, receiver, result)
+
+        return True
+
+    except Exception as e:
+        logger.warning(f"[Evolution] Run failed for session={session_id}: {e}")
+        return False
+    finally:
+        with _running_lock:
+            _running_count -= 1
+
+
+def _inject_evolution_record(
+    agent_bridge, session_id: str, channel_type: str, summary: str, backup_id: Optional[str]
+) -> None:
+    """Add an [EVOLUTION] note to the user session so the main agent can undo."""
+    try:
+        note = f"{EVOLUTION_MARKER} {summary}"
+        if backup_id:
+            note += f"\n(backup_id: {backup_id}; to undo, restore this backup)"
+        # Reuse the scheduler-output injection path: isolated execution, only a
+        # compact record lands in the user session.
+        agent_bridge.remember_scheduled_output(
+            session_id=session_id,
+            content=note,
+            channel_type=channel_type,
+            task_description="self-evolution",
+        )
+    except Exception as e:
+        logger.debug(f"[Evolution] Failed to inject evolution record: {e}")
+
+
+def _notify_user(channel_type: str, receiver: str, summary: str) -> None:
+    """Push the evolution summary to the user's channel as a new message."""
+    try:
+        from bridge.context import Context, ContextType
+        from bridge.reply import Reply, ReplyType
+        from channel.channel_factory import create_channel
+
+        context = Context(ContextType.TEXT, summary)
+        context["receiver"] = receiver
+        context["isgroup"] = False
+        context["session_id"] = receiver
+        # Channels that reply to an original message need msg=None for a fresh push.
+        if channel_type in ("feishu", "dingtalk", "wecom_bot", "qq"):
+            context["msg"] = None
+        if channel_type == "feishu":
+            context["receive_id_type"] = "open_id"
+
+        channel = create_channel(channel_type)
+        if not channel:
+            return
+
+        # Web is request-response: a background push needs a synthetic request_id
+        # plus a request->session mapping so the channel can route the message to
+        # the user's polling queue (same approach the scheduler uses).
+        if channel_type == "web":
+            import uuid
+            request_id = f"evolution_{uuid.uuid4().hex[:8]}"
+            context["request_id"] = request_id
+            if hasattr(channel, "request_to_session"):
+                channel.request_to_session[request_id] = receiver
+
+        channel.send(Reply(ReplyType.TEXT, summary), context)
+        logger.info(f"[Evolution] Notified user via {channel_type}")
+    except Exception as e:
+        logger.warning(f"[Evolution] Failed to notify user: {e}")

agent/evolution/prompts.py

@@ -0,0 +1,163 @@
+"""Prompts for the self-evolution review agent.
+
+The system prompt is intentionally English-only: it governs the agent's
+internal reasoning and is more stable / cheaper to maintain in one language.
+The user-facing summary the agent produces should follow the user's own
+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.
+  - An explicit "do NOT capture" list to avoid self-poisoning over time.
+  - Generic examples only — never bake in domain-specific business terms.
+"""
+
+# Sentinel the agent emits when there is nothing worth evolving.
+SILENT_TOKEN = "[SILENT]"
+
+# Marker prefix for the evolution record injected into the user session, so the
+# main chat agent can recognize past evolutions and honor an "undo" request.
+EVOLUTION_MARKER = "[EVOLUTION]"
+
+
+EVOLUTION_SYSTEM_PROMPT = """You are a self-evolution review agent for an AI assistant.
+
+You are given a transcript of a conversation that just went idle. Your job is to
+decide whether anything from it is worth durably learning so future
+conversations go better — and if so, to make that change.
+
+# Top principle: default to doing NOTHING
+
+Most ordinary conversations need no evolution. Only act when there is a CLEAR
+signal below. If there is none, reply with exactly `[SILENT]` and stop. Staying
+silent is the normal, correct outcome — not a failure.
+
+Greetings, small talk, acknowledgements ("ok", "thanks", "got it"), and casual
+chat are NOT signals. For these, output exactly `[SILENT]` immediately — do not
+explore files, do not write a summary, do not be polite. Just `[SILENT]`.
+
+IMPORTANT: A summary is only allowed if you ACTUALLY made a file change via a
+tool (write/edit) in this pass. If you did not change any file, you MUST output
+exactly `[SILENT]` — never describe a change you only intended to make.
+
+# Signals worth acting on (act only if at least one clearly appears)
+
+SKILL and UNFINISHED TASK are your PRIMARY value — no other mechanism handles
+them. When their signal is clear, act; do not be shy here.
+
+1. SKILL — two cases:
+   a) PATCH an existing skill: a skill used here showed a STRUCTURAL problem (a
+      missing step/section, a wrong or outdated detail, an error in its
+      content), or its OUTPUT repeatedly misses something the user flagged. Read
+      the relevant skill file under the skills directory and make a small
+      incremental edit so it never recurs.
+   b) CREATE a new skill: a clearly reusable, repeatable workflow emerged that
+      no existing skill covers and the user is likely to want again. To create
+      one, follow the `skill-creator` skill's conventions (read its SKILL.md for
+      the required structure) and write the new skill under the workspace
+      `skills/` directory. Only create when the workflow is genuinely reusable —
+      not for a one-off task.
+
+   CRITICAL — fix the SOURCE, do not just remember the symptom: when the root
+   cause of a problem lives IN a skill file itself (its instructions, content,
+   or configuration are wrong/outdated), the correct action is to EDIT that
+   skill so the problem cannot recur. Recording the corrected fact in memory
+   does NOT prevent recurrence — only fixing the skill does. Never log "skill X
+   has wrong detail Y" as a memory note in place of editing skill X.
+
+2. UNFINISHED TASK — a specific deliverable you promised but didn't produce,
+   AND you already have everything needed to finish it. DO IT now with the
+   available tools and produce the result (e.g. write the file you said you'd
+   write). If key info is missing, or the task is merely waiting on the user's
+   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.
+   - MEMORY.md is the curated long-term index, auto-loaded into EVERY future
+     conversation. Treat it as precious: writing here is RARE and reserved for
+     CORRECTING a wrong fact already in MEMORY.md (edit that line in place).
+     Do NOT append new entries to MEMORY.md — that is the nightly pass's job.
+   - For a genuinely important NEW durable fact the chat missed, append ONE
+     short bullet to today's `memory/YYYY-MM-DD.md` (not MEMORY.md). When unsure,
+     the daily file is the safe place — but first ask whether this really
+     belongs in a skill instead.
+   - Keep it to ONE short bullet. Never write paragraphs, never re-summarize the
+     conversation, never copy what the main assistant already recorded.
+   - If it is already captured anywhere (check MEMORY.md AND the daily file
+     first), do NOTHING.
+
+# Do NOT capture (these poison future behavior)
+
+- Environment failures: missing binaries, unset credentials, uninstalled
+  packages, "command not found". The user can fix these; they are not durable
+  rules.
+- Negative claims about tools or features ("tool X does not work"). These
+  harden into refusals the agent cites against itself later.
+- One-off task narratives (e.g. summarizing today's content). Not a class of
+  reusable work.
+- Transient errors that resolved on retry within the conversation.
+
+# Execution constraints
+
+- Before changing memory or a skill, READ the current content first and make a
+  small INCREMENTAL edit. Never fabricate, never rewrite large sections.
+- AVOID DUPLICATES. Before writing memory, READ both MEMORY.md AND today's
+  daily file `memory/YYYY-MM-DD.md`. If the fact/preference is already recorded
+  in EITHER (even if worded differently), do NOT add it again. The main
+  assistant likely already wrote it during the chat — only add what is
+  genuinely new or a correction not yet reflected anywhere.
+- You may only edit files inside the workspace. Built-in skills shipped with
+  the product live outside it and are write-protected; do not try to edit them.
+- Make at most the few edits the signals justify; do not go looking for work.
+
+# Output
+
+- Nothing worth evolving -> output exactly `[SILENT]` and nothing else.
+- Otherwise, after performing the edits, output a short user-facing summary in
+  the SAME LANGUAGE the user used in the conversation. Tell the user, briefly:
+    1) that you just did a self-learning pass,
+    2) what you learned and what you changed (in plain terms — no need to cite
+       exact file paths; "remembered X" / "improved the weekly-report skill" is
+       enough).
+  Keep it to 1-3 lines. Generic shape (do not copy domain words):
+    "I just did a self-learning pass.
+     - Learned: <what you learned>
+     - Changed: <remembered it / improved the <name> skill / finished <task>>
+     Reply 'undo the last learning' if this is wrong."
+"""
+
+
+def build_review_user_message(transcript: str, protected_skills: list = None) -> str:
+    """Wrap the conversation transcript as the review agent's user message.
+
+    ``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))
+        protected_note = (
+            "\n\nPROTECTED skills (built-in — never edit these): "
+            f"{names}\n"
+        )
+    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."
+        f"{protected_note}\n"
+        "<transcript>\n"
+        f"{transcript}\n"
+        "</transcript>"
+    )

agent/evolution/record.py

@@ -0,0 +1,55 @@
+"""Self-evolution record log.
+
+Session-level evolutions are appended to their OWN per-day file under
+``memory/evolution/YYYY-MM-DD.md`` (separate from the nightly Deep Dream diary
+in ``memory/dreams/``). Each day's file accumulates one short section per
+evolution pass — tagged with a timestamp and a backup id for undo — so the
+memory UI can surface "what the agent learned/changed today" on one timeline
+without ever mixing into the dream diary or the main conversation memory.
+"""
+
+from __future__ import annotations
+
+from datetime import datetime
+from pathlib import Path
+from typing import Optional
+
+from common.log import logger
+
+
+def _evolution_dir(workspace_dir: Path, user_id: Optional[str] = None) -> Path:
+    base = Path(workspace_dir) / "memory"
+    if user_id:
+        return base / "users" / user_id / "evolution"
+    return base / "evolution"
+
+
+def append_session_evolution(
+    workspace_dir: Path,
+    summary: str,
+    backup_id: Optional[str] = None,
+    user_id: Optional[str] = None,
+) -> None:
+    """Append a session-evolution entry to today's evolution log."""
+    if not summary or not summary.strip():
+        return
+    try:
+        evo_dir = _evolution_dir(workspace_dir, user_id)
+        evo_dir.mkdir(parents=True, exist_ok=True)
+        today = datetime.now().strftime("%Y-%m-%d")
+        log_file = evo_dir / f"{today}.md"
+
+        ts = datetime.now().strftime("%H:%M")
+        header = f"## {ts}"
+        body = summary.strip()
+        if backup_id:
+            body += f"\n\n_backup_id: {backup_id}_"
+
+        # Create with a title if the file is new, otherwise append a section.
+        if not log_file.exists():
+            log_file.write_text(f"# Self-Evolution: {today}\n\n", encoding="utf-8")
+        with open(log_file, "a", encoding="utf-8") as f:
+            f.write(f"\n{header}\n\n{body}\n")
+        logger.info(f"[Evolution] Recorded session evolution to {log_file.name}")
+    except Exception as e:
+        logger.warning(f"[Evolution] Failed to record session evolution: {e}")

agent/evolution/trigger.py

@@ -0,0 +1,133 @@
+"""Idle-based evolution trigger.
+
+A single background thread periodically scans live agent sessions and runs an
+evolution pass for any session that is idle for >= idle_minutes AND has enough
+accumulated signal, where "enough signal" is EITHER:
+  - >= min_turns user turns since the last evolution, OR
+  - the live context has grown past _CONTEXT_RATIO of the agent's token budget
+    (mirrors how OpenClacky / Claude Code consolidate under context pressure).
+
+Turn counting is per user turn (not per message), measured from the last
+evolution (or session start). After a pass runs, the baseline resets so a long
+session can evolve multiple times without re-judging old content.
+
+Per-session evolution state is stored on the agent instance via lightweight
+attributes set by AgentBridge.agent_reply (see _note_user_turn).
+"""
+
+from __future__ import annotations
+
+import threading
+import time
+
+from common.log import logger
+
+from agent.evolution.config import get_evolution_config
+from agent.evolution.executor import run_evolution_for_session
+
+_SCAN_INTERVAL_SECONDS = 60
+
+# Context-pressure trigger: evolve once the live context exceeds this fraction
+# of the agent's token budget, even if min_turns hasn't been reached. Kept as a
+# module constant (not user config) for now. Fallback budget matches
+# agent_initializer / config.py (agent_max_context_tokens default = 50000).
+_CONTEXT_RATIO = 0.8
+_FALLBACK_CONTEXT_BUDGET = 50000
+
+
+def _context_pressure_reached(agent) -> bool:
+    """True if the agent's live context exceeds _CONTEXT_RATIO of its budget.
+
+    Uses the agent's own (estimated) token accounting so behavior matches the
+    existing context-trimming path. Best-effort: any error -> False.
+    """
+    try:
+        with agent.messages_lock:
+            messages = list(agent.messages)
+        if not messages:
+            return False
+        est = sum(agent._estimate_message_tokens(m) for m in messages)
+        budget = getattr(agent, "max_context_tokens", None) or _FALLBACK_CONTEXT_BUDGET
+        return est / budget > _CONTEXT_RATIO
+    except Exception:
+        return False
+
+
+def note_user_turn(agent, channel_type: str = "", receiver: str = "") -> None:
+    """Record activity for a session's agent. Called once per real user turn.
+
+    Maintains, on the agent instance:
+      _evo_last_active   : epoch seconds of the last user turn
+      _evo_turns         : user turns since the last evolution
+      _evo_channel_type  : originating channel (for later notify)
+      _evo_receiver      : push target for notify
+    """
+    try:
+        agent._evo_last_active = time.time()
+        agent._evo_turns = int(getattr(agent, "_evo_turns", 0)) + 1
+        if channel_type:
+            agent._evo_channel_type = channel_type
+        if receiver:
+            agent._evo_receiver = receiver
+    except Exception:
+        pass
+
+
+def start_evolution_trigger(agent_bridge) -> None:
+    """Start the idle-scan thread once per process (idempotent)."""
+    if getattr(agent_bridge, "_evolution_trigger_started", False):
+        return
+    agent_bridge._evolution_trigger_started = True
+
+    t = threading.Thread(
+        target=_scan_loop, args=(agent_bridge,), daemon=True, name="evolution-trigger"
+    )
+    t.start()
+    logger.info("[Evolution] Idle trigger started")
+
+
+def _scan_loop(agent_bridge) -> None:
+    while True:
+        try:
+            time.sleep(_SCAN_INTERVAL_SECONDS)
+            cfg = get_evolution_config()
+            if not cfg.enabled:
+                continue
+            _scan_once(agent_bridge, cfg)
+        except Exception as e:
+            logger.warning(f"[Evolution] Scan loop error: {e}")
+            time.sleep(_SCAN_INTERVAL_SECONDS)
+
+
+def _scan_once(agent_bridge, cfg) -> None:
+    now = time.time()
+    # Snapshot to avoid holding the dict while running long evolutions.
+    sessions = list(getattr(agent_bridge, "agents", {}).items())
+    for session_id, agent in sessions:
+        try:
+            last_active = getattr(agent, "_evo_last_active", 0)
+            turns = int(getattr(agent, "_evo_turns", 0))
+            # Enough signal = enough turns OR enough context pressure.
+            enough_signal = turns >= cfg.min_turns or _context_pressure_reached(agent)
+            if not enough_signal:
+                continue
+            idle = now - last_active if last_active > 0 else -1
+            if last_active <= 0 or idle < cfg.idle_seconds:
+                continue
+
+            channel_type = getattr(agent, "_evo_channel_type", "") or ""
+            receiver = getattr(agent, "_evo_receiver", "") or ""
+
+            # Reset baseline BEFORE running so a long pass / new messages during
+            # it don't double-trigger; turns accrue fresh from here.
+            agent._evo_turns = 0
+
+            run_evolution_for_session(
+                agent_bridge,
+                session_id=session_id,
+                channel_type=channel_type,
+                receiver=receiver,
+                idle_minutes=(now - last_active) / 60 if last_active > 0 else 0.0,
+            )
+        except Exception as e:
+            logger.warning(f"[Evolution] Failed to evaluate session={session_id}: {e}")

agent/memory/conversation_store.py

@@ -13,6 +13,7 @@ Storage path: ~/cow/sessions/conversations.db
 from __future__ import annotations
 
 import json
+import re
 import sqlite3
 import threading
 import time
@@ -109,6 +110,43 @@ def _extract_display_text(content: Any) -> str:
     return ""
 
 
+# Internal markers written into the session for the agent's own bookkeeping
+# (scheduler injection / self-evolution undo). They must stay in the stored
+# content (the LLM reads them, e.g. to find a backup_id for undo) but should
+# never be shown verbatim to the user in the chat history UI.
+_SCHEDULED_DISPLAY_MARKERS = ("[SCHEDULED]", "Scheduled task")
+_EVOLUTION_DISPLAY_MARKER = "[EVOLUTION]"
+
+
+def _is_internal_user_marker(text: str) -> bool:
+    """True if a user-turn text is an internal injection marker (hide from UI)."""
+    t = (text or "").lstrip()
+    return any(t.startswith(m) for m in _SCHEDULED_DISPLAY_MARKERS)
+
+
+def _clean_display_text(text: str) -> str:
+    """Strip internal markers from assistant text for user-facing display.
+
+    Removes a leading ``[EVOLUTION]`` tag and a trailing ``(backup_id: ...)``
+    undo hint. The raw stored message is untouched, so undo + LLM context still
+    work; only the rendered chat bubble is cleaned.
+    """
+    if not text:
+        return text
+    cleaned = text
+    stripped = cleaned.lstrip()
+    if stripped.startswith(_EVOLUTION_DISPLAY_MARKER):
+        cleaned = stripped[len(_EVOLUTION_DISPLAY_MARKER):].lstrip()
+    # Drop a trailing backup_id undo hint line, e.g.
+    #   "(backup_id: 20260607-...; to undo, restore this backup)"
+    cleaned = re.sub(
+        r"\n*\(backup_id:[^\)]*\)\s*$",
+        "",
+        cleaned,
+    ).rstrip()
+    return cleaned
+
+
 def _extract_tool_calls(content: Any) -> List[Dict[str, Any]]:
     """
     Extract tool_use blocks from an assistant message content.
@@ -210,7 +248,10 @@ def _group_into_display_turns(
         if user_row:
             content, created_at, _u_extras = user_row
             text = _extract_display_text(content)
-            if text:
+            # Hide internal injection markers (scheduler / self-evolution) so the
+            # user never sees a synthetic "[SCHEDULED] self-evolution" bubble;
+            # the assistant reply that follows is still rendered.
+            if text and not _is_internal_user_marker(text):
                 turns.append({"role": "user", "content": text, "created_at": created_at})
 
         # Build an ordered list of steps preserving the original sequence:
@@ -265,6 +306,14 @@ def _group_into_display_turns(
                 step["result"] = tr.get("result", "")
                 step["is_error"] = tr.get("is_error", False)
 
+        # Clean internal markers from the user-facing assistant text. Applies to
+        # both the final content and the mirrored content step so the rendered
+        # bubble shows clean text while the stored message keeps the markers.
+        final_text = _clean_display_text(final_text)
+        for step in steps:
+            if step.get("type") == "content":
+                step["content"] = _clean_display_text(step.get("content", ""))
+
         if steps or final_text:
             turn = {
                 "role": "assistant",

agent/memory/service.py

@@ -34,13 +34,18 @@ class MemoryService:
     # ------------------------------------------------------------------
     def list_files(self, page: int = 1, page_size: int = 20, category: str = "memory") -> dict:
         """
-        List memory or dream files with metadata (without content).
+        List memory, dream, or evolution files with metadata (without content).
 
         Args:
             category: ``"memory"`` (default) — MEMORY.md + daily files;
-                      ``"dream"``  — dream diary files from memory/dreams/
+                      ``"dream"``     — dream diary files from memory/dreams/;
+                      ``"evolution"`` — self-evolution logs from memory/evolution/
+                                        merged with the nightly dream diaries, so
+                                        one tab shows everything the agent learned.
         """
-        if category == "dream":
+        if category == "evolution":
+            files = self._list_evolution_files()
+        elif category == "dream":
             files = self._list_dream_files()
         else:
             files = self._list_memory_files()
@@ -93,6 +98,26 @@ class MemoryService:
 
         return files
 
+    def _list_evolution_files(self) -> List[dict]:
+        """Self-evolution logs (memory/evolution/*.md) merged with the nightly
+        dream diaries (memory/dreams/*.md), newest first.
+
+        Both are surfaced under the unified "Self-Evolution" tab. A file's
+        ``type`` records its origin so the reader can resolve the right dir.
+        """
+        files: List[dict] = []
+        for sub, ftype in (("evolution", "evolution"), ("dreams", "dream")):
+            sub_dir = os.path.join(self.memory_dir, sub)
+            if not os.path.isdir(sub_dir):
+                continue
+            for name in os.listdir(sub_dir):
+                full = os.path.join(sub_dir, name)
+                if os.path.isfile(full) and name.endswith(".md"):
+                    files.append(self._file_info(full, name, ftype))
+        # Sort newest first by filename (date-named); ties favor evolution.
+        files.sort(key=lambda f: (f["filename"], f["type"] != "evolution"), reverse=True)
+        return files
+
     # ------------------------------------------------------------------
     # content — read a single file
     # ------------------------------------------------------------------
@@ -101,7 +126,7 @@ class MemoryService:
         Read the full content of a memory or dream file.
 
         :param filename: File name, e.g. ``MEMORY.md``, ``2026-02-20.md``
-        :param category: ``"memory"`` or ``"dream"``
+        :param category: ``"memory"``, ``"dream"`` or ``"evolution"``
         :return: dict with ``filename`` and ``content``
         :raises FileNotFoundError: if the file does not exist
         """
@@ -125,7 +150,7 @@ class MemoryService:
         Dispatch a memory management action.
 
         :param action: ``list`` or ``content``
-        :param payload: action-specific payload (supports ``category``: ``"memory"`` | ``"dream"``)
+        :param payload: action-specific payload (supports ``category``: ``"memory"`` | ``"dream"`` | ``"evolution"``)
         :return: protocol-compatible response dict
         """
         payload = payload or {}
@@ -166,6 +191,7 @@ class MemoryService:
         - ``MEMORY.md`` → ``{workspace_root}/MEMORY.md``
         - ``2026-02-20.md`` (memory) → ``{workspace_root}/memory/2026-02-20.md``
         - ``2026-02-20.md`` (dream) → ``{workspace_root}/memory/dreams/2026-02-20.md``
+        - ``2026-02-20.md`` (evolution) → ``{workspace_root}/memory/evolution/2026-02-20.md``
 
         Raises ValueError if the resolved path escapes the allowed directory.
         """
@@ -173,6 +199,8 @@ class MemoryService:
             base_dir = self.workspace_root
         elif category == "dream":
             base_dir = os.path.join(self.memory_dir, "dreams")
+        elif category == "evolution":
+            base_dir = os.path.join(self.memory_dir, "evolution")
         else:
             base_dir = self.memory_dir
 

agent/protocol/agent_stream.py

@@ -347,11 +347,14 @@ class AgentStreamExecutor:
         Returns:
             Final response text
         """
-        # Log user message with model info
+        # Log user message with model info. Truncate very long messages (e.g.
-        
+        # injected transcripts / large prompts) so logs stay readable.
         thinking_enabled = self._is_thinking_enabled()
         thinking_label = " | 💭 thinking" if thinking_enabled else ""
-        logger.info(f"🤖 {self.model.model}{thinking_label} | 👤 {user_message}")        
+        _log_msg = user_message if len(user_message) <= 500 else (
+            user_message[:500] + f" …(+{len(user_message) - 500} chars)"
+        )
+        logger.info(f"🤖 {self.model.model}{thinking_label} | 👤 {_log_msg}")        
         
         # Add user message (Claude format - use content blocks for consistency)
         self.messages.append({

agent/tools/__init__.py

@@ -14,6 +14,9 @@ from agent.tools.send.send import Send
 from agent.tools.memory.memory_search import MemorySearchTool
 from agent.tools.memory.memory_get import MemoryGetTool
 
+# Import self-evolution tools
+from agent.tools.evolution_undo.evolution_undo import EvolutionUndoTool
+
 # Import tools with optional dependencies
 def _import_optional_tools():
     """Import tools that have optional dependencies"""
@@ -135,6 +138,7 @@ __all__ = [
     'Send',
     'MemorySearchTool',
     'MemoryGetTool',
+    'EvolutionUndoTool',
     'EnvConfig',
     'SchedulerTool',
     'WebSearch',

agent/tools/evolution_undo/__init__.py

@@ -0,0 +1,3 @@
+from agent.tools.evolution_undo.evolution_undo import EvolutionUndoTool
+
+__all__ = ["EvolutionUndoTool"]

agent/tools/evolution_undo/evolution_undo.py

@@ -0,0 +1,58 @@
+"""Evolution undo tool.
+
+Lets the main chat agent roll back a previous self-evolution when the user asks
+("undo the last learning"). The rollback itself is a deterministic FILE RESTORE
+from the snapshot taken before the evolution — the model only supplies the
+backup_id it reads from the [EVOLUTION] record in the conversation. No LLM-driven
+re-editing is involved, so a restore can never make things worse.
+"""
+
+from agent.tools.base_tool import BaseTool, ToolResult
+
+
+class EvolutionUndoTool(BaseTool):
+    """Restore memory/skill files to the state before a self-evolution."""
+
+    name: str = "evolution_undo"
+    description: str = (
+        "Undo a previous self-evolution (self-learning) by restoring the "
+        "memory/skill files to their state before that learning. Use this when "
+        "the user asks to undo / revert / roll back the last self-learning. "
+        "Find the backup_id in the most recent [EVOLUTION] record in the "
+        "conversation and pass it here."
+    )
+    params: dict = {
+        "type": "object",
+        "properties": {
+            "backup_id": {
+                "type": "string",
+                "description": (
+                    "The backup_id from the [EVOLUTION] record to restore "
+                    "(e.g. '20260607-155551-850')."
+                ),
+            }
+        },
+        "required": ["backup_id"],
+    }
+
+    def execute(self, args: dict):
+        backup_id = (args.get("backup_id") or "").strip()
+        if not backup_id:
+            return ToolResult.fail("Error: backup_id is required")
+        try:
+            from agent.memory.config import get_default_memory_config
+            from agent.evolution.backup import restore_backup
+
+            workspace_dir = get_default_memory_config().get_workspace()
+            ok = restore_backup(workspace_dir, backup_id)
+            if ok:
+                return ToolResult.success(
+                    f"Restored memory/skills to the state before evolution "
+                    f"{backup_id}. The previous self-learning has been undone."
+                )
+            return ToolResult.fail(
+                f"Could not find or restore backup {backup_id}. It may have "
+                f"expired or already been rolled back."
+            )
+        except Exception as e:
+            return ToolResult.fail(f"Error during undo: {e}")

bridge/agent_bridge.py

@@ -295,6 +295,13 @@ class AgentBridge:
                 self.scheduler_initialized = True
         except Exception as e:
             logger.warning(f"[AgentBridge] Eager scheduler init failed: {e}")
+
+        # Start the self-evolution idle trigger (idempotent, daemon thread).
+        try:
+            from agent.evolution.trigger import start_evolution_trigger
+            start_evolution_trigger(self)
+        except Exception as e:
+            logger.warning(f"[AgentBridge] Evolution trigger init failed: {e}")
     def create_agent(self, system_prompt: str, tools: List = None, **kwargs) -> Agent:
         """
         Create the super agent with COW integration
@@ -547,6 +554,23 @@ class AgentBridge:
                         except Exception as e:
                             logger.warning(f"[AgentBridge] Failed to clear DB after recovery: {e}")
             
+            # Record this user turn for the self-evolution idle trigger. Skip
+            # scheduler-injected / scheduled-task sessions so internal runs do
+            # not count as user activity.
+            if session_id and not session_id.startswith("scheduler_") and not (
+                context and context.get("is_scheduled_task")
+            ):
+                try:
+                    from agent.evolution.trigger import note_user_turn
+                    ch = (context.get("channel_type") or "") if context else ""
+                    rcv = (context.get("receiver") or "") if context else ""
+                    is_group = bool(context.get("isgroup")) if context else False
+                    # Only enable proactive push for single chats (group push is
+                    # noisy); group sessions still evolve, just without notify.
+                    note_user_turn(agent, channel_type=ch, receiver=(rcv if not is_group else ""))
+                except Exception:
+                    pass
+
             # Post-message hot-reload: detect edits to ~/cow/mcp.json and
             # sync any new/removed MCP tools into the live agent in the
             # background. Off the critical path so user latency is unaffected;

channel/web/chat.html

@@ -760,7 +760,7 @@
                                         </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-moon mr-1.5"></i><span data-i18n="memory_tab_dreams">梦境日记</span>
+                                            <i class="fas fa-seedling mr-1.5"></i><span data-i18n="memory_tab_dreams">自主进化</span>
                                         </button>
                                     </div>
                                 </div>

channel/web/static/js/console.js

@@ -140,7 +140,7 @@ 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: '梦境日记',
+        memory_tab_files: '记忆文件', memory_tab_dreams: '自主进化',
         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',
@@ -342,7 +342,7 @@ const I18N = {
         skills_section_title: 'Skills', skill_enable: 'Enable', skill_disable: 'Disable',
         skill_toggle_error: 'Operation failed, please try again',
         memory_title: 'Memory', memory_desc: 'View agent memory files and contents',
-        memory_tab_files: 'Memory Files', memory_tab_dreams: 'Dream Diary',
+        memory_tab_files: 'Memory Files', memory_tab_dreams: 'Self-Evolution',
         memory_loading: 'Loading memory files...', memory_loading_desc: 'Memory files will be displayed here',
         memory_back: 'Back to list',
         memory_col_name: 'Filename', memory_col_type: 'Type', memory_col_size: 'Size', memory_col_updated: 'Updated',
@@ -4304,13 +4304,14 @@ function toggleSkill(name, currentlyEnabled) {
 // Memory View
 // =====================================================================
 let memoryPage = 1;
-let memoryCategory = 'memory';   // 'memory' | 'dream'
+let memoryCategory = 'memory';   // 'memory' | 'evolution'
 const memoryPageSize = 10;
 
 function switchMemoryTab(tab) {
     document.querySelectorAll('.memory-tab').forEach(el => el.classList.remove('active'));
     document.getElementById('memory-tab-' + tab).classList.add('active');
-    memoryCategory = tab === 'dreams' ? 'dream' : 'memory';
+    // The "dreams" tab now surfaces self-evolution logs (merged with dream diaries).
+    memoryCategory = tab === 'dreams' ? 'evolution' : 'memory';
     loadMemoryView(1);
 }
 
@@ -4327,9 +4328,9 @@ function loadMemoryView(page) {
         if (total === 0) {
             const emptyIcon = emptyEl.querySelector('i');
             const emptyTitle = emptyEl.querySelector('p');
-            if (memoryCategory === 'dream') {
+            if (memoryCategory === 'evolution') {
-                emptyIcon.className = 'fas fa-moon text-purple-400 text-xl';
+                emptyIcon.className = 'fas fa-seedling text-emerald-400 text-xl';
-                emptyTitle.textContent = currentLang === 'zh' ? '暂无梦境日记' : 'No dream diaries yet';
+                emptyTitle.textContent = currentLang === 'zh' ? '暂无进化记录' : 'No evolution records yet';
             } else {
                 emptyIcon.className = 'fas fa-brain text-purple-400 text-xl';
                 emptyTitle.textContent = currentLang === 'zh' ? '暂无记忆文件' : 'No memory files';
@@ -4346,10 +4347,15 @@ function loadMemoryView(page) {
         files.forEach(f => {
             const tr = document.createElement('tr');
             tr.className = 'border-b border-slate-100 dark:border-white/5 hover:bg-slate-50 dark:hover:bg-white/5 cursor-pointer transition-colors';
-            tr.onclick = () => openMemoryFile(f.filename, memoryCategory);
+            // In the merged evolution tab, resolve each file by its own origin
+            // (evolution logs vs dream diaries live in different dirs).
+            const fileCategory = (f.type === 'dream' || f.type === 'evolution') ? f.type : memoryCategory;
+            tr.onclick = () => openMemoryFile(f.filename, fileCategory);
             let typeLabel;
             if (f.type === 'global') {
                 typeLabel = '<span class="px-2 py-0.5 rounded-full text-xs bg-primary-50 dark:bg-primary-900/30 text-primary-600 dark:text-primary-400">Global</span>';
+            } else if (f.type === 'evolution') {
+                typeLabel = '<span class="px-2 py-0.5 rounded-full text-xs bg-emerald-50 dark:bg-emerald-900/30 text-emerald-600 dark:text-emerald-400">Evolution</span>';
             } else if (f.type === 'dream') {
                 typeLabel = '<span class="px-2 py-0.5 rounded-full text-xs bg-violet-50 dark:bg-violet-900/30 text-violet-600 dark:text-violet-400">Dream</span>';
             } else {

config.py

@@ -251,6 +251,10 @@ available_setting = {
     "enable_thinking": False,  # Enable deep-thinking mode for thinking-capable models
     "reasoning_effort": "high",  # Reasoning depth under thinking mode: "high" or "max"
     "knowledge": True,  # whether to enable the knowledge base feature
+    # 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
     "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)
 }

plugins/keyword/keyword.py

@@ -26,12 +26,12 @@ class Keyword(Plugin):
             config_path = os.path.join(curdir, "config.json")
             conf = None
             if not os.path.exists(config_path):
-                logger.debug(f"[keyword]不存在配置文件{config_path}")
+                logger.debug(f"[keyword] config file not found: {config_path}")
                 conf = {"keyword": {}}
                 with open(config_path, "w", encoding="utf-8") as f:
                     json.dump(conf, f, indent=4)
             else:
-                logger.debug(f"[keyword]加载配置文件{config_path}")
+                logger.debug(f"[keyword] loading config file: {config_path}")
                 with open(config_path, "r", encoding="utf-8") as f:
                     conf = json.load(f)
             # 加载关键词

tests/test_evolution.py

@@ -0,0 +1,660 @@
+"""Self-evolution test harness.
+
+Simulates multiple realistic conversations and checks the evolution pass behaves
+correctly: stays silent when it should, evolves (memory/skill) when it should,
+backs up before editing, notifies the user, and supports undo.
+
+Two modes:
+  - stub  (default): the review agent's reasoning is replaced by a scripted
+    output per scenario. Fast, deterministic, validates the WIRING (backup,
+    record, inject, notify, undo, protection). No model calls.
+  - real:  the review agent runs the configured model for real. Validates the
+    QUALITY of the judgement (does it correctly decide to act / stay silent).
+
+Run:
+    python tests/test_evolution.py            # stub mode
+    python tests/test_evolution.py --real      # real model mode
+"""
+
+import os
+import sys
+import shutil
+import tempfile
+from pathlib import Path
+
+sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+
+# ---------------------------------------------------------------------------
+# Fakes
+# ---------------------------------------------------------------------------
+class FakeChannel:
+    """Captures channel.send calls instead of sending."""
+
+    def __init__(self):
+        self.sent = []
+
+    def send(self, reply, context):
+        self.sent.append({"content": getattr(reply, "content", str(reply)), "receiver": context.get("receiver")})
+
+
+class FakeModel:
+    pass
+
+
+class FakeAgent:
+    """Minimal stand-in for a chat Agent."""
+
+    def __init__(self, messages, tools=None):
+        import threading
+        self.messages = messages
+        self.messages_lock = threading.Lock()
+        self.tools = tools or []
+        self.model = FakeModel()
+        self.skill_manager = None
+        self.memory_manager = None
+
+
+class FakeReviewAgent:
+    """Review agent whose run_stream returns a scripted result (stub mode)."""
+
+    def __init__(self, scripted_output, workspace, on_edit=None):
+        self._out = scripted_output
+        self._workspace = workspace
+        self._on_edit = on_edit
+        self.model = None
+
+    def run_stream(self, user_message, clear_history=False, **kwargs):
+        # Simulate the side effects a real review agent would perform.
+        if self._on_edit:
+            self._on_edit(self._workspace)
+        return self._out
+
+
+class FakeAgentBridge:
+    """Stand-in for AgentBridge wiring used by the executor."""
+
+    def __init__(self, agent, scripted_output, on_edit=None):
+        self.agents = {"session_test": agent}
+        self.default_agent = agent
+        self._scripted = scripted_output
+        self._on_edit = on_edit
+        self.injected = []
+
+    def create_agent(self, **kwargs):
+        from agent.memory.config import get_default_memory_config
+        ws = get_default_memory_config().get_workspace()
+        return FakeReviewAgent(self._scripted, ws, on_edit=self._on_edit)
+
+    def remember_scheduled_output(self, session_id, content, channel_type="", task_description=""):
+        self.injected.append(content)
+
+
+# ---------------------------------------------------------------------------
+# Test scaffolding
+# ---------------------------------------------------------------------------
+def _setup_workspace():
+    """Create a realistic temp workspace: seeded memory + real editable skills.
+
+    Mirrors a real CowAgent workspace closely enough that the model has genuine
+    content to read, reason about, and edit during a real evolution pass.
+    """
+    ws = Path(tempfile.mkdtemp(prefix="evo_test_"))
+    (ws / "MEMORY.md").write_text(
+        "# Long-term Memory\n\n"
+        "## User\n"
+        "- Name: 大锤 (David)\n"
+        "- Lives in Shenzhen, works as a backend engineer\n"
+        "- Company: a fintech startup, team of 8\n\n"
+        "## Preferences\n"
+        "- Likes detailed technical explanations\n",
+        encoding="utf-8",
+    )
+    (ws / "memory").mkdir()
+    (ws / "output").mkdir()
+    skills = ws / "skills"
+
+    # Editable skill 1: weekly report generator (has a structural gap: no risk).
+    (skills / "weekly-report").mkdir(parents=True)
+    (skills / "weekly-report" / "SKILL.md").write_text(
+        "# Weekly Report\n\n"
+        "Generate a weekly work report from the user's notes.\n\n"
+        "## Steps\n"
+        "1. Collect this week's completed items.\n"
+        "2. Summarize key progress in 3-5 bullets.\n"
+        "3. List next week's plan.\n\n"
+        "## Output format\n"
+        "Markdown with sections: 本周进展 / 下周计划\n",
+        encoding="utf-8",
+    )
+
+    # Editable skill 2: expense tracker (has a wrong currency-format step).
+    (skills / "expense-tracker").mkdir(parents=True)
+    (skills / "expense-tracker" / "SKILL.md").write_text(
+        "# Expense Tracker\n\n"
+        "Record an expense into output/expenses.md.\n\n"
+        "## Steps\n"
+        "1. Parse amount and category from the user message.\n"
+        "2. Append a row to output/expenses.md.\n"
+        "3. Format the amount with a `$` prefix.\n",
+        encoding="utf-8",
+    )
+
+    # Editable skill 3: an API caller whose SKILL.md hardcodes a WRONG endpoint
+    # host. The conversation discovers the correct host at runtime; the right
+    # fix is to edit this file's source, not just log the corrected fact.
+    (skills / "data-fetch").mkdir(parents=True)
+    (skills / "data-fetch" / "SKILL.md").write_text(
+        "# Data Fetch\n\n"
+        "Fetch records from the data service.\n\n"
+        "## Steps\n"
+        "1. Build the request payload from the user's query.\n"
+        "2. POST it to `https://api.example-wrong.com/v1/fetch`.\n"
+        "3. Parse and return the `data` field.\n",
+        encoding="utf-8",
+    )
+
+    # Protected built-in skill: must never be edited by evolution.
+    (skills / "image-generation").mkdir(parents=True)
+    (skills / "image-generation" / "SKILL.md").write_text(
+        "# Image Generation (built-in)\nDo not modify.\n", encoding="utf-8"
+    )
+    return ws
+
+
+def _point_config_at(ws):
+    """Force the global memory config to use the temp workspace."""
+    from agent.memory.config import MemoryConfig, set_global_memory_config
+    set_global_memory_config(MemoryConfig(workspace_root=str(ws)))
+
+
+def _make_messages(turns):
+    msgs = []
+    for u, a in turns:
+        msgs.append({"role": "user", "content": u})
+        msgs.append({"role": "assistant", "content": a})
+    return msgs
+
+
+# ---------------------------------------------------------------------------
+# Scenarios
+# ---------------------------------------------------------------------------
+def scenario_silent():
+    """Pure small talk -> should stay SILENT (no change, no notify)."""
+    return {
+        "name": "闲聊 (should stay SILENT)",
+        "goal": "none",
+        "turns": [
+            ("在吗", "在的，有什么可以帮你？"),
+            ("今天周五了，终于要放假了", "是呀，周末好好休息一下。"),
+            ("哈哈是的，那没事了", "好的，随时找我。"),
+        ],
+        "scripted": "[SILENT]",
+        "on_edit": None,
+        "expect_evolved": False,
+    }
+
+
+def scenario_memory_preference():
+    """User states a durable working preference -> update MEMORY.md."""
+    def edit(ws):
+        from datetime import datetime
+        p = ws / "memory" / (datetime.now().strftime("%Y-%m-%d") + ".md")
+        p.write_text((p.read_text() if p.exists() else "") + "- Prefers concise replies; avoid long explanations\n", encoding="utf-8")
+
+    return {
+        "name": "记忆-偏好 (should note in daily memory)",
+        "goal": "memory",
+        "turns": [
+            ("你能帮我解释下什么是数据库事务吗", "当然可以。数据库事务（Transaction）是一组要么全部成功、要么全部失败的操作单元，它有四个核心特性 ACID：原子性、一致性、隔离性、持久性……（接着是 5 段详细展开）"),
+            ("打住打住，太长了", "抱歉，我说得太细了。"),
+            ("对，你以后回答我都简短一点，抓重点就行，别长篇大论", "好的，明白了。"),
+            ("嗯这样挺好", "👌"),
+        ],
+        "scripted": (
+            "我刚做了一次自我学习。\n"
+            "- 学到：你希望回答简短、抓重点\n"
+            "- 调整：更新了长期记忆 MEMORY.md\n"
+            "如果不对，回复「撤销上次学习」即可。"
+        ),
+        "on_edit": edit,
+        "expect_evolved": True,
+        "expect_memory_contains": "concise",
+    }
+
+
+def scenario_memory_correction():
+    """User corrects a wrong fact the agent stated -> fix MEMORY.md."""
+    def edit(ws):
+        from datetime import datetime
+        p = ws / "memory" / (datetime.now().strftime("%Y-%m-%d") + ".md")
+        p.write_text((p.read_text() if p.exists() else "") + "- Correction: team size is now 15 (was recorded as 8)\n", encoding="utf-8")
+
+    return {
+        "name": "记忆-纠正事实 (should note correction in daily)",
+        "goal": "memory",
+        "turns": [
+            ("我们团队最近又招了几个人", "记得你们是 8 人的小团队，扩张了挺好的。"),
+            ("不对，我们现在是 15 个人了，上次你就记错了", "抱歉记错了，你们现在是 15 人团队。"),
+            ("嗯对，记住啊", "好的，记住了。"),
+        ],
+        "scripted": (
+            "我刚做了一次自我学习。\n"
+            "- 学到：团队规模已是 15 人（之前记成 8 人）\n"
+            "- 调整：修正了长期记忆 MEMORY.md\n"
+            "如果不对，回复「撤销上次学习」即可。"
+        ),
+        "on_edit": edit,
+        "expect_evolved": True,
+        "expect_memory_contains": "15",
+    }
+
+
+def scenario_skill_gap():
+    """Using weekly-report reveals a missing section -> patch the skill."""
+    def edit(ws):
+        p = ws / "skills" / "weekly-report" / "SKILL.md"
+        p.write_text(p.read_text() + "4. List risk items / blockers.\n", encoding="utf-8")
+
+    return {
+        "name": "技能-缺陷 (should patch weekly-report skill)",
+        "goal": "skill",
+        "turns": [
+            ("用周报技能帮我生成这周的周报，素材：完成了支付网关重构、修复了3个线上bug、对接了风控接口",
+             "好的，我用 weekly-report 技能生成：\n\n## 本周进展\n- 完成支付网关重构\n- 修复 3 个线上 bug\n- 对接风控接口\n\n## 下周计划\n- 联调风控接口"),
+            ("不错，但是每次周报都漏了风险项，我们 leader 每次都要问这个", "确实，风险项很重要。"),
+            ("对，你能不能让这个周报技能以后都带上风险项这一节", "可以的，我把它固化进技能。"),
+        ],
+        "scripted": (
+            "我刚做了一次自我学习。\n"
+            "- 学到：周报需要固定包含「风险项」一节\n"
+            "- 调整：给 weekly-report 技能补充了风险项步骤\n"
+            "如果不对，回复「撤销上次学习」即可。"
+        ),
+        "on_edit": edit,
+        "expect_evolved": True,
+        "expect_skill_contains": ("weekly-report", "risk"),
+    }
+
+
+def scenario_skill_error():
+    """A skill step is wrong (currency format) -> correct the skill."""
+    def edit(ws):
+        p = ws / "skills" / "expense-tracker" / "SKILL.md"
+        p.write_text(p.read_text().replace("`$` prefix", "`¥` prefix (RMB)"), encoding="utf-8")
+
+    return {
+        "name": "技能-报错纠正 (should fix expense-tracker skill)",
+        "goal": "skill",
+        "turns": [
+            ("用记账技能记一笔：午饭 35", "好的，已记录：午饭 $35（已写入 output/expenses.md）"),
+            ("怎么是美元？我们都是人民币啊", "抱歉，格式用错了，应该是 ¥35。"),
+            ("对，以后这个记账技能都用人民币符号", "明白，我修正技能里的货币格式。"),
+        ],
+        "scripted": (
+            "我刚做了一次自我学习。\n"
+            "- 学到：记账应使用人民币符号 ¥ 而非 $\n"
+            "- 调整：修正了 expense-tracker 技能的货币格式步骤\n"
+            "如果不对，回复「撤销上次学习」即可。"
+        ),
+        "on_edit": edit,
+        "expect_evolved": True,
+        "expect_skill_contains": ("expense-tracker", "¥"),
+    }
+
+
+def scenario_skill_wrong_config():
+    """A skill's SKILL.md hardcodes a wrong endpoint; the chat works around it
+    at runtime. Correct evolution = FIX the skill source, not log a memory note.
+    """
+    def edit(ws):
+        p = ws / "skills" / "data-fetch" / "SKILL.md"
+        p.write_text(
+            p.read_text().replace("api.example-wrong.com", "api.example-correct.com"),
+            encoding="utf-8",
+        )
+
+    return {
+        "name": "技能-配置错误 (should fix skill source, not log memory)",
+        "goal": "skill",
+        "turns": [
+            ("用 data-fetch 技能拉一下最新数据",
+             "好的，我按技能里的步骤 POST 到 https://api.example-wrong.com/v1/fetch …… 报错了，连接失败。"),
+            ("哦那个地址不对，正确的是 api.example-correct.com，你用这个调",
+             "好的，换成 https://api.example-correct.com/v1/fetch ，成功拿到数据了 ✅"),
+            ("嗯对，就是这个地址", "明白了，这个才是正确的服务地址。"),
+        ],
+        # The model must decide on its own to edit the skill; we still provide a
+        # scripted output for stub mode wiring.
+        "scripted": (
+            "我刚做了一次自我学习。\n"
+            "- 学到：data-fetch 的正确服务地址是 api.example-correct.com\n"
+            "- 调整：修正了 data-fetch 技能里写错的接口地址\n"
+            "如果不对，回复「撤销上次学习」即可。"
+        ),
+        "on_edit": edit,
+        "expect_evolved": True,
+        "expect_skill_contains": ("data-fetch", "api.example-correct.com"),
+    }
+
+
+def scenario_unfinished_task():
+    """A promised deliverable was not produced -> finish it now via tools."""
+    def edit(ws):
+        p = ws / "output" / "team-roster.md"
+        p.write_text("# Team Roster (backend)\n- 张伟\n- 李娜\n- 王强\n- 大锤\n", encoding="utf-8")
+
+    return {
+        "name": "未完成任务 (should finish & write output file)",
+        "goal": "task",
+        "turns": [
+            ("帮我把后端团队花名册整理成一个文件保存下，成员有：张伟、李娜、王强，还有我自己（大锤）",
+             "好的，后端 4 个人：张伟、李娜、王强、大锤。我整理成文件保存到 output/team-roster.md。"),
+            ("好的麻烦了，我先去开个会", "没问题，我现在就处理。"),
+            ("（用户离开，会话中断，文件尚未写入）", "（助手未及写入文件，对话中断）"),
+        ],
+        "scripted": (
+            "我刚做了一次自我学习。\n"
+            "- 发现：之前答应整理团队花名册但没完成\n"
+            "- 已完成：把后端成员名单写入 output/team-roster.md\n"
+            "如果不需要，回复「撤销上次学习」即可。"
+        ),
+        "on_edit": edit,
+        "expect_evolved": True,
+        "expect_output_file": "team-roster.md",
+    }
+
+
+SCENARIOS = [
+    scenario_silent,
+    scenario_memory_preference,
+    scenario_memory_correction,
+    scenario_skill_gap,
+    scenario_skill_error,
+    scenario_skill_wrong_config,
+    scenario_unfinished_task,
+]
+
+
+# ---------------------------------------------------------------------------
+# Runner (stub mode)
+# ---------------------------------------------------------------------------
+def run_stub():
+    from agent.evolution.executor import run_evolution_for_session
+    from agent.evolution import backup as backup_mod
+    from config import conf
+    # Evolution is disabled by default now; enable for the test.
+    conf()["self_evolution_enabled"] = True
+
+    passed, failed = 0, 0
+    for make in SCENARIOS:
+        sc = make()
+        ws = _setup_workspace()
+        try:
+            _point_config_at(ws)
+            # Patch channel push to capture instead of send.
+            channel = FakeChannel()
+            import agent.evolution.executor as ex
+            orig_notify = ex._notify_user
+            ex._notify_user = lambda ct, rcv, summary: channel.send(
+                type("R", (), {"content": summary})(),
+                {"receiver": rcv},
+            )
+
+            agent = FakeAgent(_make_messages(sc["turns"]))
+            bridge = FakeAgentBridge(agent, sc["scripted"], on_edit=sc["on_edit"])
+
+            evolved = run_evolution_for_session(
+                bridge, "session_test", channel_type="telegram", receiver="user_42"
+            )
+
+            ok = True
+            errs = []
+
+            if evolved != sc["expect_evolved"]:
+                ok = False
+                errs.append(f"evolved={evolved}, expected {sc['expect_evolved']}")
+
+            if sc["expect_evolved"]:
+                # memory / skill content checks
+                if "expect_memory_contains" in sc:
+                    # Evolution now writes to the dated daily file, not MEMORY.md.
+                    from datetime import datetime
+                    daily = ws / "memory" / (datetime.now().strftime("%Y-%m-%d") + ".md")
+                    mem = daily.read_text() if daily.exists() else ""
+                    if sc["expect_memory_contains"] not in mem:
+                        ok = False
+                        errs.append("daily memory missing expected content")
+                if "expect_skill_contains" in sc:
+                    sk, txt = sc["expect_skill_contains"]
+                    content = (ws / "skills" / sk / "SKILL.md").read_text()
+                    if txt not in content:
+                        ok = False
+                        errs.append("skill missing expected content")
+                # notify happened
+                if not channel.sent:
+                    ok = False
+                    errs.append("no notification sent")
+                # injection happened (undo support)
+                if not bridge.injected or "[EVOLUTION]" not in bridge.injected[0]:
+                    ok = False
+                    errs.append("no [EVOLUTION] record injected")
+                # protected skill untouched
+                prot = (ws / "skills" / "image-generation" / "SKILL.md").read_text()
+                if prot != "# Image Generation (built-in)\nDo not modify.\n":
+                    ok = False
+                    errs.append("PROTECTED skill was modified!")
+                # backup exists (undo possible)
+                backups = list((ws / "memory" / ".evolution_backups").glob("*"))
+                if not backups:
+                    ok = False
+                    errs.append("no backup created")
+            else:
+                # SILENT: nothing should have changed / been sent
+                if channel.sent:
+                    ok = False
+                    errs.append("notification sent on SILENT")
+                if bridge.injected:
+                    ok = False
+                    errs.append("injected record on SILENT")
+
+            ex._notify_user = orig_notify
+
+            if ok:
+                passed += 1
+                print(f"  PASS  {sc['name']}")
+            else:
+                failed += 1
+                print(f"  FAIL  {sc['name']}: {'; '.join(errs)}")
+        finally:
+            shutil.rmtree(ws, ignore_errors=True)
+
+    # Undo verification (uses the memory scenario's backup path).
+    print("\n-- undo tool --")
+    _verify_undo()
+
+    print(f"\nStub results: {passed} passed, {failed} failed")
+    return failed == 0
+
+
+def _verify_undo():
+    from agent.evolution.backup import create_backup, restore_backup
+    ws = _setup_workspace()
+    try:
+        _point_config_at(ws)
+        mem = ws / "MEMORY.md"
+        bid = create_backup(ws, [mem])
+        mem.write_text("CORRUPTED", encoding="utf-8")
+        from agent.tools.evolution_undo import EvolutionUndoTool
+        r = EvolutionUndoTool().execute({"backup_id": bid})
+        restored = mem.read_text()
+        if r.status == "success" and "大锤" in restored:
+            print("  PASS  undo restores pre-evolution state")
+        else:
+            print(f"  FAIL  undo: status={r.status}, content={restored[:40]}")
+    finally:
+        shutil.rmtree(ws, ignore_errors=True)
+
+
+# ---------------------------------------------------------------------------
+# Runner (real mode) — minimal: just prints the model's decision per scenario.
+# ---------------------------------------------------------------------------
+def _snapshot_ws(ws: Path) -> dict:
+    """Map every text file under the workspace -> content (skip backups dir)."""
+    snap = {}
+    for p in ws.rglob("*"):
+        if not p.is_file():
+            continue
+        rel = str(p.relative_to(ws))
+        if rel.startswith("memory/.evolution_backups"):
+            continue
+        try:
+            snap[rel] = p.read_text(encoding="utf-8")
+        except Exception:
+            pass
+    return snap
+
+
+def _print_diff(before: dict, after: dict) -> bool:
+    """Print added/changed files. Returns True if anything changed."""
+    changed = False
+    keys = sorted(set(before) | set(after))
+    for rel in keys:
+        old = before.get(rel)
+        new = after.get(rel)
+        if old == new:
+            continue
+        changed = True
+        tag = "NEW FILE" if old is None else "CHANGED"
+        print(f"      ~ {rel} [{tag}]")
+        old_lines = set((old or "").splitlines())
+        for line in (new or "").splitlines():
+            if line not in old_lines:
+                print(f"          + {line}")
+    return changed
+
+
+def run_real():
+    """Run real model evolution on each scenario and print the actual output.
+
+    Uses config.json's configured model via a real AgentBridge, so you see
+    exactly what the model decides and writes for each conversation.
+    """
+    from bridge.bridge import Bridge
+    from agent.memory.config import (
+        MemoryConfig,
+        set_global_memory_config,
+        get_default_memory_config,
+    )
+    from config import conf, load_config
+
+    # Load config.json so real API keys are available to the bots.
+    load_config()
+
+    # Default the test to deepseek-v4-flash (fast, low cost) unless overridden.
+    override_model = os.environ.get("EVO_TEST_MODEL", "deepseek-v4-flash")
+    conf()["model"] = override_model
+    conf()["bot_type"] = os.environ.get("EVO_TEST_BOT_TYPE", "deepseek")
+    # Force-enable evolution for the test regardless of config.json default.
+    conf()["self_evolution_enabled"] = True
+    print(f"[test] model: {override_model} (bot_type={conf().get('bot_type')}, "
+          f"key={'set' if conf().get('deepseek_api_key') else 'MISSING'})")
+
+    from agent.memory.manager import MemoryManager
+    import agent.evolution.executor as ex
+
+    bridge = Bridge()
+    agent_bridge = bridge.get_agent_bridge()
+
+    # Capture the user-facing reply instead of pushing it to a channel.
+    captured = {"reply": None}
+    orig_notify = ex._notify_user
+    ex._notify_user = lambda ct, rcv, summary: captured.__setitem__("reply", summary)
+
+    results = []  # (name, goal, evolved, changed, reply_ok)
+
+    only = os.environ.get("EVO_TEST_ONLY")  # substring filter on goal/name
+    try:
+        for make in SCENARIOS:
+            sc = make()
+            if only and only not in sc["goal"] and only not in sc["name"]:
+                continue
+            ws = _setup_workspace()
+            captured["reply"] = None
+            try:
+                mem_cfg = MemoryConfig(workspace_root=str(ws))
+                set_global_memory_config(mem_cfg)
+
+                sid = "session_evo_real"
+                # Fully isolated agent: tool cwd + memory_manager -> temp ws.
+                iso_mem = MemoryManager(mem_cfg)
+                agent = agent_bridge.create_agent(
+                    system_prompt="You are a helpful assistant.",
+                    tools=None,
+                    workspace_dir=str(ws),
+                    memory_manager=iso_mem,
+                    enable_skills=False,
+                )
+                # Notify path needs a channel+receiver to fire; give dummies.
+                agent_bridge.agents[sid] = agent
+                with agent.messages_lock:
+                    agent.messages.clear()
+                    agent.messages.extend(_make_messages(sc["turns"]))
+
+                before = _snapshot_ws(ws)
+
+                print("\n" + "=" * 72)
+                print(f"场景: {sc['name']}   [目标: {sc['goal']}]")
+                print("-" * 72)
+                print("【会话输入】")
+                for u, a in sc["turns"]:
+                    print(f"   用户: {u}")
+                    print(f"   助手: {a}")
+
+                from agent.evolution.executor import run_evolution_for_session
+                evolved = run_evolution_for_session(
+                    agent_bridge, sid, channel_type="telegram", receiver="tester"
+                )
+
+                after = _snapshot_ws(ws)
+                print("\n【进化结果】 evolved =", evolved)
+                changed = False
+                if evolved:
+                    changed = _print_diff(before, after)
+                    if not changed:
+                        print("      (无文件变更)")
+                else:
+                    print("      (静默，未做任何改动)")
+
+                print("\n【给用户的回复】")
+                if captured["reply"]:
+                    for line in captured["reply"].splitlines():
+                        print(f"   {line}")
+                else:
+                    print("   (无推送)")
+
+                reply_ok = bool(captured["reply"]) == bool(evolved)
+                results.append((sc["name"], sc["goal"], evolved, changed, reply_ok))
+                agent_bridge.agents.pop(sid, None)
+            finally:
+                shutil.rmtree(ws, ignore_errors=True)
+    finally:
+        ex._notify_user = orig_notify
+
+    # Summary table.
+    print("\n" + "=" * 72)
+    print("汇总 (deepseek-v4-flash 真实运行)")
+    print("-" * 72)
+    for name, goal, evolved, changed, reply_ok in results:
+        exp = "静默" if goal == "none" else "应进化"
+        got = "进化" if evolved else "静默"
+        mark = "✓" if (goal == "none") != evolved else "✗"
+        print(f"  {mark}  {name:42s} 预期={exp} 实际={got}")
+
+
+if __name__ == "__main__":
+    if "--real" in sys.argv:
+        run_real()
+    else:
+        ok = run_stub()
+        sys.exit(0 if ok else 1)