diff --git a/agentmain.py b/agentmain.py
index ffd8205..b2839ac 100644
--- a/agentmain.py
+++ b/agentmain.py
@@ -38,8 +38,7 @@ def get_system_prompt():
class GeneraticAgent:
def __init__(self):
script_dir = os.path.dirname(os.path.abspath(__file__))
- temp_dir = os.path.join(script_dir, 'temp')
- if not os.path.exists(temp_dir): os.makedirs(temp_dir)
+ os.makedirs(os.path.join(script_dir, 'temp'), exist_ok=True)
from llmcore import mykeys
llm_sessions = []
for k, cfg in mykeys.items():
@@ -99,10 +98,11 @@ class GeneraticAgent:
sys_prompt = get_system_prompt()
script_dir = os.path.dirname(os.path.abspath(__file__))
handler = GenericAgentHandler(None, self.history, os.path.join(script_dir, 'temp'))
- if self.handler and self.handler.key_info:
- handler.key_info = self.handler.key_info
- if '清除工作记忆' not in handler.key_info:
- handler.key_info += '\n[SYSTEM] 若开始新任务，先更新或清除工作记忆\n'
+ if self.handler and 'key_info' in self.handler.working:
+ ki = re.sub(r'\n\[SYSTEM\] 此为.*?工作记忆[。\n]*', '', self.handler.working['key_info']) # 去旧
+ handler.working['key_info'] = ki
+ handler.working['passed_sessions'] = ps = self.handler.working.get('passed_sessions', 0) + 1
+ if ps > 0: handler.working['key_info'] += f'\n[SYSTEM] 此为 {ps} 个对话前设置的工作记忆。若已在新任务，先更新或清除工作记忆\n'
self.handler = handler
self.llmclient.backend = self.llmclient.backends[self.llm_no]
user_input = raw_query
@@ -154,7 +154,6 @@ if __name__ == '__main__':
threading.Thread(target=agent.run, daemon=True).start()
if args.task:
- script_dir = os.path.dirname(os.path.abspath(__file__))
d = os.path.join(script_dir, f'temp/{args.task}'); rp = os.path.join(d, 'reply.txt'); nround = ''
with open(os.path.join(d, 'input.txt'), encoding='utf-8') as f: raw = f.read()
while True:
@@ -193,8 +192,9 @@ if __name__ == '__main__':
except Exception as e:
if once: raise
print(f'[Reflect] drain error: {e}'); result = f'[ERROR] {e}'
- script_dir = os.path.dirname(os.path.abspath(__file__))
- open(os.path.join(script_dir, './temp/reflect.log'), 'a', encoding='utf-8').write(f'[{datetime.now():%m-%d %H:%M}]\n{result}\n\n')
+ log_dir = os.path.join(script_dir, 'temp/reflect_logs'); os.makedirs(log_dir, exist_ok=True)
+ script_name = os.path.splitext(os.path.basename(args.reflect))[0]
+ open(os.path.join(log_dir, f'{script_name}_{datetime.now():%Y-%m-%d}.log'), 'a', encoding='utf-8').write(f'[{datetime.now():%m-%d %H:%M}]\n{result}\n\n')
if on_done:
try: on_done(result)
except Exception as e: print(f'[Reflect] on_done error: {e}')
diff --git a/ga.py b/ga.py
index ee1d731..e5b64c1 100644
--- a/ga.py
+++ b/ga.py
@@ -246,8 +246,7 @@ class GenericAgentHandler(BaseHandler):
'''Generic Agent 工具库，包含多种工具的实现。工具函数自动加上了 do_ 前缀。实际工具名没有前缀。'''
def __init__(self, parent, last_history=None, cwd='./'):
self.parent = parent
- self.key_info = ""
- self.related_sop = ""
+ self.working = {}
self.cwd = cwd; self.current_turn = 0
self.history_info = last_history if last_history else []
self.code_stop_signal = []
@@ -408,11 +407,12 @@ class GenericAgentHandler(BaseHandler):
'''
key_info = args.get("key_info", "")
related_sop = args.get("related_sop", "")
- if "key_info" in args: self.key_info = key_info
- if "related_sop" in args: self.related_sop = related_sop
+ if "key_info" in args: self.working['key_info'] = key_info
+ if "related_sop" in args: self.working['related_sop'] = related_sop
+ self.working['passed_sessions'] = 0
yield f"[Info] Updated key_info and related_sop.\n"
- yield f"key_info:\n{self.key_info}\n\n"
- yield f"related_sop:\n{self.related_sop}\n\n"
+ yield f"key_info:\n{self.working.get('key_info', '')}\n\n"
+ yield f"related_sop:\n{self.working.get('related_sop', '')}\n\n"
next_prompt = self._get_anchor_prompt()
#next_prompt += '\n[SYSTEM TIPS] 此函数一般在任务开始或中间时调用，如果任务已成功完成应该是start_long_term_update用于结算长期记忆。\n'
return StepOutcome({"status": "success"}, next_prompt=next_prompt)
@@ -477,14 +477,14 @@ class GenericAgentHandler(BaseHandler):
h_str = "\n".join(self.history_info[-20:])
prompt = f"\n### [WORKING MEMORY]\n\n{h_str}\n"
prompt += f"\nCurrent turn: {self.current_turn}\n"
- if self.key_info: prompt += f"\n{self.key_info}"
- if self.related_sop: prompt += f"\n有不清晰的地方请再次读取{self.related_sop}"
+ if self.working.get('key_info'): prompt += f"\n{self.working.get('key_info')}"
+ if self.working.get('related_sop'): prompt += f"\n有不清晰的地方请再次读取{self.working.get('related_sop')}"
try: print(prompt)
except: pass
return prompt
def next_prompt_patcher(self, next_prompt, outcome, turn):
- if turn % 35 == 0 and 'plan' not in str(self.related_sop):
+ if turn % 35 == 0 and 'plan' not in str(self.working.get('related_sop')):
next_prompt += f"\n\n[DANGER] 已连续执行第 {turn} 轮。你必须总结情况进行ask_user，不允许继续重试。"
elif turn % 7 == 0:
next_prompt += f"\n\n[DANGER] 已连续执行第 {turn} 轮。禁止无效重试。若无有效进展，必须切换策略：1. 探测物理边界 2. 请求用户协助。如有需要，可调用 update_working_checkpoint 保存关键上下文。"
diff --git a/llmcore.py b/llmcore.py
index c7011d1..479045a 100644
--- a/llmcore.py
+++ b/llmcore.py
@@ -413,23 +413,28 @@ class ToolClient:
self.total_cd_tokens = 0
def chat(self, messages, tools=None):
- if self._should_use_structured_messages(messages):
- return (yield from self._chat_structured(messages, tools))
- full_prompt = self._build_protocol_prompt(messages, tools)
- print("Full prompt length:", len(full_prompt), 'chars')
script_dir = os.path.dirname(os.path.abspath(__file__))
- with open(os.path.join(script_dir, f'./temp/model_responses_{os.getpid()}.txt'), 'a', encoding='utf-8', errors="replace") as f:
- f.write(f"=== Prompt === {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n{full_prompt}\n")
- gen = self.backend.ask(full_prompt, stream=True)
+ log_path = os.path.join(script_dir, f'./temp/model_responses_{os.getpid()}.txt')
+ if self._should_use_structured_messages(messages):
+ backend_messages = self._build_backend_messages(messages, tools)
+ print("Structured prompt length:", sum(self._estimate_content_len(m.get("content")) for m in backend_messages), 'chars')
+ prompt_log = self._serialize_messages_for_log(backend_messages)
+ gen = self.backend.raw_ask(backend_messages)
+ else:
+ full_prompt = self._build_protocol_prompt(messages, tools)
+ print("Full prompt length:", len(full_prompt), 'chars')
+ prompt_log = full_prompt
+ gen = self.backend.ask(full_prompt, stream=True)
+ with open(log_path, 'a', encoding='utf-8', errors="replace") as f:
+ f.write(f"=== Prompt === {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n{prompt_log}\n")
raw_text = ''; summarytag = '[NextWillSummary]'
for chunk in gen:
- raw_text += chunk;
+ raw_text += chunk
if chunk != summarytag: yield chunk
print('Complete response received.')
if raw_text.endswith(summarytag):
self.last_tools = ''; raw_text = raw_text[:-len(summarytag)]
- script_dir = os.path.dirname(os.path.abspath(__file__))
- with open(os.path.join(script_dir, f'./temp/model_responses_{os.getpid()}.txt'), 'a', encoding='utf-8', errors="replace") as f:
+ with open(log_path, 'a', encoding='utf-8', errors="replace") as f:
f.write(f"=== Response === {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n{raw_text}\n\n")
return self._parse_mixed_response(raw_text)
@@ -506,24 +511,6 @@ class ToolClient:
logged.append(msg)
return json.dumps(logged, ensure_ascii=False, indent=2)
- def _chat_structured(self, messages, tools):
- backend_messages = self._build_backend_messages(messages, tools)
- print("Structured prompt length:", sum(self._estimate_content_len(m.get("content")) for m in backend_messages), 'chars')
- script_dir = os.path.dirname(os.path.abspath(__file__))
- with open(os.path.join(script_dir, f'./temp/model_responses_{os.getpid()}.txt'), 'a', encoding='utf-8', errors="replace") as f:
- f.write(f"=== Prompt === {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n{self._serialize_messages_for_log(backend_messages)}\n")
- gen = self.backend.raw_ask(backend_messages)
- raw_text = ''; summarytag = '[NextWillSummary]'
- for chunk in gen:
- raw_text += chunk
- if chunk != summarytag: yield chunk
- print('Complete response received.')
- if raw_text.endswith(summarytag):
- self.last_tools = ''; raw_text = raw_text[:-len(summarytag)]
- with open(os.path.join(script_dir, f'./temp/model_responses_{os.getpid()}.txt'), 'a', encoding='utf-8', errors="replace") as f:
- f.write(f"=== Response === {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n{raw_text}\n\n")
- return self._parse_mixed_response(raw_text)
-
def _build_protocol_prompt(self, messages, tools):
system_content = next((m['content'] for m in messages if m['role'].lower() == 'system'), "")
history_msgs = [m for m in messages if m['role'].lower() != 'system']
diff --git a/memory/autonomous_operation_sop.md b/memory/autonomous_operation_sop.md
index 324610f..3cb8bf8 100644
--- a/memory/autonomous_operation_sop.md
+++ b/memory/autonomous_operation_sop.md
@@ -15,7 +15,7 @@
## 执行
**启动**：
-- update_working_checkpoint: `自主行动｜报告→./autonomous_reports/R{XX}_简短描述.md｜≤30回合｜收尾：重读sop，写报告+更新history+标记TODO`
+- update_working_checkpoint: `自主行动｜报告→./autonomous_reports/R{XX}_简短描述.md｜≤30回合｜收尾：重读sop，写报告+更新history+标记TODO | 报告严禁放错位置`
- 读 `./autonomous_reports/history.txt` 推断下一编号RXX + 了解历史避免重复
**执行**：
diff --git a/memory/skill_search/SKILL.md b/memory/skill_search/SKILL.md
index 9844b3e..e89b34b 100644
--- a/memory/skill_search/SKILL.md
+++ b/memory/skill_search/SKILL.md
@@ -1,116 +1,64 @@
-# Skill Search — API 客户端
+# Skill Search — 105K 技能卡检索
-> 从 10 万+ 技能卡中智能检索最适合当前环境的 skill。
+> 从 105K+ 技能卡中语义搜索最匹配的 skill。零依赖，内置默认 API 地址，开箱即用。
-## 架构
-
-```
-┌─────────────┐ HTTPS/JSON ┌──────────────────┐
-│ 客户端 CLI │ ──────────────────▶ │ Skill Search │
-│ (本项目) │ ◀────────────────── │ API Server │
-└─────────────┘ └──────────────────┘
- • 环境检测 • 105K+ 技能卡索引
- • 结果格式化 • 四层漏斗检索引擎
- • 零数据依赖 • 环境过滤 → 安全标注
- → 语义匹配 → 质量排序
-```
-
-## 快速开始
-
-```bash
-# 设置 API 地址
-export SKILL_SEARCH_API="https://your-server.com/api"
-export SKILL_SEARCH_KEY="your-api-key" # 可选
-
-# 搜索
-python -m skill_search "python testing"
-
-# 限定类别
-python -m skill_search "docker deploy" --category devops
-
-# JSON 输出（适合程序集成）
-python -m skill_search "git workflow" --json
-
-# 查看环境信息
-python -m skill_search --env
-
-# 查看索引统计
-python -m skill_search --stats
-```
-
-## 编程接口
+## 最简调用
```python
-from skill_search import search, detect_environment
-
-env = detect_environment()
-results = search(query="python testing", env=env, top_k=5)
+import sys; sys.path.append('../memory/skill_search')
+from skill_search import search
+results = search("python send email") # ⚠️ 必须用英文查询，中文匹配效果极差
for r in results:
- print(f"{r.skill.name} (score: {r.final_score:.2f})")
- print(f" {r.skill.one_line_summary}")
+ s = r.skill
+ print(f"[{r.final_score:.2f}] {s.name} — {s.one_line_summary}")
+ print(f" key: {s.key} category: {s.category} tags: {s.tags[:3]}")
```
-## 文件结构
+## API 签名
-```
-skill_search/
-├── SKILL.md # 本文档
-└── skill_search/ # Python 包
- ├── __init__.py # 公开 API
- ├── __main__.py # CLI 入口
- ├── engine.py # HTTP 客户端（替代本地检索）
- ├── index.py # SkillIndex 数据模型
- ├── env_detect.py # 本地环境检测
- └── formatter.py # 结果格式化输出
+```python
+search(query, env=None, category=None, top_k=10) -> list[SearchResult]
+# env: 自动检测，一般不传
+# category: 可选过滤，如 "devops"
+# top_k: 返回数量，默认10
```
-## 环境变量
+## 返回结构
-| 变量 | 说明 | 默认值 |
-|------|------|--------|
-| `SKILL_SEARCH_API` | API 服务地址 | `https://skill-search.example.com/api` |
-| `SKILL_SEARCH_KEY` | API 密钥（可选） | 无 |
+```
+SearchResult
+ .final_score float 综合评分 (0~1)
+ .relevance float 语义相关度
+ .quality float 质量分
+ .match_reasons list[str] 匹配原因
+ .warnings list[str] 警告
+ .skill SkillIndex ↓
-## API 协议
-
-### POST /search
-
-请求:
-```json
-{
- "query": "python testing",
- "env": { "os": "windows", "shell": "powershell", ... },
- "category": "coding",
- "top_k": 10
-}
+SkillIndex (常用字段)
+ .key str 唯一标识/路径
+ .name str 名称
+ .one_line_summary str 一句话摘要
+ .description str 详细描述
+ .category str 类别
+ .tags list[str] 标签
+ .form str 形式(sop/script/...)
+ .autonomous_safe bool 是否自主安全
```
-响应:
-```json
-{
- "results": [
- {
- "skill": { "key": "org/repo/skill", "name": "...", ... },
- "relevance": 0.85,
- "quality": 7.2,
- "final_score": 0.78,
- "match_reasons": ["完整短语匹配", "标签匹配: python"],
- "warnings": []
- }
- ]
-}
+## CLI
+
+```bash
+python -m skill_search "python testing"
+python -m skill_search "docker deployment" --category devops --top 5
+python -m skill_search "git" --json
+python -m skill_search --stats
+python -m skill_search --env
```
-### POST /stats
+## 配置
-请求: `{}` 或 `{"env": {...}}`
-
-响应:
-```json
-{
- "total": 105586,
- "safe_count": 98234,
- "categories": { "coding": 45000, "devops": 12000, ... }
-}
-```
\ No newline at end of file
+| 项 | 默认值 | 说明 |
+|---|---|---|
+| API地址 | `http://www.fudankw.cn:58787` | 环境变量 `SKILL_SEARCH_API` 可覆盖 |
+| API密钥 | 无(可选) | 环境变量 `SKILL_SEARCH_KEY` |
\ No newline at end of file