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https://github.com/zhayujie/chatgpt-on-wechat.git
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ba777ed706
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).
661 lines
26 KiB
Python
661 lines
26 KiB
Python
"""Self-evolution test harness.
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Simulates multiple realistic conversations and checks the evolution pass behaves
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correctly: stays silent when it should, evolves (memory/skill) when it should,
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backs up before editing, notifies the user, and supports undo.
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Two modes:
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- stub (default): the review agent's reasoning is replaced by a scripted
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output per scenario. Fast, deterministic, validates the WIRING (backup,
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record, inject, notify, undo, protection). No model calls.
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- real: the review agent runs the configured model for real. Validates the
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QUALITY of the judgement (does it correctly decide to act / stay silent).
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Run:
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python tests/test_evolution.py # stub mode
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python tests/test_evolution.py --real # real model mode
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"""
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import os
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import sys
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import shutil
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import tempfile
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from pathlib import Path
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sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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# ---------------------------------------------------------------------------
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# Fakes
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# ---------------------------------------------------------------------------
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class FakeChannel:
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"""Captures channel.send calls instead of sending."""
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def __init__(self):
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self.sent = []
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def send(self, reply, context):
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self.sent.append({"content": getattr(reply, "content", str(reply)), "receiver": context.get("receiver")})
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class FakeModel:
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pass
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class FakeAgent:
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"""Minimal stand-in for a chat Agent."""
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def __init__(self, messages, tools=None):
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import threading
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self.messages = messages
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self.messages_lock = threading.Lock()
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self.tools = tools or []
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self.model = FakeModel()
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self.skill_manager = None
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self.memory_manager = None
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class FakeReviewAgent:
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"""Review agent whose run_stream returns a scripted result (stub mode)."""
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def __init__(self, scripted_output, workspace, on_edit=None):
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self._out = scripted_output
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self._workspace = workspace
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self._on_edit = on_edit
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self.model = None
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def run_stream(self, user_message, clear_history=False, **kwargs):
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# Simulate the side effects a real review agent would perform.
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if self._on_edit:
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self._on_edit(self._workspace)
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return self._out
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class FakeAgentBridge:
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"""Stand-in for AgentBridge wiring used by the executor."""
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def __init__(self, agent, scripted_output, on_edit=None):
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self.agents = {"session_test": agent}
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self.default_agent = agent
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self._scripted = scripted_output
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self._on_edit = on_edit
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self.injected = []
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def create_agent(self, **kwargs):
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from agent.memory.config import get_default_memory_config
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ws = get_default_memory_config().get_workspace()
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return FakeReviewAgent(self._scripted, ws, on_edit=self._on_edit)
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def remember_scheduled_output(self, session_id, content, channel_type="", task_description=""):
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self.injected.append(content)
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# ---------------------------------------------------------------------------
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# Test scaffolding
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# ---------------------------------------------------------------------------
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def _setup_workspace():
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"""Create a realistic temp workspace: seeded memory + real editable skills.
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Mirrors a real CowAgent workspace closely enough that the model has genuine
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content to read, reason about, and edit during a real evolution pass.
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"""
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ws = Path(tempfile.mkdtemp(prefix="evo_test_"))
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(ws / "MEMORY.md").write_text(
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"# Long-term Memory\n\n"
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"## User\n"
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"- Name: 大锤 (David)\n"
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"- Lives in Shenzhen, works as a backend engineer\n"
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"- Company: a fintech startup, team of 8\n\n"
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"## Preferences\n"
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"- Likes detailed technical explanations\n",
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encoding="utf-8",
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)
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(ws / "memory").mkdir()
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(ws / "output").mkdir()
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skills = ws / "skills"
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# Editable skill 1: weekly report generator (has a structural gap: no risk).
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(skills / "weekly-report").mkdir(parents=True)
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(skills / "weekly-report" / "SKILL.md").write_text(
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"# Weekly Report\n\n"
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"Generate a weekly work report from the user's notes.\n\n"
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"## Steps\n"
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"1. Collect this week's completed items.\n"
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"2. Summarize key progress in 3-5 bullets.\n"
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"3. List next week's plan.\n\n"
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"## Output format\n"
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"Markdown with sections: 本周进展 / 下周计划\n",
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encoding="utf-8",
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)
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# Editable skill 2: expense tracker (has a wrong currency-format step).
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(skills / "expense-tracker").mkdir(parents=True)
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(skills / "expense-tracker" / "SKILL.md").write_text(
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"# Expense Tracker\n\n"
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"Record an expense into output/expenses.md.\n\n"
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"## Steps\n"
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"1. Parse amount and category from the user message.\n"
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"2. Append a row to output/expenses.md.\n"
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"3. Format the amount with a `$` prefix.\n",
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encoding="utf-8",
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)
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# Editable skill 3: an API caller whose SKILL.md hardcodes a WRONG endpoint
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# host. The conversation discovers the correct host at runtime; the right
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# fix is to edit this file's source, not just log the corrected fact.
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(skills / "data-fetch").mkdir(parents=True)
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(skills / "data-fetch" / "SKILL.md").write_text(
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"# Data Fetch\n\n"
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"Fetch records from the data service.\n\n"
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"## Steps\n"
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"1. Build the request payload from the user's query.\n"
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"2. POST it to `https://api.example-wrong.com/v1/fetch`.\n"
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"3. Parse and return the `data` field.\n",
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encoding="utf-8",
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)
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# Protected built-in skill: must never be edited by evolution.
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(skills / "image-generation").mkdir(parents=True)
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(skills / "image-generation" / "SKILL.md").write_text(
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"# Image Generation (built-in)\nDo not modify.\n", encoding="utf-8"
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)
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return ws
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def _point_config_at(ws):
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"""Force the global memory config to use the temp workspace."""
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from agent.memory.config import MemoryConfig, set_global_memory_config
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set_global_memory_config(MemoryConfig(workspace_root=str(ws)))
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def _make_messages(turns):
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msgs = []
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for u, a in turns:
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msgs.append({"role": "user", "content": u})
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msgs.append({"role": "assistant", "content": a})
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return msgs
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# ---------------------------------------------------------------------------
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# Scenarios
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# ---------------------------------------------------------------------------
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def scenario_silent():
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"""Pure small talk -> should stay SILENT (no change, no notify)."""
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return {
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"name": "闲聊 (should stay SILENT)",
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"goal": "none",
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"turns": [
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("在吗", "在的，有什么可以帮你？"),
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("今天周五了，终于要放假了", "是呀，周末好好休息一下。"),
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("哈哈是的，那没事了", "好的，随时找我。"),
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],
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"scripted": "[SILENT]",
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"on_edit": None,
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"expect_evolved": False,
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}
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def scenario_memory_preference():
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"""User states a durable working preference -> update MEMORY.md."""
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def edit(ws):
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from datetime import datetime
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p = ws / "memory" / (datetime.now().strftime("%Y-%m-%d") + ".md")
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p.write_text((p.read_text() if p.exists() else "") + "- Prefers concise replies; avoid long explanations\n", encoding="utf-8")
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return {
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"name": "记忆-偏好 (should note in daily memory)",
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"goal": "memory",
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"turns": [
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("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）"),
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("打住打住，太长了", "抱歉，我说得太细了。"),
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("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。"),
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("嗯这样挺好", "👌"),
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],
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"scripted": (
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"我刚做了一次自我学习。\n"
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"- 学到：你希望回答简短、抓重点\n"
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"- 调整：更新了长期记忆 MEMORY.md\n"
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"如果不对，回复「撤销上次学习」即可。"
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),
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"on_edit": edit,
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"expect_evolved": True,
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"expect_memory_contains": "concise",
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}
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def scenario_memory_correction():
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"""User corrects a wrong fact the agent stated -> fix MEMORY.md."""
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def edit(ws):
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from datetime import datetime
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p = ws / "memory" / (datetime.now().strftime("%Y-%m-%d") + ".md")
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p.write_text((p.read_text() if p.exists() else "") + "- Correction: team size is now 15 (was recorded as 8)\n", encoding="utf-8")
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return {
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"name": "记忆-纠正事实 (should note correction in daily)",
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"goal": "memory",
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"turns": [
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("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。"),
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("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。"),
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("嗯对，记住啊", "好的，记住了。"),
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],
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"scripted": (
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"我刚做了一次自我学习。\n"
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"- 学到：团队规模已是 15 人（之前记成 8 人）\n"
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"- 调整：修正了长期记忆 MEMORY.md\n"
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"如果不对，回复「撤销上次学习」即可。"
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),
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"on_edit": edit,
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"expect_evolved": True,
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"expect_memory_contains": "15",
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}
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def scenario_skill_gap():
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"""Using weekly-report reveals a missing section -> patch the skill."""
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def edit(ws):
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p = ws / "skills" / "weekly-report" / "SKILL.md"
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p.write_text(p.read_text() + "4. List risk items / blockers.\n", encoding="utf-8")
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return {
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"name": "技能-缺陷 (should patch weekly-report skill)",
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"goal": "skill",
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"turns": [
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("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",
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"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"),
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("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。"),
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("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。"),
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],
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"scripted": (
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"我刚做了一次自我学习。\n"
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"- 学到：周报需要固定包含「风险项」一节\n"
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"- 调整：给 weekly-report 技能补充了风险项步骤\n"
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"如果不对，回复「撤销上次学习」即可。"
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),
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"on_edit": edit,
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"expect_evolved": True,
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"expect_skill_contains": ("weekly-report", "risk"),
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}
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def scenario_skill_error():
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"""A skill step is wrong (currency format) -> correct the skill."""
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def edit(ws):
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p = ws / "skills" / "expense-tracker" / "SKILL.md"
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p.write_text(p.read_text().replace("`$` prefix", "`¥` prefix (RMB)"), encoding="utf-8")
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return {
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"name": "技能-报错纠正 (should fix expense-tracker skill)",
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"goal": "skill",
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"turns": [
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("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）"),
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("怎么是美元？我们都是人民币啊", "抱歉，格式用错了，应该是 ¥35。"),
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("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。"),
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],
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"scripted": (
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"我刚做了一次自我学习。\n"
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"- 学到：记账应使用人民币符号 ¥ 而非 $\n"
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"- 调整：修正了 expense-tracker 技能的货币格式步骤\n"
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"如果不对，回复「撤销上次学习」即可。"
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),
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"on_edit": edit,
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"expect_evolved": True,
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"expect_skill_contains": ("expense-tracker", "¥"),
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}
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def scenario_skill_wrong_config():
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"""A skill's SKILL.md hardcodes a wrong endpoint; the chat works around it
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at runtime. Correct evolution = FIX the skill source, not log a memory note.
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"""
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def edit(ws):
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p = ws / "skills" / "data-fetch" / "SKILL.md"
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p.write_text(
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p.read_text().replace("api.example-wrong.com", "api.example-correct.com"),
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encoding="utf-8",
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)
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return {
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"name": "技能-配置错误 (should fix skill source, not log memory)",
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"goal": "skill",
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"turns": [
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("用 data-fetch 技能拉一下最新数据",
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"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。"),
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("哦那个地址不对，正确的是 api.example-correct.com，你用这个调",
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"好的，换成 https://api.example-correct.com/v1/fetch ，成功拿到数据了 ✅"),
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("嗯对，就是这个地址", "明白了，这个才是正确的服务地址。"),
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],
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# The model must decide on its own to edit the skill; we still provide a
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# scripted output for stub mode wiring.
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"scripted": (
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"我刚做了一次自我学习。\n"
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"- 学到：data-fetch 的正确服务地址是 api.example-correct.com\n"
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"- 调整：修正了 data-fetch 技能里写错的接口地址\n"
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"如果不对，回复「撤销上次学习」即可。"
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),
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"on_edit": edit,
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"expect_evolved": True,
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"expect_skill_contains": ("data-fetch", "api.example-correct.com"),
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}
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def scenario_unfinished_task():
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"""A promised deliverable was not produced -> finish it now via tools."""
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def edit(ws):
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p = ws / "output" / "team-roster.md"
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p.write_text("# Team Roster (backend)\n- 张伟\n- 李娜\n- 王强\n- 大锤\n", encoding="utf-8")
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return {
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"name": "未完成任务 (should finish & write output file)",
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"goal": "task",
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"turns": [
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("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）",
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"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。"),
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("好的麻烦了，我先去开个会", "没问题，我现在就处理。"),
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("（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）"),
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],
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"scripted": (
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"我刚做了一次自我学习。\n"
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"- 发现：之前答应整理团队花名册但没完成\n"
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"- 已完成：把后端成员名单写入 output/team-roster.md\n"
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"如果不需要，回复「撤销上次学习」即可。"
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),
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"on_edit": edit,
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"expect_evolved": True,
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"expect_output_file": "team-roster.md",
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}
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SCENARIOS = [
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scenario_silent,
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scenario_memory_preference,
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scenario_memory_correction,
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scenario_skill_gap,
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scenario_skill_error,
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scenario_skill_wrong_config,
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scenario_unfinished_task,
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]
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# ---------------------------------------------------------------------------
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# Runner (stub mode)
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# ---------------------------------------------------------------------------
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def run_stub():
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from agent.evolution.executor import run_evolution_for_session
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from agent.evolution import backup as backup_mod
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from config import conf
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# Evolution is disabled by default now; enable for the test.
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conf()["self_evolution_enabled"] = True
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passed, failed = 0, 0
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for make in SCENARIOS:
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sc = make()
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ws = _setup_workspace()
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try:
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_point_config_at(ws)
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# Patch channel push to capture instead of send.
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channel = FakeChannel()
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import agent.evolution.executor as ex
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orig_notify = ex._notify_user
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ex._notify_user = lambda ct, rcv, summary: channel.send(
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type("R", (), {"content": summary})(),
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{"receiver": rcv},
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)
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agent = FakeAgent(_make_messages(sc["turns"]))
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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)
|